Analytics helps to optimize various business management.
Big Data is hot, and Data Analytics is at top priority of forward-looking businesses these days, back to fundamental, what is business analytics? what’re the noble business purposes can be achieved by deploying it? What’re critical steps in reaching BA maturity?
- Analytics is "the systematic selection, transformation, and presentation of data, through technological and quantitative processes with algorithmic methods, to automate or support business decisions."
- Business Analytics is the systematic and methodological selection of data to track the behavior of the past decisions to support the future business directions
- Business Analytics is the logical process of examining data and applying management science to support decision-making in an enterprise. Business Analytics is a reliable process that transforms raw data into relevant, accurate and usable strategic knowledge with the purpose of increasing profitability
- Business Analytic (BA) is the qualitative or quantitative scientific algorithm (SA) or generalized algorithm from observable seeming regularity to get the objective optimization (OO) at the given restricted feasible risk (RFR) assuming participation in the business tests or actions. SA improvement leads to diminishing RFR under the same OO or, for the given and "frozen" RFR, OO ought to be improved.
- There are three parts to the analytics eco-system: technological (getting and storing the data); quantitative / interpretive; and decision support/decision making. And there are three steps towards "analytics maturity". Companies will start with simpler statistical analysis ("what happened?"); then move to predictive analytics ("what will happen?"), and finally prescriptive analytics ("what should we do next?"). Surely a simulation into what will/may happen is both predictive AND prescriptive as it will almost certainly suggest appropriate actions
- From noble business purpose perspective: Analytics helps to optimize various business management, directly or indirectly related to long-term revenue. Include traditional optimization (operations research, six sigma), root cause analysis, and statistical analysis / machine learning / data mining to boost efficiency of marketing campaigns, price optimization, inventory management, finance and tax engineering, sales forecasts, product reliability, fraud and risk management, user retention, product design, ad spend, employee retention and predicting success of new hires, competitive intelligence leveraging external data source, guessing new trends based on automated analysis of user feedback and much more. And the role as an analyst is to understand the business context, leverage data and come up with recommendations that can be acted on