Every company should first figure out what it wants from its data to manage it efficiently. A data governance framework consists of many things, including data quality, master data management, data security, ownership of data, conflict management, and stake holder management (access to the data). What a company undertakes depends on the way it operates in terms of the systems, information technology (IT) and human resources maturity (structure and rules). The role of data determines a company’s data governance policy. Data governance should be undertaken as part of the security program or data quality initiative.
Get the users on your side: Data and technology management can be explained with a simple illustration of plumbing pipes. A pipe is a technology but the water that flows through it is data. Data is the heart of IT, which helps figure out what really happened once a transaction takes place on the system. IT is just a custodian of the data; the real ownership lies with the users. Hence, data governance frameworks should be solely driven by the users. A very strong participation from them is mandatory for the success of your data governance framework.
Role in mergers & acquisitions (M&A): Having a good data governance framework is an asset during M&As and helps makes the process easier. Many companies try to force their processes onto the acquired company, by implementing their technology systems. The way sales, procurement, management, accounting is done cannot be easily merged into the other. There may be a competing thought process with regards to how the two companies manage their data. If the acquired company’s management reporting matches the reports generated in the parent, the need to overtake the systems may not arise. These can stay this way for an extended period of time until a solution is sought. It is more important to consolidate the data and information into the systems. Having good data quality and governance makes this more feasible, and reduces change management efforts.
Funding the framework: Business support is critical when it comes to the funding requirements of a data governance framework. Risks such as bad data quality, security issues in terms of access, and lack of good reports, associated with not having a data governance framework need to be highlighted. Stress should be laid on how inaccurate reports bother business users. The cost involved in hiring analysts, who work on the data can also be emphasized.
Set up a data governance council: The data governance framework can be implemented by first setting up a data governance council. However, before forming a council, appoint a business champion to identify issues such as data quality, master data management, reporting problems, especially if business intelligence is used, and time taken for manual massaging of data. The champion would have to collate all these points and propose them to the management.
Overcoming roadblocks: Building a data governance framework is a difficult task, and nobody is willing to take responsibility. The volume of data is generally huge and it is tough to create energy and enthusiasm in the team for this. Making the management realize the value of the endeavor may be a problem, as issues of change management may crop up.
Pilot projects, involving a single department can be undertaken to demonstrate value. Getting the involvement of the business is an art, as the business users are very busy people. A data governance framework is considered by them as an added burden, as there is no immediate return on investment. Hence, it is imperative to emphasize the value to get their participation. The level of interest generated is crucial. Once there is a buy in for the data governance framework, they will be willing to depute people for these programs.
About the author: Sachin Mittal is the Head of BI & Data Governance at Essar Steel Limited. He brings to the table more than 13 years of international business consulting, technology consulting and entrepreneurial experience.
(As told to Sharon D'Souza)