Gone are the days or more appropriately “era”, when businesses used to operate or prosper based on decisions, taken purely on a hunch! Now, we are in the era, where each business decision needs to be advanced analytics augmented by data and facts. Especially due to easy availability of big data and technological advancement, every business executive is supposed “know” the facts to take decisions instead of “feeling strong or weak” while taking decisions. More importantly, there is a “cost” associated with decisions taken by hunch instead of facts. One more reason, to have trusted and intelligent “Business Analytics” implemented and incorporated into the overall business process. Any analytics ultimately aim to provide better business decisions, so that humans can take better business decisions augmented by relevant, trustworthy information.
What is Business Analytics?
“Business Analytics” is a process, explorative, investigative and iterative like most of the business process, which refers to the skills, technologies, applications to gain insight using historical data and drive business planning. Business Analytics consists of two major components, Business Intelligence and Advanced Analytics.
Business Analytics Vs Business Intelligence:
Business Intelligence traditionally focuses on measuring performance metrics of business which are predefined historically and track business planning based on those metrics. It mostly consists of querying, reporting and online analytical processing (OLAP) and usually answers questions like “What happened”, “How Many” and “How often”.
Advanced Analytics goes beyond business intelligence by using statistical modelling technique to understand trends from historical data and predict future leveraging that trend analysis. It is additional process on top of business intelligence to give answers to business questions such as “Why is this happening (descriptive)”, “What is the best that can happen (optimisation of resources)”, “what will happen next (predictive)”. Since advanced analytics is by nature inclusive of business intelligence, more and more businesses are focusing on robust business analytics, combining and merging both into one organisational process.
Components of Business Analytics:
Business Analytics, comprising of business intelligence and advanced analytics encompasses below components:
- Descriptive analytics is totally dependant on live data and helps to provide information about “What” is really happening within the business.
- It comprises of reports, dashboards, KPIs.
- Traditionally known as “Business Intelligence”.
- This provides all the required details around the business to help take the business decisions.
- Human Intervention for a business decision is comparatively higher in addition to descriptive analytics
- This type of analytics involves automated root cause analysis. It examines data or content to answer questions “Why did it happen”
- It usually comprises of robotic process automation (RPA) for root cause analysis (RCA)
- Modern solutions to diagnostic analytics do have solutions involving machine learning, as definitely machines are more capable of learning patterns, detecting anomalies in identifying KPI drivers.
- There is less human intervention in diagnostic analytics, as more and more analysis is done by exploring machine learning capabilities.
- It encompasses a variety of statistical technique, from data mining, predictive modelling and machine learning that analyzes current and historical facts to make predictions about future.
- It is all about planning for the future based on the current trend and historical facts to answer business questions like “what will happen next?”
- Human intervention is little as again more and more outcomes are based on statistical modelling to predict future augmented by facts and information.
- Prescriptive analytics, often the final step for business analytics, encompasses all other analytics such as what will happen (descriptive analytics), when will it happen (predictive analytics) and also why it will happen (diagnostic analytics), based on which it suggests a business decision about how to mitigate future risk or how to take advantage of any situation
- It shows the implication of every business decision by taking references of outcomes of all other analytics systems.
- Human intervention is very minimal to nonexistent as there is the capability to generate business decisions. However, the systems or analytics solutions need to be robust and trustworthy to achieve this level of artificial intelligence for any organization
Above diagrammatic representation is just to elaborate:
- How each component of analytics supports overall business decision making and how much human intervention is needed at every step of analytics.
- As you go incorporating more and more analytics, human intelligence is replaced more and more with artificial intelligence and automation.
- Thereby, return on investment and in general intrinsic business value also increases, as there are more automated business decisions augmented by data and statistical data models.
- With prescriptive analytics, there is maximum optimisation of resources which enables automated and faster decision making.