When you read about future trends in data governance, you want to learn how to get the data quality for your business without a lot of clunky and costly bureaucracy.

Maybe you hope you can buy a piece of data governance software, set it, and leave it without much thought.

However, any data platform, even automated data quality software, requires comprehensive data governance to guide it. DZone supports this requirement by confirming the data governance global market size will grow to USD $5.7 billion by 2025.

Fortunately, we’ve forecasted the latest industry trends in data governance that will promise you a thrifty foundation to maximize data quality automation with business operations and people.

Read further to learn about the nine crucial data governance trends in 2021 that are set to make a positive difference.


1. Hesitance Among Small and Mid-Sized Firms to Implement Data Governance:

Some small to mid-sized firms will try to forgo data governance in 2021 and beyond, for as long as possible, according to a BARC Business Intelligence (BI) survey. These smaller companies see investment in other business priorities as more essential and perceive data governance as blocking innovation.

Fewer employees, simpler technology infrastructures, and smaller data sets mitigate a lack of formal data governance at small companies. However, as the business adds more transactions, people, or systems, critical data quality issues will creep up to a crisis, affecting security and access.

2. More Spending by Larger Companies to Become More Data-Driven:

Larger companies see data governance as more relevant and have a rough formal data governance structure to become data-driven, making important decisions based on data analysis. Throughout 2021, some of these data governance practices will mature while company culture will remain a sticking point for data governance investments.

Only a third of Fortune 1000 companies say that they have transformed business outcomes with data, leading to a perceived decline in investment return. This result provides small to mid-sized companies a windfall to surpass larger firms’ data quality.

Related: Everything You Need To Know About Master Data Management

3. Lack of Data Governance in Artificial Intelligence (AI) and machine learning (ML) projects:

About 85% of companies use artificial intelligence (AI) and machine learning in their deployed applications. However, only about a fifth of businesses have implemented a formal data governance program to complement these AI and ML projects. Without the attention to data governance and resulting good data quality processes, firms will continue to lose ground from their AI and ML initiatives.

4. More Confusion Between IT and Data Governance:

In response to increased remote business and security vulnerabilities, companies have placed IT governance as a higher priority over data governance. While IT sets physical access to different resources, it does not comprehensively handle data quality and data privacy.

A Forrester blog recommends taking data governance out of IT. With self-serving data quality tools, like from WinPure, businesspeople can take charge of their data quality through their data governance roles.

Related: GDPR & Data Governance

5. Data Quality Remains a Central Theme

As insideBigData attests, “data quality will always remain central to data strategy and data governance.” To be cost-effective in 2021 and beyond, you need to have a data strategy, a combination of data management and data governance perspectives, to inform data quality activities. 

When taking a data governance view, you assign roles and create cleansing processes to your automated data quality software. You save money by tailoring your data strategy to your business and set up ways to collaborate, cross-departmentally, about data quality.

6. Inclusion of Third Parties:

Tejasvi Addagada, a Principal Data Consultant, predicts that data governance will extend to “data collection from customers and third parties.” In this process, data quality will be corrected at s data source using data rules. Automated data quality with a data governance program will provide a simple solution for good data quality to start.

Related: Data Governance Vs Data Management

7. Increased Data Literacy:

An increased data literacy, the ability to read, analyze, transform, and interpret data will play a dual purpose in data governance and data quality. First, as managers become more data literate, they will be more comfortable leveraging data governance. Second, data governance improves employees’ data literacy, at the very least by providing examples and stories to teach and inspire others using data well and keeping its quality.

8. Greater Number of Data Privacy Regulations.

data privacy

Since 2019, several regions and countries have enacted data privacy legislation. In the meantime, many US states have either passed or have data privacy bills in the works. 

To do business in any of these areas, you need to comply with these privacy laws. You must have the data quality to trust your handle your customer data well in all business operations, requiring good data governance.

Related: 2021 Master Data Management Trends

9. Greater Enforcement of Data Privacy Legislation

Regions with existing data privacy legislation will see greater enforcement through 2021. Violations will become more punitive and costly, as some data privacy laws have been in existence for a few years. 

So, bad data quality practices because of confusion and ignorance will be less tolerated by regulators. To adapt, companies will have to adopt data governance and mature their existing frameworks. Small companies are not immune.

Final Words

Data governance industry trends in 2021 make it a top priority for small and large businesses to remain competitive and compliant with data privacy laws. Small and mid-sized companies have a small window of opportunity to get data governance, while large companies regress slightly.

Enacting IT governance does not handle comprehensive data quality needs. Combining data governance guidance with data quality automation tools, guided by a data strategy, allows you to run your business operation quicker and thriftily.

Written by Michelle Knight

Michelle Knight has a background in software testing, a Master's in Library and Information Science from Simmons College, and an Association for Information Science and Technology (ASIST) award. At WinPure, she works as our Product Marketing Specialist and has a knack for explaining complicated data management topics to business people.

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