Several terms like ‘Data warehousing’, ‘Business Analytics’, ‘Data mining’, ‘Business Intelligence’ and many more are used quite loosely and interchangeably in business discussions today.
While there are multiple interpretations for each of these terms, the general consensus is that these are all techniques that enable businesses to take correct decisions by leveraging operational data.
With that in mind, to understand the key distinctions between the two terms in question here – Business Intelligence (BI) and Business Analytics (BA) – let us focus on how a ‘business’ generally works.
Consider a fledgling retail start-up selling a niche range of handbags online. Like you would expect, in the first few weeks, the focus would be on finalizing the product designs and developing an easy to use e-commerce portal. Next, the attention will shift to marketing the brand and gaining traction with the target audience.
As the company starts delivering its first few orders, it will become necessary for the company to measure its performance on a periodic basis. At a minimum, it will have to keep track of its
- Sales performance – read number of handbags sold per month
- Marketing performance – read number of unique website visitors
- Logistics performance – read number of orders fulfilled on time
To enable this tracking on an on-going basis, reports will have to be designed and the necessary data will have to be pulled together into these reports to generate accurate values for these performance indicators month on month.
That is exactly what Business Intelligence (BI) refers to. BI enables you to get a detailed dynamic view of how your business is performing.
With time, as the start-up expands its operations to multiple regions and diversifies into more product lines, a variety of visualization techniques that include drill down reports, graphical dashboards, and real-time scorecards will become the norm across the organization.
Automatic alerts will be put in place to notify relevant teams as and when specific threshold values for key performance indicators (KPIs) are crossed. At this stage, ‘retail business analytics’ may be a commonly used term across the company but it still is operating in the Business Intelligence domain.
With time, as the competition grows, margins will tend to decrease and it will become more and more difficult to retain customers and drive brand loyalty. At this stage, it is not enough to just track past performance. It becomes imperative to stay one step ahead of the competition by developing an in-depth understanding of customer preferences, price sensitivity, brand engagement and other softer parameters to incorporate them into the product as well as portal design. On the logistics side, optimizing stock becomes essential to control costs and forecasting sales is required to prevent stock-outs.
This is when operational data comes to be leveraged not just for performance visibility but for strategic decisions impacting future operations. This is when an organization graduates to the Business Analytics (BA) domain.
To contrast with the KPIs mentioned earlier, following are the types of strategic questions retail business analytics will help to answer,
- Sales insights – What effects will a change in the price of the handbags have on the number sold per month?
- Marketing insights – Which source of unique website visitors should be focussed on for consistent high margin referrals?
- Logistics insights – How can product returns be minimized by tracking customer behavior and product defect patterns?
Business analytics inherently demands an industry specific outlook and specialized solutions are available to cater to various industries – in this case, a retail business analytics solutions would be best for the company described above.
Also implementing business analytics within an organization generally requires data scientists to design and apply statistical algorithms and techniques to the operational data. In contrast, BI tools can be easily used, even by business users, to create and schedule sophisticated KPI reports as long as the data is readily available. Popular BI tools include SAS, Cognos, and Microstrategy. BA tools include Qlik View, Revolution R enterprise among others.
The above description of the growth journey of a start-up is just meant to present the key distinctions between BI and BA in an easy to understand manner. It does not in any way intend to suggest that business analytics is only feasible and useful for mature large-scale enterprises. In fact, with a number of web-based analytical tools online and data scientists available on demand, more and more small and medium enterprises, especially in the retail sector are leveraging retail business analytics.
At ComTec, our retail industry specialists help organizations of all sizes and types – brick and mortar as well as online – to gain actionable insights across business processes from merchandising, ordering and fulfillment, product and pricing design to customer relationship management.
We are just an email way – firstname.lastname@example.org.