Soon businesses of all sizes will have so much amount of information that dashboard software will be the most invaluable resource a company can have. Visualizing the data and interacting on a single screen is no longer a luxury but a business necessity. That’s why we welcome you to the world of highly interactive dashboards. Take a comfortable seat, enjoy the power of engaging business dashboards, leave your spreadsheets behind, and utilize the advantages of interactive dashboard design and its capabilities.

But before we delve into the bits and pieces of our topic, let’s answer the basic questions: What is an interactive dashboard, and why do you need one? In essence, in this post, we will explain all the details needed for dashboard reporting and creation, compare interactive vs. static reporting, and provide tips and tricks to make your company perform even better. Let’s get started.

What Is An Interactive Dashboard?

An interactive dashboard is a data management tool that tracks and monitors key business metrics in a centralized way. With the help of various interactive features, users can explore the data on a deeper level and make well-informed, data-driven business decisions.

Dashboards are used within the BI environment, creating a link between managers and the company’s strategy, allowing departments to collaborate more effectively, and enabling employees to perform with an increased productivity level.

The point of such dashboards is to simplify the working environment and analytics processes since there are massive volumes of data collected on a daily level, and companies need solutions that will bring them to the right answer at the right moment. Now that we’ve explained the interactive dashboard meaning, we continue on to the next important aspect.

Interactive Dashboards vs. Static Reporting

There’s no doubt about it: interactive dashboards provide businesses with insights that have never been possible before. A professional dashboard maker enables you to access data on a single screen, easily share results, save time, and increase productivity. Gone are the days of static presentations, stagnate reports, and waiting on analysts to pull reports and then having out-of-date data. Thankfully, it’s also the moment to take a step back from your spreadsheets and slides. While Excel and PowerPoint, and various other spreadsheet and presentation applications, remain important tools for many, their interactivity options are limited.

Here are some key benefits that interactive dashboards provide, compared to traditional, static reporting:

1. Agility for decision-makers: Interactiveness during the analytical process empowers users to answer critical questions on-demand with the most up-to-date data. Additionally, data can be looked at from different perspectives and points of view with just a few clicks. Zooming in and out, detailing time intervals, filtering countries, or showing and hiding specific parameters that you don’t need enables you to look at data in the most holistic way, like never before.
2. Avoid redundant reports: You need only one tool with state-of-the-art interactive capabilities to quickly adapt the displayed data instead of creating 10 static PowerPoint slides. Reports use real-time data, with implemented intelligent data alerts that enable users to completely eliminate spreadsheets and presentations. The alarm will notify the user when an anomaly occurs, while neural networks will ensure smart detection and future forecasts.
3. Less IT involvement: By empowering users to perform their own ad hoc data analysis, a company can save valuable IT resources since the number of requests for database queries or customizations will significantly decrease. The IT department can then concentrate on other urgent or valuable tasks while users can get answers to important business questions quickly.
4. Speed: There is no doubt, swiftness today is a crucial element for any company trying to survive in our cutthroat digital age. When using traditional spreadsheets or PowerPoint presentations, data is inserted once and updated manually. With modern reporting tools, there is no need to do so. Real-time dashboards enable real-time data and that is the beauty and power of BI at its core.
5. Productivity: While static reports have been a useful tool for increasing productivity, in today’s modern economies this is simply not enough. The amount of data that is collected, and needs to be analyzed is continuously growing, and numerous static or paper sheets or millions of rows and columns cannot help as much as they used to. The rise of self-service BI tools has enabled users to tinker with the data on their own, and use modern technologies that will increase their productivity levels.

In essence, why do you need robust interactive dashboard reporting? They quickly engage end-users with a wide range of technical understanding and provide an intuitive experience and easily digested insights. They provide ROI by quickly highlighting trends and dig out irregularities.

Business dashboards aren’t just for management, they can be easily capitalized on by all teams across a company. They enable you to easily visualize your data, filter on-demand, and slice and dice your data to dig deeper. They can also be fun, and here you can look at some of the best data visualization examples, most of them made out of stunning interactive dashboard templates.

Are you sold on the power of these kinds of dashboards? Before rushing off to buy software, or requesting it from the powers that be, there are several important features you should look for. We’ve already written about the 18 necessary attributes a dashboard software must have, so now we have to determine our must-have interactive dashboard features.

Our Top 14 Interactive Dashboard Features

To take your data and insights to the next level and drastically improve ease of use, keep reading! If you want to experience the power of some of these features in action, you can also check out this short video:

1) Dashboard Chart Filter / Click-to-Filter

A well-built interactive data dashboard provides a variety of ways to dissect data. You should be able to easily explore your data to discover a wide range of insights. A great feature to further enable interactivity is a click-to-filter option. This allows users to utilize the dimensions of the dashboard’s charts and graphs as temporary filter values. It’s as easy as clicking on any data set in your charts. This action seamlessly creates a quick filter that applies to the data and delivers new insights in an instant, whether it is used on an executive dashboard or operational dashboard.

Let’s further explore this feature in more detail. Maps are important data visualizations and at datapine, we love utilizing them in our dashboards. Maps get even better when using them with click-to-filter.

Say you want to visualize your revenue data by country. With click-to-filter, you simply click on a specific country (or countries!) on the map on the dashboard. You then activate this as a filter for your whole dashboard. The data visualized on the dashboard is now just for that chosen country. You can easily click out of it to return to an unfiltered view or click into another country to access that data. With just a couple of quick clicks, users can easily interact with the data, finding what is pertinent to them. There is now no need to build data visualizations for every country like you would have to do in Excel!

This example filtrates the dashboard for Australia and February using a click-to-filter feature.

Another valuable feature of the global filter that we just explained above, is expressions. Essentially, expressions enable users to go one step further with their analysis by giving them the option to easily generate custom calculations. This is possible by merging multiple predefined functions such as UMIF, NPS, CONCAT, or IIF, which can be used also as a global dashboard filter as well. On the one hand, this helps in manipulating the visualized data at your will, and on the other hand, it offers new possibilities for merging multiple filters from different data sources to answer any questions that might arise during the analytics process.

2) Drill-Throughs

A drill-through is an interactive dashboard software feature that shows you additional, more specific, and detailed information about a particular element, variable, or KPI, without overcrowding the dashboard. Imagine you want to visualize the exact net profit or sold units of your management strategy. By simply clicking on the specific KPI (in this case the net profit), the drill-through will enable you to visualize this data in a clear manner, without the need to be presented on the main screen. While there are many KPI examples you can choose specifically for your organization, this is just one that shows the tip of the iceberg of the power of interactivity.

This example shows additional information for the net profit: the top 5 product categories by using a drill-through.

If you want to show, for example, details about your active customers, sold units, or net profit, the drill-through option is certainly one feature that you will want to add in order to dig deeper into the various details of the selected charts. Do you want to find out what are top-selling products during last year or where are your customers located? The drill-through will help you answer these questions with a couple of clicks.

3) Drilldowns

If you want to create an interactive analytics dashboard, drill-downs are one of the attributes you should look for and get familiar with. This option enables you to add more levels to charts, meaning by simply clicking on your visualization, you can drill into a lower level of the X-axis. Drilldowns give you the possibility to “nest” an additional variable into the chart, and by simply clicking on it, you will be able to change the chart according to your parameters.

A drill down feature example showing the number of customers by channel and country.

As you can see in our example, we wanted to take a look at the performance of our customer numbers by channels in selected countries. When we applied the drill down, the graph itself changed and we could visualize the results of each channel in various countries, in this case, Austria, Belgium, Canada, Netherlands, the UK, and the United States.

In general, drill downs can be added to any type of chart. Sometimes referred to as nested charts, they are especially useful in tables, where you can access additional drill down options such as aggregated data for categories/breakdowns (e.g. by channel or country) as well as change the aggregation type (sum, average, median, etc.). Therefore, nested tables deliver even more compressed information in comparison to utilizing drill downs in other types of charts, as shown in the picture below:

**click to enlarge**

Similar to drill-throughs, this feature is used in an interactive data dashboard when we don’t want to overcrowd the visuals with multiple charts but simply dig deeper into the data right at our fingertips and provide additional information to the questions that might arise. Thanks to modern data science tools, such analysis is easy to create and demonstrate.

4) Cross Tab Filters

The best interactive dashboards will enable you to apply filters through different tabs in order to save precious hours and speed up the analytics process. Essentially, the cross-tab filter will give you the opportunity to dynamically synchronize and apply filters on different dashboard tabs. In practice, let’s say you have created a dashboard with 4 different tabs, and you want to be able to apply one filter to all the tabs that you’re working on. By utilizing a cross-tab filter, all other tabs will change, without the need to create them separately, 4 different times.

As mentioned, this filter will enable you to save time, and increase your productivity levels because you won’t need to manually apply filters on each tab and lose some working hours. Moreover, these smart data analysis techniques are used to easily connect the information in different parts of the dashboard, making your analysis much easier and quicker.

5) Time Interval Widget

Another built-in feature you should look for is a time interval widget. These widgets allow you to enhance individual time scales on different charts on your dashboards with a drill-down function.

With a time-interval widget, you choose the time interval of the data displayed on your charts using a date or time field on the X-axis with a click of your mouse. Use this function to move from a yearly to a monthly, weekly, or daily view of your data without changing the time period displayed on your whole online dashboard. This is especially helpful if you want to change the time intervals of single charts really fast without affecting other data visualizations on your dashboard.

Easily look at revenue & sales across the day, week, month, and year time intervals with the help of the time interval widget.

6) Chart Zoom

While you should be able to open a well-designed dashboard and immediately glean a story, a robust interactive dashboard provides multiple layers of knowledge allowing you to get a 30,000-foot view of your data or zoom into the minute. A chart zoom feature helps you do this.

