The Westphalia DataLab
We, the Westphalia DataLab, are a start-up founded in autumn 2017 by a team around Professor Dr. Reiner Kurzhals, Cornelius Brosche and the 145-year-old logistics company FIEGE as a strategic investor. In this combination we unite 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, we have the technological and scientific freedom to try out and exploit new state-of-the-art technologies that enable us to achieve our goals as quickly as possible. With Professor Dr. Rainer Kurzhals as Managing Director, we have an expert in the field of data analysis and corporate management on board. His close connection to universities in Münster also allows us to continually expand our team with specialized employees. Our team now consists of more than 45 experienced data experts. We specialize in developing data-based solutions together with our customers for a wide range of challenges. Using artificial intelligence and machine learning technologies, we set new standards with our innovative product approaches and support our customers on their way to become data-driven companies. Amongst others, our customers include CLAAS, Westfalen AG, Remondis, LBS West, TSR Recycling and FIEGE itself.
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 us. 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 us 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 us usually takes place in three agile steps:
The abbreviation DAaaS stands for "Data Analytics as a Service". As a customer, you collect data that you make available to the WDL. We carry out a data analysis by using additional external information to analyze the current status for you and provide a forecast for your company. It is important to us that this data analysis is carried out in the way that best suits you and your company: You can give us factors that we look at more closely and then focus on areas that you want to have analyzed more closely. Alternatively or additionally, we explore your data and develop insights that have strategic added value for your corporate management. Another advantage that the WDL provides you with is our individual service after the first analyses. We do not offer classic, one-off consulting projects. With us, you can decide for yourself how long and in what intensity and scope you want to enjoy our support. You decide on a certain level of permanent support by the WDL and still have the freedom to adjust this service to your current situation at any time or to cancel it monthly.
How to automate Data Science Use Cases?
A data science project with us usually runs in three agile steps:
What's the WDL Dashboard?
The term dashboard comes from English and originally refers to the dashboard of a car. In our environment, this means an interactive graphical user interface with which information can be clearly presented to the user. This bundled information makes it easy for the administrator to keep track of all company information, monitor processes and react just-in-time to developments. As a WDL customer, you are interested in finding out what will happen in your company in the future and where you can intervene in good time. Artificial Intelligence (AI) methods are used to analyze your company's historical data in combination with additional external data, for example to predict future sales figures or customers willing to terminate their contracts. A WDL project consists of three agile steps in which a customer-specific project is first identified and this Proof of Concept (PoC) is converted into an individual dashboard. 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. The third step is to 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.
What's data science?
The term "data science" is often referred to as data science in the German-speaking world. In terms of content, this refers to the extraction of knowledge from data, for which the WDL uses various scientifically based methods and algorithms. The aim is to identify new findings and patterns for the user by evaluating large company data and thus, for example, to create added value for further planning. Data Science comprises many methods which have in common that they can be applied in particular to extremely large data sets. For example, the WDL deals with the following methods:
What is artificial intelligence?
Artificial Intelligence (AI) is a branch of computer science that deals with the automation of intelligent behavior and machine learning. By programming a computer, certain human decision structures are simulated so that the machine can solve tasks relatively independently. Such tasks otherwise require intelligence when processed by humans. AI methods are used in particular where the cognitive capacity of humans is insufficient and the amount of data is extremely large. Small and medium-sized companies in particular often ask themselves whether the use of AI methods is also suitable for their own company and whether there is sufficient suitable data available in their company. The WDL can support you in this decision: Anyone who already knows today what is likely to happen tomorrow will be better placed to hold their own in the market. We know from experience that even smaller companies have sufficient data for the efficient application of AI. Often, however, it is not the missing data that is problematic in an analysis, but a bad data model or a missing objective. The correct preparation and meaningful combination of different data sources is relevant in order to be able to use data as a basis for future corporate decisions. A target-oriented data preparation and strategy are important for chances of success and competitive advantages. The WDL with more than 25 years of experience in Advanced Analytics. We can guarantee you a safe and meaningful application of AI methods, so that you as a small or medium-sized company can also be a big step ahead of your competitors in the future.
What is Machine Learning (ML)?
Machine learning is an area of artificial intelligence (AI) in which knowledge is generated from experience. The artificial system learns patterns and laws from given example data sets and can generalise what it has learned after the learning phase has ended. IT systems can dynamically recognize patterns in the given data and then assess unknown data and transfer the learned knowledge to new data in order to develop solutions or improve results and predictions based on learning processes. The technology behind machine learning is neural networks. The WDL uses machine learning, for example, to detect cases of fraud, analyze stock markets or determine future customer buying behavior. If you are interested in such questions, a data science project with us would be a possibility to generate such information. We help you to find out what will happen in your company in the short and long term.
What's the difference between Machine Learning (ML) and artificial intelligence (AI)?
Machine learning and artificial intelligence are two keywords that are often not used in a differentiated enough way in the big data context. Artificial intelligence is understood as a broader concept in which machines are able to perform tasks in an "intelligent" way. Machine Learning is understood as a current application of the AI, in which the machines are given access to data and can then learn for themselves.
Definition of term
Predictive analytics, a field of data mining, is about predicting future data and trends at short notice. The focus here is on an individual customer or a unit from your company for which future behaviour is to be calculated and for which interest is to be shown. If, for example, you want to know what you can do to increase sales of a specific product as quickly as possible or which offer you can present to customers who are willing to migrate so that they remain loyal to you, the WDL can help you with your decision. In order to be able to determine such information, we use the available historical data of your company and concentrate additionally on the history of the product to be viewed or the relevant customers. With the help of appropriate state-of-the-art technologies, we can evaluate the extensive amount of data with regard to the current issue. In addition, we enrich the evaluations with a large amount of external data in order to better explain the behavior of individual customers and the sales figures of certain products. During the analysis external information about holidays, weather, season or the introduction of new competing products or companies to the market can be helpful. The focus of Predictive Analytics is on the short-term decision horizon as well as the set focus. The difference with another data forecasting option, forecasting, which the WDL uses for more long-term data forecasting for your entire organization, is in the focus and timeframe set.