What is data governance?
Data governance is a system for defining who within an organization has authority and control over data assets and how those data assets may be used. It encompasses the people, processes, and technologies required to manage and protect data assets.
What to achieve from Data Governance?
Data governance may best be thought of as a function that supports an organization’s overarching data management strategy. A data governance framework provides your organization with a holistic approach to collecting, managing, securing, and storing data.
- Data architecture: The overall structure of data and data-related resources as an integral part of the enterprise architecture
- Data modeling and design: Analysis, design, building, testing, and maintenance
- Data storage and operations: Structured physical data assets storage deployment and management
- Data security: Ensuring privacy, confidentiality, and appropriate access
- Data integration and interoperability: Acquisition, extraction, transformation, movement, delivery, replication, federation, virtualization, and operational support
- Documents and content: Storing, protecting, indexing, and enabling access to data found in unstructured sources and making this data available for integration and interoperability with structured data
- Reference and master data: Managing shared data to reduce redundancy and ensure better data quality through standardized definition and use of data values
- Data warehousing and business intelligence (BI): Managing analytical data processing and enabling access to decision support data for reporting and analysis
- Metadata: Collecting, categorizing, maintaining, integrating, controlling, managing, and delivering metadata
- Data quality: Defining, monitoring, maintaining data integrity, and improving data quality
Benefits of Data Governance:
- Better, more comprehensive decision support as a result of consistent, uniform data across the organization
- Clear rules for changing processes and data that help the business and IT become more agile and scalable
- Reduced costs in other areas of data management through the provision of central control mechanisms
- Increased efficiency through the ability to reuse processes and data
- Improved confidence in data quality and documentation of data processes
- Improved compliance with data regulations
Data Governance Principles :
According to the Data Governance Institute, eight principles are at the center of all successful data governance and stewardship programs:
- All data governance participants must have integrity in their dealings with each other. They must be truthful and forthcoming in discussing the drivers, constraints, options, and impacts for data-related decisions.
- Data governance and stewardship processes require transparency. It must be clear to all participants and auditors how and when data-related decisions and controls were introduced into the processes.
- Data-related decisions, processes, and controls subject to data governance must be auditable. They must be accompanied by documentation to support compliance-based and operational auditing requirements.
- Data governance must define who is accountable for cross-functional data-related decisions, processes, and controls.
- Data governance must define who is accountable for stewardship activities that are the responsibilities of individual contributors and groups of data stewards.
- Checks-and-balances. Data governance will define accountabilities in a manner that introduces checks-and-balances between business and technology teams, and between those who create/collect information, those who manage it, those who use it, and those who introduce standards and compliance requirements.
- Data governance will introduce and support standardization of enterprise data.
- Change management. Data governance will support proactive and reactive change management activities for reference data values and the structure/use of master data and metadata.
Mistakes to avoid during Data Governance :
1. Assuming your data is ready to use — and all you need
You need to check both the quality and volume of the data you’ve collected and are planning to use.The majority of your time, often 80 percent of your time, is going to be spent getting and cleaning data.
2. Not exploring your data set before starting work You may
You may have theories and intuitions about what your data set will show, but data teams should take the time to look into data in detail before using it to train a data model.
3.Starting with targets rather than hypotheses
It’s tempting to look for a data model that can offer specific improvements, like getting 80 percent of customer support cases closed in 48 hours or winning 10 percent more business in a quarter, but those metrics aren’t enough to work from. It’s better to start with a hypothesis when you can. Test your hypothesis about what will improve things, either with a control group or by exploring the data.
4.Letting your data model go not updated frequently
If you have a data model that works well for your problem, you might think you can keep using it forever, but models need updating and you may need to build additional models as time goes on.
5.Forgetting the business experts
It’s a mistake to think that all the answers you need are in the data and a developer or data scientist can find them on their own. Make sure someone who understands the business problem is involved.
6.Picking too complex a tool
The cutting edge of machine learning is exciting and new techniques can be very powerful, but they can also be overkill.
7.Misunderstanding fundamentals like cross validation
Cross validation helps you estimate the accuracy of a prediction model when you don’t have enough data for a separate training set. For cross validation, you split the data set up several times, using different parts to train and then test the model each time, to see whether you get the same accuracy no matter which subset of your data you train with. But you can’t use that to prove your model is always as accurate as its cross-validation score.
If you need any assistance with Data Governance, contact Hippalus Technologies.
Hippalus Technologies | Data Governance
