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Forecasts are difficult, especially when they concern the future. What sounds so flippant and easy is often a great burden in everyday business life, as most decisions have to be made under great uncertainty. Nobody knows what the future holds and which decision will turn out to be the right one in the end. In their article, our enomyc authors Stefan Frings and Mario Trapp show how modern business intelligence and business analytics solutions can also help SMEs to make better-informed and therefore sounder decisions.

One of the basic assumptions of business administration is that decisions in companies are generally made under uncertainty. This means that those responsible do not have the necessary information to fully foresee the consequences and risks associated with the decision.

Against this backdrop, it has been a key objective of application-oriented business administration since its beginnings to provide methods and tools for rational decision-making - precisely in order to reduce the level of uncertainty. This in turn, and this brings us full circle, requires better information.

In their search for more reliable assumptions and better decisions, managers and entrepreneurs can quickly get lost in the jungle of countless IT tools available today. Modern business intelligence or business analytics solutions promise to structure and process large volumes of data - even with limited computer capacities.

BI and BA: from descriptive to prescriptive

Business intelligence (BI) applications are tools that can be used to analyse business data and convert it into actionable insights. Well-known solutions include Power BI, Tableau and Qlik. They create the basis for more informed decisions by displaying large amounts of data visually on dashboards with just a few clicks and allowing interactive analyses on intuitive user interfaces. The ability to visualise data in real time makes it possible to quickly identify patterns, optimise business processes and make better-informed decisions. Such BI tools are particularly useful when it comes to carrying out descriptive (what is happening?) and diagnostic (why is it happening?) analyses.

Business analytics (BA) tools, on the other hand, use company data to forecast trends and results, for example to optimise complex issues in a targeted manner. Programming languages such as Python and R are real game changers in the field of data analysis. They enable statistical analyses with the help of machine learning and artificial intelligence. This enables companies to carry out predictive (what is likely to happen?) and prescriptive (how can a certain result be achieved?) analyses. Python and R can also be used to develop complex models that not only take past data into account, but also forecast future trends and make recommendations for optimal business decisions.

There are also major differences between BI and BA in terms of the focus and complexity of the respective tools and methods. While BI tools such as Power BI, Tableau and Qlik are primarily used to display and analyse business data, Python and R go one step further and offer advanced analysis functions that go far beyond pure data visualisation.

For example, companies can use Power BI to monitor sales trends in different regions or to find out which products are selling best. At the same time, they can use Python to develop predictive models for future sales trends based on current data and external factors such as market conditions or seasonal fluctuations.

To summarise, the integration of BI tools and modern programming languages into the business strategy offers great added value. It helps companies to improve their decision-making, minimise risks and thus gain a competitive advantage. At a time when data is considered "the new oil", medium-sized companies should also utilise these resources.

How are the tools used in practice?

In principle, all questions of optimisation that require the analysis of large amounts of data are potential use cases for BI and BA, such as tendering logistics services like courier, express and parcel deliveries. Typically, the prices of shipping service providers are based on postcodes and levels of service such as weight and speed. If you now analyse the shipment structures, the number of possible combinations of postcodes and levels of service quickly increases to a million data fields and more. In addition, the quotations of the service providers participating in a tender must be imported and graphically implemented.

With a suitable visualisation, it is very easy to see which service provider is the cheapest in which areas. The combination of absolute transparency and visual implementation allows optimal preparation of contract award negotiations, which means that potential savings of ten percent or more can be realised in the short term. Typical questions of operations research can also be solved. One example of this is the so-called "sweet spot analysis". This involves searching for a warehouse location that can be operated with minimal transport costs from inbound and outbound logistics.

However, BI applications can also support sales optimisation. For example, visualising turnover and sales by postcode shows key areas and "white spots" so that targeted measures can be identified and implemented for regions with low turnover.

Production site structures can also be analysed and improved with the help of BI applications. The analysis of customer-plant assignments in a company that specialises in surface finishing, for example, makes it clear that a historically grown customer-plant assignment can be suboptimal. The reasons: On the one hand, excessive transport costs are accepted because the customer has been assigned to a more distant production site. On the other hand, capacities are not optimally utilised due to the existing assignment. By reallocating, one of our customers was not only able to close a site, but transport costs were also reduced by around eight per cent.

The findings of complex data analyses will also be available to smaller companies in future

Using BI tools can significantly improve the cost-benefit ratio. Optimisation questions that were previously reserved for larger companies due to the leverage effect can now also be answered in smaller SMEs thanks to the significantly reduced analysis effort.

Automotive OEMs like to talk about "democratisation" in this context. This always takes place when equipment features that were previously reserved for the luxury and upper mid-range are also available in lower vehicle classes. Understood in this way, BI applications make an important contribution to the "democratisation" of analysis tools and concept ideas for SMEs.

What questions do you have about business intelligence and business analytics tools? Please feel free to contact us. We look forward to hearing from you.

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