Data Governance
as a Service

Data governance is the process of elaborating standards and policies that ensure data accuracy, usability, availability, and security. These standards rely on both internal principles and external rules and regulate data collecting, processing, storage, and disposal.

Data governance services encompass all the measures, tools, and human resources required to create a data governance framework and integrate it into the enterprise data strategy.

When an organization processes large quantities of customer data, there is always space for mistakes and duplicate information. As a result, further advanced analytics and business intelligence get hampered and reveal a distorted picture. When implementing data governance, you can adjust business-critical data to a common standard, make it easily reachable and usable for all departments, and get a comprehensive view of your customer pool.

Data governance is a beneficial yet challenging process that requires dedicated professionals like chief data officers, data managers, and data stewards. On the one hand, you may allocate time and budget to compile such an in-house team by yourself. On the other hand, it's way more lucrative and faster to delegate data management to a data governance service provider, i.e., to resort to data governance-as-a-service.

Data governance consultants are well-versed specialists who help organizations define the best-fit data governance program by analyzing initial data quality and prioritizing data assets.

Located in the USA, SAS Institute is a software company renowned for its data analytics solutions. A statistical analysis system (SAS) is one of them. With SAS data governance, you can boost the collaboration between data stewards, executive managers, and IT teams and implement effective data governance.

The key ingredients of a properly implemented data governance are people, tools, and processes. Therefore, when elaborating the data governance strategy to answer your business objectives, try to hire experienced specialists, pay attention to their tool sets, and integrate effective data management practices into your organizational processes.

The right combination of tech specialists, business executives, and data experts is the groundwork for a successful process delivery. Here are the key responsibilities of each group:

• A chief data officer or data governance manager takes charge of all the decisions regarding the data governance program and leads the team responsible for data framework development;
• Business executives and data owners form a data governance committee that approves or rejects the internal standards and policies for data protection, usage, access, and disposal;
• Data stewards keep control over data models and metadata to make sure the predefined standards and policies are implemented. Data stewardship is an integral part of data protection procedures.

In the broadest sense, data can be:

1. Structured (concise information in a machine-readable format);
2. Unstructured or semi-structured (data that requires human interpretation entirely or partly);
3. Big data (data sets too huge to be processed by the existing technology).

Master data is the lifeblood information associated with the core processes of enterprises. There can be product, customer, and financial master data types that answer the questions Who, What, and Where.

While data governance focuses on creating standards that meet regulatory compliance requirements, master data management (MDM) does the utmost to implement these standards in practice. In other words, data governance is just part of data management.

The four components that allow companies to govern their data in the most streamlined manner are:

• Data stewardship;
• Reliable data protection mechanisms;
• Data quality enhancement practices;
• Best-in-breed data tools.

A combination of policies, standards, rules, and technologies for enterprise data management that are properly documented and presented is called a data governance framework. Here's how its development may unfold:

1. Assess the current data state and analyze current compliance levels;
2. Choose proper tools;
3. Elaborate the data governance strategy;
4. Inform all the departments about the upcoming changes;
5. Keep your framework compliant with the latest regulatory requirements.