Data Management Disciplines – Separate Specialities or Better Together?
In today’s data-driven world, organisations face the challenge of managing vast amounts of their biggest asset – data. And with so many different data disciplines, and different data teams within an organisation being responsible for such an important asset, it’s no surprise that sometimes the lines of who is responsible for what and how teams work together can get blurred.
In order to understand where Data Governance fits and how we can work together, we must first understand what Data Governance is, and more crucially, what it is not…
What is Data Governance?
Data Governance is a collection of processes, roles, standards, and metrics that ensure the effective and efficient use of information in enabling an organisation to achieve its goals. It establishes the processes and responsibilities that ensure the quality and security of the data used across a business or organisation. Data Governance defines who can take what action, upon what data, in what situations, using what methods.
A well-crafted Data Governance approach is fundamental for any organisation that works with data, and will explain how your business benefits from consistent, common processes and responsibilities. Business drivers highlight what data needs to be carefully controlled with your Data Governance Framework and the benefits expected from this effort.
Data Governance ensures that roles related to data are clearly defined, and that responsibility and accountability are agreed upon across the enterprise. A well-planned Data Governance framework covers strategic, tactical, and operational roles and responsibilities.
What Data Governance is not
Data Governance is frequently confused with other closely related terms and concepts, including data management and master data management – but it is neither of these things.
Data management refers to the management of the full data lifecycle needs of an organisation. Data Governance is the core component of data management, tying together nine other disciplines, such as data quality, reference and master data management, data security, database operations, metadata management, and data warehousing.
Master Data Management (MDM) focuses on identifying an organisation’s key entities and then improving the quality of this data. However, there is no successful MDM without proper governance. For example, a Data Governance program will define the master data models (what is the definition of a customer, a product, etc.), detail the retention policies for data, and define roles and responsibilities for data authoring, data curation, and access.
So how does Data Governance interact with other disciplines?
Data quality is a fundamental aspect of effective data management. Data Governance provides the necessary structure and oversight to establish data quality standards, define data quality metrics, and monitor data quality throughout its lifecycle. By collaborating with data quality management, Data Governance ensures that data is accurate, reliable, and fit for purpose, ultimately enabling better decision-making and reducing operational risks.
By aligning data integration and ETL processes with Data Governance principles, organisations can avoid data silos, improve data accessibility, and promote a unified view of data across the enterprise and by integrating Data Governance with MDM, organisations can establish data ownership, resolve data conflicts, and ensure data consistency across systems, thereby enabling accurate reporting, streamlined processes, and enhanced decision-making.
Collaboration between Data Governance and data privacy/security disciplines allows organisations to identify and classify sensitive data, define access controls, and monitor compliance with data protection regulations. By working together, Data Governance and data privacy/security disciplines help safeguard data assets, mitigate risks, and maintain stakeholder trust.
Data Governance stands as a crucial bridge between various data management disciplines. By collaborating with data quality management, data integration, metadata management, master data management, and data privacy/security, Data Governance ensures the consistency, integrity, and security of an organisation’s data. This collaborative approach establishes a robust data management framework that enhances data-driven decision-making, mitigates risks, and drives organisational success.
Embracing the collaboration between Data Governance and other data management disciplines is imperative for organisations aiming to derive the maximum value from their data assets in today’s data-centric landscape.
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Originally published on www.nicolaaskham.com