Four Data Governance Mistakes Enterprises Need to Look Out For

Four Data Governance Mistakes Enterprises Need to Look Out For-01

Organizations that design a well-thought-out data management strategy and leverage technologies that work well together are the most likely to succeed in unlocking their data’s value in the future.

Data is only as good as its governance strategy. With enterprises collecting ever-increasing amounts of data, the desire to increase data accessibility and generate more value from it is growing. As a result, data governance challenges — such as how businesses manage, use, and exchange data across the value chain – have become more prominent than ever.

According to a Gartner report “Predicts 2019: Data and Analytics Strategy,” up to 90% of corporate strategies will consider data as a significant business asset by 2022, making governance in the digital era a crucial event for all governance professionals.

Enterprises and IT leaders should be on the lookout for the following four mistakes that should be avoided at all costs in order to prevent their company from falling into a trap that will render their data governance strategy ineffective.

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Impact assessments being overlooked

The best method to understand who, what, when, where, why, and how data is collected, used, disclosed, and processed is to combine privacy impact assessment (PIA) and data protection impact assessment (DPIA). Organizations that do not conduct a complete PIA/DPIA risk being at a disadvantage because they do not understand the data they handle/maintain or how to secure it from unauthorized use/disclosure.

When data is managed inappropriately, organizations risk costly regulatory fines and penalties, as well as a loss of customer trust.

Defining data governance without the necessary infrastructure

Many IT leaders make the mistake of implementing data governance policies before ensuring that all essential enterprise parties have the tools and skills to effectively apply them.

If leaders design policies centrally and pass over a new cloud data platform with no centralized management system, business teams will create their own tools to handle data in their own way.

Instead, they should provide the tools and platforms teams needed to fully implement the data governance policy before implementing it. Data governance teams can ensure that enterprise standards are being maintained while tracking anything that can go against policy by allowing all activity to dwell in a central location. This method decreases the total data management burden on business teams, allowing employees to spend more time working with data instead of governing it.

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Losing sight of the fact that data governance education is on-going

Failing to acknowledge the reality of an evolving workplace that encourages employees to use new data-sharing platforms can leave a data governance policy in shambles over time.

Information sharing through unauthorized apps occurs much too frequently simply because employees are unaware of the tools available or the ramifications of using unauthorized applications to the organization.

Failure to appoint a strong project leader

The buck should stop with a project leader when designing a data governance strategy. This senior member of the IT team will get down with business colleagues to hammer out a detailed and firm policy that satisfies all of the objectives. To keep the company’s data clean, the leader should assist in the creation and enforcement of policies. The data governance chief should also be in charge of gathering IT and management colleagues to revise and update the governance document on a regular basis.

Organizational data can become siloed without a well-designed governance structure, when each business unit or department deploys a distinct transaction system with unique data meanings and rules. Subtle inconsistencies can emerge as these different systems begin to create and collect data over time, making it impossible to find one version of the truth with each system reporting different outcomes. With a good enterprise data governance program that contains data definitions and formats that will be utilized across the organisation, these inconsistencies can be avoided.

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Prangya Pandab is an Associate Editor with OnDot Media. She is a seasoned journalist with almost seven years of experience in the business news sector. Before joining ODM, she was a journalist with CNBC-TV18 for four years. She also had a brief stint with an infrastructure finance company working for their communications and branding vertical.