Detecon Study: Still a Lot of Backlog When It Comes to the Data-Centric Company
- Still room for improvement in added value, culture, and data management
- Compliance plays a key role for data-driven innovation projects
- Mesh governance broadens range of options for data users
Cologne, 30 May 2023. Although most companies are pursuing big data approaches, they fail to crystallize information, knowledge, and added value from the data they have collected. This is one of the key conclusions of the qualitative study “Data Centric Company” prepared by the management consultancy Detecon. It has determined that there are especially striking gaps in the early identification of potential business values, in the development of data culture (including the associated skills), and in the design of suitable data architectures. One possible solution is data mesh, a platform based on decentralization that gives users tremendous freedom when generating valuable insights from data. The Detecon study also identifies problem areas within the category of “Compliance” for companies that have been forced to forego data-driven innovation projects while dealing with new technologies.
What should a good data strategy include?
According to the authors of the “Point of View: Data-Centric Company” paper, companies today are neglecting these factors.
- Demonstrate business value
A major obstacle encountered when conceptualizing data strategies is the frequent lack of clarity about the business value and the consequent lack of support from top management. The authors’ advice: demonstrate unambiguously the potential added value by clearly identifying focal points and specific application areas in advance. Pilot projects or proofs of concept (PoC) and, in more advanced phases, the scaling of multiple use cases that create added value are useful in this respect.
- Empower organization
Data-centric action by an organization demands a corporate culture that creates a broad democratization of data intelligence; all employees — not just developers or data scientists — must be trained in the requisite skills that ensure data’s place at the heart of decision-making and business procedures. Change management initiatives based on a company-wide vision, ongoing skills assessments, and continuous competency development fostered by hands-on training, coaching, and communities are among the means that can be used to promote such a culture.
- Improve data engineering
Classic fundamentals such as professional data management are still not mapped satisfactorily at most companies. More attention must be paid to issues such as poor data quality, inadequate data access, and a lack of processing and analysis tools. The entire data added-value chain must be considered: the collection, management, transformation, analysis, visualization, and storage of data that will support data-centric added value for the enterprise.
“Experience shows that data-driven projects and solutions run into difficulties at any number of points that may differ drastically from one another. Methods and approaches combining multiple disciplines are recommended to ensure that data-driven solutions are implemented quickly and successfully,” explains Steffen Kuhn, study author and director of the Detecon Digital Engineering Center. “This is where our Data Thinking Framework, which combines principles from design thinking, CRISP-DM (standard process for data mining), and agile development, comes into play as a possible option,” according to Kuhn. Volker Rieger, head of Value Creation & Strategy at Detecon, unequivocally emphasizes the tremendous importance of data-centric ecosystems: “Added value today is networked and digital. Data link companies with their customers, partners, and suppliers. No one who is not a master of data exchange on platforms will be able to compete and survive.”
Mesh governance as a pillar of data democracy
Another conclusion of the Detecon study refers to the growing role of the phenomenon of data democracy. Its premise is to provide access to corporate data to as many employees as possible so that they can use actually use the information for the improvement of organizational operations. “In other words, data should be treated as a product that is ready to use and reliable,” explains Marcus Berlin, Principal at Detecon. In many cases, a data mesh platform that has been deliberately conceptualized as a decentralized data architecture can be a great fit. Instead of top-down decisions about the format in which data are stored for future users, data are stored in their original format, and future users are at complete liberty to decide themselves what transformations are expedient for their specific requirements. “Most players want a single-source data platform that they can use to perform analytics and gain meaningful insights without relying on the support of a central IT team,” notes Marcus Berlin.
Data-driven innovations — especially difficult in Europe?
One commonly expressed opinion is that the strict data protection laws in Europe are causing the countries on this continent to fall far behind China and America. The presumable consequence is that few data-driven innovations are coming out of Europe. Detecon’s analysis has now identified three major problem areas for companies that have been forced to forego data-driven innovation projects while dealing with new technologies.
1) The impact and requirements of the specific data protection regulation were not given due consideration from the very beginning of the development.
2) In-house compliance processes are regarded as static, complex, and time-consuming and consume substantial resources for audits and consulting.
3) Some companies prohibit the inclusion of certain technologies such as the use of machine learning or proprietary data as a general principle owing to their complexity and ambiguities in interpretation.
In their analysis, the Detecon authors emphasize that the compliance sector is a key player who acts as a bridge and translator between companies and legislators whenever new technologies such as artificial intelligence or machine learning appear. “Intelligently established, however, it can specifically promote and accelerate data-driven innovations — by specifying future-proof guidelines and methods, for example," emphasizes Isabell Neubert, expert for digital ethics and innovation management at Detecon.