From Rock Stars and Roadies or why Rock Stars need a stage and “Trusted Data” is the basic requirement for every intelligent enterprise
Data Scientists are the obvious Rock Stars of digital transformation and are currently in great demand. Every Rock Star needs a solid stage that he trusts to be made of high quality and suitable material and every data scientist needs Trusted Data to do his job.
Nobody would think of letting a Rockstar build his stage by his own. Couriously this is exactly what happens in many companies when it comes to the procisioning of data. The Rockstar would need by far too much time and would not be able to show his artistic talents. His wings would be cut, so to speak. Similarly, nobody hires data scientists to prepare data, but to create and train AI models.
For good reason there are the “Roadies”, the silent heroes of every rock concert. Transferred to the business world, they are the counterparts of specialists for data management . It is their very own task to provide quality-assured data in a manner that is compatible with the legal framework.
Data driven organizations are 23 times more likely to acquire customers, 6 times as likely to retain those customers, and 19 times as likely to be profitable as a result (Source: McKinsey & Company)?
Reason for this are already digitized business processes. This can only be achieved on the basis of Trusted Data to make better decisions, improve and automate business processes, increase productivity and monetize data.
But why are so many companies struggling to create the data foundation for business agility that is so essential right now?
1. The basic step of every data management activity is to identify the right and relevant data for the respective business goal.
2. Isolated data from different data sources have to be connected and eventually transformed in order to be able make correct relations.
3. Data quality must be managed over the entire data life cycle, which requires continuous measurement.
4. Chief Data Officer has to define Data Governance Guidelines in order to prevent data from being copied and moved in an uncontrolled way for individual purposes.
5. No matter how great a digital process , it will never make it to production when data privacy and protection is at risk. Personal data must be deleted if there is no longer a legal basis and may have to be obfuscated based on the respective use case.
6. A clear structure of responsibility and defined data ownership is an essential prerequisite for the treating data as a strategic asset. As with a real supply chain, the data supply chain must be managed end to end accordingly.
7. Data monetization comprises of identifying and marketing data or data-based products to generate monetary value.
- How to use your data to make your company fit for all challenges.
- How to provide users with the right data in real time in the context of the task at hand. How to use Trusted Data as an innovation catalyst.
- How to ensure compliance with external data protection guidelines and internal governance guidelines.