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.

 

90% transparency
78% more flexibility
100% compliance

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.

0SAP data repositories
0worldwide locations
0weeks time-to-value

Data Quality Disasters

Data quality is an innocuous term. Upon first encounter, the association is usually big tables filled with numbers, some of which erroneous, math, and complex statistics. The consequences, however, can be very real. In my previous article “Data Cleaning: Pitfalls and solutions” I  shed some light on some of the shapes data quality issues can take. I also talked about a few approaches towards improving data quality and shared some insight on the business impact of inadequate data quality. Today, [...]

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