6 Reasons You Need Data Governance

Much has changed since we last wrote about data management and governance. Data science has seen significant developments enabled by artificial intelligence. Leaders now recognize data governance as critical to a successful business. It ensures data quality, compliance, and usability for making business decisions. It isn't easy to directly measure data governance, but its value is immense. Leaders of large corporations have saved millions of dollars through effective administration, surpassing the benefits of analytics and digitalization. 

Data governance differentiates firms that achieve this from those that fall short. Companies that ignore or underinvest in governance find it hard to make reliable decisions, make working with data tedious and time-consuming, and may face regulatory consequences. 

Data Science 

Data science methods have advanced using machine learning, deep learning, and AI-driven approaches.

• Scientists have learned to collaborate with domain experts, statisticians, and engineers to solve complex problems. 
• We are more aware of ethical considerations, fairness, and bias in algorithms, driven by transparency and a growing body of experience, guidance, and regulation. 
• Machine learning models for automated decisions have become commonplace. They now drive automated decisions in areas like credit approvals, security screening, etc.
• Data governance enables scalable data science by establishing rules, monitoring compliance, and enforcing corrective actions for data assets.
  

• Trust and Quality: Good data quality includes accuracy, availability, completeness, relevance, reliability, timeliness, granularity, and decision-making support. 
• Democratization: Trustworthy data is essential for democratizing data science across an organization. If your people don't trust your data, they won't trust you. 

The Intersection of Data Governance and Data Science

Data governance is the foundation for data science's success, underlining reliability, trustworthiness, and growth even as new methods and collaboration shape the discipline. The confluence of these disciplines is central to impactful data-driven decision-making. 

• Accountable Algorithms: Ensuring governance of AI technologies and accountable algorithms is a societal interest and a government priority worldwide. 
• Digital Era Governance: Data governance plays a central role in the digital era governance model, where data are valuable commodities and contribute to discovery and agility.
• CDO Focus: Chief Data Officers prioritize data governance, strengthening data quality and cybersecurity procedures. 

Why You Need Data Governance 

The short answer to whether you need data governance is always. People in your company need to know and apply the rules for data creation, storage, transmission, and preparation to prevent bad data from having a huge impact on your business.

Formal data governance is required when:

1. Your firm's size or the complexity of technology outgrows the ability to manage the data. 
2. You are in a compliance-driven industry. 
3. You may need to break down data silos and organizational barriers. 
4. When privacy, compliance, or security requirements require Data Governance.
5. You have inconsistency across your organization.
6. When you need data consistency across your extended enterprise.

Do You Have Any of These Symptoms? 

As data becomes more democratized, the need for governance grows. Enforcing standards was easy when IT owned the data and created the reports. Today, everyone in an organization creates and uses data. Without governing principles, data becomes chaos. 

Here are the symptoms we have seen in our work. 

• You can't trust your data. 
• You're missing opportunities because getting the necessary information takes too long. 
• Your competitors are beating you because you can't react fast enough. 
• Analysts spend more time preparing and cleaning data than using it.
• Data costs more than it should because you spend so much correcting it. To learn more, read our article on the cost of bad data. 

Data Governance Isn't Data Management 

Michelle Goetz at Forrester points out the conflation of governance and management terms. She points out that marketers have made it worse by making it simple. 

The trouble starts with the concepts themselves. Here are the definitions from TechTarget: 

• "Data management is ingesting, storing, organizing and maintaining the data created and collected by an organization." 
• "Data governance is managing the availability, usability, integrity, and security of the data in enterprise systems, based on internal data standards and policies that control data usage." 

We can understand why the two terms can become conflated. It's hard to see where one ends and the other begins. Data management requires data governance, but you could exercise data governance if you were still using the processes of the 17th century. 

Here's an example: ISO 3166 defines two sets of country codes. One set (alpha-2) has two letters, and the other (alpha-3) uses three. 

• Data management is making sure that your apps use the same codes.
• Governance is deciding which one you will use and ensuring everyone knows what they are and how to use them. 

Getting Started

We recommend connecting with the Data Governance Institute or your software vendor to learn what it can mean for you. Affordable membership at DGI can help you build your governance framework. Initial training is free for members. 

Conclusion 

Data management and governance are more critical today than ever. With today's privacy, compliance, security requirements, and the need for agility to change strategy, anything else is a recipe for disaster.

To avoid those pitfalls, you must understand the rules governing data creation, storage, transmission, and preparation.

If you don't invest in Data Governance, you may experience any of these symptoms.
The most critical is the impact on business decisions.

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