5 Tips For Designing Data Governance that Delivers Value

Updated: 2 days ago


In today's fast-paced and increasingly digitized business world, the significance of data is unquestionable. It is the foundation of decision-making, the driver of initiatives, and the fuel that powers organizations to outpace competitors. For some businesses, data is etched into even the most basic day-to-day operations.

Therefore, data availability, quality, consistency, relevance, and trustworthiness are crucial for a business's ability to function and achieve its objectives. As organizations define Data Governance services (what they get out of it), we wanted to help you by identifying five to keep in mind to make sure value creation is at the top of the agenda. This will also guide any Data Governance assessment you will do.

The Importance of Designing Data Governance that Delivers Value

Organizations around the world suffer from ineffective Data Governance services. The problem usually starts at the top with C-level executives that do not fully recognize Data Governance's value-adding potential.


An organization that does not implement proper Data Governance misses opportunities to generate revenue and optimize operations. Because the quality of analytics is compromised, decision-makers do not get reliable information to chart the right course of action.

According to studies, data scientists in global enterprises spend 45 percent of their working hours on non-value-added tasks because of poor Data Governance.

Furthermore, the lack of a properly designed Data Governance program denies a business the chance to standardize data formats and definitions across its systems. The data inconsistencies that prevail complicate integration efforts undermine business intelligence, and inhibit regulatory compliance initiatives.

Cleaning up data errors can consume a great deal of your analytics team's time, resulting in employee frustration. According to studies, data scientists in global enterprises spend 45 percent of their working hours on non-value-added tasks because of poor Data Governance.


As an executive, you might struggle to attach direct value to Data Governance services. Indirectly, however, good Data Governance can result in significant savings and revenue. It draws the line between consistently profitable and struggling organizations.

How To Position Data Governance for Value

Designing an effective and scalable Data Governance program is a challenge that only a few organizations have overcome. Many dump efforts on a support function like the IT team and wonder why they are not capturing any value from their data.


With the steps below, you can shift from Data Governance based on loosely followed policies to one that actively drives the achievement of your strategic goals.

Rethink your organizational design

More often than not, the difference between companies that excel at Data Governance and those that fail derives from the size of investment they have made to educate and involve everyone in the business.


Data Governance is not the sole responsibility of the IT department. The entire organization must band together to identify priority data assets, define policies that support maximum value creation, and assign these assets to designated custodians across the company. This approach can improve the company's efficiency of standing up priority domains, reduce data clean-up times, and accelerate analytics use-case delivery.

A well-designed Data Governance model typically comprises the components below:

  • An Executive Data Committee, led by the Chief Data Officer (CDO), whose members set the policies according to a well-defined data strategy. The members represent all the main functions of the organization.

  • Data stewards, who are assigned individual roles organized by domains. They are responsible for the day-to-day implementation of the Data Governance program.

  • A Data Council, which brings the teams and data stewards together. It ensures that activities align with the company's overall strategy and priorities.

This structure is a foundation of Data Governance and accepted best practices. It balances data utilization and strategy while placing the decision rights in the hands of the business users who create data and use it. If you do carry out a Data Governance assessment, ask your team how they will implement collaboration and communication.

Secure the backing of C-level management


A successful Data Governance implementation requires unwavering support from an organization's leadership. The CDO's responsibility is to engage the C-suite, understand their needs, and explain how Data Governance helps the business. It is also beneficial to present tangible ways of tracking progress and value creation. These can include measuring the time data scientists spend positioning data for priority use cases or the financial loss associated with data errors. These metrics can ensure the attention and continued support of top executives.

In some organizations, the Data Council includes one or two C-level executives to ensure the set policies and standards sit well with the overall business strategy. These executives must devote some time to understand the Data Governance program, including the core elements of the data architecture and relevant regulations.

While a lot will be done in the trenches, we cannot underscore enough the need for executives to give their strong support for Data Governance to be successful

Having the top management's backing helps avoid the usual challenges of poor role definement and employee empowerment. Everyone involved will understand that their work is a business priority and give it the utmost attention.

