Sales departments are preparing their transition to an entirely Data-Driven model, marketers are gearing up to anticipate the imminent disappearance of third-party cookies, and e-tailers are leveraging Data to boost their sales volumes... At a time when data is increasingly determining the relevance of decision-making and competitiveness, the concepts of Data Management, Data Governance and Data Monitoring are gaining in importance. Data Enso offers you a practical guide to these key concepts of the Data transition.
#1 Data Management: the conscious, voluntary and controlled management of data
Data Management refers to all the techniques and processes used to manage data within a company, from ingestion (collection and centralization) and discovery (consolidation of raw data) to analysis and governance.
In its Data Management Body of Knowledge(DMBOK2), the Data Management Association (DAMA) offers a summary definition of the concept: " Data management is the development, execution and oversight of plans, policies, programs and practices that deliver, control, protect and enhance the value of data and information throughout its life cycle ".
For its part, Gartner takes a more practical approach to data management, linking it to the goal of business performance: " Data management consists of the practices, architectural techniques and tools that ensure consistent access and delivery of data across all data domains and data structure types in the enterprise to meet the data consumption requirements of all applications and business processes.
Modern data management is based on 6 main pillars:
- Data architecture. This is a global map that provides visibility over all the tools, processes, plans and interdependencies needed to manage data.
- Data integration to rationalize the storage of data in different formats and from different sources in a data warehouse or data lake.
- Data preparation, which involves cleansing and transforming raw data into information useful for decision-making.
- The data catalog, which consists of bringing together useful, contextualized information to make it accessible, actionable and shareable to users.
- Data security management, which covers all the processes, flows, people and tools needed to secure stored data.
- Data governance, which we will develop in the next section.
In short, Data Management is a global expression that designates the conscious, voluntary and controlled management of data useful to a company's business. Governance is part of this.
#2 Data Governance: the "planning" part of Data Management
Data Governance is a specialization within Data Management, which is the general framework within which the company's various Data initiatives are carried out. Data Governance is therefore an iterative practice that aims to define the best procedures, practices and tools for optimizing the storage and use of data within the enterprise.
While data management relies heavily on the technology stack, Data Governance is more about strategy and planning. Forrester offers a definition along these lines: " Data Governance is a strategic business program that identifies and prioritizes the financial benefits that data brings to organizations, and prevents the business risks associated with poor Data ".
Data governance has several components:
- Data quality measurement, ideally in real time;
- Managing the risks associated with data collection and storage ;
- Ensuring that data conforms to the usual standards;
- Controlling (and reducing) the cost of data management ;
- Data security ;
- Data warehousing.
Governance imagines policies and procedures, Data Management executes them. Without Data Management, Data Governance is merely documentation. To use an analogy, data governance designs and creates the construction plan for a building, data management executes that plan to build it. The company can construct this building without a plan, but it will be less stable, less energy-efficient and probably architecturally defective.
#3 What is Data Monitoring?
Data Monitoring is the regular examination of critical data to ensure that it complies with quality control rules, notably with regard to accuracy, uniqueness, completeness, usefulness, reliability and timeliness (or freshness).
If data quality problems are detected, an alert is sent to an administrator informing them of the rule violation, so that the data can be checked and, if necessary, (re)brought into compliance. Data rules can be created and modified as necessary to apply new data quality objectives as they arise. Data Monitoring is part of Data Management, and its processes are formalized by Data Governance. To discover other terms related to Data Quality, consult our glossary.
To find out more...
Data Enso, data for global performance
On average, between 15% and 25% of corporate databases are unusable. The cause: erroneous, incomplete, obsolete, duplicated data, etc. This is the result of months, years or even decades of poor data management, and translates into wasted time and money, customer dissatisfaction, sales underperformance, unprofitable marketing campaigns and a damaged corporate image.
To turn your Data capital into a real competitive advantage, Data Enso has developed simple, 100% RGPD-compliant solutions. Put us to the test!