While a time interval widget helps you choose your desired interval, you may need to dig even deeper. This is when you need a chart zoom. This function lets you drill down into the smallest unit of time for charts using any date or time field on the X-axis.

datapine’s charting zoom option allows you to simply drag the mouse over the part of the visual you wish to view on a more granular basis and narrow the period displayed, depending on the selection. In the video below, we have selected the month of February. After the selection, you get the daily overview of what happened during the whole month, and dig deeper into your data:

The zoom filter in action: shows the revenue and sales changed from a monthly to a daily overview.

While we would all love to get perfect data all the time, that isn’t practical. Data visualization is the easiest way to surface data irregularities.

Surfacing these irregularities provides some of the biggest ROI from business intelligence software. A chart zoom feature is an especially useful way to drill down and investigate your data after finding these irregularities.

For example, after looking at the past week using a time interval widget, you identify a day with 50% less daily revenue. Using a chart zoom, you can hone in to analyze revenue on this day by the hour and look for inconsistencies. Maybe your online shop was down for several hours last Friday. Now, you can easily correlate your downtime and lack of revenue.

You can even use these insights to justify investing in a better fail-safe! Saving money by zooming in on a chart, is the power of interactive dashboards.

7) Custom Chart Tooltips

There are many interactive dashboard examples that provide customization levels based on their level of sophistication. One prominent feature that can be adjusted to show details of a specific element is the custom chart tooltips. This option enables you to adjust the shown information when you hover over it with your mouse providing a small snippet. Let’s imagine you have a stacked bar graph with various categories. You can adjust your hovering information on:

• Just one category
• All categories
• Adding custom text to a category (for additional explanations, for example)
• Additionally add sums, averages, percentages, etc.

The image below shows that we added the percentage, besides showing just the absolute revenue per month:

A small snippet showing additional details of the performance was created with the help of custom chart tooltips.

8) Advanced Data Options

When you’re focusing on interactive reporting, you might want to make sure that you have instant access to the raw data of the chart that you’re exploring. That’s where advanced data options come in handy, especially when additional questions arise and you want to take a look at the raw data itself. That way, you will have the possibility to easily explore each part of the chart in its raw form, and export it if needed. Moreover, you will have the opportunity to swipe through each element that will show you exactly what kind of data the visualization is consisted of. Thanks to modern business intelligence solutions, tinkering and exploring business information has never been easier. Let’s see this through an example.

The data option will show the raw data behind a chart.

In this example, we have shown data options for the revenue and sales chart and we want to see the data in its raw form, in this case, a table with columns and rows filled with our information such as the date, amount, product ID, and previous periods. By simply clicking on the option to show data, another pop-up will open and you will immediately see the revenue and sales information in its raw form. Swiping left or right will enable you to explore other charts such as customers by region, top 10 products by profit margin, new customers signups, etc. That way, you have immediate access to a raw table where you can see what kind of information is present, and adjust if needed.

9) Show or Hide Chart Values

A business often has many datasets and sources. It’s most likely that your data isn’t living in one spot. To further complicate things, the data can also be in multiple “languages”. For example, you may have different SQL databases, Google Analytics, and sales data in a CSV. They all host invaluable data for your business. Before business intelligence and dashboard software, it was very difficult to combine these data sources and even harder to analyze them together. A cutting-edge, interactive dashboard tool allows you to combine and visualize multiple datasets in one dashboard, in just a few clicks.

However, when combining datasets, it’s imperative you’re comparing apples to apples and can easily filter across the combined data. Poorly built filters lead to misinterpreted data and misinformed decisions related to your business.

A “show or hide chart values” feature is one of the many ways to better manage blended data. With a show or hide chart values feature, charts containing more than one dataset are presented with a dynamic legend at the bottom. With a simple click on a dataset variable, the data point will be excluded from the charts. This includes the ignored selected data in all calculations of totals or accumulations in your charts.

For example, let’s say your interactive dashboard design contains a chart of five different sales categories and you only want to dig into two of them. The example below shows all of the categories; all you do is deselect the categories in the legend you don’t want to view, and that data will disappear from all the related data visualizations:

An example of showing or hiding chart values, in this case, the sales performance by category.

10) Dashboard Widget Linking

Your business doesn’t live in a vacuum, and neither should your dashboards. Each of the ones you create should be a live snapshot of your business. Combining and connecting these snapshots takes your BI to the next level.

Widget linking helps to further unify your dashboards. It enables you to add links to any widget on your dashboard, whether a chart, textbox, or image, and redirects users and viewers to other related content. You may link to another dashboard tab or even to an external website or resource.

Let’s say one of your dashboards contains a high-level key performance indicators (KPI) tab that provides snapshots of all your departments. You then have subsequent more detailed tabs for each individual department. If you don’t have this resource, this is a dashboard best practice and we highly recommend layering your data like this!

By using a widget link you can easily move from an overview chart, for sales for example, on the KPI tab to a related sales dashboard tab, where you find many other more detailed charts and sales-focused KPIs, like in the video below:

The widget linking option gives you the opportunity to link additional charts and dashboards.

11) Hierarchical Filter

The feature hierarchical filter provides an in-depth overview of how one filter influences the selection of another. Let’s say you want to generate insights into the product category and a specific product. By selecting a specific product category, the product name filter gets automatically updated, deselecting all products, that don’t belong to the selected product category, like in the example below:

The hierarchical filter is expressed on product categories.

By adding this filter to the dashboard, you can eliminate all products that are not relevant to your current analysis. For example, you are interested only in cameras, so you deselect every other product (like computers or games) that is in your dashboard and observe how it automatically selects just the cameras in your portfolio. Besides having a standard global dashboard filter (that filters the whole dashboard for selected variables with a few clicks), hierarchical filters help you to manage related filters effectively. Simple, and no manual work is needed.

12) Ignore Filter Options

By continuing our filters series, we need to stress that interactive BI dashboards need to give the option to ignore filters upon need. Your data tinkering and analysis won’t be complete if you don’t have the possibility to apply various kinds of filtering options, but also ignore some of them, depending on your use case.

This feature gives you the chance to ignore all filtering that is applied on a dashboard or exclude a chart from that same filter. In other words, you may prefer that one chart is not affected and shows static values. Let’s take a look at an example:

The ignore filters option feature is applied to active customers.

In this case, we have a management overview on which we have applied multiple filtering options such as the time, product category, and product name, but maybe you have also applied additional quick filters. We can see the number of active customers, but we want to see the total number of customers without any filter interference. By simply applying features of modern online BI tools, we can edit the chart and show the static value of the numbers.

In another case, this feature is useful if you want to compare the total revenue with the selected markets, for example, or simply use it as a comparison value in case the presented data is already filtered. Hence, there are different scenarios where you can ignore filters in order to quickly show the original numbers and easily compare the selected chart with the rest of the dashboard.

Paired with being able to ignore dashboard filters on specific charts, a single chart filter enables you to do the opposite thing and add a filter only to a specific chart on the dashboard. This means you are able to explore the data on a single visual without the need to change all of the others. There are multiple use cases where this makes sense, for example, when you want to have dynamic benchmarks filtered by time but want to ignore all other filters.

13) Dynamic Text Boxes/Images

Businesses these days face the issue of monitoring their performance on a daily basis. Exporting reports manually, or writing endless documents takes an enormous amount of effort. This causes an increased risk of overlooking valuable information when you need to know whether your business trends are performing well or need additional adjustments. This can be easily resolved with dynamic textboxes or images, as shown in the pictures below:

A checkmark showing that the profit margin has performed well while the exclamation mark that we still haven’t reached established goals.

If you have an implemented value or criteria that are under your specified benchmark, a clear exclamation mark will show you that this KPI is underperforming and needs attention. On the other hand, a check-mark will provide a clear signal that the KPI is performing well and the viewer will have an obvious cue of the overall metric’s functioning. This filter can then be implemented on various KPIs within the dashboard and provide additional insights for each and every data needed. It is extremely useful since the dashboard itself will alert you if any differences happen, so manual calculations are no longer necessary.

14) Information Tooltips

There is much information you have to present and explain to your audience, whether as a finance report or management presentation. An interactive dashboard software will also have a function that will enable users to add specific explanations or additional information to text boxes and images, similar to the custom chart tooltips feature, like in the picture below:

Additional information is expressed on the profit margin with the help of information tooltips.

It is triggered once you hover over it with a mouse which enables you to see, for example, a definition of a specific KPI or notes about the data you need or present at a meeting. This is useful to include in an interactive dashboard design since definitions and specific explanations are available just by hovering, so the user doesn’t necessarily need to remember every little detail s/he wanted to say or explain.

Interactive Dashboard Examples

To keep putting the value of these interactive features and filters into perspective, we will now analyze three examples that cover some of the various business areas that can truly benefit from an interactive analytics process. It is important to note that the examples presented below are static and only used for representational purposes. If you want to see these capabilities in action you can watch the video presented at the beginning of the post or, if you want to experience them for yourself and build your own dashboards, then we recommend you subscribe to datapine dashboard software for a 14-day free trial.

1. Sales Analysis Dashboard 

Our first example offers insights into important sales KPIs like revenue and profit coming from different countries and products. With a range of filters on top making this template highly interactive, users can extract deeper conclusions without the need to migrate to another dashboard or overcrowd the current one with lots of information. This is especially useful when holding collaborative meetings as different discussions can be supported thanks to the diverse filtering options available. 

