Data is at the heart of every business. Handling it is an unavoidable but complex task. This challenge can only be met by moving away from infobesity towards the management of "useful" information. Every company needs to implement data governance (data management) in order to optimize its information assets.
Massive, anarchic collection of information brings no value. Today, more than ever, companies need a strategic, structured and relevant approach to all their data. It's all about making the most of your information assets.
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 data is just as important as the solutions chosen to integrate it into its Information System. These fundamental objectives enable data to be exploited by transforming it into useful information. This is where value is created.
Data-Management : The Lifecycle
But the successful exploitation of data and information assets is no simple matter. It's a complex undertaking. It requires proactive management based on specific policies and skills throughout the data lifecycle. Let's remember in passing that this perennial data management is an obligation to comply with the RGPD.
The General Data Protection Regulation (GDPR) emphasizes the need for all businesses involved to ensure the security of such data throughout its lifecycle.
Beyond this new constraint, companies must rely on the RGPD precisely to implement data governance.
This approach relies in particular on MDM (Master Data Management), in close collaboration with the DPO. This Data protection officer (or Délégué à la protection des données) is a key position for the RGPD.
The aim of MDM is to create a quality repository. Four stages are given priority:
- Identifying the company's information assets: It is essential to know where the reference data is located;
- Defining the quality of decision-making data: this stage consists of sorting them according to their qualities: accessibility, validity, accuracy and usefulness;
- Data preparation: much information contains errors. Checking and rectifying these errors is a time-consuming and costly operation. It is therefore advisable to give priority to processing essential data. This involves checking and consolidating the data, cleansing it of outliers, filling in missing values and formatting it;
- The definition of a data consumption mode that is consistent with the organization's constraints and objectives, as well as internal and external uses.
Culture data-driven
To meet these challenges, data management must be seen as an "administrative" process of varying length and complexity. It involves the acquisition, validation, storage, protection and processing of data. All these steps are essential to ensure that data is accessible (as efficiently as possible), reliable and up-to-date for the company's various business functions.
It's an ambitious project, and one that may seem daunting to many companies who don't yet have this vision. But the time spent planning and implementing effective data management far outweighs the costs involved!
For information system managers, these processes require the support of a number of essential cogs in the wheel.
- Data lakes: the integration of similar and disparate information in data warehouses can create new assets and improve decision-making. Data can be structured, unstructured or both.
- Business Intelligence (BI) solutions: shared between different employees, BI solutions produce highly detailed, contextual analyses. They enable the discovery of new perspectives and the design of predictive models.
- Data architecture: The data structure must not only meet business requirements, but also the regulations specific to the company's sector of activity.
The current context is forcing us to break our habits. It also calls into question many practices that are no longer adapted. Particularly with regard to the RGPD, which requires companies to only process data that is "strictly necessary" for their business!
Companies need to build a data-driven culture. This requires Agile adaptation of individuals and teams.
