The IT industry likes mantras, condensed expressions to convey complex concepts and repeat them until the incantation works: “digital transformation”, “agile business”, “client centric business” and now “data centric business “. Even if the challenges related to data are identified in the majority of companies, few companies are today data centric and capable of developing a real disruption.
Where do we start and with what skills and ways of working? How not to drown in the data centric enterprise? Let’s find out with roadmap PowerPoint, the way to open the right choices for you. Its mission: to define and prioritize the capabilities of the Ipsen data platform for Business Intelligence, Machine Learning, data transfer, governance and reuse of internal and external data projects.
Becoming a data centric company is a fundamental step in disruption to support agility
- First of all, remember that it is essential to invest in all activities related to processing and maximizing the use of data. For three reasons:
- The IS must be more agile and for that it is necessary to perpetuate the stable bricks and make variable elements adjustable.
- Getting knowledge of the data is a sine qua non condition for the disruption to be created, in particular through artificial intelligence and data visualization.
- The strengthening of regulations (such as the GDPR and, in the future, a trustworthy AI) and the need for ethics make it necessary to know your data well, how to use it, and to protect it.
- Today, lessons from the past teach us that there is no agility if it is not based on data and their exchanges in processes (via APIs). In fact, following the disruptions observed and decades of analysis of the evolution of large IS, we realize that the only stable element is the data and the services rendered using the data.
Who says data centric company says data governance
The data centric business concept puts data governance at the center of thinking, but we also see that the term is broad and vague enough to give some cold sweats to those who are responsible for making it a reality in the company.
The company gives a very good description of the governance of IS, but it is a target vision with a lot of variation according to the companies (cf. the Guide of audit of the governance of the information system of the digital company). Data governance is a common vocabulary, knowledge of the owners of the data and associated systems, quality management and uses shared with business. Architecture, security and the DPO (Data Protection Officer) can participate in the modeling, classification and access rights to data, or globally mitigate this role partially if business governance is not yet installed. Ethics, quality and legal aspects can also become more or less important depending on the sector of activity. In pharmacy and aviation, for example, quality is essential.