**click to enlarge**

Paired with the filters you can see on top, the template can also be explored by directly interacting with the different charts and graphs. A click-to-filter feature lets you click on a specific territory on the map and the whole dashboard will adapt to show that data. This is an easy and interactive way to dig deeper into the performance of each country and optimize it based on its needs and potential to improve. 

On that same note, if you want to keep digging deeper into the performance of a specific country, you can also use a drill down filter to look at lower levels of data. For example, if you are looking at the data from Australia, a drill down into the revenue and profit chart allows you to click on a specific quarter and see how each month developed. This information can be complemented by looking at which products are the most or the least profitable for a specific month and gaining more profound conclusions on where you should focus your efforts to ensure continuous growth. The same situation applies to other areas of the dashboard where you can also filter by a sales representative to see how each of their performances is impacting the end result. 

2. Procurement Quality Dashboard 

Next, we have an interactive procurement KPI dashboard tracking relevant quality indicators. Getting this detailed overview can help businesses make better-informed purchasing decisions as well as ensure that supplier relationships and contracts are going as expected. Let’s look at how filters can make this template even more effective. 

**click to enlarge**

A traditional report would require its users to migrate from one document to another to see the performance of the different suppliers. Our template gives users the possibility to visualize all of these metrics for a specific supplier, category, project, and month all in one central location. Making it easier to perform advanced analysis and compare the different variables that go into assessing procurement quality. By being able to navigate metrics such as the return costs, vendor rejection rate, emergency purchase ratio, and more, users are able to maximize the business value coming from the different procurement strategies. 

Additionally, our custom chart tooltips feature allows you to hover over a value of a chart to get specific data. For instance, looking at the project analysis chart on our procurement template above, if a user wants to look at the exact value for project LA-273 in July 2021, all he or she needs to do is hover over it to see the exact percentage which is 66% in this case. Since this is the lowest percentage of the three projects in all of 2021, it is definitely something that needs to be looked into to find the reasons. 

3. Customer Demographics Dashboard 

Last but not least, we have a market research dashboard tracking the results of a five-year-long customer demographics survey performed by a technology manufacturer. Understanding its customer’s demographics is a key process that any company that wants to be successful in delivering a product or service should perform. Our interactive dashboards make the analysis easier and more accessible by providing a set of filters that enable users to navigate through the different sections of the dashboard. 

**click to enlarge**

The template above provides insights into 5 key demographic metrics: gender, age, education, household income, and technology adoption. These indicators allow companies to build buyer personas and promote targeted campaigns to increase sales as well as develop products that are tailored to their target’s interests. For instance, in the image above, we can see that the female share of customers has grown over the past 5 years. Filtering the entire dashboard based only on female respondents can help users get a complete picture of the female customer profile and make decisions based on it. The same process can be applied to compare different ages, education levels, incomes, and more. Through this, researchers can perform advanced analysis with all the needed data in one single place. 

Interactive Dashboard Software: Additional Features

Extracting the maximum potential out of all the aforementioned features would not be possible if the dashboard creator that you choose to invest in doesn’t offer complementary features to make the analytical experience as efficient and accessible as possible. To help you get a complete overview of what you should expect from these tools, here we present you with 5 capabilities that you should look for before investing in one. 

1. Customizable dashboards: As a part of achieving full interactiveness, dashboards should be customizable. This means being able to position your graphs and charts in a way that benefits your data story as well as being able to add the colors, logo, and font of your business for an extra professional look. In time, this will make your dashboards more accessible and focused. 
2. Mobile-friendliness: Accessibility is key when it comes to dashboarding. Considering the fast-paced world that we live in where professionals are moving from the office to their homes and to other locations, mobile friendliness is a must. BI tools such as datapine offer the possibility to access your dashboards from any device with an internet connection. In case you want to access your data from the phone, the dashboard will automatically adapt to fit the size of the screen.  
3. User-friendliness: Since a successful analysis requires every relevant stakeholder to be involved regardless of their technical knowledge, a user-friendly interface to generate interactive dashboards is a critical point to consider. For example, a drag-and-drop feature allows users to easily place their most important KPIs into the dashboard with just a few clicks. No need for coding or any other advanced task. This way, you open the analytical doors to everyone in the company and data-driven culture will be formed across the organization. 
4. Multiple visualization options: Visualizing everything in traditional tables or bar charts is not necessarily the most efficient way to present your data. Depending on the aim of the analysis and the data being presented, there will be different types of visuals that will serve the purpose better. For this reason, including multiple visualization options is another important aspect to consider. This gives you the flexibility to tell your data story in the way that you think fits best with the help of interactive graphics.  
5. Real-time data access: Staying on top of any new developments that happen with your data is another important aspect of success. Dashboards that provide real-time data access enable users to make important decisions as soon as something good or bad happens. This way, they avoid wasting resources on strategies that are not successful and can spot any potential opportunities to exploit in real time.  

Interactive Dashboard Features: A Summary 

We have answered the question of what is an interactive dashboard, provided examples, interactive dashboard features, tips, and tricks on how to use them in action, and what to look for when choosing your solution. To summarize our article, here are the top 14 features:

1. Dashboard chart filter/click-to-filter
2. Drill-throughs
3. Drill down
4. Cross tab filters
5. Time interval widget
6. Chart zoom
7. Custom chart tooltips
8. Advanced data options
9. Show or hide chart values
10. Dashboard widget linking
11. Hierarchical filter
12. Ignore filter option
13. Dynamic text boxes/images
14. Information tooltips

Modern software, like datapine, is helping to drive business with real-time interactive dashboards. This kind of tool is easier to use than spreadsheet programs and provides a wider range of options and possibilities. With innovative dashboard software that provides these 14 functionalities, you can prepare stunning interactive data visualizations of your business data. By simply utilizing interactive business intelligence dashboards, you have the chance to examine countless data sources on a single screen, without profound technicalities. Better yet, you can easily do it on your own with a few clicks and with no advanced IT skills.

Don’t believe us? Start your free trial and check for yourself. You can create your first charts and dashboards in minutes.

The post Move Beyond Excel, PowerPoint & Static Business Reporting with Powerful Interactive Dashboards appeared first on BI Blog | Data Visualization & Analytics Blog | datapine.

But being an inquisitive Sherlock Holmes of data is no easy task. There are a number of tools available on the market, and knowing which one to choose to increase performance can be time-consuming, and often confusing. Not to forget various areas of data scientists employed in, from academia to IT companies.

But we will not focus on their choice of industry, instead, we will take a closer look at the basic definition of these tools, and consider the most popular data science tools and software on the market in order to maximize the scientist’s role in an organization and/or company. Finally, we will expound on data science visualization tools that create powerful interactive dashboards by using a modern dashboard creator.

Let’s get started.

What Is A Data Science Tool?

Data science tools are used for drilling down into complex data by extracting, processing, and analyzing structured or unstructured data to effectively generate useful information while combining computer science, statistics, predictive analytics, and deep learning.

In the past, data scientists had to rely on powerful computers to manage large volumes of data. Thanks to modern online data analysis processes, today the costs are decreased since all the data is stored on a cloud that, ultimately, speeds up the process to make better business decisions.

The usage of modern and sophisticated tools has the goal to make data science faster, deeper, more effective while blending together several hundreds of routines that enable the standardization and cleanup of the data for you. Therefore, there are numerous data science tools and techniques that provide scientists with an easier, more digestible workflow and powerful results. To learn more about different analytical possibilities, you can explore our resource on self-service analytics tools.

Here, we list the best data science tools and software used in various industries and take a closer look at key functions and usage of each in order to make your choice of utilizing such solutions easier and lighter.

What Software & Tools Do Data Scientists Use?

The tools for data science benefit both scientists and analysts in their data quality management and control processes. But they’re not just limited to this, therefore, we will mention the top tools that data scientists utilize to delve into data, and extract actionable insights. Let’s start with RStudio and the R programming language.

1. R (And RStudio)

We begin our list of the top data science tools with R and RStudio. As most data scientists have probably heard of, RStudio is an open-source solution that allows to clean, manipulate, but also analyze data. It helps to automate and makes the usage of the R programming statistical language easier and much more effective. R users typically come from science, education, and various other industries that need statistical computing and design in their processes. Big companies that utilize R in their analytics operations, such as Google, Facebook, and LinkedIn, usually are finance and analytics-driven, as R has proved to be the top mechanism for data analysis, statistics, and machine learning.

**Source: RStudio

R is platform-independent, meaning it can be easily applied in each operating system. The major features that this data science tool offers are the ability for extensive data exploration combined with integration with other languages such as C++, Java or Python. Many users also report its power in constructed-in capabilities and libraries, data manipulation, and reporting. Whether the company needs a comprehensive financial analytics strategy or process, R has become one of the most used data science tools to explore and manage data.

Key functions and usage:

• available in both open-source and commercial use
• provides the user with visualizations, code editor, and debugging
• perfect for statistical computing and design

2. Python

We continue our list of the top data scientist tools with Python. Together with R, this programming language makes the state of the art towards data science tools and techniques. In an ideal world, learning both would be a perfect solution, but we will not concentrate much on that.

Instead, we will talk about how Python enables data scientists to begin their journey into this exciting field, but also want to explore the world of programming since Python was primarily developed as a programming language. It offers a wide range of libraries attractive for both programmers and data scientists such as seaborn or TensorFlow. But its popularity within data science is also based on the possibility to clean, manipulate, and analyze data, just like R. They do have differences, and the user has to finally decide which one fits better in their needs to work with data, but Python has emerged as one of the most prominent data scientist tools out there. In fact, there are numerous tools built with or connected with Python, such as SciPy, Dask, HPAT, and Cython, among others, which makes this programming language among the top choices of striving data scientists who would love to grow in the field.

