When developing and implementing a successful project, one of the most important resources is data. However, just having data isn’t beneficial enough; it’s analyzing and making decisions based on that data that is even more important. That is why business intelligence – or BI as it can be commonly referred to – is so crucial. But what exactly is it? Gartner in their IT Glossary defines business intelligence as “an umbrella term that includes the applications, infrastructure and tools, and best practices that enable access to and analysis of information to improve and optimize decisions and performance.” In this post we will explore more about why BI is so important and best practices for using it.
Why BI?
BI is a way to help analyze your data to deliver the best possible product. Maybe you want to get a better idea of what is working and what is not, or perhaps you are trying to measure efficiency in a certain area. BI allows you to generate reports on-demand for various scenarios, eliminating guesswork. Best of all – this information is in one place allowing you to get the data you need as efficiently as possible. If you’re wondering if BI is worth the investment and resources – yes! Although it is important to remember that different projects can require different needs, and there is not a one-size-fits-all approach. Any project can benefit from implementing BI, but the level and resources needed can vary based on scope and scale.
Setting Yourself Up for Success
Since you’ll be dealing with a massive amount of data with BI, one crucial element is having a data warehouse to store all this information. Data warehouses allow you to store large amounts of data and retrieve this information more effectively. Another important tool to have is good data mining that will allow you to extract the information you need and analyze it efficiently. There are also sorts of data mining techniques, as explained in this article from Datafloq. Essentially, good data mining involves not just taking data, but also seeing what sort of patterns or trends exist for maximum efficiency. But remember that reporting is only as good as the data that it starts with, so make sure that your data is essentially “clean” before starting, as explained here.
Analyze This!
Collecting and analyzing data is great, but what about reporting it? An analytical reporting application is an essential part of data analysis. Some of the most popular reporting applications include Cognos, Oracle Utilities Analytics, and Crystal Reports. All of them offer their own strengths and weakness based on the needs of your project, so we recommend doing your research before investing to see which one is right for you. Do keep in mind, though, that while selecting and implementing a reporting application can be a great first step, it is important that your end users are trained on how to use it, too.