Did you know: The world's data volume will grow at a staggering 40% per year? That's according to the Aureus Analytics report that projects growth trends from 2021-2026. As far back as the early 2000s, enterprises recognized data as a strategic asset of the company to guide strategic decision-making, promote experimentation to learn and improve, and deliver better business results.
But after public data breaches jolted well-known brands like Facebook and Yahoo, data security has become a top priority for enterprises. This led to the demand for regulatory data governance.
Search “definition of data governance” in Google or Bing, and you'll find many explanations that are sometimes confused with data management. According to the Data Governance Institute (DGI), data governance is “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.”
Gartner's definition is the following: it encompasses a collection of processes, roles, policies, standards, and metrics that guarantee the efficient and effective use of information, allowing an organization to reach its goals.
These data governance definitions indicate robust governance follows internal data standards and policies to ensure data is used with integrity. It stipulates who can take what action, in what situations, upon what data, and what methods.
As new data privacy laws and regulations are passed, it will become critical for organizations to develop, implement, and follow ethically sound data governance frameworks. A concrete data governance framework covers operational roles and responsibilities, as well as tactical and strategic objectives.
Now that we've looked at the definition of data governance, let's discuss who's responsible for the implementation.
Effective data governance involves the entire enterprise. Large organizations typically designate a data governance team responsible for setting goals and priorities, architecting the governance model, gaining budget approval, and selecting appropriate technologies to use. Below is a breakdown of the most common team designations:
This role should be assigned to a senior manager, who specifies the organization's requirements on data and data quality. They need to be able to take initiative and make decisions for the entire organization. Their role is business-orientated. Data owners are accountable for the state of the data as an asset.
This is a technical role. Data stewards are also referred to as data architects. They ensure all data standards and policies are adhered to daily. Often they are part of a central management team or IT department as they need to be subject-matter experts for a data entity or/and a set of data attributes.
Data stewards provide standardized data element definitions and formulas as well as profiling source system details and data flows between systems. They are either taking care of the data as an asset or providing consultation on how to do so.
Also called data operators, data custodians create and maintain data based on an organization's standards. This includes business and technical onboarding, updates, and maintenance of data assets. Data custodian roles can be bestowed onto employees in established business units, or bundled together with dedicated support functions, for example, shared services.
Data governance committees approve policies and standards that have to do with the governance of data. A governance committee is also responsible for handling escalating issues and may be divided into sub-committees if you have a large organization. For example, you may have sub-committees for customers, vendors, products, and employees.
These committees ensure that data requirements, priorities, and issues are aligned between different entities. In addition to subcommittees, most organizations have two boards; one for strategic data management topics and another for tactical data management issues.
In a perfect scenario, a data governance team should include a manager, a solutions and data governance architect, data analyst, data strategist, and compliance specialist, who pool their expertise to make informed and compliant decisions on behalf of their organization.
Data governance provides clarity and safeguards against poor data management and non-compliance. IBM recently reported that in the U.S. alone, businesses lose $3.1 trillion every year due to poor data quality.
When data quality is low, it affects every aspect of a business, from marketing insights to financial planning, and hinders the achievement of important KPIs. It's impossible to make accurate decisions or take calculated risks when data quality is poor.
Despite some initial challenges, data governance allows enterprises to remain agile in saturated markets while still being compliant with ever-changing legislation.
Data security includes protecting the machines where it resides as well as the data itself. Implementing access controls, such as three factor authentication can help, but PII is still at high risk of exposure and extortion if it is not anonymized. In non-production environments personally identifiable information (PII) must be anonymized to meet data compliance standards and mitigate exposure.
A vigorous data governance program keeps your data clean. Shared responsibility ensures regular cleansing, updating, and purging of data. Dealing with data is laborious, but the process can be less tedious if your data management team keeps everything up-to-date and relevant.
An effective data policy enables organizations to find and maintain useful information and reduces ROT (redundant, outdated, and trivial information). For example, when dealing with many data entry points, some data will inevitably be duplicated and/or incorrect. Your data policies should enable your team to eliminate these errors to create a single source of truthful, high-quality data.
We live in an age where data has become the critical driver of business decisions. A strong data governance allows authorized users to access the same data, erasing the danger of data silos within a company. IT, sales, and marketing teams work together, share data and sights, cross-pollinate knowledge, and save time and resources. Increased data centralization
Along with better decision making comes faster compliance. Businesses are able to choose from a low code or no code approach, dependent on their specific needs, both of which achieve the benefit of faster compliance. Data governance software can transform the process of using masking as a data protection technique, allowing organizations to become compliant much more quickly. As a result, months or years of training are no longer necessary.