**Source: python.org

The industry loves Python since scientists are usually looking for tools that will enable them a simple programming experience, without much hassles and potential complexities. It’s a general-purpose programming language, preferred by 55% of data scientists with less than 5 years in the field. That only confirms that Python is one of the top data science software on our list.

But not only, as the TIOBE index confirms that the popularity of Python is increasing. As a matter of fact, Python was declared as one of the top 3 most popular languages in 2020, and it will surely grow in the future as well.

Key functions and usage:

• offers a wide range of libraries; connected with numerous other tools
• used for cleaning, manipulating, and analyzing data
• preferred by almost half of the data scientists with less than 5 years in the industry

3. BI Tools And Applications

Business intelligence has developed into one of the most powerful solutions for companies that look for smart data analysis, predicting the future, and utilizing BI tools for generating actionable insights. There are many differences between business intelligence and data science, but with the recent development of BI tools, both became closely interconnected and dependent on each other.

The use of machine learning, predictive analytics, and various data connectors that enable the user to work with enormous amounts of databases, flat files, marketing analytics, CRM, etc., and share them with just a few clicks while all the information is stored on a cloud, enabled data scientists to use virtualization to their advantage, and utilize their selected data science software not just as a powerful tool, but also as a working environment to work with data on a scalable solution. There are also numerous business intelligence examples that illustrate what kind of value it can bring to the business bottom line. To put this into perspective, let’s take a look at an example:

The example above shows us a visual of the drag and drop interface created in datapine for a 6 months forecast based on past and current data. This is extremely useful in scenarios where future predictions can provide a backbone for defining forthcoming business strategies and decisions which would, otherwise, be based on possible human errors and “hunches.” The predictive analytics possibilities are developed in a simple, yet straightforward way, where you only need to enter your specified data points based on your past data, choose the confidence interval and the forecast engine will do the rest.

It is a fact that companies have become more data-driven, require a deep dependence on information, but need someone who can own the process of managing their data, often also of sensible nature. Business intelligence solutions not only provide the possibility to manipulate the data but also create powerful dashboards and reports that translate the work of data scientists to real business scenarios protected with high-security levels. These tools for data science offer additional aspects in dealing with information. They might implement a MySQL report builder to relieve the IT department from carrying out SQL queries and, therefore, save enormous amounts of resources and create a cost-effective business environment.

The possibilities within business intelligence are endless, and by using modern data science visualization tools in the form of BI software, scientists can become the backbone of a successful business strategy.

Key functions and usage:

• cloud data storage; availability 24/7/365
• scalable and adjustable according to the needs of the user
• connecting data sources and predicting future outcomes

4. Jupyter

Jupyter, know as a computational notebook, is one of the open-source data science tools that was born out of the Python Project back in 2014 and, since then, became renowned for its possibilities to combine software code, support scientific computing across all programming languages such as Python, Julia, R, and Fortran, among dozens of others (more than 40, to be exact). An interesting fact about Jupyter is that it will be used in astronomy to process terabytes of data each night for the Large Synoptic Survey Telescope (LSST) project. This notion definitely confirms that the data migration power of the tool is undeniable.

**Source: jupyter.org

The tool works as a computational notebook, as mentioned, that contains live code, equations, visualizations, and text. It’s consisted of the language of choice, sharing notebooks, interactive outputs such as images, videos, HTML, or custom MIME types, and integration with other data analysts tools and big data solutions such as Apache Spark. They also provide a hub for pluggable authentication, centralized deployment, and container friendly features so it’s not unusual to see giants such as IBM, Google, or Soundcloud that are currently using it.

Key functions and usage:

• supports more than 40 programming languages
• interactive computing features based on computational Kernels
• integrates with other big data solutions such as Apache Spark

5. BigML

Our data science tools and technologies list wouldn’t be complete without machine learning (ML), therefore, you might want to consider BigML. It’s a scalable machine learning platform that enables users to solve and automate regression, classification, cluster analysis, anomaly detection, and time series forecasting, among other prominent features. Their philosophy is focused on making machine learning simple, and if you want to use it for educational purposes, you can obtain a free account (they currently serve more than 600 universities across the globe). Additionally, you can sign up for a prime account that will enable you to collaborate on projects with team members.

**Source: bigml.com

The product can be used as an online data science tool as well as locally, embedded into applications and it’s a comprehensive ML platform for both supervised and unsupervised learning (from logistic regressions, deepnets to topic modeling, and Principal Component Analysis). All predictive models within the tool come with interactive visualizations and explainability features. That way, you can easily interpret the visuals such as Partial Dependence Plots while BigML models can be easily exported via JSON PML and plugged into Google Sheets, Amazon Echo, or Zapier, among others.

With BigML, you can share your machine learning resources using their project management and granular team capabilities while libraries that are available for multiple popular languages such as Python, Java, Swifts, or Ruby, will enable you to easily program and trace your workflow at any time.

Key functions and usage:

• focused on machine learning capabilities
• provides supervised learning: classification, regression (linear regressions, trees, etc.), and time series forecasting
• offers also unsupervised learning: association discovery, cluster analysis, anomaly detection, etc.

6. Domino Data Lab

Domino Data Lab is a data science platform that enables users to build and implement models, run on machines with up to 2 terabytes of RAM, or on specialized GPUs for deep learning without being a DevOps expert. Their Reproducibility Engine automatically tracks and organizes all results of each conducted data experiment while the preconfigured computing stacks for research, including popular languages such as R, SAS, Jupyter, Tensorflow, or Python, will enable you to utilize a comprehensively managed data science platform for various industry functions and objectives.

**Source: dominodatalab.com

You can also build your own custom environment and share it with your colleagues but also reconstructing experiments and results thanks to the mentioned Reproducibility Engine. They also support multiple modes of delivery, meaning you can fit models into existing workflows, send scheduled reports, or use self-service web forms. It can run in the cloud, on-premise, and hybrid environment, and is one of the tools for data scientists that tend to eliminate DevOps tasks.

You can deploy your work into their Kubernetes compute grid where you can monitor your model performance, and publish interactive apps built in Shiny or Flask. Additionally, their Control Center will enable you to check your team’s performance, allocate tasks more effectively, and gain a complete overview of your team’s work. Tutorials and pre-built environments can also help in faster onboarding so any team member can pick up the work where others have left off – project’s artifacts and history is available at all times.

Key functions and usage:

• includes solutions such as model management and cloud data science
• the Reproducibility Engine reconstructs experiments and results
• tends to eliminate DevOps tasks

7. SQL Consoles

The tools of a data scientist list wouldn’t be complete without SQL consoles such as MySQL Workbench, which we will give our focus on – this language is used for database management, querying, and analysis. In fact, MySQL Workbench is a visual tool that provides “data modeling, SQL development, and administration tools for server configuration, backup, and much more,” according to the product listing at the MySQL website. It has numerous features such as creating and viewing databases, executing and optimizing SQL queries, viewing server status, performing backup and recovery, and much more.

This is one of the most prominent data science visualization tools offered both for open-source and commercial use. This database management tool offers a variety of possibilities to keep a data-driven application running smoothly which is critical for organizations that need their databases clean and effective.

**Source: mysql.com

The field requires storing data in an accessible, and uncomplicated way; therefore, this language is the most convenient method a data scientist can use. Additionally, if you want more details about the topic and broaden your knowledge, you can read our resources on SQL reporting tools.

Key functions and usage:

• database management, analysis, querying
• performing backup and recovery
• offered both as open-source and commercial use

8. MATLAB

We finish our list of the best data science software tools with MATLAB. It’s still utilized in most of the academic areas, especially in (scientific) research. Computational mathematics is in the heart of this language, typically used in algorithm development, modeling and simulation, scientific and engineering graphics, data analysis, and exploration. It offers many statistics and machine learning functionalities such as predictive models for future forecasting.

**Source: mathworks.com

This is one of the tools of a data scientist that utilizes physical-world data by offering native support for real-time formats (sensor, image, video, binary, etc.). It also provides thousands of pre-built algorithms for financial modeling, image and video processing, control system design, and much more. Learning MATLAB is an excellent bonus for those who want to pursue a career in (academic) research.

Key functions and usage:

• utilized mostly in academia with a strong focus on computational mathematics
• offers many statistics and machine learning abilities
• thousands of pre-built algorithms

9. KNIME Analytics Platform

KNIME Analytics is a platform that can be used for enterprise data science as well as an open-source solution through 2 main products: KNIME Platform and KNIME Server. Here we will focus on the Platform that features visual workflows, a choice from over 2000 nodes, including native ones and from different domains, and the possibility to use publicly available workflows or a workflow coach. With the platform, you can combine text formats, unstructured or time series data, and connect to a host of databases and data warehouses to integrate your data, including Oracle, Apache Hive, Load Avro, Parquet, etc.

**Source: knime.com

You can shape your data by deriving statistics, including mean, quantiles, and standard deviation, or applying statistical tests, and integrating dimensions reduction and correlation analysis. Sorting, filtering, and joining data can be done on your local machine or in distributed big data environments while you can clean your data through normalisation, data type conversion, and missing value handling.

If you want to go a step further, you can build machine learning models for classification, regression, dimensions reduction, or clustering by using advanced algorithms such as deep learning. You can also visualize your data with classic bar charts and scatter plots, for example, and export as .pdf or a PowerPoint presentation.

