What is the Westphalia DataLab?

The Westphalia DataLab

We are a start-up that was founded as a strategic investor in autumn 2017 by a team led by Professor Dr. Reiner Kurzhals, Cornelius Brosche and the 145-year-old FIEGE company. In this combination we combine the best from different areas: The family-run and down-to-earth company FIEGE backs us up with its trust and constant support in all areas. This way, for example, we can make use of the technological and scientific freedoms of a start-up, which bring us to our goal as quickly as possible. In our WDL, there are few fixed specifications regarding the technology to be used and no major obstacles to trying out and using new state-of-the-art technologies, as is often the case with large, hierarchical companies. With Professor Dr. Rainer Kurzhals as Managing Director, we have an expert in the field of data analysis and corporate management on board and through him good connections to the universities in Münster. This close contact with science also enables us to continually supplement our team with suitable and specialized employees. The WDL team now consists of more than 45 experienced data experts. The focus of our work is on data analysis for companies for which we can obtain significant insights from their data. Using machine learning and artificial intelligence methods, we combine internal data from past months and years with additional external information such as weather, season or traffic conditions. In three agile steps, we help our customers to become automated and data-driven companies. Our existing and current customers include CLAAS, Westfalen AG, Remondis, LBS West, TSR Recycling and FIEGE itself, for example.

Which use cases are relevant for my company?

As a rule, most companies know for themselves what has happened to them recently and which developments should be examined and analysed more closely by the WDL. In an initial analysis, we can use your experience in creating a proof of concept (PoC) to identify customer-specific projects. These can be special questions concerning the sales development of a specific product or groups of customers willing to migrate, for example, or long-term recommendations for action regarding sales increases or cost reductions within the company. In addition to your own questions and ideas, the WDL can also explore the available data in order to identify relevant use cases. The WDL draws on more than 25 years of experience in Advanced Analytics to evaluate the large amounts of data available in such a way that relevant information can be discovered for your management. We use machine learning and artificial intelligence methods to evaluate internal company data and additional external data in order to provide you with concrete recommendations for action. The WDL supports you in several ways and helps you to find out what will happen in your company in the short and long term and which use cases are relevant for your company so that you can intervene in a timely manner.

What requirements does my company have to meet for a data science project?

If you are interested in the future of your own company, you have already fulfilled the greatest prerequisite for working with the WDL. If you want to know how to form customer groups, avoid cancellations or create optimal route and warehouse plans or how to identify "high potential" customers and products or detect cases of fraud, a data science project with the WDL is a good way to get one step closer to these goals. For a comprehensive evaluation we need such data, which has been stored for years in most companies, but is often not further evaluated. It doesn't matter whether there was a lack of know-how, technology or capacity. The WDL now takes over this work: You provide us with your internal data and we discuss the objectives with you. Internal data typically includes customer and contact history or transaction data in a common standard format such as .xls or .csv. Then we perform an individual analysis with additional external data in order to provide you with answers to your questions and concrete recommendations for action to increase sales and reduce costs. If you like the results of the first project and you recognize the added value of Data Science in your company, your company will be transformed into a data-driven one. You can individually adjust the level of support in regular operation by the WDL.

How's a data science project going?

You yourself know best what has happened in your company in recent years and what important topics and questions are in corporate management. The WDL can support you in finding out what will happen in your company in the coming months and years, so that you can intervene in a timely manner. With Artificial Intelligence (AI) procedures, your existing company data as well as external data are analyzed in order to predict, for example, sales figures for a product in the coming days, weeks and months or to identify customers in good time who are otherwise highly likely to cancel. A data science project with the WDL usually takes place in three agile steps:

  1. In the first step, the Proof of Concept (PoC), a customer-specific project is identified within three weeks. Your own questions will be recorded and analysed here.
  2. This PoC will be transferred into an individual dashboard within four weeks in a second step. With this graphical user interface, which is tailored to your company and suitable for analysis, you can view all important key performance indicators and analysis results and filter and aggregate them user-specifically depending on the issue at hand.
  3. Our project is not a classic, one-time consulting project, which becomes apparent in the third step, the transfer to regular operation. Here we automate the dashboard for regular operations, where you have as much new information as possible about data-driven business management. Here you decide for yourself how strong our support is and how long you want to act as a data-driven company on the market.

Cookies erleichtern die Bereitstellung unserer Dienste. Mit dem Klick auf OK erklären Sie sich damit einverstanden, dass wir Cookies verwenden. Mehr dazu