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Best Practices To Overcome Data Governance Challenges

By Sunil Gupta posted Mon March 07, 2022 06:26 AM

  

Data is omnipresent abundantly, which does not mean sharing or owning a vast amount of it guarantees success for data-driven businesses. It is crucial to have a data management structure if they desire to take advantage of the vast data they have. Data governance is the solution to this dilemma. 

Data governance is a component of data management. It is guided by dos & don’ts, policies, and practices to ensure that the organization receives high-quality data. Building a data culture means making decisions based on data. So, an appropriate data governance strategy will result in better analytics. It facilitates company executives to make better decisions and drive huge revenue. It even helps in avoiding data inconsistencies that can cause faulty analytics, poor decisions, and several other challenges. 

Challenges 

Data governance initiative accompanies plenty of challenges. There is an array of sources –

  • Leaders who don’t approve.
  • Limited resources and funding or competition to gain them.
  • Uncontrollable 3rd party data.
  • Complicated and detailed flow of data.
  • Partial data access.
  • No or vulnerable tools & systems.
  • Incorrect preconception about data governance.
  • Silos or fragmented operational & IT teams.
  • Insufficient knowledge about data governance across the organization.

Best practice to overcome the challenges

Data Management University is the best place to start with for data governance training courses. You get good knowledge about how to initiate and strategize your data governance program. 

Good data governance = High data quality

Understand senior leaders’ mentality because it often is equivalent to better data but for less money. Therefore to increase data value –

  • Create a trusted data source [what staff can use].
  • Remove guesswork through consistency and transparency.
  • Lower the costs via streamlining data flow, decrease internal & regulatory reporting efforts, lessen the project’s implementation times as well as standardize data approaches like policies, systems, procedures, and criteria. 
  • Resolve data issues at the source as well as enhance data quality tracking and other data-related struggles thus offering relief.
  • Provide control proofs with documented standards and policies along with correct data lineage to help fulfill regulatory and audit requirements.
  • Automate processes to reduce the risk as well as save time.

Attain team goals

  • Create data governance value – Creating a self-sustainable data quality environment as well as resolving issues increases the odds to drive value. It removes the process for entering into frustrating arguments with the top leaders to get new tools or funding. 
  • Transform the data culture – It is hard but communicating with governance values the culture will change. Through newsletter communicate topics associated with existing and anticipated data culture. Even highlight the areas where governance has helped in reducing the risk, saved time as well as offered great performance. 
  • Create team credibility - Include skilled data literate members in the forefront. They can help to resolve everyone’s data-related issues and even sell data to the leaders later.
  • Interrelated goals - The data governance team goals have to interrelate with the business goals. 

The senior leader in data governance can offer the strength and keep the reluctant team members and management side personnel to stay involved and motivated. 

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