Why is data analysis necessary in the office environment? Are we really analyzing data when we create diagrams? What is the difference between data analysis and data visualization? How can we support data-based decision-making? How can a company become data-driven? And last but not least, what do business intelligence tools have to do with it all? We will seek answers to these questions in the following four-part article series.
What do we mean by a business intelligence tool?
This is the general name for tools that support data analysis and data visualization for business users. A wide range of these products have been released recently. Basically, they all support becoming data-driven by making data accessible to those who do not have deep professional knowledge of using databases or data analysis. However, there are big differences between them in several aspects. Before the introduction, it is worth determining well for what tasks and for what professional background users we are choosing a tool, because based on these there are big differences between the individual software.
But why would we need anything when we can use good old Excel? After all, we can easily put together any chart in it and, with the help of a few macros, we can also do data preparation. Well, the answer is simple: Excel is not a data visualization tool. It is even less suitable for data discovery, data preparation, and data analysis. Nothing shows this better than the fact that Microsoft itself developed a BI tool, PowerBI.
What makes such a tool different? What does it offer users and decision makers more? For example, have you ever tried to refresh data behind an Excel report? It's at least cumbersome, if not completely hopeless. Tools designed for this purpose (like Tableau and PowerBI) have built-in connectors to almost any data source - thus ensuring automatic data refresh and access to data for business users.
This feature is just one of the many automations that these softwares provide. The really great thing about them is that by using them, you don't have to re-create regular reports every month. We go through the steps once, the report is created, we add the appropriate views, we upload it to a common interface where everyone involved has access to the views and data that are public to them, and finally we set an update frequency, and we have already freed up the part of our working time that was previously wasted! Which we can then use to perform tasks that truly create business value – for example, to create predictive analyses that can reveal previously hidden trends, opportunities or sources of errors in the organization.
But this is just one (albeit fundamental) advantage among many. After all, the efforts of data preparation / data cleaning are also significantly simplified by the built-in functions. After all, we only have to perform the transformations once. With each subsequent update, it goes through the steps: it organizes, transforms, slices, formats the data or connects it with data from other sources. If you don't believe it, check it out - the possibilities are endless, and what's more, they are incredibly user-friendly.
In the next part of our article series, we will discuss the most important steps in the data analysis process and the tools that can be used for each stage.