Big Data gives the Controlling Team new homework. Controllers must handle the massive volumes of data, bring them into a meaningful relationship, and aggregate them into management decisions. No easy task in these volatile times, but predictive analytics solutions evolves to be a great help. They use not only historical data but also current information for a data-driven look into the future using statistical and mathematical models. More accurate and more frequent forecasts lay the foundations for dynamic decisions to achieve the set goals or exploit savings and cost optimization potential.

But what belongs to a successful predictive analytics solution?

1. Selection of the right KPIs

The selection of suitable KPIs is crucial for the success of the new Predictive Analytics strategy. The metrics must be targeted to the value drivers of your business that affect your top financial KPIs.
Proven practice examples in controlling include:

  • Prediction / early warning system of extraordinary expenses, Digital Forecast
  • Identification of new sales potential, cross-selling prediction
  • Prevent customer turnover, churn prediction
  • Optimizing the disposition, sales volume forecasting, optimizing the supply chain
  • Prediction of price peaks in purchasing, price prediction
  • Fraud detection
  • Creation of a company-wide process controlling

2. Build a powerful big data structure

Most companies have already some experience with large amounts of data: Data Warehouses and Business Intelligence (BI) applications are successfully used by SMEs for many years now. Nevertheless, the analyses are limited to certain data segments and only give a glimpse into the retrospective – usually with a considerable delay.
A predictive analytics solution combines data of different origin, e.g. from the databases, web archives, IOT data or classical spreadsheets to discover new causal relationships on the fly.
The data structure must be prepared correctly with regard to the defined KPIs, and, most importantly, with the appropriate foresight. The following questions help with the structure:

  • Where is the data stored?
  • Where should the data be available?
  • Which data belongs to which business processes?
  • Which processes are influenced by the available data?

Once you have the answers for these questions, you can define and build the data models considering benefits and security. You should already have the next step, the data analysis, in mind.

3. The data analysis

Now we check the quality of the data and whether they can be used for data analysis. To be able to perform an effective analysis, the data should consist of numerical values, images or names are rather inappropriate. Of course, sufficient records of a characteristic must be available (for example, customers and products of the last 3 years). Then the data should also show correlations (male customers in NRW are increasingly buying product A). Last but not least, the dimensions must be mutually dependent and not dependent on external influences (for example, stock price slumps due to hysteria sales).

When it is decided what information from the existing data should be used, the analysis begins. The selected data features then run through various statistical methods and machine learning techniques (data mining) to develop algorithms that merge into a forecasting model. R and Python are cost-effective open source software solutions that are technological leaders in terms of functionality and integration.

The developed algorithms are trained by using a manageable amount of data from the past to identify specific patterns. In the following, a gradual adaptation of the algorithm takes place to optimize the hit rate. If a satisfactory prediction is achieved in the test runs, the developed algorithm can be used to deliver accurate predictions with the entire data set.

4. Integration into the existing infrastructure

Of course, the new Predictive Analytics solution should not open a new data silo. Imagine using state-of-the-art analysis techniques to present the results as an Excel spreadsheet or PowerPoint template. The forecasts or recommendations for action are only effective if they are available to the companies as an integrated service. e.g. integrated into the existing BI system, on the desktop, mobile or in the cloud.

5. External support

To get predictive analytics projects to be successful and bring benefits, companies must overcome several hurdles. The biggest challenge is the lack of specialist Know how within the company. That’s why companies are well advised to use an external service partner for predictive analytics projects, especially for technical support. Selbach Information Systems is an expert in technical consulting and places a high priority on seamless integration into existing management information systems. By using pre-designed logic and case-specific modules, the solutions are particularly simple, transparent and ready to use.
If you want to know more about how you

  • Implement predictive analytics solutions quickly and easily with ready-made algorithms
  • Integrate data-driven decision-making into existing MIS systems,
  • Make future forecasts available on the desktop, mobile or in the cloud,

then call us on +49 2104 13880.

Selbach Information Systems GmbH develops comprehensive IT solutions for successful corporate management for more than 20 years.

Our employees are experts in data modelling, reporting, dashboarding, planning, predictive analytics, Industry 4.0 and more. Our focus is on modern software architectures, apps and cloud applications.

What is important to us:
Quality 100%
Customer Satisfaction 100%
Reliability 100%
Long-term partnership 100%

When it comes to high technical requirements, flexible solutions and user-friendly design, then Selbach Information Systems GmbH is the right partner.