Link Data Governance to existing data transformation activities

Data Governance demonstrates the most value when it links to other transformation efforts in an organization. Suppose your company is already working on a project like digitization or resource-planning modernization; in that case, Data Governance will be a much-welcome focus point.

Including Data Governance to your existing transformation initiatives makes it easier to rally your organization behind the program and cultivate responsibility. Say, for instance, your marketing department is exploring omnichannel marketing. Attaching Data Governance to this project will shift the mindset and focus the efforts on practical elements that people need to solve and show just how effective it can be. This move will ensure that practices are also integrated right to data production and consumption. Project managers can become data leaders and channel executives, data-domain owners.

This approach orients the Data Governance program to real and ongoing business activities, hastening its implementation and maximizing its effectiveness. Any Data Governance assessment should highlight which of the on-going initiatives would benefit most from some support.

Prioritize data domains and data elements

Many organizations start implementing Data Governance by looking at the data as a whole. However, taking up such a broad scope creates the risk that time may be spent working on low-value data assets at the expense of high-value ones.

So, once you have identified your data domains and allocated them to data stewards, do not dive right into execution. Instead, prioritize them based on important considerations like potential value, ongoing data transformational efforts, and regulatory requirements, and create a road map for deployment. Start small by focusing on the two to three highest-priority domains.

You may also want to go a step further and prioritize the data elements within your domains. Critical data typically makes up at most 20 percent of the total data in an organization.

Suppose you have chosen Marketing as a critical priority domain. In that case, you can start with essential elements like customer name and address and put seldom-used information like customers' previous service providers on a back burner. This strategy narrows down the scope of Data Governance to the most critical data.

Implement a leaner and agile-inspired Data Governance approach

Data Governance programs vary dramatically across industries and organizations. Banks, for instance, require sophisticated models to comply with regulations like BCBS 239. Most other sectors do not face the same level of regulatory pressure. Therefore, they do not need to implement Data Governance in the same vigor.

Successful companies employ a "needs-based" approach. They deploy just the level of Data Governance that is appropriate to their regulations and data complexity.

A global bank may choose a comprehensive structure that comprises an Executive Governance Council with C-suite leaders involved, a high degree of automation, and a broad domain scope. On the other hand, a regional Fintech company may have a council that only includes top management periodically and a narrower domain scope based on use-case prioritization.

Leading organizations also adjust Data Governance efforts across data domains. For instance, traditional Data Governance standards are often heavily inclined toward regulating data quality and access. However, while this approach is excellent at risk management, it also misses value-creation opportunities. To achieve a balance, a firm can apply lighter governance to data used in stages like exploration and testing and more robust principles to sensitive applications direct interactions with customers. The shift in where to focus will allow it to devote some efforts to derive more value from data assets.

Data Governance is only useful if the whole organization is involved and committed to its implementation. Therefore, invest in change management to garner support across your company and motivate people to improve data quality earlier in the data value chain, ideally at the source.

Top leadership should become role models of acceptable data practices and start recognizing employees that drive improvements. You can also offer training and qualifications, and if possible, create more career opportunities within data management. The strategy you choose must be geared towards building holistic excitement and responsibility around data. This will guide the definition of Data Governance services. You can find more on how to craft a Data Strategy here.

Conclusion

Every organization has data, but only a handful manage to unlock its full potential. Improper data management is costing you tremendously in missed opportunities, suboptimal business decisions, and time lost in cleaning data.

With proper governance, you can achieve the accuracy, consistency, and quality needed to capture maximum value from your data and maintain full compliance with regulations. All you need is a shift in mindset from thinking of governance as policies and standards to embedding it into the way your company works every day.

Align your Data Governance program to your organization's continued business needs. Moreover, prioritize implementation based on value and use a lean and focused deployment approach. Finally, do not forget to loop in top-level management to campaign for Data Governance. You will start realizing the value you expect in no time.



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