Data management for the retail industry2020-05-29T14:12:12+02:00

Effective data management for successful data use in retail

Data management is the basis and framework for rapid digital transformation

When preparing your data, we focus on efficient long-term solutions. Correct data management is the basis of all other applications from key figure dashboards to machine learning models. After successful data preparation, we also support our customers with automated data warehouse solutions to make the entire data pipeline process as simple as possible. This way, we make your data immediately ready for use and available at any time.

Retail companies usually have many different data sources from which they can extract key figures if required but this is often difficult and time-consuming. With a well-structured data warehouse and a clearly defined ETL (Extract, Transform, Load) process within the company, unnecessary manual efforts can be eliminated. Companies can thus quickly save costs and time. In addition, data quality and availability in the company can be permanently increased to continuously improve data-based planning and forecasts.

 

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Case Study: Motul is now running with data like clockwork

The lubricant manufacturer Motul faced the problem of poor data quality and unclear data lakes. Therefore, the company decided to cooperate with our data experts to structure the ETL process from different data sources and to support them in building a data warehouse. This enabled Motul to create a long-term, stable basis for data-driven decisions within a few months. The data was prepared and transformed in such a way that the company can now access its data in a targeted manner, at any time and from any location. This uncomplicated access to the data, the real-time availability and the clear data structure save the industrial company a lot of time and enables the rapid implementation of further data analytics and data science projects.

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Data Cleaning: Pitfalls and Solutions

As the interest in machine learning and artificial intelligence grows, companies regularly find themselves confronted with the dissatisfying quality of their data. This discovery is either made early-on with a structured approach, or a lot later, when poor data quality is identified as the root-cause of poorly performing models. In either case, the next step should be a methodical exploration of the available data, followed by a series of steps to remedy the identified issues. In this article, I will [...]

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