Two of the hottest tech buzzwords are business intelligence (BI) and data analytics. Both of these terms are used far more often than they are understood, requiring additional context for understanding by an audience.
I’ve noticed their interchangeable use – e.g. the concept they intend to describe is data analytics, but they call it BI. On one level, this is very understandable because these terms are not as well defined as they should be.
If the definition truly matters, it is very important to ask someone to explain what they mean in order to create common ground and mutual understanding. This runs the risk of seeming ignorant, but once the awkward part of the conversation is over they will hopefully realize the value in clarifying the definition.
If the person knows what they are talking about, this shouldn’t take long at all – and if they don’t know, it provides a good opportunity to build trust and explain “what my definition of BI is” or “how I think about leveraging data analytics in a BI solution”. At Bsquare, our best conversations with our customers come from such an open and clarifying approach. Of course, having that firm definition yourself is important if the conversation goes there!
Here is the simple way that I think about the difference between business intelligence and data analytics:
Business Intelligence (BI) refers to the T’s: tools and talent. BI tools make use of business data. These tools transform the data into meaningful visuals, reports, or dashboards that provide context for analysts and other subject matter experts (the talent) to make business decisions. BI can range from very general contextual awareness to very specific use cases; but ultimately BI is focused on situational awareness and developing context from meaningful business data.
Analysts are often able to “play” in the data and “dig” for insights to identify new opportunities or optimize corporate strategies. BI is a powerful business performance enabler but we need to recognize that it is not focused on solving specific business questions. It’s a relatively passive solution, limited to descriptive statistics, pivot table type queries, and standard data visualizations.
Data Analytics is a much more sophisticated evolution of informing business decisions through applied math, data wrangling, and subject matter expertise. Most simply I would describe data analytics as the application of data science to solving business problems. Data analytics certainly provides an input into BI solutions. For example, I could build a data analytics solution that enriches my business data (for example, through business rules and heuristics that leverage the subject matter experts in my business) and generates a higher-quality data to feed to plug into a BI dashboard. Perhaps I am generating risk scores for specific components of a process, and I am feeding those risk scores into a BI tool so that my analysts can check on the daily status of my system. Under the hood, this solution would include both a data analytic solution (generation of the risk scores) and a BI solution (descriptive dashboard of risk scores, perhaps depicting changes in last 24 hours).
I’ll provide more detail on Data Analytics i.e. demystifying data science in a future blog. Stay tuned!