Key functions and usage:

• a choice of over 2000 nodes (native and from different domains)
• you can derive statistics, integrate dimensions reduction and correlation analysis
• offers the possibility to build machine learning models by using deep learning

10. Microsoft Excel

We finish our roundup of the tools for data scientists with Excel as one of the traditional solutions that are still present today in numerous analysis processes and it’s a fairly classical tool for analyzing, manipulating, calculating, and visualizing your efforts through a spreadsheet. Various formulas, filters, slicers, and tables will enable you to customize your analytical efforts, and explore your data, but on a smaller scale in comparison to other data scientist software from our list such as datapine or Python. This is one of the business-specific data science tools since it can fit departments that need to create a procurement reporting process, for example, and present their numbers in a spreadsheet that can be easily shared or manipulated by using columns and rows.

**Source: microsoft.com

That said, it’s highly popular for spreadsheets calculations, and data scientists can use it for cleaning as it’s fairly simple to use in order to edit 2-dimensional data (in essence, tables). Oftentimes considered as a bridge between advanced data scientists and average business users, Excel has its power to provide a set of features and possibilities that can accommodate both, especially if you’re just getting started in data manipulation and analytical processes. Excel is also considered as one of the tools that you can combine with others and it certainly gives us a reason that it survived and thrived since it was published back in the 90s.

No matter if you need to build a sales chart or simply calculate endless rows and columns, Excel will give you some basic features that you can start with, and then you can decide if you want to utilize more advanced tools from our list.

Key functions and usage:

• good for analyzing and cleaning 2-dimensional data
• uses historical data, analyzed on a smaller scale
• works well for beginners as most people are acquainted with Excel

We have listed the best data science software tools for various fields that can be used in, whether small business analytics or scientific research. It contains so many areas that this list could go on, but, in the end, it all depends on what the focus area of the data scientist is, and how advanced his analysis needs to be.

The success of the modern analytics strategy, whether academic, business, or industrial, depends on the data. Utilizing the right data science software and tools is crucial to develop practical values of the projects, but the context in which data exists is completely dependent on data scientists. We hope that this list of the best software for data science has given you a good overview of the possibilities of each and made your choice much easier, no matter if you need an enterprise software solution or a more simple and open-source.

If you want to have practical experiences of data and science, learn about various ways of analyzing your data, developing practical insights, monitoring and extracting your findings, then try modern business intelligence software like datapine for a 14-day trial, completely free!

The post Top Data Science Tools That Will Empower Your Data Exploration Processes appeared first on BI Blog | Data Visualization & Analytics Blog | datapine.

When it comes to implementing and managing a successful BI strategy we have always proclaimed: start small, use the right BI tools, and involve your team. We know that the best approach is an iterative and flexible approach, no matter the size of your company, industry or simply a department. When encouraging these BI best practices what we are really doing is advocating for agile business intelligence and analytics.

That said, in this article, we will go through both agile analytics and BI starting from basic definitions, and continuing with methodologies, tips, and tricks to help you implement these processes and give you a clear overview of how to use them. In our opinion, both terms, agile BI and agile analytics, are interchangeable and mean the same. Therefore, we will walk you through this beginner’s guide on agile business intelligence and analytics to help you understand how they work and the methodology behind them. Without further ado, let’s begin.

What Is Agile Analytics And BI?

Agile analytics (or agile business intelligence) is a term used to describe software development methodologies used in BI and analytical processes in order to establish flexibility, improve functionality, and adapt to new business demands in BI and analytical projects.

It’s necessary to say that these processes are recurrent and require continuous evolution of reports, online data visualization, dashboards, and new functionalities to adapt current processes and develop new ones. In essence, these processes are divided into smaller sections but have the same goal: to help companies, small businesses, and large enterprises alike, adapt quickly to business goals and ever-changing market circumstances. To build your company even more, we suggest you read our article on the subject of enterprise software applications.

It’s often the case that businesses need to develop an agile BI methodology in order to successfully meet companies’ requirements of strategic developments as well as operational ones. No matter if you need to develop a comprehensive online data analysis process or reduce costs of operations, agile BI development will certainly be high on your list of options to get the most out of your projects.

The term “agile” was originally conceived in 2011 as a software development methodology. 17 software developers met to discuss lightweight development methods and subsequently produced the following manifesto:

Manifesto for Agile Software Development:

Individuals and interactions over processes and tools
Working software over comprehensive documentation
Customer collaboration over contract negotiation
Responding to change over following a plan

That is, while there is value in the items on the right, we value the items on the left more.

And like that, agile was born. As a software development methodology, agile is a time-boxed, iterative approach to software delivery that builds software incrementally, instead of trying to deliver the entire product at the end. Due to the success of its methodology, agile has successfully migrated beyond its initial scope and is now being used successfully as a project management methodology across numerous industries. With an emphasis on adaptivity over rigidity and collaboration over hierarchy, it’s easy to see why agile is becoming the chosen methodology for so many.

Source: www.pmi.org

To look into these processes in more detail, we will now explain the agile BI methodology as well as for analytics and provide steps for agile BI development.

Agile Business Intelligence & Analytics Methodology

Business intelligence is moving away from the traditional engineering model: analysis, design, construction, testing, and implementation. In the traditional model communication between developers and business users is not a priority. Also, developers are more focused on data and technology than answering more important questions:

• “What business questions do we want to answer with the available data in order to support the decision-making process?”
• “What do our users actually need?”

Through agile adoption, organizations are seeing a quicker return on their BI investments and are able to quickly adapt to changing business needs. To fully utilize agile business analytics, we will go through a basic agile framework in regards to BI implementation and management. You may find different versions of this to adopt but the underlying methodology is the same. Let’s start with the concept.

1. Concept

This is the stage where you start to develop a loose BI vision. The agile BI implementation methodology starts with light documentation: you don’t have to heavily map this out. A whiteboard meeting will suffice, where you can explain the initial architecture, consider the practical aspects of delivering the project, and identify the prioritization between them. Details will be taken into consideration later, therefore, focus on the concept and develop from there.

2. Inception

The inception stage is the critical initiation stage. This is when you first implement active stakeholder participation. You also:

• Train project stakeholders in agile fundamentals
• Determine BI funding and support
• Identify key business requirements and needs. This includes understanding the business questions to be answered through the BI system
• Discover the available data sources
• Understand the expected information delivery avenues: reports, dashboards, ad hoc reporting, etc.
• Then prioritize key business requirements and needs with time and budget constraints in mind. An effective prioritization technique is to write user stories for each business question identified. Then use a frequency vs. difficulty quadrant to prioritize them. The top right quadrant includes the business questions that are most frequently asked and are the most difficult to answer with existing data. These stories can be considered as a high priority. The bottom right corner of least difficult and most requested might be some good low hanging fruit as well!

Source: www.thoughtworks.com

• During this stage, you are also researching and vetting which online business intelligence software to use. You need to determine if you are going with an on-premise or cloud-hosted strategy. Then, you need to choose AND set-up the right BI solution for your organization!

3. Construction Iterations

During construction, you are delivering a working system that meets the evolving needs of stakeholders. You will continually cycle through this stage to stage 4 at set increments, usually 1-3 weeks long. Eventually, after stages 3 and 4 are done you move to stage 5 (production). But before production, you need to develop documentation, test driven design (TDD), and implement these important steps:

• Actively involve key stakeholders once again
• Collaboratively develop reports
• Utilize the “just in time” (JIT) modeling: identify an issue that needs resolving, grab a few co-workers and explore the issue, and then everyone continues as before. This is also known as model storming, one of the practices in agile analytics development
• Test BI in a small group and deploy the software internally

4. Transition (aka Release or End Game)

During this stage, you release the previous construction iteration into production. You then return to iteration and then return to transition again to release those changes to production. During transition, you:

• Involve key stakeholders (yes, still!)
• Finalize testing
• Finalize documentation, where necessary
• Pilot release to small subgroup
• Train end-users
• Train production staff
• Deploy into production

These steps are critical in the adoption of agile in business intelligence and it’s important to stress that you need to support your team in delivering value in a timely manner, but not stick to a ‘single truth’ as different departments have different ways and styles of working. After the tinkering of transition and iterations is done, you will move to the next step in BI and agile analytics development.

5. Production

Production is where you operate and support everything that has come out of the construction and transition iterations into production. During this stage, you:

• Operate and support the system, dashboards, and reports
• Identify defects and enhancements. Any of these changes must start at the construction stage and work their way to production.

In essence, production is the stage where you will need to keep an eye on the overall system, utilize a dashboard maker, and support the release.

These basic steps will enable you to deliver agile data analytics and BI methodology into practice, no matter the size of your company. Always remember to focus on users and understand how people will potentially use your BI system and reach your business goals, both short and long-term.

Now that you know the basic framework and how it works, we will divert our attention to additional tips to make sure you don’t miss any important part of successfully developing an agile analytics methodology and increase the quality of final projects.

Top 10 Tips For Agile BI & Analytics Development

To make sure your BI and agile data analytics methodologies are successfully implemented and will deliver actual business value, here we present some extra tips that will ensure you stay on track and don’t forget any important point in the process, starting with the stakeholders.

1. Active stakeholder engagement

It is so important we are stating it again. Stakeholder involvement is critical throughout every stage of your BI project. In agile, stakeholders and product owners experience team progress at regular intervals throughout the process, and increased stakeholder input means better overall business value. Stakeholders are critical throughout the project, and they need to be included in most of the steps since you need regular feedback, no matter if it’s the direct user in question, senior manager, staff member,  developer or program manager. Typically, you need to develop a close collaboration with stakeholders in order to finally update the solution based on their feedback and overall understanding of what they actually need. While dealing with stakeholders, remember to be flexible, educate senior management, and understand their importance. That way you can save yourself lots of potential bottlenecks into delivering the final project and results.

