Data governance refers to enterprises having extraordinary quality of data management throughout the lifecycle of that data in the organization. Given the current competitive market, effective policies, and controlled data management procedures direct organizations to achieve enhanced business outcomes and power business growth.
To implement effective and value adding data governance in your business and make use of its excellence, this article will provide you a step-by-step approach to help you drive data governance swiftly and flourish steadily.
Step1: Identify and Assign Roles and Responsibilities:
These are the foundational components to establish data governance. Privacy and security are critical domains of data governance undoubtedly, but for enhanced operational efficiency, well-informed decision making and escalating business value, it is essential to define and diversify the representatives involved in the process. So, firstly, take notes of your data governance steering committee. The committee should include all business units’ representatives and should be cross-functional.
Once done with assembling the steering committee, move further to assign a data owner for each business units and functions who must establish and ascertain the policies and compliance procedures that will pave the way to rectify data-quality issues. A team should be there to help data owners in adherence to the policies implemented. Lastly, there should be a data management team comprising of the technical IT staff and takes the responsibility of handling the data throughout the lifecycle of the data – from creation till expiration. It ensures adherence to the security and privacy policies through audits and evaluates the quality of data for completeness, relevance, and value.
Step 2: Extend your ecosystem to manage new data:
As the initial policies are defined in the first step, now the ecosystem needs to be flexible enough to inculcate new data coming from various sources and to help you pivot quickly as the business requirements change. Long-term scalability and reusability must be the conclusive factors and not speed or any other quick fixes available.
Step 3: Identify the data domains and data workflows:
Identification of data domains include establishing your data elements that are majorly involved in your reports, determining the data types as well the data values correlated with them. This can be a real game changer in the process of filtering out your stakeholders as the ones impacting your operating model would be well-defined. Subsequently, prioritizing the data to sustain workflows is unquestionably a primary task.
Step 4: Establish and optimize data quality controls:
This is where the heart of data governance lies. The focus has now pivoted to establishing pertinent controls and processes to elevate the entire process of data quality and integrity management. The tedious task of defining metrics and thresholds for data starts here. A report process needs to be started that provides information about the usage and ingestion of data. To identify, analyze and resolve the data related issues, an iterative feedback mechanism is obligatory.
Step 5: Automate the formal data management practices:
The concluding step is to code and formalize the data management tools and practices for continuous data quality. Excellence of data management programs is evaluated by them having support for automated data-quality auditing and monitoring, data quality reporting through metrics or KPIs, data storage operations and automated resource allocation. Following these steps will help you build a great data governance program and that will lead to organization-wide transformation by leveraging the data. Ultimately you will be facilitated by speed, swiftness, tremendous business results.