One can find different definitions of Data Governance in influential sources:
- According to DAMA it is “the exercise of authority and control (planning, monitoring and enforcement) over the management of data assets.” (DAMA-DMBOK, Chapter 3)
- Gartner defines it as “the specification of decision rights and an accountability framework to ensure the appropriate behavior in the valuation, creation, consumption and control of data and analytics.” (Gartner glossary – Information Technology)
- Wikipedia claims that it is “the capability that enables an organization to ensure that high data quality exists throughout the complete lifecycle of the data, and data controls are implemented that support business objectives… Data governance encompasses the people, processes, and information technology required to create a consistent and proper handling of an organization’s data across the business enterprise.” (source: Wikipedia)
- Finally, Forrester describes it as “the process by which an organization formalizes the “fiduciary” duty for the management of data assets critical to its success.” (Forrester Glossary)
Regardless of the definition, however, it is now widely accepted that Data Governance is key to creating value, managing risks in an organization and improving business processes. All businesses, regardless of their industry and size, make data-related decisions. And the ever-growing volumes of data produced daily by various internal and external systems and sources require leveraging intrinsic value of one’s data assets.
Therefore, Data Governance is the ability to manage data as an actual business asset. To achieve it, one needs to follow certain “rules”, or a Data Governance framework. It ensures the data is appropriately conducted with respect to the business objectives.
Why adopt a Data Governance framework?
Implementing a Data Governance framework has numerous benefits, including:
- Better control of risks, both financial and reputational, including those related to legal issues and standards, such as respect for privacy (e.g. GDPR).
- Increased data security. Having established various levels of importance for data, one can properly define “visibility funnels” and focus on protecting the critical data. For example, being responsible for the data processing, it is necessary to have full control of where the data is stored, who updates and accesses it and for what purposes.
- Greater transparency and auditability that makes the use more efficient and reduces management costs.
- An enterprise-wide vision of data (Business Glossary and Data Catalog) designed to create a common business language. It would lead to fewer misunderstandings and easier communication.
- Efficient development and monitoring of processes to address both issues and opportunities in data assets management, including using calculation metrics.
- Optimized vendor management, control of data-related contracts such as cloud storage, external data acquisition, etc.
- Facilitating the Data Quality reporting and issue resolution processes.
- Improved decision-making and conflict resolution processes.
Conversely, without a Data Governance framework a number of organizational problems may emerge. These include, for example:
- Difficulties in understanding between departments due to inconsistent definitions;
- Creating policies and rules that are not in line with or, worse still, contradictory to the business strategy;
- Data management entrusted exclusively to IT and not involving the business, and, as a consequence, the distancing between the departments;
- Increased effort in resolving issues related to business processes management;
- Lack of technical integration, which may bring the risk of scattered and duplicate data and the “impossibility” of overcoming the notorious “data silos” logic;
- Decisions based on erroneous data.
The first crucial task is to define a data strategy. Without it, we run the risk of launching Data Governance initiatives deemed to remain isolated projects that never achieve the stated goals. To help the organization in practice, Data Governance should be seen as a continuous, structured and regulated process. And it should be adequately sponsored by the business and the management. A Data Governance framework should have clearly defined operational guidelines. These include roles and responsibilities, application priorities and scopes, technological processes and choices. The latter, in particular, must be addressed accurately and in compliance with the strategic goals to produce timely results for achieving business objectives.
Who in the company actively participates in Data Governance initiatives?
Some of the most important (and the most arduous) first steps on the way to success are:
- to spread data culture in the entire company,
- to identify roles and responsibilities for those involved in data asset management at all levels,
- to determine the modes of participation in business processes for these individuals. Some companies already have real communities. Credem’s Data Heroes is one of the best known examples in the Italian market.
In larger and more complex organizations, a well-designed Data Governance structure typically includes:
- a governance team led by a Chief Data Officer or a Data Governance manager whose primary task is to draw up the policy and standards and to oversee the functioning system,
- a steering committee that acts as a governing body and resolves possible doubts about intervention priorities and/or inter-departmental conflicts,
- several Data Owners, each entrusted with the formal responsibility to oversee the data in a particular business area,
- several Data IT specialists/Data Technologists responsible for Data Management in computer systems,
- in many cases, a number or Data Stewards, who, in addition to other duties, can act as intermediaries between the worlds of business and IT.
These roles and departments may have different names in different companies. Some organizations may have additional figures engaged in overseeing data.
Data Governance involves the entire company, and all departments play their part. Hence it is crucial to manage the interactions between Data Specialists in a structured way. A concrete example of this is Data Quality Governance, i.e. organizing people and collaborative processes with the aim of ensuring Data Quality.
Why is it easier to achieve Data Governance business goals with Irion EDM?
The use of specialized tools facilitates the definition and efficient execution of Data Governance processes. Furthermore, it helps to orchestrate, automate and monitor them (e.g. verifies and manages risk data anomalies) and guarantees unique tracing of the entire data life cycle and of responibilities of individual actors involved (including for audit purposes). The organizational culture must learn to value its data assets in order to benefit from them. As expected, promoting data value culture is essential to effective Data Governance. Even with the best data strategy, data governance and management plans will not succeed if the organization does not embrace and manage change.
Irion EDM facilitates optimal and structured Data Management as well as data sharing and collaboration between different roles.
- is a completely metadata driven all-in-one Enterprise Data Management system. It is based on a freely configurable model for creating Data Catalogs and Business Glossaries;
- automatically coordinates the collaboration of several teams managing the same project. It is designed for use by all Data Specialists with personalized visibility funnels and features for individual roles;
- is inspired by the Declarative Thinking principle. It allows to focus on defining the context and the goals, while the tool automates all the technical aspects and optimizes the performances;
- is an ideal Data Lineage and impact analysis tool. It provides a graphical interactive representation of the relationships between data;
- can quickly manage millions of data;
- enables Serviced Oriented Data Governance to create and organize a catalog of Data Governance services useful for different business departments.
These features, along with a complete and developed functionality that includes special components for applying AI/ML or Augmented Data Management techniques, make Irion EDM the optimal tool for simpler management of data assets and for putting their value at the service of the business.
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Among the subjects considered:
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