Data is the biggest asset of every organization as it drives the business, so it is very important to implement data governance correctly to avoid making wrong decisions that will pull an organization down, with fines, with a lot of frustrations, and with wrong business decisions.
Data Governance defines rules on how the data should be used and it is not just data management. It will ensure we have all rules, policies, and roles that will help the organization to better manage the availability, usability, security, and integrity of the data. It is driven by the business side of the organization. For Large Organizations, the Chief Data Officer office is responsible for Data Governance implementation.
Data Governance Framework is a set of rules and guidelines for creating models to manage the data. It involves organizing the activities involved in decision-making and data.
Implementing the Data Governance Program is a very complex activity that needs the involvement of multiple technologies, teams, and other factors.
Why is Data Governance required for an organization?
Every company that operates within the European Union has to abide by the GDPR (General Data Protection Regulations) rules. WhatsApp has been fined 267 million dollars for not completely fulfilling the data subject rights. Amazon has been fined dollar 886 million dollars by GDPR
If proper data governance is not implemented, it will affect an organization in many ways than just fines. For example, wrong data with bad quality will lead to wrong business decisions, and if the data is not structured correctly, it will lead to inconsistent data issues.
Data Governance needs to be implemented in every organization to secure the data, improve the data quality, improve the efficiency and trust in the data, ensure compliance with regulations and data privacy laws, avoid inconsistent data issues and help the organization take better decisions.
Core Principles of Data Governance:
The Future of Big Data
With some guidance, you can craft a data platform that is right for your organization’s needs and gets the most return from your data capital.
There are four core data principles as defined by Robert Seiner,
- Data must be recognized as a valued and strategic enterprise asset. Data must be accurate to make good decisions.
- Data must have a clearly defined accountability.
- Data has to be managed to follow internal and external rules and regulations.
- Data quality must be defined and managed consistently across the data lifecycle.
Data Governance Program Roadmap:
A data governance program roadmap includes,
- Creating the data governance charter
- Creating the data governance operating model
- Defining the KPIs, rules, policies
- Deciding on the tools to be used
- Implementation of the program, monitoring
- Improving the existing program
Steps to implement Data Governance Program:
Identifying the need to implement the data governance and scope is the first step to start along with identifying the data governance leadership. It is good practice to start with a narrow scope instead of establishing it for the whole organization. It will allow us to be more agile, move more quickly and provide quick wins.
The second step is to define a strategy to develop the data governance program for an effective data governance team. A data governance charter needs to be developed to define the strategy, purpose, scope, the responsibilities of the data governance committee, its goals, and its membership. The third step in your data governance journey is to choose a model for the data governance team. There are many different types of models, and we need to choose the one that fits our organization.
The fourth step is to select who will be part of the Steering committee. The Steering committee usually consists of high-level executives in the business. The Steering committee is usually involved in improving things such as the charter and the strategy for data governance and approving different funding requests. The role of the steering committee is also to make sure that there’s a resolution to all the conflicts.
The fifth step is to set up the data governance office. They have the authority to enforce policies and propose to the steering committee which projects to fund money. They will coordinate the different business groups and define the success metrics and are responsible for reporting on the success of the program to the steering committee within the organization.
The sixth step is to set up the data governance working group. This group approves data standards, business rules, procedures, policies, best practices tools to use, and resources to be used. They identify data governance projects, set goals for the program, and oversee its progress. This Council will identify the data stakeholders and assign data stewards to resolve data issues.
The seventh step is to select the data governance support team. It includes Data Owners, Data Stewards, Data architects, Data modelers, and Data Analysts. The next step is to create policies, procedures, and rules and start enforcing them. The last step for the long-term success of a data governance program is to establish teams within the business and within IT that will help to drive this forward.
Reasons for Data Governance failure:
The major reason for the failure of data governance is a lack of understanding of what data governance should include. The organization works in department silos without centralized decision-making from the management. Data experts included in the data governance team do not have enough bandwidth to focus and spend time on data governance activity. The executives do not understand the importance of data governance and they believe it is an IT or data thing and not related to business. Many organizations implemented the data governance framework successfully, but they fail in executing it.
Data governance tools widely used in most of the organizations are Collibra, Informatica, Talend, Erwin by quest, IBM, ASG technologies, and SAP
Data Governance scorecards are used to measure the success of the Data Governance Program. A scorecard can show the progress of data quality, data control, data infrastructure, and finance.
