Most of us know that data creation and collection has accelerated over the last few years. Along with that has come an increase in data privacy regulations and the prominence of the idea of “data governance” as something companies should be focused on and concerned with. Let’s see what’s driving the focus on data governance, define what “data governance” actually is, look at some of the challenges, and how companies can implement data governance best practices to build a modern enterprise data governance strategy.
Data Governance History
The financial services industry was one of the first to face regulations around data privacy. The Gramm–Leach–Bliley Act (GLBA) of 1996 requires all kinds of financial institutions to protect customer data and be transparent about data sharing of customer information. This was followed by the Payment Card Industry Data Security Standard (PCI DSS) in 2006. Then the Financial Industry Regulatory Authority (FINRA), founded in 2007, established rules institutions must follow to protect customer data from breach or theft.
Perhaps not surprisingly, healthcare was another industry to face early data regulations. The first sensitive data to be covered in the US was private health data – the Health Insurance Portability and Accountability Act of 1996 (HIPAA) required national standards to protect sensitive patient health information from being disclosed without the patient’s consent or knowledge. More recently, data privacy regulations like the European Union’s GDPR and California’s CCPA privacy regulation have expanded coverage to all variety of “personal data” or Personal Identifiable Information (PII). These laws put specific rules around what companies can do with sensitive personal data, how it must be tracked and protected. And US data privacy guidelines have not stopped there – Colorado, Connecticut, Virginia and Utah have all followed their own state-level privacy regulations. So today, just about every company deals with some form of sensitive or regulated data. Hence the search for data governance solutions that can help companies comply.
What is Data Governance? – a Definition
Google searches for “data governance” have doubled over the last five years, but what is “data governance” really? There are a few different definitions depending on where you look:
- The Data Governance Institute defines data governance as “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 Management Association (DAMA) International says it is “planning, oversight, and control over the management of data and the use of data and data-related sources.”
- According to the Gartner Glossary, it’s “the specification of decision rights and accountability framework to ensure the appropriate behavior in the valuation, creation, consumption, and control of data and analytics.”
You could probably find a hundred more data governance definitions, but these are pretty representative. Interestingly, it’s either called a “system” or a “framework,” – which are very process-oriented terms.
At a high level, “data governance” is about understanding and managing your data. Enterprise data governance projects are often led by data governance teams, security teams or even cross-functional data governance councils who map out a process and assign data stewards to be responsible for various data sets or types of data. They’re often focused on data quality and data flows – both internally and externally.
As you can see, data governance is not technology. Still, technologies can enable the enterprise data governance model at various stages. And due to increased regulatory pressures, more and more software companies offer “data governance” solutions. Unfortunately, many of these solutions are narrowly focused on the initial steps of the data governance strategy—data discovery, classification or lineage. However, data governance can’t just be about data discovery, cataloguing or metadata management. While many regulations start with the requirement that companies “know” their data, they’ll never be fully in compliance if organizations stop there. In addition, fines and fees are associated with allowing data to be misused or exfiltrated, and the only way to avoid those is by ensuring data is used securely.
Data Governance Challenges
Companies can run into many data governance challenges – from knowing what data they have to where data is to understanding where the data comes from and if they can trust it or not. You can solve many of these challenges with the various data catalog solutions mentioned above. These data catalogs do a great job at helping companies discover, classify, organize and present a variety of data in a way that makes it understandable to data professionals and potential data users. You can think of the result as a data “card catalog” that provides a lot of context about the data but does not provide the data itself. Some catalog solutions even offer a shopping cart feature that makes it very easy for users to select the data they want to use.
That leads to the following data governance challenge: controlling access to data to ensure that only the people who should have access to specific data have access to that data.
This goes beyond the scope of most data catalog solutions – it’s like having a shopping cart with no ability to check out and receive your item. Managing these requests is often done manually via SQL or other database code. It can become a time-consuming and error-prone process for DBAs, data architects and data engineers as requests for access to data pile up. This happens very quickly once the data catalog is available – as soon as users within the organization can easily see what data is available, the next step is undoubtedly wanting access to it. In no time, those tasked with making data available to the company spend more time managing users and maintaining policies than they do developing new data projects.
Data Governance Benefits
While data governance can be a challenging task, there would not be so much focus on it if the benefits didn’t outweigh the effort. With a thoughtful and effective data governance strategy, enterprises can achieve these benefits:
1. Avoid hefty fines and stringent sanctions on leaked PII
As mentioned above, every company that deals with PII is subject to regulations regarding data handling. In the US, the regulatory landscape is still patchy but targeting the most stringent requirements is the easiest path. A robust data governance practice can ensure companies meet their obligations and avoid fines across all their spheres of operation.
