Left to their own devices, your finance, marketing, and supply chain departments will all come up with different ways to organize their data. Likely, they don’t even define essential business terms like ‘customer’ the same way, which makes coordinating information across departments impossible. To create master data that shows connections across functions and provides a clear view of business processes, you need to establish some guidelines and a way to enforce them. That’s called master data governance.
What is master data governance?
Master data governance creates a system of rules and the policies and procedures enforcing them to ensure data quality and consistency.
Master data governance includes definitions, policies, workflow, and the roles and responsibilities of data stewards and users to ensure your data is accurate, complete, and connected across the enterprise. It’s an ongoing process that matches new data entering the system with current records, validates its accuracy, and makes sure it’s structured properly to provide business insights and drive business goals. It’s a complicated business, but certain master data management (MDM) platforms, like Reltio Connected Customer 360, employ machine learning to streamline MDM governance.
Why is master data governance important?
Garbage in, garbage out. We know you’ve heard it before. But consider: Multiple studies cite poor data quality as the biggest stumbling block to any big data initiative in a business. If you’re making business decisions based on disconnected, disjointed, and inaccurate data, you’re likely making the wrong ones, and you won’t know how wrong they were until you’ve lost customers and market share.
Since master data touches every function in your business, data silos or poor data quality leads to bad outcomes all around:
- Marketing misses customer insights
- Financial reporting is inaccurate
- Supply chains get disrupted
- Customer orders are fulfilled inaccurately or slowly
- Operations cost more than they should
Lack of master data governance sows inefficiency into your business, costs the bottom line, and restricts your agility. Data governance is a prerequisite for leveraging innovations like IoT and Big Data as part of your MDM platform, and without it, you can’t make the connections necessary to win in the experience economy.
Proper master data governance provides:
- A single best version of the truth across the enterprise
- Standard definitions and business rules for creating and categorizing data
- Workflows to change and approve data
- Audit trails for any changes in master data
- Policies and procedures to enforce data standards
- Quality data that maps to your required business insights and goals
What is the difference between MDM and Data Governance?
Data governance is an essential component of master data management. MDM creates an overarching system to bring master data together, make connections, structure the data to reveal business insights and make that data accessible to users. In order to do any of that, the data needs to be organized and defined the same way.
- Master Data Management: includes all the processes from the creation of data to its delivery
- Master Data Governance: includes all the rules and policies to make data clean and consistent
You’re not managing master data if you don’t have proper data governance. Master data governance creates the rules to ensure quality, consistency, and security in the master data. It regulates changes in the data and makes sure that new incoming data adheres to the system rules so that it can be useful. An advanced MDM platform has built-in tools that simplify MDM governance and allow your business to incorporate data from multiple domains.
Does ERP or CRM provide data governance?
Enterprise resource planning software creates a centralized system to access and share data within your organization. CRM does too. Business success relies on effective communication and data sharing across all departments, which ERP supports, but winning in the experience economy requires consistent data across these and hundreds of other enterprise systems, plus, data from outside your organization.
Your operational systems will gather the data that your internal business processes generate, but most sell data governance as an add-on. Such systems are also not built to handle Big Data or unstructured data from social media and customer help desks. A multi-domain MDM platform brings data together from your ERP with CRM, SRM, and external data sets so you can see a full view of your customers, supply chain, and financials.
With proper master data governance in place, not only will your internal business data become more actionable, but you’ll have a pathway to bring in outside data that can power business transformation. Innovative MDM platforms like Reltio with big data architecture and automated smart matching allow better data taxonomy and easy ways to view and monitor the MDM hub.
What is a data governance framework?
A data governance framework is a set of data rules, roles, processes, and policies that bring everyone in the organization on the same page. It’s the blueprint for your master data governance and how to leverage MDM platforms for the best data quality. It can get complicated quickly, and tools like the Reltio platform can make it easier.
Every MDM governance platform should include a few key elements:
- A Data Strategy: That outlines how your master data contributes to business goals and outcomes and what business processes and transformations you hope to power through master data.
