Business data can provide powerful leverage that boosts productivity, enhances market accuracy and powers competitiveness. However, data also comes with significant risks that can endanger the very existence of the organization. Regulatory agencies in particular have intensified their scrutiny of how businesses handle data and continue to demand proof of compliance with data privacy, security and integrity laws.
In this context, many business leaders are turning to (or contemplating) an overarching data governance framework that use a cross-functional approach to streamline data flow, bolster stakeholder trust and increase the value of business assets.
We’ll take a closer look at what a data governance framework is, why you need one and some of the steps you can take to make the framework a reality for your business. We’ll delve into why you must take time to identify, develop and utilize the most appropriate governance structure for your organization.
What is Data Governance?
Data is the lifeblood of the modern enterprise. Businesses handle a rapidly growing volume of information that plays a fundamental role in the realization of their objectives. But while data is such an invaluable asset, it’s arguably the worst managed, protected, leveraged and utilized.
Whereas the Digital Age has been with us for decades now, business leaders continue to struggle to give as much attention to intangible assets such as electronic data as they do physical and financial assets.
Ergo, data governance refers to the rules, regulations, standards and influences that are applied to data as a means of setting and overseeing an appropriate data handling policy. A data governance model establishes the decision-making rights as well as the security controls needed to ensure data security, accountability and integrity.
Governance isn’t the active, everyday oversight of data but rather the foundational body of principles and policies that underpin a data management system. The objective of a data governance framework is to encourage and facilitate the correct and consistent use of enterprise data.
A Brief History of Data Governance Frameworks
Data governance as a concept (even though not necessarily labeled as such) has been around since at least the 1980s when the burgeoning computing boom precipitated the development of data quality and metadata management tools. These tools were usually designed and used within one department of an organization. They were often a means of supporting data warehousing and database marketing efforts.
However, it wasn’t until the early 2000s that data governance started to assume the form we know today. Several major corporations such as Enron and Adelphia suffered catastrophic collapse due to financial problems that weren’t explicitly disclosed in their reports. In came the Sarbanes-Oxley Act (SOX) that required the highest level of management to take personal responsibility for the accuracy and completeness of the data their businesses publish.
Data governance frameworks have rapidly evolved and matured since then. They now encompass a much broader body of rules that are driven by multiple regulations and standards such as GDPR, HIPAA and PCI DSS.
Common Data Governance Pitfalls
Most C-suite executives understand why business data must be treated as an indispensable asset. New roles such as the chief data officer have emerged to help drive through data governance.
Yet, while this acknowledgement has been pivotal in setting in motion a set of good data governance practices, many organizations still fall short because they do not have a clearly defined data governance framework. A framework helps organizations steer clear of the pitfalls that regularly bedevil governance initiatives. These pitfalls include:
- Governance that isn’t clearly defined.
- A perception that anything to do with electronic data is exclusively a technology issue with no input or participation required from business managers and non-tech C-level executives.
- Failing to take the organization’s culture into account.
- Fragmented company structure that doesn’t foresee centralized decision-making.
- Treating data governance as an academic exercise or a finite project.
- Disregarding or overlooking the opinions of existing teams and committees that have the knowledge or clout needed to push through the data governance project.
- Poor execution and a failure to manage data in a tactical, structured and repeatable way.
- Unclear return on investment (ROI) that makes it difficult to link governance activity to actual business value.
- Strained resources that make it difficult to tackle governance challenges adequately.
Despite your best intentions and efforts, these hurdles can make it difficult to successfully deploy a data governance model. A data governance framework is your best bet for getting the breadth, depth and flexibility needed to surmount the most common points of failure of data governance.
Developing a Pragmatic and Holistic Data Governance Framework
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Kicking off a data governance framework initiative can be daunting. After all, you are establishing policies and strategies that will determine how your enterprise-wide data assets will be used. You are effectively committing the business to handle data as an asset in the same way it does buildings, intellectual property, bank accounts and employees. For a data governance framework to be effective, it must be both pragmatic and holistic.
Pragmatic in the sense that it should be cognizant of the cross-departmental challenges and political struggles that are part and parcel of every organization. Therefore, the tactical deployment of a data governance program should be broken down into multiple phases that ensure quick wins and minimize fatigue.
Holistic in the sense that every element of data capture, storage, maintenance and usage has to be factored into the overarching vision of the data governance framework.
To be pragmatic and holistic, data governance must touch both internal and external systems, and drive decision-making techniques that break down organizational silos. It has to deliver data quality accountability at the enterprise level. A data governance framework should minimize data dysfunction through a well-planned and coordinated initiative. The initiative needs two key elements around the creation and management of data.
- Business inputs that feed into strategy decisions via a well-defined policy development process.
- Technology levers needed to keep tabs on production data.
Structure of a Data Governance Framework
While there’s no single data governance framework that can be applied to every organization, the structure and components are largely the same across the board.
At the top of the framework are the strategic elements of governance including the rationale and need for data governance. These strategic pillars are geared toward improving customer experience, ensuring compliance with relevant data management regulations, enhancing decision-making, streamlining operations, facilitating M&As and driving project management.
Defining these strategic pillars is crucial in nurturing buy-in from the topmost leadership of the organization and consensus across business units. Where possible, you should tie the strategic data governance pillars to the most important business goals such as profitability, conversion rates and risk governance and mitigation.
When employees and department heads realize that the framework will make it easier for them to achieve their goals, they’ll be more enthusiastic about seeing the framework thoroughly to successful adoption.
Governance Methods and Solutions
Below the strategic elements are the data governance, management and stewardship methods and solutions. These drive desirable outcomes such as data definitions, architecture, security and quality. They dwell on the tactical aspects of governance including execution of policies and the everyday processes that drive data management.
There must be a clearly defined set of stakeholders, performance indicators and measures of ROI. There should also be a clear delineation of who is responsible for doing what. It’s about planning a future vision of data management and crafting a road map that allows you to get there.
Data stewards will be tasked with acting on directions from the data governance council in terms of harmonizing definitions, defining domains, reporting metrics and determining usage. The data stewards are usually business-oriented, but they often have the kind of basic understanding of IT that allows them to be an intermediary between business units and the IT department.
Note that the data steward is different from the data custodian who is the point person for IT departments in the enforcement of data security and data quality policies.
As organizations become more dependent on data, their long-term success will eventually hinge on their ability to have a coherent view of this information. Data that is clearer and better means improved insights and better decisions.
A data governance framework provides the requisite structure for organizations of all sizes to establish and drive a data governance program. With that you can provide timely, trusted, high-quality data to the entire enterprise. This results in a more responsive, efficient and effective organization.
We mentioned earlier on the need to breakdown the implementation of a data governance framework into bite-sized milestones that reduce the likelihood of fatigue and resistance while increasing the chances of success. This however must be combined with a strategy that considers short- and long-term objectives in order to give the business the opportunity to enjoy the benefits of good data governance early.
The good thing about data governance frameworks is that no organization really starts from scratch. There are already a wide range of existing governance mechanisms that can be plugged into a new data governance framework. The key is identifying what works well, what can be improved, and what must be discarded going forward. Implementing this will give you continuous improvement within your organization.
This is the fourth post in a five-part blog series on data governance. Make sure you check out the rest of the series to learn more about:
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