Analytics Governance vs. Master Data Management
Data analytics has become a valuable tool for businesses and organizations to drive better decision-making, enhance customer experience, and increase revenue. For effective data analytics, two important concepts come into play - analytics governance and master data management (MDM).
While analytics governance and MDM share similarities, they serve different functions in data analytics. In this blog post, we will explore the differences between analytics governance and MDM and their relevance in data analytics.
Analytics Governance
Analytics governance involves implementing policies, procedures, and controls to manage data analytics effectively. Analytics governance ensures that data analytics aligns with the goals, objectives, and strategies of an organization. It also ensures that data analytics complies with regulatory requirements, ethical standards, and data privacy laws.
A typical analytics governance framework comprises three key components - people, processes, and technology. The people component provides oversight, guidance, and decision-making for data analytics. The processes component includes planning, execution, and monitoring of data analytics activities. The technology component involves selecting, implementing, and maintaining data analytics tools.
Analytics governance helps to mitigate risks associated with data analytics, such as biased or inaccurate results, unauthorized data access, and data breaches. Analytics governance provides a structured approach to managing data analytics, which improves the accuracy, consistency, and reliability of data analytics outputs.
Master Data Management
Master Data Management (MDM) is a process of managing the reference data or master data of an organization. Master data is the core data that defines the business entities, products, customers, suppliers, and other operational areas of an organization.
MDM ensures that master data is consistent, accurate, and up-to-date across all systems and applications in the organization. MDM also provides a single source of truth for data analytics. MDM helps to eliminate data silos, data inconsistencies, and data redundancy, which enhances data analytics efficiency and effectiveness.
MDM also provides a comprehensive view of the business entities, products, customers, and suppliers, which enables better decision-making, faster innovation, and improved customer experience.
Analytics Governance vs. Master Data Management
Analytics governance and MDM share similarities, such as the goal of improving data quality and reducing risks. However, analytics governance focuses on managing data analytics activities, while MDM focuses on managing master data.
Analytics governance ensures that data analytics aligns with the overall goals, objectives, and strategies of an organization. On the other hand, MDM ensures that master data is consistent, accurate, and up-to-date across all systems and applications of an organization.
Analytics governance provides a framework for managing data analytics people, processes, and technology, while MDM provides a process for managing reference data across the organization.
Conclusion
In conclusion, analytics governance and MDM are critical components of data analytics. Analytics governance ensures that data analytics aligns with the strategic objectives and regulatory requirements of an organization. MDM ensures that master data is consistent, accurate, and up-to-date across all systems and applications of an organization.
Both analytics governance and MDM help to improve data analytics efficiency and effectiveness, which leads to better decision-making and enhanced customer experience.
So let's wrap it up - with analytics governance and MDM, you can become a master of your data universe!
References
- Rob Karel, "Master Data Management Vs. Data Governance: What’s the Difference?" Forrester, 2018.
- "What is Analytics Governance?" Datalicious, 2020.
- "What is Master Data Management (MDM)?" Oracle, 2020.