- Data Policies and Processes: Set clear standards for data quality, how you will monitor it, and enforce data rules.
Reltio Use Case:
Reltio provides a fine-grained user, role, and group framework to enforce data access and governance policies. Every Reltio customer has development, test, and production environments to test the impact of policy changes.
- Information Quality Standards and Semantics: Create consistent definitions and standards for incorporating data into the MDM.
Reltio Use Case:
More than 20% of Reltio customers use our data as a service capability to ensure accurate information from industry sources such as NPI, DEA, IQVIA, and Onekey.
- Governance Workflow: Structures roles and responsibilities for the data governance team as well as their task management and KPIs.
Reltio Use Case:
Our life science customer uses the Reltio platform to gather and process physician and healthcare organizational updates from 10,000+ field sales reps in 60+ countries, while monitoring, analyzing and reporting on their activities and progress.
- Data Stewardship: Methods to enforce the data standards and data governance policies.
Reltio Use Case:
Reltio leverages machine learning to analyze the data quality for trends, assist manual match resolution, and continuously grade customer data profiles. Pre-configured analytics provide data quality metrics. Exception alerts support investigation with full match and merge histories and point-in-time comparison.
Who is responsible for data governance?
Everyone who creates or updates your business’s master data is responsible for MDM and data governance. The more your stakeholders adhere to master data governance guidelines, the less data stewardship work is required. It makes sense then to involve your business stakeholders in setting the data governance framework, especially at the early stages of creating a data strategy.
Business stakeholders can become better stewards of data quality if you take the time to create a business glossary and a data dictionary as part of your data governance framework.
Data dictionary: defines master data elements and the relationship between them (how they’re used together, where they come from, their descriptions).
Business glossary: connects elements of the data dictionary to business terms to improve business understanding of how data is used.
Even with full user alignment on the data governance strategy, data errors are inevitable. Outside data sources also need to be analyzed, matched, and merged. Reltio’s platform makes this process easier, but best practices in MDM governance suggest several key roles and responsibilities:
A data owner usually is the one within your business that knows the most about a particular set of data and can make decisions about how it should be governed. Typically, data owners are the strategists within their function who know what their data needs to look like and have enough authority to be accountable for the data’s quality.
Data stewards are your policy enforcers. They make sure records adhere to data standards. Typically, they are subject matter experts within their function and are knee-deep in the data on a day-to-day basis. A data steward does the heavy lifting on data quality responsibilities and makes the judgment calls when data doesn’t quite fit the policy norms.
Data custodians come in for the after-work clean up. They do the updates and other data asset maintenance, assure that records update to all sources properly and onboard new data assets.
Owners, stewards, and custodians are defined roles essential to MDM governance. They make up the MDM user and owner environment and these roles are rarely the stakeholder’s sole responsibility. By contrast, organizations taking the leap to full digital transformation often add data governance offices and/or data governance committees to their roster of full-time business roles.
A data governance office includes roles like:
- Manager of Master Data Governance: Heads the data governance team in executing the MDM governance framework.
- Data Strategists & Analysts: Study trends in the data to develop new governance strategies and advise on business implications.
- Compliance specialists: Monitor data quality to ensure it conforms to regulatory standards.
A data governance committee includes stakeholders from throughout the business to discuss any changes to policies or updates that become necessary with changing any business process. Master data governance is an ongoing discipline, and having input from across the enterprise to ensure it serves business insights and goals can be essential to adherence.
Reltio’s Connected Customer 360 puts data governance at the heart of master data management. Our platform was purpose-built to be multi-domain, with a user interface that makes MDM governance, collaboration, and workflows intuitive. Machine learning allows the platform to continually improve matching and make more accurate predictions, giving you a competitive advantage in the use of your data. Ready to fuel your business processes and accelerate your ability to get customer insights? Reltio’s master data management solution can be implemented in any MDM implementation style to meet your business requirements. Say goodbye to data silos and poor data governance and get connected today.