Implementing a data governance system makes it easier for your organization to be 100% compliant with the latest laws, including the European Union's General Data Protection Regulation (GDPR), U.S.' Health Insurance Portability and Accountability (HIPAA), the Payment Card Industry Data Security Standard (PCI-DSS), and more.
Of all the motivating benefits, compliance should be at the top of your list. Legislation around data privacy will continue to evolve as technology does. Adopting a comprehensive data security policies ensures adherence to the law and avoidance of paying penalties or fines for breaching legislation. Also, obeying current regulatory standards protects company data from getting into the wrong hands.
An average user spends 1.8 hours a day looking for the right data because of insufficient data management, which remains a foundational challenge for enterprise teams.
Data governance spans multiple departments within the business and requires clear leadership from the top down. A successful data governance program requires cross-functional collaboration.
Industry trends indicate that Chief Data Officers (CDOs) now possess the same level of prominence as Chief Information Officers (CIOs). If not a CIO, an organization needs someone in senior management whose role is focused on data policy and procedural alignment. They must enforce their authority when advocating for budget and resource allocation and be committed to upholding good data governance.
Organizations that struggle to implement strong data governance tend to rely too heavily on data scientists and expect them to shoulder most of the responsibilities that have to do with data. Data governance contains several components that are not within a data scientist's skill set, such as setting up policy procedures.
Data governance is best managed by a group of data stakeholders responsible for different parts of operational procedures and meeting compliance standards.
Often there is a lack of clarity on ownership, access, management, and usage, which means that data is stored in systems that may not be accurate. This can result in issues of ROT and general mismanagement, which has an adverse compound effect. Technology investments won't improve the quality and value of present data as data cannot govern itself and must be adequately understood for effective utilization.
Data management is not the same as data governance. The latter establishes policies and procedures around data. The former enact those policies and procedures to compile and use data for decision-making. Poor data management results in unsecured data, opaque processes, data silos, and a lack of control over processes. Without consolidating policies and processes, organizations face high-security risks and non-compliance.
Since its establishment in 2003, the Data Government Institute (DGI) has provided a benchmark for data governance best practices. Its framework is used by hundreds of organizations all over the world. Below are fundamental principles of good data governance:
An organization must define its data governance team with clear job descriptions, responsibilities, and duties. This includes determining who is accountable for cross-functional data-related decisions, processes, and controls.
Data governance programs must define accountabilities in a way that introduces checks-and-balance between business and technology teams to ensure everyone is working effectively towards a common goal.
Data-related decisions, controls, and processes must be auditable and accompanied by documentation to support compliance requirements. Furthermore, the framework must support the standardization of enterprise data governance.
Everyone in the organization must work with integrity when dealing with each other and data. They must be honest in discussions and feedback around data-related decisions.
Data stewardship processes require transparency, so all participants and auditors know when and how data-related decisions and controls are introduced into processes.
Lastly, effective data governance programs must support proactive and reactive changes made by management to ensure the proper handling of data processes.
As data and applications have become crucial for organizations, the importance of data governance tools to safeguard the integrity of data assets has increased.
Most data governance tools can help you:
Empowered decision making
Improved data quality
Streamlined data management
Higher data interoperability
Superior data lineage
But picking the right tools for your data governance framework is not so much about the tools as it is about knowing the goals and objectives of your own data governance strategy.
Data governance is important wherever data resides. Often data governance procedures are strong in production environments, but less so in non-production, such as dev/test and analytics, but those environments are also contain personally identifiable information (PII). In these environments PII must be anonymized to meet data compliance standards and mitigate extortionware threats and data exposure.
Automating data processes, such as delivery, refresh, and migration, also reduce the risk of manual errors that expose data. In addition, versioned, immutable data should always be available for quick recovery to reduce downtime and possibly thwart the intentions of ransomware gangs.
With the ability to continuously capture change data from sources down to the second or transaction, Delphix preserves a more granular data history than backup solutions. It also enables an automated approach to testing data block consistency and integrity, safeguarding against attacks that might go undetected using closed-box backup approaches.
Delphix compliance solutions are built specifically to eliminate risk in non-production databases. Through automation, Delphix provides discovery of sensitive data and irreversible masking, so data is anonymized and contains no “real” sensitive or PII data. Delphix also provides continuous detection of database changes and an isolated vault of immutable versioned data, ready to recover application environments to the point just before an attack.
Learn achieve data governance and protect your business-critical application data with Delphix Data Masking.