Getting Started with Data Governance

Jitendrudu Lacaraju By: Jitendrudu Lacaraju

What is Data Governance?
Let’s begin with understanding the term Data Governance: it is an umbrella term that encompasses several different practices, such as data management, data quality, business processes, and risk management. Data Governance is not just about Technology, but rather, it is a combination of people, process, knowledge bases, and technology.

A formal definition:
“The formal orchestration of people, processes, and technology to enable an organization to leverage data as an enterprise asset.” – MDM Institute

and for a more worded definition,

“Data governance is a system of decision rights and accountabilities for information-related processes, executed according to agreed-upon models, which describe who can take what actions with what information, and when, under what circumstances, using what methods.” - The Data Governance Institute

A key component of Data Governance is the stakeholders that take responsibility for turning raw information into Data Assets for the Enterprise by setting up processes on how they interact with information as well as how and why it is used.


The Need for Data Governance
With the elevated use of technology-dependent business solutions, the data captured by an Enterprise usually grows at a rate of 1.5 to 3 times in a year. In the face of such unprecedented data expansion, an Enterprise that lacks effective data governance policies will quickly find itself with a mass of poor-quality data. Examples of such include a lack of institutional knowledge, inconsistent data definitions, costly data duplication, missing fields, and many other data integrity issues that lead to real business impact in terms of reporting, regulatory compliance, and data privacy. A Data Governance Framework will aid in ensuring the quality and integrity of data, which will help increase oversight and remove risks.
It is not to say that most Enterprises lack any framework; however, over time, it can be challenging to keep up with changing technology trends and information management best practices. Furthermore, organizational challenges and turn over may impede progress. Developing an Enterprise-wide Data Governance Framework can be perceived as a daunting and overwhelming task with a high cost of implementation.  The more assets, applications, and people involved, the higher that cost seems.  Though, without a robust Data Governance strategy, the unforeseen expenses of poor-quality data are often a more significant burden to the Enterprise in the end.


Summarizing the Importance:

  • Save Money: A Data Governance framework increases efficiency by avoiding duplication of effort, time, and resources spent in data tracking, and making data readily available for customer, finance, and analytical initiatives.
  • Avoid Risks: Organizations can avoid issues with privacy, poor-quality data and regulatory compliance.
  • Get Clarity: Data is more visible, standardized and accurate which instills confidence in the organization to perform key performance metrics and to analyze their own data for future use to be a more data-driven organization.


How do we help?

We provide steps and best practices along the full life cycle of standing up a Data Governance Framework from the initial planning phase to implementation. We leverage an open and iterative approach to Framework development that spans across the domains of people, process, and technology that can be catered to suit both short and long-term needs and budgets. This approach is demonstrated in the following graphic.


  1. Recognize the areas of interest that are easy to target and can solve many underlying issues in a short span of time, such as improved data availability for analytics. Some aspects to think about for the long-term are what the intended results/deliverables will be such as better marketing outreach, regulatory compliance, or an effective Customer Relationship Management (CRM).
  2. Make the data Available and Accessible, which includes data from various systems including ERP, CRM, Finance, legacy applications, geospatial, and external sources from mission partners. Often, due to various data silos and lack of Data Governance, it becomes hard for organizations to map, trace and make data available when needed. Creating a data-flow procedure that will take care of privacy, permissions, and security are part of this process.
  3. Core to a Data Governance Framework is People who have key roles, responsibilities and follow rules to guide the Data Governance process. In most cases, all staff within the organization are potential stakeholders of a Data Governance Framework- business users, IT professionals, data groups, etc. Organizing people around the Data Governance Framework is an iterative process.
  4. Data Integrity follows the functions for data quality improvements usually following a standard approach of data profiling, transformation and standardization, data enrichment, and data monitoring. This portion of the Data Governance must be taken seriously to ensure that there are no gaps/roadblocks for short or long-term.
  5. Data Governance Framework development requires that stakeholders make important Decisions towards achieving the common goal, by which any processes, technology implementation, or data decision should be made with proper communication and acknowledgement of agreements of all parties involved.
  6. Accountability of People in the Data Governance  framework is important, so all the agreed upon policies and processes are taken seriously, responsibilities of data assets are considered (data integrity), and everyone involved has the right technology and training at hand to ensure success to avoid ‘cracks’ in governance.
  7. A feedback mechanism, Commentary so to say, is required to ensure that People have a channel to put their ideas, concerns and questions.
  8. Finally, the Technology infrastructure framework will enable all of the above, including but not limited to Data Storage, Master Data Management, Data pipelines (ETL), Reporting Engines, Quality gates, Data Visualization, and Commentary.

What is next?
Timmons Group has years of experience working with clients to tackle difficult data challenges at enterprise scale and is well-versed in the difficulties faced by private and governmental entities when it comes to Data Governance and strategy.  With over 100 highly qualified technology professionals on staff, we have the resources to help support your team and the consulting experience to custom fit your budget.  We would love to get the chance to help you on your Data Governance journey!  Please reach out to us if you would like more information.
Additionally, read our previous post on Master Data Management, which is key to successful implementation of your Data Governance Framework.


About the Author
Jitendrudu Lacaraju

Jitendrudu is the Lead Software Architect at Timmons Group with over 16 years of experience in Software Development and Data Management. He is leading efforts on providing data-driven solutions that enhance the business and experience of our clients in their respective industry areas. He is proficient in MS SQL Server, .NET, R, SQL, Python, GIS, Tableau and Power BI. He has a Master’s degree in Remote Sensing and GIS from ISRO, Department of Space, India.