What Is Data Governance? A Modern Outlook

Data Perspective

What is Data Governance?

Data Governance is the strategy and process of data management in an organization that determines how data will be managed and used throughout the data lifecycle. It refers to the program or rules that define how to govern data. Organizations apply a data governance framework to define how to manage data from collection to deletion, including how to define, access, protect, and share data. A data governance program guides organizations to maximize the value and minimize the risk of data, maintain high data quality, and promote data-driven decisions.

As organizations are becoming more data-driven, there is an increased demand for data needed for analysis and decision-making. While this is a smart move for business outcomes, it means that organizations are experiencing increased data use which comes with an increased interest in making sure that the data is high quality and that all data users have a common understanding of the data so that it is not misused, misinterpreted, or overshared.

Why is Data Governance Important?

Organizations generally want to enable data users to have access to the information that they need for business growth and essential operations, but they need to also enforce guidelines to protect data. Data governance is important to maintain compliance with policies and regulations, and be mindful of how to use and protect personal and sensitive data so that they don’t become the next headline for a data breach and lose customer trust.

What are Some Data Governance Challenges?

Varied and Siloed Data

Data exists in various formats and in various sources, often owned by the department that is using or creating it. In some organizations, data lives in business siloes and is managed and governed by those independent siloes. The challenge with siloed data is that the data is managed by different internal policies depending on where it lives in the organization. Without policies that span the siloes to determine how data should be managed, defined, quality checked, accessed, or used, the data is at risk of not being managed consistently. This siloed approach to data management limits the benefit of the data because information would be more valuable if it could be shared across the organization and managed in a consistent way, with understanding so that other departments will find it valuable.

Data Sprawl and Sharing

Data governance is a challenge also as data sprawls internally and externally. Organizations have deep relationships with customers, vendors, suppliers, distributors, partners, and often data is shared between entities, so data teams need to set the rules for what data can be shared, and how, and with whom.

Remote Work and Collaboration

Another challenge of managing data is the increase in remote work. Data is everywhere, and now in addition to living in on-prem databases, data lives in our home offices, laptops, phones, and in cloud environments. Data teams need to set the rules about who has access to what data to minimize the risk of overexposure. As teams want to collaborate in a remote environment, understanding who has access to what data and how it is used is a challenge. Many Chief Data Officers (CDOs) building a data culture want to promote data collaboration. Now faced with remote work environments and distributed workforce, it is even more essential to make sure that data is understood and interpreted correctly. Analysis based on incorrect data interpretation could lead to the wrong conclusions and negative business outcomes.

Global Expansion and Regulations

Organizations are expanding globally and facing new challenges with regulations like GDPR, CCPA, and beyond. Data teams need to know what data is sensitive and applies to specific regulations and how to apply policies for compliance. The first step is knowing what data exists in the data environment, and defining what data is considered sensitive and must be protected according to which regulations.

Where to start with Data Governance

  1. Start with Discovery to take an inventory of the data in your current environment. Know what you are starting with to identify where the gaps are and define a process to get to your ‘ideal’ state.
  2. Realize that data is an ongoing growing and transforming asset. Forward-thinking organizations will establish programs to manage and protect the data in their current environment and are also aware that their technologies and policies will need to accommodate for future growth – new data sources, new lines of business, new regulations. Knowing what data exists in your environment to begin with, where it is, what is sensitive and needs to be protected is a good place to start.
  3. Consider adopting a tool to scale data management – for broader collaboration, improved data quality, and consistency across your catalog.

Data Governance Tools

The scale of data that modern organizations are dealing with is impossible to manage using manual data stewardship alone. Enterprise organizations realize that traditional methods are no longer effective, so data teams are actively seeking modern processes and technology solutions to manage their current data programs. Adopting technology like BigID is critical to get a current and consolidated view of all data to know what needs to be managed, enable dashboards for measurement, and oversee policies and audit enforcement. Using automation and machine learning (ML) is necessary to enable proactive control of the data environment. Deploying the right processes and policies supported by the right technology is the best solution for success.

How to use BigID for Data Governance

BigID Data Intelligence Platform is a single platform for data governance. With BigID, organizations can scan all of their varied data sources, on-prem or in the cloud, and get a complete unified view of structured and unstructured data to know all of the data in their environment with enhanced metadata in an ml-augmented data catalog

Where data teams otherwise struggle with manual methods, BigID is able to automatically identify personal and sensitive data, overexposed data, and data that is affected by regulations and policies. Organizations use BigID apps to evaluate data quality, document and share data definitions, and even set retention policies for lifecycle management, including when to remediate expired data or apply policies to retain data for legal holds.

Data teams deploy BigID for data governance to:

  • Know what data is in their environment
  • Identify sensitive data
  • Increase data trust by proactively managing data quality
  • Enable understanding with consistent definitions to limit misunderstanding
  • Implement privacy policies and regulations for compliance
  • Protect personal and sensitive data against misuse
  • Minimize the risk of data breaches
  • Establish, execute, and audit data lifecycle policies
  • Measure, audit, and report data governance results

To learn more about how BigID can help you build a data governance program with automation and machine learning, request a demo to get started.