2. Embrace an evolutionary approach

It is a given: requirements, or at least your understanding of them, will change throughout the lifecycle of your project for a variety of reasons. To best develop a solution that meets stakeholder needs you have to take an evolutionary (iterative and incremental) approach to development. Keep in mind the need for methodological flexibility as every team is unique, various technologies require various techniques, and there is no ‘one size fits all’ approach to agile methodology in data analytics and BI. It is possible to work with different teams, no matter if their focus is on data management or agile business intelligence platforms implementation. The important notion is that you need to be prepared to work in an evolutionary manner and deliver your project incrementally, over time, instead of one big release. This concept can be new to data professionals as well as traditional programmers, but it will certainly help in modern software processes.

3. Document only when necessary

This tip should be a favorite. Where traditional methods require a great deal of time in planning and writing documentation, agile relies on daily scrums and face-to-face interactions for team communication. By minimizing documentation, teams are able to respond quickly to project obstacles and remove redundancies. We’re not saying to completely lose the documentation but only to focus on what’s necessary. Effective teams usually focus on activities such as developing reports instead of just documenting what you need to deliver at some point. You will measure your success by delivering the project, not by the level of documentation you’re producing, therefore, documentation should be developed only when necessary. It’s better to have regular feedback on the final product so that you know what needs to be updated and improved instead of filling endless documentation. That way, your feedback cycle will be much shorter, workflow more effective, and risks minimized.

4. Accept change

If you can act on a changed requirement late in the lifecycle, it could result in a competitive advantage.  Instead of adopting strict change management processes, adopt an agile approach to change management. With the agile methodology, stakeholders can easily change their minds as progress progresses. But not only, as agile BI solutions and services look to deliver projects which are both high-quality and high-value while the easiest way is to implement high-priority requirements first. That way, the stakeholder’s ROI can be maximized while agilists can truly manage change instead of preventing it. There are numerous reasons why change happens, from missing a requirement, identifying a defect, legislation or even marketplace can change. The main point is not to set in stone the requirements early in the lifecycle so that you have space to adapt and deliver what stakeholders asked for. This is essential in BI and for effective organizations in order to reach success.

5. Test throughout the lifecycle

Remember agile business intelligence is a continual process and not a one-time implementation. Data changes. Organizations change. You will need to continually return to your business dashboard to make sure that it’s working, the data is accurate and it’s still answering the right questions in the most effective way. Testing will eliminate lots of data quality challenges and bring a test-first approach through your agile cycle. This is a continuous process throughout the project and the goal is always the same, as we mentioned before: to deliver high-level quality results. Usual methods that are used in agile testing include:

• Behavior driven development (BDD): Here, the goal is to improve communication between stakeholders so that each understands features before the beginning of the development process
• Acceptance test driven development (ATDD): This method is used to create a set of acceptance tests so that the customers, developers, and testers incorporate their perspective into the agile business analytics development
• Exploratory testing: In this case, customer collaboration and generally individuals included in the project are more important than the process itself as well as comprehensive documentation.

Each method has its own set of features and scenarios where they can be implemented, with additional benefits such as saving countless hours and, therefore, costs.

6. Choose the right BI software

Don’t go through all this effort to be agile and then use agile business intelligence platforms that are stuck in traditional methods. Make sure your BI software:

• Supports quick iterations: iterations will take longer if your tool is cumbersome, hard to use, or does not work well together with other systems and data sources.
• Makes basic features easy to use: self-service BI tools allow even not so technically-savvy end-users to participate in all stages.
• Facilitates easily delivery to a large audience: valuable feedback will be lost if the software restricts the number of end-users that can provide feedback and engage in the process. You want an organization-wide buy-in of your business intelligence strategy. To this end, everyone that should have access must get access.
• Supports collaboration: to foster active stakeholder participation the tool must make collaboration between these users easy.
• It allows you to easily publish reports: the whole point of agile is to get the product out there. Find a business reporting software that allows you to rapidly deploy new dashboards and reports. Just make sure you can easily make changes to them moving forward.

7. Automate as much as possible

To succeed in agile, automating as many processes as possible is the key. Building automation will help in the preproduction environment (or demo) where you need to build a version of your system that completely works. Agile analytical tools can help teams in automating any process that’s done more than once. That way you can focus on feature development and avoid duplicate processes, leading to greater operational efficiency. For example, if you use white label BI, make sure they have automation features in place so that your analytical team doesn’t have to deal with many manual tasks and, additionally, have seamless integration into your existing applications. The more processes you can automate, the more benefits you will gain in the long run.

8. Evaluate your key performance indicators

Regularly turning to KPIs in an agile environment is necessary in order to effectively evaluate progress, reflect on the performance, and improve discussions. The entire team should be introduced to KPIs that will evaluate the success of the agile framework, and each member should know the role they need to fulfill which are then presented to senior leadership on a regular basis. It might make sense to follow-up on specific operational metrics on a weekly or bi-weekly basis so that any issues or potential bottlenecks can be addressed quickly. For example, you can collect the amount of business information fed into a data lake weekly, therefore, have the advantage to react immediately if issues arise.

9. Ensure the quality of production

The point of agile is to gradually evolve to the best possible BI solutions instead of building constant (and hollow) prototypes. As mentioned earlier, ruthless testing is needed throughout the project and the quality of production is achieved when users are satisfied with the delivered value and developers proud of their work. Each feature must be tested and debugged on time in order to ensure the quality of the production, and, finally, considering it ‘done’ when all stakeholders are accepting the final product.

10. Support collaboration and self-management

In traditional settings, the development team often bears the burden of respecting deadlines, managing budgets, ensuring quality, etc. Agile methodology in data analytics and business intelligence acknowledges that there is a much broader community that needs to share the responsibility to successfully deliver the project’s success such as technical experts, project managers, business owners, stakeholders, etc. Collaborating daily with the technical team is important as well as collaborating throughout the project community in order to become successful in agile. This collaboration requires also a self-managing approach, where teams can decide on their own how much time they need for certain developments.

Use An Agile Strategy To Get Your Business Intelligence Off the Ground…

….and keep it relevant and effective.

Agile analytics embrace change, viewing it not as an obstacle but a competitive advantage. The result is a more flexible and more effective BI that is situated for success in a continuously evolving industry. You can start by using datapine to implement agile business intelligence at your organization for a 14-day trial, completely free, and reap the benefits across the board.

The post Accomplish Agile Business Intelligence & Analytics For Your Business appeared first on BI Blog | Data Visualization & Analytics Blog | datapine.

But data is only valuable if you know how to handle it effectively.

With so many digital insights available in our hyper-connected age of information, a  professional report tool is the most effective means of collecting, curating, organizing, and analyzing your most valuable business data.

To help you understand data-driven reporting and propel your business to the next level, we’re going to explore the difference between static reports and dynamic reports.

In addition to exploring the difference between static and dynamic, we will also look at 2 working dynamic reports examples.

Let’s get started.

What Is Static Reporting?

A static report offers a snapshot of trends, data, and information over a predetermined period to provide insight and serve as a decision-making guide. After initial use, a static report is usually filed away and used for the purposes of historical data analysis.

Static reports are those that include static information relating to a specific area of business, from inventory to sales, customer service, and beyond.

These traditional reports are generated in Excel, Word, or PowerPoint and exported into HTML or PDF format. While static reporting is a reliable source of information, there is no scope to drill down further into the insights displayed on them, meaning that these informational documents have a short shelf life.

We’ve explored our static report definition in greater detail. Now, it’s time to take a look at our dynamic or real time reporting definition.

What Is Dynamic & Real Time Reporting?

Dynamic (or real time) reports offer 24/7 access to the most up to date information while enabling the user to interact with data through functionalities such as interactive features and other capabilities in order to conduct basic and advanced analysis of data.

Most dynamic real time reporting software is powered, to some extent, by machine learning (ML) capabilities, meaning that it’s insightful, intuitive, and enables you to use your data as a past, predictive, and live decision-making resource.

As a result of their interactive nature, dynamic reporting dashboards also help businesses become more responsive to unexpected issues or sudden changes in direction by gaining quick-fire access to visual data as it unfolds—a priceless capability regardless of your industry.

Dynamic vs. Static Reports: What’s The Difference?

We’ve offered a clearcut real time reporting definition and touched on the value of real time reporting tools. Now, it’s time to dig a little deeper by pinpointing the difference between static and dynamic reports.

It’s clear that dynamic reporting offer a greater level of depth than those of the static variety, with functionality that allows users to interact with the insights before them rather than merely viewing them on-page. A KPI reporting software can even automate and offer the most recent data in all your reports.

While real time reporting tools offer a seemingly endless level of scope for improving decision-making and fostering a culture of business intelligence (BI), static or traditional reporting methods are worth archiving for historical performance reference. In short, we shouldn’t rule our traditional reporting techniques completely, but we should acknowledge that they are somewhat antiquated in our tech-driven digital age.

Let’s further clarify the key differences between static and real time reporting.

Dynamic vs. static reporting:

• Cohesion: As static-style reports offer a snapshot of data, in order to examine and analyze insights from a longer timeframe, it’s necessary to pull up multiple reports from different sources, causing fragmentation and consuming hours of time sifting through information. As your average dynamic reporting tool consolidates relevant data in one central location, comparing insights and viewing metrics from broad timelines is quick, intuitive, and designed for swift decision-making, whether you need to create a finance report for your department or the entire organization.

• Accessibility: It is possible to export static report data into various formats and share these insights digitally, but the process is fairly manual and takes time. It also leaves more room for error. With dynamic data reports, users can log into a dashboard from anywhere across multiple devices for instant insight and analysis.

• Digestibility: Every robust dynamic reporting tool offers a multitude of stimulating visuals based on clearcut key performance indicators. As humans, we respond far more effectively to visual stimulation than text-based information, which means that interactive reporting makes data and dashboard storytelling more effective. This, in turn, produces more powerful, business-boosting results. As static data is more text-centric and devoid of interactive functionality, extracting insights is a slower, more laborious process.

