We are drowning in information
and starving for knowledge
– John Naisbitt
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Analytics
Analytics is the systematic process of collecting, organizing, and analyzing data to discover meaningful patterns and insights. It is crucial for making informed decisions, understanding trends, and predicting future outcomes across various fields.
Business Analytics
Business analytics applies analytical techniques to business data to gain a deeper understanding of performance, identify oppor- tunities, and improve decision-making. It helps organizations optimize processes, increase efficiency, and achieve strategic goals.
Advanced Business Analytics
Advanced business analytics utilizes sophisticated tools like machine learning and predictive modeling to uncover hidden patterns and forecast future trends. This enables businesses to proactively adapt to changing market conditions, identify new revenue streams, and achieve sustainable, profitable growth.
Advanced business analytics are applied in the course of recurring financial planning and budgeting, non-recurring events planning (e.g. investments) and 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 advanced business analytics is to gain insights from data in order to make better decisions (defined by INFORMS)
- Advanced business analytics is the process of transforming data into insight for making better decisions and actionable insights.
Advanced business analytics must be focused on value creation : insights must be converted into productive actions (‚actionable‘). The status of „analysis paralysis“ must be avoided. The John Naisbitt quote (top of this page) says : the flood of information and analysis results will not provide for profitable growth, but the insight gained turned into purposeful actions.
Business Analytics Maturity Model
The stages of business analytics can be displayed by use of Gartner’s Business 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 |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
A short note on descriptive analytics
Descriptive analytics may seem trivial at first glance, as it is purely about the presentation of past or current data.
However, practice shows that in many companies a lot of management-relevant information is not available and simply cannot be retrieved at the push of a button. The visibility of events is poor.
Examples: (1) Sales pipeline filling and flows per sales representative, per deal, per customer or per phase. (2) Time series of incoming orders per month compared to incoming order targets; incoming order deviations per month and cumulatively.
Simply setting up such data flows and providing and visualizing the data increases the visibility of events in management and can increase management effectiveness.
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 events did happen
Predictive Analytics
Predictive Analytics provides forecasts and predicitions for management decisions. It answers the question:
What will happen ?
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’s the best action to take to achieve a desired outcome?
It is characterized by techniques such simulation, optimization, machine learning and artificial intelligence.
Focus : what must be done to achieve the targets
Pro : fast decisions, immediate actions, proactive approach
Con : trust in algorithms may lack, feeling of „loss of control“
Benefit : rolling optimization enables for proactive management and for goal achievement
A short note on prescriptive analytics
Descriptive analytics, diagnostic analytics, and predictive analytics are fundamental and essential tools. These methods can be implemented with simple means. They add value if not yet in place, but the principle is: „oldies but goldies.“
In contrast, prescriptive analytics is really new and offers very high potential for added value in terms of profitable growth. This is made possible by state-of-the-art hardware and software technologies such as digital twins, simulation optimization, reinforcement learning, and artificial intelligence.
Advanced Business Analytics – from Data to Insights to Actions
The four stages of advanced 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
Advanced Business Analytics – from Data to Insights to Actions to 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).