Wednesday, December 7, 2022

Top 5 Enterprise-Wide Data Management Strategies for DataOps Teams

By Nikhil Sonawane - September 06, 2022 3 Mins Read

Enterprises today need to have effective data management strategies implemented throughout their organization to ensure business success rates.

With the advent of big data and industry 4.0, DataOps teams find it challenging to manage the huge influx of data into the enterprise repositories. Without a comprehensive data management strategy to gather, organize, govern, evaluate and deploy information will create a data mess. The Chief Data Officers should consider developing and setting the best data management practices throughout their organization to improve business productivity. Efficient data management strategies will enable organizations to make strategic decisions, minimize risks, improve productivity and generate new revenue streams. Following are a few ways that CDOs can leverage to unlock the power of the enterprise-wide data:

Understand the data at granular levels

Organizations need to understand the data generated at a microscopic level to determine where the data will be live, the type of data collected from which source, and how the data will be structured. DataOps teams need to have a clear understanding of the enterprise-wide data structure to enhance their work process.

Lack of visibility in the data structures will result in siloes in a comprehensive data management approach. CDOs should consider setting effective workflows to streamline the workflows that gather, store, and process data throughout the organization.

Minimize the gap between Business Intelligence (BI) and DataOps teams

While designing enterprise-wide data management strategies, the business decision-makers need to determine the teams that will be involved in the process and set realistic expectations for BI. Enterprises with siloes in the data management strategy will make it challenging for the BI to function smoothly. Business intelligence teams. DataOps teams should have a clear understanding of the business logic for every team and the data structure that is collected through them. Business leaders should consider minimizing the gap between business intelligence and DataOps teams to streamline the data flow throughout the organization to get actionable insights.

Assign data quality ownership

Once the enterprise minimizes the siloes between the BI and data teams, they need to assign ownership of the data quality. Giving data quality ownership to the different owners at various levels in the pipeline will help the enterprises to make teams accountable for the data generated or processed through their channel.

Set vigilant data governance policies

DataOps should consider data governance as a top priority while developing efficient enterprise-wide data management strategies. It is one of the effective ways to set filters and regulations to ensure smooth and secure data flow. DataOps teams can develop data governance policies based on coding standards, documentation processes, and access management requirements. Just implementing governance policies will not help; enterprises need to set an adherence mechanism to check their compliance with the protocols. Enterprises that embrace stringent data governance policies and adherence mechanism has enabled to have optimized data management strategy.

Audit the data management strategies regularly

DataOps teams can ingrain a data maturity model in their information and workflows. It will require the teams to assess their data management strategy to make necessary changes. Once the enterprise starts scaling exponentially, it might need to focus on a distributed model instead of a centralized one. Auditing data management strategies on a regular basis will enable businesses to make strategic changes based on actionable insights to improve efficiency.

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AUTHOR

Nikhil Sonawane

Nikhil S is a Tech Journalist with OnDot Media. He is a media professional with eclectic experience in communications for various technology media brands. He brings his eye for editorial detail and keen sense of language skills to every article he writes.

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