Editor's Note: Take a look at our featured best practice, Data Governance: Roles & Responsibilities (24-slide PowerPoint presentation). Data standardization, precision, and security aren't the sole dominion of folks working within the IT department. These tasks involve participation of a number of stakeholders from across the organization. Incoherent data creates hurdles in Data Analytics and business operations. Data [read more]
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Data Governance is a set of practices that outline the roles and accountabilities related to data and support the organization’s Business Model by generating and consuming data. It’s all about overseeing the accessibility, practicality, reliability and safety of enterprise data.
Data Governance is guided by in-house data standards and guidelines to monitor data usage. The process safeguards data uniformity, dependability, and fair use. The success of a Data Governance initiative should be measured by its expected business benefits or organizational growth—not just data security and usability.
With increasingly stringent data confidentiality laws, the need for implementation and adoption of Data Analytics to improve decisions and enterprise operations is further amplified.
With huge data lakes and Big Data comes greater responsibility to manage risks associated with data collection, processing, and storage. Data Governance aids in evading any untoward issues arising during the data collection, storage, retrieval, and utilization process. In data-driven organizations, the importance of Data Governance is supreme to generate value. For instance, Data Governance assist in:
- Busting inconsistent data silos.
- Standardizing data definitions and developing a common data understanding.
- Improving quality of data.
- Providing executives reliable insights to make decisions.
- Ensuring compliance with data regulations.
- Reducing data handling costs.
- Creating competitive advantage for organizations.
To gather across-the-board support from the stakeholders and reducing conflict against the Data Governance initiative, research suggests the following best practices:
- Involving business people—alongside data owners and governance committee—to decide on standards and policies of the governance program.
- Consistently reporting the governance program’s status through workshops, newsletters, emails, or reports.
- Setting up and updating security protocols for all organizational technology systems and units.
- Defining a customized Data Governance methodology to guide individual business processes.
- Keeping the emphasis on creating results and business value for the enterprise.
- Encouraging all stakeholders to agree on the roles and responsibilities.
- Regularly training all stakeholders on Data Governance, data usage rules, confidentiality, and data consistency.
The execution of Data Governance programs commence by preparing a Data Governance plan. The Plan entails:
- A document delineating rules and procedures governing data generation, gathering, and utilization.
- These rules and procedures focus more on security policies.
- Most employees know that the plan exists somewhere, but majority do not bother to read it.
- The plan is often quite bureaucratic and vague, specifically due to the plurality of data, which makes it tough to present all potential scenarios in one place.
Dimensions of Data Governance
Most large enterprises comprises multiple business units where each produces and uses data of varied degrees of sensitivity. A uniform approach to Data Governance based on the data with highest degree of sensitivity would not work for all categories of data.
The discrete business units need an agile and flexible Data Governance methodology; one that inspires Digital Innovation. Such an approach to Data Governance is grounded on 5 dimensions that cover different spheres of executive Decision-making:
- Life cycle
Let’s talk about the dimensions of Data Governance in detail.
Principles are at the foothold of the Data Governance approach. Executives need to first define the key principles which act as the foundation for the data to function as a valuable resource for the corporation.
The Quality dimension demarcates the prerequisites for usability of data and the methods to evaluate that those prerequisites are fulfilled.
Metadata expresses the meanings to clarify and use data—e.g. Metadata in a data catalog is employed by data scientists to use large data sets available in a data lake.
Accessibility defines data access, security and risk mitigation needs, privileges, and procedures.
This dimension deals with the creation, retention, and disposal of data.
Interested in learning more about the dimensions of the approach to Data Governance? You can download an editable PowerPoint on the Data Governance Primer here on the Flevy documents marketplace.
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About Mark BridgesMark Bridges is a Senior Director of Strategy at Flevy. Flevy is your go-to resource for best practices in business management, covering management topics from Strategic Planning to Operational Excellence to Digital Transformation (view full list here). Learn how the Fortune 100 and global consulting firms do it. Improve the growth and efficiency of your organization by leveraging Flevy's library of best practice methodologies and templates. Prior to Flevy, Mark worked as an Associate at McKinsey & Co. and holds an MBA from the Booth School of Business at the University of Chicago. You can connect with Mark on LinkedIn here.
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