We are drowning in information and starving for knowledge
John Naisbitt
Business Analytics
Business data analysis, aiming to improve business performance and business development has a long tradition. Formats include financial analysis (P&L, balance sheet & cash flow analysis) or classic MBA-style analysis (e.g. SWOT, 5-Forces, ABC, XYZ, portfolio analysis etc.)
Data analytics are applied in the course of recurring financial planning and budgeting, of non-recurring events planning (e.g. investments) and of results control. Operative management analyses and monitors performances by use of key performance indicators.
Data analytics is applied to almost all functional areas and disciplines : Finance, Marketing and Sales, Product Management, Procurement, Production and Supply Chain Management.
The purpose of analytics is to gain insights from data in order to make better decisions, as e.g. defined by INFORMS : Analytics is the scientific process of transforming data into insight for making better decisions and actionable insights.
Business analytics must be focused on value creation : insights must be converted into oroductive actions (‚actionable‘). The status of „analysis paralysis“ must be avoided. The John Naisbitt quote (top of this page) says : the flood od information and analysis results will not provide for Profitable Growth, but the insight gained turned into purposeful actions.
Analytics Maturity Model
The stages of business analytics can be displayed by use of Gartner’s Analytics Maturity Model
It shows four stages :
- Descriptive Analytics
- Diagnostic Analytics
- Predicitive Analytics
- Prescriptive Analytics
Descriptive Analytics
Descriptive Analytics is the examination of data or content, usually manually performed, to answer the question “What happened?”
(or What is happening?), characterized by traditional business intelligence (BI) – often spreadsheet based – and visualizations
(e.g. pie charts, bar charts, line graphs and tables ).
Focus : what happened ?
Pro : simplicity of application, special know-how is not necessary
Contra : insights gained are oriented towards the past, are isolated, are not actionable
Benefits : management is informed about what happened and how
Diagnostic Analytics
Diagnostic Analytics is a form of advanced analytics which examines data or content to answer the question “Why did it happen?”, and is characterized by techniques such as drill-down, data discovery, data mining and correlations.
Focus : why did it happen ?
Pro : deep insight into past cause and effect relations
Con : actionable insights are related to the past
Benefit : management is informed about why something did happen
Predictive Analytics
Predictive Analytics provides forecasts and predicitions for management decisions. A wide spectrum of methods is available : from simple Linear Regression at the low end and (e.g.) VARIMA in the mid range up to (e.g.) LSTM networks at the high end.
Focus : what will happen ?
Pro : trends (up and down) are realized early on
Con : the culture to initiate actions based on forecasts may be at an early stage of development
Benefit : predictions do support proactive management practices
Prescriptive Analytics
Prescriptive Analytics is a form of advanced analytics which examines data or content to answer the question “What should be done?” or “What can we do to make _______ happen?”.
It is characterized by techniques such simulation, optimization, machine learning and artificial intelligence.
Focus : what must be done to achieve the targets (similar to the „next best action marketing“ – autopilot principle)
Pro : fast decisions, immediate actions, enables proactive approach
Con : trust in algorithms may lack, feeling of „loss of control“
Benefit : forecasts and rolling optimization enable for proactive management and for the achievement of objectives with certainty
Business analytics – from data to action
The four stages of business analytics may lead
- from basic management systems with focus on human activities (descriptive analytics)
- towards semi-automated management systems
(e.g. management support systems like diagnostic and predicitve analytics) - towards high and full automated management systems (prescriptive analytics), based on intelligent, self-optimizing systems
Business analytics – from data to action to optimum Profitable Growth
Profitable Growth can be initiated and supported at each stage of the business analytics model. The more intelligent the analytics system is and the better it’s integration in the enterprise management system, the better and the safer will be the effect on Profitable Growth.
Nevertheless it is observed, that analytics is carried out entirely right from data capturing towards the definition of optimal sets actions, but no growth effect is created because the necessary actions are not executed appropriately – analytics as a crystal palace.
Under such circumstances the realisation of Profitable Growth is not exclusively a subject for data and insights, but a subject for management regarding execution, controlling and ongoing optimization. In this case the challenge is the transition from an open loop management system (as is often the case with a passive admin or reactive focus) towards a closed loop management system (with a focus on proactive practices).
Contemporary business analytics is carried through by means of easy to operate applications, which visualize results, insights and next best actions immediately.