If you’ve got so much as a passing interest in business intelligence trends, you may notice that BI is often mentioned in the same breath as data analytics. If you’ve implemented, are planning on implementing a business intelligence solution or simply just curious like me, the following questions will likely spring to mind: Can BI and DA work as a synergy? What are benefits of data analytics to a business intelligence solution?
Well, let’s find out, shall we.
First off, what’s Business Intelligence and what’s Data Analytics?
I tried a little google search once and ended up more confused about these terms. Too many market players, vendors and service providers are using too many different definitions – some of which, with the little I know, are incomplete. Some sources define business intelligence and data analytics as two different concepts while others use them interchangeably. After my own research, I came up with two definitions that work for me:
Business Intelligence (BI) refers to the technology driven process of collecting, integrating, analysing, and presenting business information so business management can make informed decisions.
The 6 main components of Business Intelligence include:
- Source data
- Extract, Transform and Load (ETL)
- Data warehouse
- Online Analytical Processing (OLAP)
Data analytics basically is the process of analysing data sets in order to discover insights about the information they contain. You can say that if business intelligence is focused on decision making, data analytics is focused on asking probing questions about the decisions. So it is the route to business intelligence. Data analytics mainly involves:
- Data mining
- Predictive and prescriptive analytics
- Diagnostic and descriptive analytics
- Big data analytics, etc.
Is a BI and DA synergy possible?
Businesses are already creating richer BI reports by integrating data analytics, which provides additional insights via complex algorithms and statistical methods. Here are some ideas on how business intelligence and data analytics can work well:
- Cohort Analysis is a branch of behavioural analytics that takes data on people from a specific subset such as a game, or online store, and uses particular characteristics users share to create groups or ‘cohorts’. The actions or activities of these cohorts are usually then analysed over a period of time. For example, an analyst can create a cohort of gamers who opened a new Steam account in certain counties and within a time period. Such a group can become a dimension in the OLAP cube. Decision makers can compare them by seasonality, revenue, order distribution, etc. to create a custom marketing strategy.
- Regression analysis helps us to measure the relationship between variables. This statistical technique provides insights in a way historical data alone cannot. For example, you can investigate the pages most viewed on your blog. But with regression analysis, you can also find out if social engagement correlates with page-views.
- Time series analysis is the collection of data at different intervals over a period of time, and is used to identify trends, cycles, and seasonal variances to aid in forecasting.