Data is at the heart of any business. His treatment is a must, but complex. This challenge can only be met by moving from inferiority to managing “useful” information. Every company must set up data governance in order to optimize its information assets.
The massive and anarchic collection of information brings no value. Today more than ever, businesses must have a strategic, structured and relevant approach to all their data. It’s about taking advantage of its informative heritage.
The most successful companies pay particular attention to this asset. Not as an afterthought, but rather as a central element in the definition, design, and construction of their information systems and databases.
The way a company uses and manages the data is just as important as the solutions chosen to integrate them into its Information System. These fundamental objectives make it possible to exploit the data by transforming it into useful information. That’s when they create value.
The cycle of life
But the successful exploitation of data and information assets is not simple. It is a complex site. It requires proactive management based on specific policies and skills throughout the data lifecycle. Remember that this sustainable management of data is an obligation to be in compliance with the RGPD.
The General Data Protection Regulation (personal) emphasizes that all the professions concerned must ensure the security of such data throughout their life cycle.
Beyond this new constraint, companies must rely on the RGPD to precisely set up data governance.
This approach is based in particular on MDM (Master Data Management), in close collaboration with the DPO. This Data protection officer is a key position for the RGPD.
MDM’s goal is to build a quality repository. Four stages have priority:
Identifying the information assets of the company: It is essential to know where the reference data are;
Definition of the quality of decision data: this step consists of sorting them according to their qualities: accessibility, validity, precision, and usefulness;
Data preparation: many pieces of information contain errors. Verification and compliance is a long and costly process. It is therefore recommended that priority is given to the processing of essential data. You have to control them, consolidate them, clean them out of the way, complete the missing values and then put them in shape;
The definition of a mode of consumption of these data which is in coherence with the constraints, the objectives of the organization, but also the internal and external uses. Coupon: BLT8B
To meet these challenges, data management must be considered as an “administrative” process that is more or less long and more or less complex. It includes the acquisition, validation, storage, protection and processing of data. All these steps are essential to ensure accessibility (the most efficient possible), reliability and timeliness of the data for the various businesses of the company.
This project is ambitious and may seem daunting for many companies that do not yet have this vision. But the time spent planning and implementing effective data management is much more than the cost of implementing it!
For information system managers, these processes involve relying on some essential elements. Data Lake: Integrating data warehouses with similar and disparate information can create new assets and improve decision-making. Data can be structured, unstructured, or both.
Business Intelligence solutions: shared between different employees, BI solutions produce very fine and contextual analyzes. They make it possible to discover new perspectives and to design predictive models.
Data architecture: the data structure must meet the requirements of the business, but also the regulations specific to the business sector of the company.
The current context requires breaking habits. It also calls into question many practices that are no longer appropriate, especially with respect to the RGPD which requires companies to only process data “strictly necessary” to their business!
Companies need to build a data-driven culture. This goal requires an agile adaptation of individuals and teams.