2. Leverage data-driven decisions for competitive advantage
A key reason there are growing regulations around collecting and using personal and sensitive data is that companies would like to use this data to understand their customers better gain insight into optimization opportunities, and increase their competitive advantages.
In a Splunk survey of data-focused IT and business managers, 60 percent said both the value and amount of data collected by their organizations will continue to increase. Most respondents also rate the data they’re collecting as extremely or very valuable to their organization’s overall success and innovation. In a recent Snowflake survey with the Economist, 87% say that data is the most important competitive differentiator in the business landscape today, and 86% agree that the winners in their industry will be those organizations that can use data to create innovative products and services. A data governance strategy gives companies insight into what data is available to gather insight from, ensures the data is reliable and sets a standard and a practice for maintaining that data in the future, allowing the value of the data to grow.
3. Improve customer trust and relationships
In a 2019 Pew Research Center study, 81% of Americans said that the potential risks they face because of data collection by companies outweigh the benefits. This might be because 72% say they personally benefit very little or not at all from the data companies gather about them. However, a recent McKinsey survey showed that consumers are more likely to trust companies that only ask for information relevant to the transaction and react quickly to hacks and breaches or actively disclose incidents. Coincidentally, these are some of the requirements of data privacy regulations – only gather the information you need and be upfront, timely and transparent about leaks.
What is data governance in healthcare?
Data governance in healthcare is very focused on complying with federal regulations around keeping personal health information (PHI) private. The US Health Insurance Portability and Accountability Act of 1996 (HIPAA) modernized the flow of healthcare information. It stipulates how personally identifiable information maintained by the healthcare and healthcare insurance industries should be protected from fraud and theft, and addressed some limitations on healthcare insurance coverage. It generally prohibits healthcare providers and healthcare businesses, called covered entities, from disclosing protected information to anyone other than a patient and the patient’s authorized representatives without their consent. With limited exceptions, it does not restrict patients from receiving information about themselves. It does not prohibit patients from voluntarily sharing their health information however they choose, nor does it require confidentiality where a patient discloses medical information to family members, friends, or other individuals not a part of a covered entity. Any entity that has access to or holds personal health information on an individual is required to comply with HIPAA.
Data Governance Best Practices
Today, organizations utilize massive amounts of data across the enterprise to keep up with the pace of innovation and stay ahead of the competition. But making data available to users throughout the business also increases the risk of loss and the potential costs of a breach. It seems like an impossible choice: use data or protect it. But unfortunately, it’s not a choice; organizations must protect data before sharing it.
This requires a solution that includes these enterprise data governance best practices:
- Data discovery, classification and lineage – to ensure regulated data governance, companies must be able to identify, locate and trust it.
- Automated data access controls – as the need for data across the business grows, manual granting of access requests becomes infeasible. Manual controls slow down access to data and introduce the possibility of human error, potentially creating compliance issues instead of avoiding them. Role-based access controls are more efficient in ensuring that only authorized users get access to the data they need.
- Data usage visibility and tracking – once data has been logged and access granted, there must be visibility into who is using what data, when and how much. This helps companies prepare for an audit while ensuring appropriate data usage. It can also provide valuable insight into normal usage patterns to identify out-of-normal areas for concern more easily
- Automated policy enforcement – after data access has been granted, there must still be the ability to automatically alert, slow or stop any out-of-policy activity to prevent or halt credentialed access threats.
In addition, a solution must make the implementation of data governance easy for groups across the company. It’s not just data, security or governance teams responsible for keeping data safe – it’s everyone’s job.
Data Governance: the Future
There’s zero chance that data collection, use and regulation will decrease in the coming years. IDC predicts that the global datasphere will double in size from 2022 to 2026. Regulations also show no sign of slowing – a US federal privacy bill was making its way through approvals as of July 2022.
Both of these trends mean that if companies don’t have a data governance strategy in place now, they will soon need to. As a result, the number of data governance solutions will continue to increase rapidly. Some of these will come from legacy players seemingly offering soup to nuts; some from energetic new startups providing a fix for a single task with very little expertise. We expect the industry to move toward an enterprise data governance solution that helps companies meet global privacy requirements while being easy to use, manageable and scalable to keep up with growing data and regulations.