• Scalability: Static data provides value for a short time as reports of this nature are set in stone. However, interactive reports are customizable both in terms of content and functionality, which means it’s possible to tweak and improve them over time, allowing you to remain responsive to the commercial landscape around you as you evolve and grow — scalability guaranteed.

Now that we’ve explored dynamic vs. static reports, we will take a closer look at real industry examples.

Essential Dynamic Reporting Examples

We’ve explored the key differences between two of the world’s most powerful business reporting types. Now, it’s time to look at two quite different but equally inspiring dynamic reports examples—starting with our financial KPI dashboard.

1. Financial KPI dashboard

**Click here to open the dashboard in full-screen mode**

Primary KPIs:

• Working Capital
• Quick Ratio / Acid Test
• Cash Conversion Cycle
• Vendor Payment Error Rate
• Budget Variance

When you create dynamic reports, it’s important to work with a balanced mix of KPIs and visuals. The financial dashboard above is a testament to that notion.

With five key performance indicators that apply to almost every industry and sector, the dynamic real time data provided within this report includes all of the financial analytics information required to answer critical monetary questions based on liquidity, invoicing, budgeting, and the general financial stability of your business. From current assets to working capital, it’s possible to gain an up-to-the-moment insight of all critical financial performance data while drilling down into specific metrics with ease.

For monitoring your financial health while gaining the intelligence required to improve internal processes and fix inefficiencies, this interactive reporting tool is second to none. A powerful financial tool that scales seamlessly with business growth.

If you want to tackle deeper into the financial health of your organization, you can explore our rich library full of financial KPIs.

2. Sales & order dashboard

**Click here to open the dashboard in full-screen mode**

Primary KPIs:

• Total Orders
• Total Sales by Region
• Order Status
• Perfect Order Rate
• Return Reason

 “Data is what you need to do analytics. Information is what you need to do business.” – John Owen

The retail sector is fast, furious, and rife with competition. Regardless of your niche or what you’re selling, gaining access to dynamic live insights is essential if you want to get ahead of the pack and stay there.

Our sales & order overview is interactive, engaging, visually balanced, and equipped with all of the ingredients for growth and success in the retail sector.

This retail dashboard has five focused key performance indicators geared towards improving your fulfillment processes, handling orders more efficiently, and understanding any customer-facing issues with your service or products.

By gaining access to this depth of dynamic information, you stand to make your retail analytics more productive, more efficient, and more profitable. For ambitious retailers, interactive reports don’t get any better.

“It is a capital mistake to theorize before one has data.” – Sherlock Holmes, “A Study in Scarlet” (Arthur Conan Doyle)

Now that you’re well versed in the power of data analysis, you’ll be able to create dynamic reports that will help your business become bigger, better, and stronger than ever before. No matter if you need to track call center metrics or create stunning visualizations, these reports will boost your bottom line and provide you with the most accurate data available.

In the Age of Information, we’re swimming in digital data, and it’s those who embrace its power today that will survive and even thrive tomorrow.

It’s true that static reporting offers some value—they are based on critical business data, after all. But to squeeze every last drop of value from the information available at your fingertips, dynamic reporting is the way forward.

Dynamic reporting tools will empower you to dig deeper into the information that you know offers genuine value to your business, interacting with it in a way that will open your eyes to a world of business-boosting opportunities that you never knew existed. Dynamic data will help you make better decisions, develop narratives that will help others in your organization do their jobs better, and make your business more responsive to change. All this will catalyze your success for years to come—and surely, that’s the aim of the game, isn’t it?

To ensure you start with your own dynamic reporting practice which is intuitive and easy to digest, we offer a 14-day trial, completely free! Start building your reports with just a few clicks and see how dashboards and smart modern tools benefit your bottom line!

The post Everything You Need To Know About Static, Dynamic & Real Time Reporting appeared first on BI Blog | Data Visualization & Analytics Blog | datapine.

Business intelligence steps up into this process by creating a comprehensive perspective of data, enabling teams to generate actionable insights on their own. With the introduction of online BI, companies today have the chance to create additional value, and, ultimately, profit.

At its core, business intelligence (BI) encompasses the strategies and technologies used by companies for the detailed online data analysis of key business-based information. BI technologies offer historical, current, and predictive insights into various aspects of business operations, thus helping a company to make informed decisions on activities centered around finances, marketing, sales, competitor research, social outreach, internal processes and more.

Business intelligence is vital in our digitally-driven world as it essentially gives you an additional sense: a commercial vision that can help you see and process far more than the information that presents itself on the surface. And there are business intelligence examples and insights out there that demonstrate that every notion.

To put the power of business intelligence into perspective, here are 4 key insights you should know:

• Businesses using analytics are five times more likely to make better, quicker decisions, according to an article published on BetterBuys.
• Forecasts for the worldwide business intelilgence software applications market for 2024 are 17.6 billion, increasing from 14.9 billion in 2019.
• Businesses will create and manage 60% of the world’s data by 2025.
• 85% of business leaders believe that big data will change the way they do business, significantly, especially in the personalization potential of intelligence.

It’s clear that BI and the tools that facilitate better business intelligence are vital to the future of any company competing in the digital arena, regardless of industry or sector.

Here we explore 13 BI examples based on real-life case studies, scenarios, data, and discoveries. These business intelligence examples will showcase the power and potential of big data analytics in the modern age and how it can make your venture smarter, stronger, more scalable and more successful.

Without further ado, here are 13 inspirational examples of business intelligence.

1) Improving The Decision-Making Process

One of the primary benefits of BI is the ability to make better and more valuable decisions, and this business intelligence example is based on that very idea.

In the first of our business analytics examples, the CEO and founder of a budding fintech company was presented with the challenge of changing his business’s internal culture with a view to making all business data more accessible across the board.

To avoid the IT department having sole control over the data, and thereby preventing other departments from working collaboratively and making informed decisions that benefit the business, the company’s CEO deployed a dashboard reporting software for an automated data reporting process. As a direct result of this decision, not only is the company’s data now decentralized and digestible, improving the decision-making process across the board, but it has also saved 40 valuable hours per week on report preparation. This is one of our business insights examples that don’t stop here.

Speaking on this BI triumph, the fintech CEO said, “All departments now can access their own real-time dashboards, no matter if they are in the office or at a meeting. All decision-makers have quick, easy access to ad-hoc analysis and reports, even on their tablets.”

2) Uncovering Fresh Business Insights

The second of our business analytics examples is focused on discovering new business insights that can ultimately help streamline commercial processes, thereby improving productivity and boosting the bottom line.

A forward-thinking online food ordering business wanted to gain a better insight into the life cycles of its customers while gaining the ability to optimize sales reports and marketing campaigns in a time-efficient, cost-saving, and autonomous way.

By gaining self-service access to real-time analytical information the company was able to streamline its marketing and sales activities, make better, swifter decisions based on real-time information and uncover fresh insights that have served to improve its level of customer experience, resulting in increased brand loyalty.

The use of a real-time dashboard has empowered the budding online food giant to monitor all significant business operations through customized KPIs. Moreover, the new business analytics platform has made the business more able to rise to challenges as they unfold in days, rather than weeks or even months later. With the help of sales graphs and charts, the data was easily interacted with, and presented on a single screen.

This is one of our examples of business analytics that demonstrates how quickly the power of business intelligence affects the decision-making processes and creates a backbone for sustainable growth.

3) Boosting Productivity

Today’s consumers crave ratings, opinions, and reviews from their peers to help them make decisions, particularly when it comes to travel. That said, a travel-based rating business should be able to deliver an exemplary level of customer experience and support to its users.

In the third of our business intelligence examples, a hotel rating company based in Berlin turned to business intelligence analytics software as its data was fragmented, diluting its impact across the company, impairing its productivity and service levels as a result.

By rolling out a SaaS-based analytics solution that requires minimal IT intervention and making it accessible across the organization, the travel company was able to consolidate its key data in one accessible space.

When it comes to big data examples in real life, this travel business made a wise BI-based move that resulted in improved internal efficiency, better interdepartmental cohesion, and the new level of insight has also enhanced the company’s level of customer support beyond the CEO’s wildest expectations.

4) Increasing Sales

Number 4 of our inspiring BI examples demonstrate that by using big data analytics to your advantage, you can increase your sales – which is one of the primary aims for all business worldwide. By taking advantage of well-established sales KPIs, each business can improve its bottom line. Let’s see this through one of our top examples of companies using business intelligence.

A rising online retail player was suffering from an inconsistent and somewhat erratic sales performance for some time and was unable to evolve its strategy despite a host of efforts.

After turning to BI methodologies and incorporating a sales dashboard to solve this ongoing issue, it became apparent, almost immediately, that force rather than data drove the company’s sales strategy. By realigning its strategy and drilling down into the data available at its fingertips, the company’s sales grew by 24% while rep attrition fell by 90%. A better organized target-setting process, as well as streamlined sales strategies driven by data, has ensured that the company not only continues to scale, but its sales team continues to surpass its targets.

5) Improving Financial Efficiency

Without a doubt, your financial department is one of the beating hearts of your organization because, without steady cash flow and the capital to invest back into the business, the entire operation would grind to halt.

That said, the fifth of our business analytics examples focuses on evolving a company’s business intelligence to identify potential cash flow issues and improve financial efficiency. In this scenario, a disease diagnostics brand turned to an online reporting software and business intelligence methodologies as, despite a period of rapid growth, the company’s percentage collections were low, and accounts receivables, as well as claim denials, had reached a record high.

