The verdict is in: Data is big and business intelligence (BI) and data visualization tools are here to stay. Organizations of all sizes are now able to easily connect to their various data sources, interact and make sense of their data, and share their analyses. As the BI marketplace matures, organizations are realizing they need robust tools that can handle a wide range of analysis types. Just as there are various dashboard types and different data visualization types tell different data stories, there are multiple intentions for data reporting and analysis. There are two overarching business intelligence buckets that can help determine what type of data report to build: data exploration reports and data presentation reports. Let’s dig deeper into each one and showcase the different data visualization techniques between the two.

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The significant leap in information technology development we have been witnessing since the last couple of years has resulted in the increased popularity of business intelligence solutions. Companies constantly look for tools that would help them leverage the potential of large volumes of data gathered in heaps by any business. Turning to business intelligence is no longer a novelty in the business world but rather a necessity. In other words, nowadays everyone should get to grips with business intelligence, even if it is only to be able to choose the best self-service business intelligence tool without the need to ask an IT expert for help. We prepared a list of business intelligence vocabulary that we hope will enlighten you and turn the struggle through various BI-specific issues into a pleasant walk.

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For most of non-technical users data analysis using MySQL seems to be a complicated task reserved only for IT specialists. Indeed SQL is a ‘Structured Query Language’ which is the most common standardized programming language used to access databases. A database is broadly defined as a structured collection of data. It may be anything from a simple shopping list to a picture gallery or the vast amounts of data of an international corporation. To add, access, and process data stored in a computer database, you need a database management system.

However, the procedures of MySQL data analysis aren’t much more difficult from what you do every time you build tables in Excel or Word. It always starts from collecting data, then cleaning it from any mistakes and finally visualizing them in an analysis-ready format. In the following article we will perform a short data analysis using MySQL language and a workbench tool to illustrate this process in 3 easy steps.

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Business intelligence tools have a reputation for being cost-prohibitive. Maybe it’s the vendors themselves who brought about this notoriety – they usually shy away from providing a precise sum as an answer to how much a BI solution costs. It’s because the cost goes far beyond the one-time implementation of software and the total of upcoming expenses is hard to estimate. However, there are some costs that will always be there – we gathered all business intelligence costs in this brief summary.

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Collecting data can be a chore. So much so that many business owners simply don’t do it. Unfortunately, data is the best way to know if your business is performing and provides a pathway for how to run your business in the future. Without it, you are leading your business blindly and hoping that your choices align with the data you are failing to collect.

I used to be the same way about collecting data. As it seemed too complicated and time consuming, I simply failed to do it. This was a mistake.

After seeing colleagues of mine grow their sales numbers at a fast pace, I finally gave in and began to find out what I needed as far as data. After learning everything I could, I came to the conclusion that sales data is the #1 most important set of figures that businesses should collect.

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