For data to deliver business value, a lot of investment is made in data governance initiatives. It includes smart planning, choosing the right team including data stewards, executives, and specialized tools.
How tools can help in attaining data governance goals?
The data governance process comprises organizing, storing, managing, securing, and presenting data using technologies and procedures to ensure it stays consistent, accessible, and correct.
The goal of data governance is to enhance data quality, makes locating it easy, simple to understand, implement data literacy, and support data analytics. The use of the right tool and technology is a crucial part of consistent and efficient data governance, which makes achieving the goal much easy.
The appropriate tool enables the data governance team to take steps in quality improvement, to use reliable data for analysis that will help to make decisions confidently. EWSolutions is a data literacy consulting agency that can help you choose the right data governance tool because it can be helpful in multiple ways. For example,
- Compliance is necessary but hard to attain because of internal regulations and external data protection requirements.
- Current data governance security measures and policies are inconsistent.
- There are no consistent terms and definitions, so every user makes use of different terms to define the same thing.
- Data sharing on an enterprise level is challenging, which creates data silos.
- Businesses lack data literacy.
What capabilities to look for in a data governance tool?
Integrated data catalog
Data discovery is crucial for business intelligence and analytics. An integrated data catalog will list and categorize every data asset in different forms – reports, files, and tables. All the data is drawn from different sources. It becomes easy to find data for resolving specific business issues, which could otherwise have taken weeks or months to achieve the relevant data.
Sensitive data discovery
Sensitive data can be classified and tagged automatically. It saves the time taken for defining, analyzing, and managing sensitive data, especially when your organization handles a massive amount of data.
Data quality assessment & maintenance
Solutions like data editors, data mining tools, data link tools, version control, etc. can be used to achieve better data quality. Data scrubbing or cleansing is a part of a data quality initiative. Even duplicate data occurrences are identified and eliminated
Reward & attribution systems
It encourages participants to maintain data quality and offers an incentive of sharing knowledge.
Data ownership & stewardship
Data ownership provides access to data and data stewardship manages data quality associated with accessibility, completeness, consistency, accuracy, and updates.
Dynamic data masking
It is a way of protecting sensitive data assets. It hides sensitive data and the non-sensitive data is made available to everyone. Access to sensitive data is granted to verified users only.
Business data glossary
Organizations need to build a business glossary that clearly defines the company’s terms and data language.
Automated data lineage
It gives an idea of where the data originated and where it was processed and stored. Lineage is depicted in graphical form for easy understanding. Automated data lineage creation saves time.
Auditing & reporting
Regulatory auditing is common but there may be a need for data usage review to enhance data governance initiative success.
Choose a data governance tool with an agile framework!