The Data Platform dissolves data silos in the company, covers topics such as Data Quality Management and Data Privacy and offers a valid and therefore trustworthy basis for the generation of corporate key figures. The structure of the Data Platform with Single Point of Truth is not a magic wand and can specifically cover all requirements in the specialist departments. Nevertheless, many companies do not have a holistic strategy when it comes to the central consolidation and availability of their data.
The digital transformation is still a much discussed topic in many companies in this country. Although most decision-makers have now understood that a digital strategy is absolutely necessary for survival in the market, it often fails to be implemented, especially in the case of medium-sized companies, because resources in the form of money and personnel cannot be redistributed as flexibly as with larger market participants.
First holistic approaches of global digitisation strategies are always accompanied by one and the same concise term: Single Point of Truth. Without this powerful central collection point for corporate data, a consistent digitization strategy cannot be implemented because data for reporting, analysis and planning purposes is trapped in data silos and cannot be made available at all or only with difficulty in other areas or for management. Companies with data silos often waste a lot of time discussing data validity and moving Excel sheets from department to department. Compliance requirements further complicate the exchange of data between departments. Even management reports are often classified as untrustworthy because there is a high level of mistrust in the data basis due to data quality deficiencies. The marriage of data models is thus not possible at all or only to a limited extent. Data-driven goodbye!
Sooner or later, most decision-makers reach the point where the topic of a central data platform becomes inevitable. At the latest when a flexible, holistic and reliable reporting system is to be established, the single point of truth must be established, because data management is the driving force for reporting and analysis. However, this insight poses a number of challenges: Technologies must be researched and validated and the large number of manufacturers on the market does not necessarily make the decision easier. However, the most important basis for decision-making is not the technology to be implemented, but the business requirements for the Data Platform.
The design of the central data platform is not only about ensuring the accessibility of all data at the single point of truth. Other topics critical to success, such as data quality management, data governance, data integration or data security must also be taken into account in order to generate added value from corporate data.
Customer data that contains errors or gaps, for example, can only be evaluated inadequately and, in the worst case, can damage the customer relationship if the customer journey is to be built on a database. Inadequate warehouse data also leads to delivery bottlenecks or overfilled shelves. In addition, management dashboards that contain incorrect key figures lead to blind control and serious mistakes in decision-making.
In order to find out with which components a data platform must be equipped for all relevant company areas, the recording of specialist requirements in the various departments plays an essential role. It is possible that relational data in tabular form will be sufficient for monthly reporting to the controlling department for the time being. If, however, some time later the topic of planning optimization comes up, multidimensional databases are required. At the same time, production may have large amounts of sensor data, want to create real-time dashboards for production controlling and therefore require a streaming data solution based on data-lake technology. Covering all requirements simultaneously is not possible in most companies due to limited human resources. Does the Single Point of Truth have to be able to directly consider all requirements at the same time?
The answer is no. Data platforms can be built up modularly and successively in the context of subprojects. The inspired phrase "Think big - start small" is the order of the day: without a consistent vision, one or the other often gets lost in technologies that may not fit optimally into their medium-term digital strategy. In all cases, it is advisable to organize requirements workshops with representatives from different departments and to incorporate all results into a consistent concept. Once the first guideline for the digital strategy has been drawn up, the various use cases are classified and prioritized according to topic. This prioritization is also followed by the establishment of the Single Point of Truth, because who needs streaming data today when production controlling will only have capacities and budget for predictive analytics projects in a few years' time? At the beginning it is important that the choice of technologies enables the modular structure of your data platform. In this way, future new requirements can also be taken into account.
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