Businesses that are data and insight-driven are ruling the roost today. Enterprise-wide initiatives such as digital transformation, crisis management, and sustainability require strategic interpretation of complex enterprise data. A recent Forrester research suggests that 41% of business leaders find data-driven decision-making extremely challenging due to a lack of instant access to high quality, reliable data, and analytics. To fuel data-driven decision-making, MDM Master Data Management needs to be optimized with dynamic data governance and stewardship strategies.
Deriving value and efficiencies
Mature data governance and stewardship are the panaceas that enable enterprises to create value from their master data. Organizations must invest in operationalizing data governance and MDM to ensure fit-for-use data and insights. Greater the insight derived from data, the greater the value created.
Building data stewards
Data governance requires incredible accountability and management skill. It is indispensable for organizations to have stewards in the form of dedicated governance teams that could be a top-down or bottom-up collaboration. Common roles in steward teams include Chief Data Officer (CDO), Chief Information Officers (CIO), data owners, data scientists, and specialists. This team will define processes and rules that store, archive, backup, and protect enterprise data from internal issues, data theft, and cyber-attacks.
Companies can cut millions in cost from their data ecosystem, by training CXOs and senior leadership teams in data governance, enabling enterprise-wide digital analytics, and protecting their businesses from expensive regulatory risks.
Ensuring regulatory compliance
The drastic shift in global regulatory and compliance landscape has forced organizations to rethink how they collect, store, use, and dispose of enterprise data. Reiterating the importance of data governance, industry-specific data regulators have issued stringent laws to mitigate data breach and protect customer, product, and business data. Implementing the right data governance process and procedure is crucial for enterprises to comply with regulatory requirements.
- Smart data governance strategy is the need of the hour to manage regulatory compliance and reap the benefits listed below.
- Standardized MDM processes for superior decision making
- Manage data quality issues involving data inconsistency, integrity, fidelity, and security
- Increased scalability through clear data lifecycle management procedures
- Centralized control mechanisms to optimize data management cost
- Increased data confidence through quality-assured and certified data, and documentation of data processes
- Assured security for enterprise data by monitoring and reviewing privacy policies
- Increased data processing efficiency by reducing extended coordination
- Clear and transparent enterprise-wide communication
7 steps to build a data governance strategy
Step 1: Assess and prioritize
It is important to objectively assess and prioritize areas where improved data governance can bring the most instant benefit. The governance program can be extended from the prioritized areas to the others.
Step 2: Maximize data availability
Irrespective of how disparate the current data architecture is, organizations need to leverage best practices in data integration so that enterprise data becomes easy to access. Data that is readily available and accessible can be governed effectively.
Step 3: Build data stewardship teams and processes
Choosing the data steward and establishing formal roles, responsibilities, and rules are key for efficient governance.
Step 4: Enhance and ensure data quality
Ensuring the integrity of enterprise data is a crucial part of data governance. Data needs to be profiled with predefined quality metrics, analyzed, validated, enriched, and monitored on a regular basis to ensure data quality.
Step 5: Create an ownership infrastructure
Holding data owners responsible through an accountability infrastructure is critical to keep data integrity and quality high.
Step 6: Transform to master data culture
For successful and effective implementation of data governance programs, businesses must transform into an MDM based culture. Legacy systems prevent organizations from leveraging the maximum potential of their data to support and align with business strategies.
Step 7: Ask for feedback
A feedback mechanism that enables continual assessment is key for process improvement in data governance.
Shifting to effective MDM helps businesses achieve superior data governance by enabling global identification, linking, and synchronization of information related to enterprise-wide entities. A ‘single version of the truth’, the valuable master record of key business elements, is created in MDM to provide a unified, consistent, accurate view of these entities to stakeholders.
Therefore, helping assess, manage, use, monitor, improve, maintain, and protect data, robust data governance and stewardship structures are key to effective master data management. Businesses must take a need-based approach to choose the level of data governance sophistication and rigor required for their organization.
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