Descriptive Analytics

In our comprehensive guide, we reveal what lies behind the term ‘descriptive analytics’ and how you can successfully implement descriptive analytics in your company – for long-term business success.

Descriptive analytics is a crucial component of modern data analysis in companies. It transforms historical data into valuable insights, visualises trends and enables informed decisions. Despite its advantages, however, many companies fail due to the quality and structure of their data. How can the potential still be fully exploited?

In a nutshell, descriptive analytics is a type of data analysis. Descriptive analytics involves evaluating large amounts of historical data to identify patterns and trends. The aim is to understand the past in order to make informed decisions in the present.

Companies can use descriptive analytics to:

• Identify customer segments: A retail chain can analyse sales data to understand which customer groups prefer certain products.
• Recognise sales trends: A company can analyse which products experience an increase in sales at certain times of the year.
• Monitor supply chains: By evaluating logistics and inventory data, bottlenecks become visible and the efficiency of supply chains is improved.
• Discover growth opportunities: Companies can analyse market data and customer behaviour to identify new target groups or product areas with growth potential.
• Prepare financial reports: Detailed financial reports can be generated based on historical financial data, providing a clear overview of a company's financial performance and stability.
• Perform condition monitoring: Machine and sensor data can be evaluated to monitor the condition of equipment and identify potential failures at an early stage.

The advantages of descriptive analysis are as diverse as its areas of application in companies. Descriptive analysis offers a whole range of advantages:

• Better understanding of customer behaviour: Analysing customer data provides insights into preferences, purchasing behaviour and needs. This enables targeted decisions to be made.
• Identification of success factors: By examining past successes, companies can derive targeted measures that will ideally have a positive impact on future projects.
• Basis for further analysis steps: Descriptive analyses create the necessary data basis for efficiently performing prescriptive and predictive analyses.
• Transparency in operational processes: A comprehensive data overview allows bottlenecks and inefficiencies in operations to be quickly identified and addressed.
• Improved communication with stakeholders: The visual presentation of data in the form of diagrams, for example, makes it easier to present complex issues in an understandable way and support decision-making.

Descriptive analysis is the first step in the comprehensive data analysis process and forms the basis for informed, data-driven decisions. Through careful analysis of historical data, it provides companies with a clear view of past events, thereby laying the foundation for further analysis.

Descriptive analyses answer the central question ‘What happened?’ by examining data from the past. This allows trends, patterns and anomalies to be quickly identified. Descriptive analysis leads to the following results, for example:

• Sales have declined.
• Customer satisfaction has increased.
• The bounce rate on the company website has remained constant over the last 3 months.

While descriptive analysis shows what happened, diagnostic analysis goes one step further and investigates the reasons behind it. It uses advanced techniques such as correlations and cause-and-effect analyses to find answers to questions such as these:

• Why has sales declined?
• What factors have contributed to increased customer satisfaction?
• Why has the bounce rate on the company website remained constant over the past 3 months?

Predictive analytics uses past data and statistical models to make predictions about the future. By building on the insights gained from descriptive analytics, it helps companies minimise risks and exploit opportunities in a targeted manner. Predictive analytics can lead to results such as the following:

• Without changes to the corporate strategy, sales could fall by a further 5% per quarter in the coming months.
• If the current course is maintained, customer satisfaction could increase by a further 10% in the next year.
• If the design and content remain the same, the bounce rate on the company website is expected to remain stable, but could increase slightly due to changes in the market environment.

The final stage in the analysis process is prescriptive analytics. This combines data from descriptive and predictive analytics to not only identify potential scenarios, but also directly implement the best measures. This allows recommendations for action based on the previous analyses to be implemented independently. Real-life examples from everyday life include:

• Descriptive analytics: A pedestrian is detected on the road and wants to cross the street.
• Predictive analytics: The system calculates the probability of a collision and issues a warning.
• Prescriptive analytics: The vehicle brakes or swerves automatically to protect the pedestrian.

• Descriptive analytics: The platform recognises that a user visits the website several times and repeatedly views the same flights or hotels.
• Predictive analytics: The algorithm calculates that the probability of a purchase increases with each additional visit.
• Prescriptive analytics: On the next visit, prices are specifically increased to boost margins, as the purchase is very likely to be completed.

Descriptive analyses are key to extracting valuable information from historical data. The process comprises a total of five steps that must be carried out in a structured manner in order to deliver clear and useful insights. Descriptive analysis consists of the following steps:

In addition to numerous advantages, descriptive analysis also presents a few challenges:

Many companies underestimate the strategic value of descriptive analytics and see it merely as a reporting tool. In reality, however, it is essential for making informed decisions and preparing further data analyses. A deeper understanding of descriptive analytics throughout the entire analytics process can help to better recognise and leverage its importance.

For companies that are still in their infancy, introducing descriptive analytics can be a strategic challenge. Successful implementation requires the careful integration of data sources, the selection of suitable tools and, in many cases, a reorientation of the data strategy.

Simply analysing data is not enough – the ability to interpret key figures correctly is crucial. Often, there is a lack of expertise in both of these areas:

• Reviewing and interpreting key figures: In the worst case, misinterpretations can lead to wrong business decisions. Support from experienced analysts and consultants is recommended here.
• Data mining: This is a key discipline for identifying patterns and trends in large amounts of data. However, many companies lack the necessary expertise. Working with experienced data scientists and providing targeted training and further education for the team are useful measures here.

Often, incomplete, inconsistent or incorrect data is available, which can significantly distort the results of descriptive analyses. Comprehensive data cleansing and uniform standards are essential here before analysis can begin.

Historical data plays a central role in modern corporate management and creates transparency about past events. However, many companies find it difficult to exploit the full potential of their existing data. Common hurdles include fragmented data sources, poor data quality and a lack of expertise in using analysis tools. Descriptive analytics is key to understanding past events and creating a solid foundation for strategic decisions.

This is where MaibornWolff comes in with a holistic approach that combines various services:

• (Initial) consultation: We offer a free consultation to find out how we can support you step by step with descriptive analyses.
• Targeted workshops: In workshops on data thinking, we work with you to analyse the initial situation and your business goals.
• Development of data platforms: Based on the initial situation and the respective goals, we support our customers in the design and implementation of modern data platforms that enable automated and efficient descriptive analysis.
• Implementation of automated analysis pipelines: We implement automated analysis pipelines that consolidate data from various sources, clean it up and make it available for analysis.
• Integration into existing systems: We also take care of the seamless integration of new data platforms and analysis processes into your existing IT landscapes. In this way, we not only provide solutions that are powerful, but also compatible with your current systems.

Descriptive analytics forms the basis of modern data analysis by evaluating historical data and identifying trends. With clear goals in mind, high-quality data and the right tools, descriptive analytics can be implemented successfully. This enables companies to fully exploit the potential of their data and make informed decisions based on the analysis results – for sustainable business success.