Data governance is a term used to describe the policies, processes, and roles a corporation uses to ensure high data security, integrity, quality, and availability are all performed in a controlled and responsible manner
Why is Data Governance Important?
The primary role of data governance is to safeguard a business from data leaks that can leave it open to ransomware threats, regulatory fines, and potential litigation. Secondly, it can improve data quality as it catalogs where data is, its quality, how important it is, and drives consistency. A data governance audit will invariably find silos of duplicated, out-of-sync data. Resolving the duplication of data will save money in the long run.
Governance and Compliance
There was a time when data compliance was focused on regulated industries such as financial institutes through PCI DSS regulations and healthcare around HIPAA. Today, almost every business needs to do its part in preventing identity theft and protecting individuals’ personal data, including its employees. Recent regulations such as GDPR (General Data Protection Regulation) have the power to seriously impact a corporation’s profitability if its data governance is weak.
Data Governance Components
Starting a data governance initiative requires the definition of organizational roles to create governance policies and controls to implement best practices. This central body for data governance is often the Data Management Office (DMO). The DMO links efforts across the business and ensures consistency of practices. The leader of the DMO often chairs a data council to provide strategy and goals and approves funding for the function. Many business functions will have a data governance officer responsible for domain-specific controls. This role can be full-time or an additional responsibility for a manager within a business unit or central function. When it is part-time, assigning a local data steward role can be effective.
Business Intelligence dashboards are essential to get the whole program on the same page across the business. Specialist governance dashboards can remove some of the challenges of defining your own metrics for the program.
Key Facets of Successful Data Governance
Every business implements data governance differently. Nonetheless, below are some helpful guiding principles:
- Involve senior management, and get buy-in by making the data governance council function a senior management responsibility. Senior management needs to understand the importance of protecting the business from the consequences of data loss.
- Digital transformation is an excellent vehicle for implementing and refining practices.
- Prioritize efforts by auditing data assets, so the focus is on the most valuable data. Focus on the business value and outcomes for the organization when setting priorities.
- Governance priority and data labeling are essential metadata in a corporate data catalog. The same data catalogs can be used to track data lineage, increasing the knowledge of a particular data set’s trustworthiness.
- Use an iterative approach to periodically review, refine, and automate controls that can be reused across datasets. In addition to driving reuse, data governance can also maximize the use of existing data.
- Use a collaborative approach which delegates responsibility and builds trust.
Provide training and updates on the program to keep the program effective.
Increasing communication across department silos can be beneficial.
Data Governance Challenges
Every governance program faces headwinds. Below are some examples of common governance challenges:
- Funding is often predicated on demonstrating business value. Using the potential costs of failed governance can make a strong business case. Recovering from reputational damage can take many years.
- Big data and data lakes that contain unstructured data can be hard to audit. Often metadata is all you know about a set of files. Software that transcribes audio, uses text analysis and detects keywords like company names can help with this challenge.
- Metrics must be collected to measure data loss, quality, and error rate improvements to maintain funding and interest in such programs.
Data Value
Data is the lifeblood of an enterprise. Beyond compliance, data needs to be protected because it is often the biggest differentiator of a business. For example, suppose you are competing to become a successful autonomous taxi service. The telemetry database you compile to train your AI/ML-powered navigation system is a valuable resource.
Customer data must be protected from competitors and is often subject to non-disclosure terms in the sales contract. When the business is in litigation, all digital data, including emails and text messages, are subject to disclosure. This makes data retention policies a critical aspect of the subject.
Actian Can Help With Data Governance Initiatives
The Actian Data Platform includes many capabilities that assist organizations in implementing data governance. These include the availability of the same platform on-premise and in the cloud to improve consistency. Robust data profiling and transformation functions make data more consistent. Support for structured and semi-structured data in the database engine and the ability to maintain metadata and associated linkages to unstructured data stored outside the platform simplify access.
