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Posted By Adeptia Inc. on 09/17/2018 in Technology

Best Practices for Effective Data Governance

Best Practices for Effective Data Governance

IT Governance is a multi-nuanced undertaking that takes several intricate operations to get right. It is an important initiative but also inherently challenging. Growth in structured or unstructured data makes data governance even more difficult. With the right strategy, organizations can enable the best path for IT governance and beat back the challenges of data complexity. 

Gartner predicts that data proliferation will increase by 40 zettabytes in organizations. Data generated by next-generation analytics, sensors, gadgets, mobile devices, loyalty management tools will pose a huge number of challenges before organizations. This terrific growth in structured and unstructured data will demand end-to-end data governance. 

Regulatory compliances mandate enterprises to manage data in a cohesive and orchestrated manner. Healthcare and financial institutions face difficulties in aligning with these compliances. Gartner mandates organizations to maintain a culture of data governance to avoid fines. In this blog, I will discuss some best practices to implement data governance in an organization. 

Data Governance Best Practices

Understand Data Governance Needs

Enterprises need to understand their data spread across a multi-dimensional ecosystem. Enterprises need to evaluate data governance needs around their cloud and on-premise data sources. After this, they should prepare a strategy for master data management, data quality, and data security. The strategy should cover the following aspects: 

•    Knowledge Performance Indicators (KPIs)

•    Master Data Management

•    Technology Framework for Data Governance

•    Information silos and master data management

Establish Policies, Procedures, and Standards

Enterprises need to set up policies and procedures for data management, and data setup. Meta management setup should be deployed for representing data lineage, data models, and data dictionaries. Enterprises should define policies for data transparency, and authorization. 

Setup Metric Control & Framework

After this organizations should set up metrics to assess the performance and effectiveness of data governance. The framework should enable organizations to compare metrics and business goals. These triggers help in defining the roadmap for achievement. Enterprises should:

•    Data governance roles, policies, and procedures 

•    Set up mechanisms for governance, structure, and roles

•    Prepare a communication plan for the smoother moment of data

•    Ensure effective management of data stewardship 

Designate a Chief Data Officer

Enterprises should appoint a Chief Data Officer who can assess oversee the data governance operations. The data officer should be responsible for the end-to-end management of data governance projects. He should help organizations in setting up a future-ready framework for data governance. The CDO should be responsible for usability across all segments.

Data Security

Data security is an essential aspect of data life-cycle management. Enterprises should have drivers for consistent consumption of data. Users should be able to encrypt data from threat actors and hackers. Organizations should adopt a layered approach for authenticating users and securing data. 

Data Integration

Enterprises are deploying advanced enterprise systems with a view to gain a competitive advantage. However, they still have many old legacy systems. In this process, they create an ecosystem which is too complex and hard to maintain. Data integration addresses this need by automating application and data integration. It can help operational users in marshaling systems and orchestrating data in simple & easy ways.

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