Business intelligence and data analytics are not the same thing and using them interchangeably can cause confusion.
It would be useful to make a clear distinction between the two, perform an in-depth overview of both business intelligence and data analytics.
The name itself is pretty self-explanatory, as data analytics is the analysis of the information obtained through data mining.
Don’t let this oversimplification fool you though as it is so much more than a simple analysis and it is an integral process for successful business development.
Basically, data analytics can be divided into 3 categories:
In other words, we use data analytics to see what happens, predict what is going to happen, and plan what to do about it. It is important to note though that the quality and relevance of descriptive analytics will be reflected in both predictive and prescriptive analytics results.
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To put it bluntly, business intelligence is a decision-making process or future planning that is growth-oriented. This is done based on data analysis which is one of the main reasons why the two terms get mixed up so often.
The easy answer would be that data analytics is simply a more broad term, whereas business intelligence is a form of data analytics within an organization.
However, this type of oversimplification doesn’t do the whole topic of justice, so let’s do a side by side comparison instead.
If there is one key takeaway from all this is that the business intelligence team relies on data analytics to guide its thought process.
Data analysts rely on business intelligence needs in order to generate useful feedback.
This kind of means that all descriptive analytics is closely tied to business intelligence as they are generated based on the needs of a certain company.
The business intelligence unit is typically tasked with addressing how a company operates.
Creating execution guidelines that help companies meet their goals, and reducing risks that result in loss of profit or production capacity. For them to be successful they use inputs from processed data mining feedback.
Data analytics deals with issues on how to collect relevant data more effectively, how to clean that data and validate the results. It also deals with categorizing data in a meaningful way so that management teams can do their own SWOT analysis, create product road maps, implementation strategies, etc.
Due to the importance of both of these processes, it’s not too difficult to notice how a lot of organizations are going the extra mile in order to capture relevant data.
User surveys, AB split testing, alpha and beta testers, purchasing browser activity/history and other methods are used to collect information that retailers ultimately use to boost their sales. If you compare your user experience now to the one 10 or 15 years ago you will notice a significant amount of differences.
The ads you see seem way more relevant to your Google search, e-commerce platforms try to upsell products way more often.
The surveys you are asked to complete are more detailed rather than just inquiring about overall user experience, etc.
However, the products have improved as well, and more so-called quality life features get added on top of the core product. Most importantly, there are way more community managers nowadays who are in charge of collecting direct user feedback and building a better relationship with the user base.
Data analytics is a process of adapting, formatting and cleansing unstructured or raw information into meaningful feedback. Business intelligence uses data analytics inputs to come up with strategies for company growth.
The quality of data analytics is in direct correlation with the efficiency and success of business intelligence teams.