To tackle this potentially devastating issue, the company implemented an intuitive financial reporting system that allowed them to drill down into a wealth of relevant account-based metrics but also utilizing a wealth of financial graphs that helped them see data in a visual and straightforward way.

As a result of this savvy BI initiative, and the most financially-driven of our examples of business intelligence, the business diagnostics was able to, well, diagnose the issues by leveraging the power of financial reports, uncovering the source of the claim denials, and recovering millions of dollars’ worth of claims in the process.

6) Streaming Internal Processes

A rapidly growing US-based healthcare company suffering from a raft of disjointed internal processes and commercial inefficiencies sought the power of BI in the sixth of our analytics examples.

Due to a lack of cross-departmental compatibility arisen out of poor data handling, collection, and analytics processes, the business was unable to use this wealth of digital insight to its advantage. However, by working with a BI partner to develop and deploy a unified business intelligence system that integrates multiple data sources into one single platform, the healthcare company was able to analyze their data in on an efficient, valuable and accessible format, empowering it to make increasingly smart decision to benefit the business. This is one of our business insights examples that shows us how healthcare companies can perform on a much higher level.

Armed with the tools required to perform their jobs better, departments including finance, billing, marketing, and sales began to work more productively, evolving internal processes, and boosting cross-departmental collaboration.

This scenario is perhaps one of the most valuable of our business analytics examples as it serves to showcase how big data in healthcare and business intelligence can help foster a culture of continual evolution, which is an invaluable asset in today’s fast-paced digital world.

7) Saving Marketing Dollars

One of our industry BI solutions examples focuses solely on the marketing department of a company. A US-based e-commerce company suffered from low conversion rates, despite the fact their campaigns were monitored on a daily, weekly, and monthly basis with the help of traditional spreadsheets. The team was disparate across 19 US cities, and their communication was oftentimes slow. By the time the campaign manager analyzed the campaign data, another campaign already needed to be launched. This blind approach to their promotional activities cost them days and weeks of proper planning, analyses, and reporting process. Losing time in marketing is a significant issue that costs dollars, and, ultimately, conversions.

A centralized BI solution saved the team 4 working days per week by automating the reporting processes, alarming the designated campaign manager when an anomaly in the campaign occurred, and predicting future campaign results.

They utilized a similar marketing dashboard such as this one:

**click to enlarge**

This example of business intelligence shows the top 3 campaigns by spent budget, the total number of impressions, clicks, acquisitions, and the CPA by a campaign for the last 12 weeks. The whole team had this dashboard automatically delivered and updated in their inbox each week – insights were made fast, campaigns were planned better, and campaign managers across the US had the same data at the same time so their communication and cohesion also improved.

8) Reducing IT Involvement

A financial company was having difficulties in their analytical processes involving the IT department. Their daily analytical and ad hoc reports were often times late, and employees didn’t have a proper insight into the massive amounts of data they were collecting. The IT department was simply overburdened with requests from each department of the company, and when you add the additional tasks they needed to handle, they were overpowered with a shortage of time and efficiency.

The company wanted to decentralize the decision-making process, and grant business users across the company the possibility to extract, administer, and derive insights while creating their own reports, without the need to wait for the IT department for hours, even days. Since they needed to combine multiple data sources, internal and external, a business intelligence solution was a logical step forward for implementing into their operational and strategic management.

The company quickly saw an improvement of their reporting and analytical processes, not only via automated standard reports but also in ad hoc analysis, where they only needed to utilize a drag-and-drop interface to generate immediately actionable insights. The IT department could also resort to the database reporting tool since business intelligence provides beginner and advanced features for business users across industries and departments.

This is one of the business analytics examples that show how to unburden staff and create a working culture that saves time and increases productivity.

9) Connecting Departments

Another real-world business analytics example centers around a fashion label based in Washington, D.C. with multiple stores across the city.

Their challenges arose when they needed to combine sales and marketing data in real-time, optimize their promotional activities to deliver the best possible results, and create a comprehensive overview of the customer lifecycle. That meant that a vast amount of data and KPIs needed to be managed and successfully analyzed to get the best possible results.

They decided to implement business intelligence into their operations to be able to monitor real-time data, ensure employees have access to marketing and sales analytics and use a dashboard builder to visualize all their business information.

It not only improved customer loyalty (we will talk about this in another example of business intelligence), but by connecting various datasets from different departments, the company managed to utilize these business intelligence KPI tools for various purposes such as aligning promotional activities with the goal of closing more sales deals. Ultimately, the sales department was better informed about marketing activities, and the marketing department could better plan their promotions to fit the overall customer lifecycle.

10) Increasing Employee Performance

We continue with examples of companies using business intelligence by mentioning an HR department of a US-based company that was experiencing issues when employees started to increase their overtime hours, the productivity decreased, but the number of sick days steadily grew. This was an unusual situation in HR since it showed them that there are issues with the workforce, but they couldn’t afford the time to speak with each employee, it would have taken them weeks of time, even months.

The manager decided to take advantage of HR analytics software and utilize business intelligence for their department. By analyzing workforce behavior and performance, the department had a better overview of the issues that needed to be solved.

The most prominent HR KPIs that were looked upon are the overall labor effectiveness, overtime hours, absenteeism rate, and the training costs to see if new hires would make sense. After looking at the data on interactive visualizations that gathered historical and present information of the whole department, the manager could clearly conclude that the department was understaffed, chronically tired, and the company needs new hires or the sick days will increase exponentially, thus creating substantial business issues in the long run.

This is one of our operational business intelligence examples that showed us how workforce management can be streamlined and upscaled so that the whole company doesn’t deteriorate because the staff simply needs more help.

They used an HR dashboard similar to this example below:

**click to enlarge**

This enabled them to advance their employee productivity and the overall performance since the analytics process empowered the manager to make a better-informed decision and create regular HR reports that provide accurate data. And it didn’t take weeks or months to do it, visualizations were generated with a few clicks.

Now we will focus on examples of business analytics that improved a manufacturing business located in France.

11) Enhancing Manufacturing Processes

A plethora of data is managed in the manufacturing industry, and when you add the increased use of robotics and artificial intelligence, this industry is one of the pioneers when it comes to utilizing business intelligence.

The issues this particular German-based company was facing were related to streamlining their production process since they started to experience more problems with their equipment so the production volume decreased, and business concerns started to arise.

By having a birds-eye view of all the manufacturing analytics needed to successfully operate the production process, the company managed to make the most out of intelligent data alerts that enabled the production workers to be immediately alarmed when an anomaly would occur. This ensured that no machinery is out of order any more as their repairs and management could go under immediate inspection, even before the actual breakdown occurred, the production process stopped, and enormous amounts of revenue lost.

This is one of our real-world business analytics examples that puts a spotlight on artificial intelligence, and how it improves the maintenance of production facilities that need the lowest production downtime possible, one of the most important manufacturing KPIs, alongside with the production volume and costs.

Now we will take a look at our next business intelligence solutions examples focused on retail and a web-based electronics supplier.

12) Improving Customer Loyalty

In number 12 of our business analytics examples, a clothing retailer in the early stages of its rising success was struggling to scale its business further. After a year of impressive growth, the business saw its profits and customer acquisition levels plateau while seeing a rise in customer churn. The company was able to maintain its momentum so it decided external help was needed for continued success.

By opting for a retail dashboard customized to display a host of invaluable demographic data about its existing users and target audience and with retail KPIs focused on increasing customer value and new customer acquisition, the company began to grow its audience once again.

The business was able to identify its strengths and weaknesses, spot emerging trends, and segment its audience accurately to ensure it offered the right personalized deals or offers to the right set of consumers, resulting in significant growth in its customer base and increased brand loyalty.

13) Optimizing Inventory

The thirteenth and final of our operational business intelligence examples, or business analytics examples, is centered on stock or inventory optimization. Around 46% of SMBs either don’t track their inventory or use a laborious manual method to do so, costing time, money, and a host of other valuable resources.

A tight-knit web-based electronics supplier that deals with a large warehouse of ever-changing stock began to feel the effects of a poor inventory management process when it began to lose track of high-value items and ensure an increasing number of in-house damages.

Before the situation spiraled out of control, the company adopted a decision support system (DSS) so it could use all of its inventory-based data to make informed choices regarding the way it stored, quantified, and managed its stock on a sustainable basis. This exhaustive and incredibly smart analysis of historical data in addition to stock-taking metrics for warehouse product not only helped the business to keep track of its various items, but it also prevented damages and ensured all of its popular products remained stocked at all times.

We live in an age rich in data, and those that use it wisely today will reap endless rewards tomorrow, and beyond. These examples of business analytics prove that BI is no longer a process used solely by specific industries, but its implementation is welcomed and successfully employed by managers, employees, average business users, and IT specialists who want to work with a seamless SQL report builder and build their analysis with this popular language.

We hope that these BI examples have inspired you to improve your organization’s processes and for further reading, explore these 10 essential data visualization techniques.

To summarize our subject matter, here are the most prominent business intelligence examples that businesses use for:

1) Improving The Decision-Making Process

2) Uncovering Fresh Business Insights

3) Boosting Productivity

4) Increasing Sales

5) Improving Financial Efficiency

6) Streaming Internal Processes

7) Saving Marketing Dollars

8) Reducing IT Involvement

9) Connecting Departments

10) Increasing Employee Performance

11) Enhancing Manufacturing Processes

12) Improving Customer Loyalty

13) Optimizing Inventory

If you want to be a part of your own business intelligence story, you can try our online data visualization software for a 14-day trial, completely free!

The post 13 Analytics & Business Intelligence Examples Illustrating The Value of BI appeared first on BI Blog | Data Visualization & Analytics Blog | datapine.