Increasingly I speak to clients from the perspective of line of business use of data. As an old-time “MDM-er” this makes perfect sense. I talk with leaders in supply chain, manufacturing, distribution, sales, marketing, procurement, operations, customer service, development, and so on. Of course, master data sits at the center of business processes and the supporting business applications – so this is not surprising. What is surprising is that once again, my phone is ringing off the hook from such clients; and more usefully, the advise we can now give has matured nicely. It used to be that the answer was MDM. That answer has been over simplified (and abused) by some.
Even though master data and an MDM program might figure, the broader more flexible framework we explore with clients now spans the following dimensions:
- Business objects (e.g. customer, product, account, citizen etc.
- Business process objects (e.g. reference data)
Several kinds of applications:
- Broad suites (packaged or developed, on-premise or cloud) such as ERP, core banking
- Domain, BU or departmental specific such as SCP, CRM, or some innovative custom built or cloud app
For the purposes of:
- Information governance (the work and act of policy setting as well as policy enforcement)
- Sharing (who needs the data even if they don’t need to participate in setting or enforcement of policy)
- Integration (what IT will do with the data at a physical level to meet the above requirements – the API level, if you will)
Across degrees of entanglement:
- Most widely shared
- Somewhat shared
- Least or not shared
Taking into account value, opportunity or risk.
For some background reading and introduction to the complexity, check out:
- Postmodern ERP Requires a Pace-Layered Information Strategy to Succeed
- Resolve Postmodern ERP Data Complexity and Consistency Through Effective Engagement of Business Process Analysts
And for a means to connect Master Data Management (MDM) with Application Data Management (ADM) as well as more generally “how to” determine who should set (data and analytics) policy and enforce it, informing smarter persistence and integration efforts, try this:
It turns out this is a pretty neat way to look at the puzzle. It does not give you the answer, or the output, but it provides a neat framework to determine where you need to work. We have produced a set of notes to help support the framework
Library of Notes to Support Development of Governance Framework for Data used in Business Applications
- Designing Your Pace-Layered Information Strategy
- This captures most of the notes below, as a kind of “top view” but even these notes miss the odd note. So the list below is a little more up to date but this is a good place to start.
- Gartner’s Three Rings of Information Governance Help You Prioritize Different Types of Data
- The original note that introduces a simple model to capture the complexity, overlap and intersection of increasingly shared data (and analytics, for that matter) between business processes, apps, business units, and departments etc.
- Pursue a Pace-Layered Information Strategy to Support Your Business Applications
- Explores the “degrees of entanglement” at more detail – a “step 2” if you will
- Toolkit: Use Value Stream Mapping to Optimize Your Product Information Supply Chain
- Use this as an example (you may not focus on product information or supply chains) to document example information flow or lifecycle patterns for data across business processes, systems and apps.
- Information Governance Requires a Comprehensive and Interrelated Range of Policy Types
- A support document you will need to ensure you are aware of all the possible policy types that need to be selected, prioritized and then set (policies are applied to data as part of the policy setting, and enforcement work).
- Toolkit: Assessing Key Data Quality Dimensions
- Data consistency, trust and quality are one of the primary policies that need focus. This helps you explore the dimensions of quality.
- Toolkit: How to Classify Information Assets to Be Governed in Applications
- The main toolkit you can use to capture the output (deliverable) of the dialog with business users and IT for what needs governing (policy setting) and stewarding (policy enforcement), where (which organization) and by whom (which user/role), and at what level (policy and goal). This (should!) drive IT’s subsequent integration strategy.
- How to Manage Your Master and Metadata Data Models for More Effective Program Management
- Consider this (now old but perfectly good) note to understand, over time and visually, how the scope and scale of what data is being governed – spanning all (important and necessary) data and applications.
Hand off the final toolkit to IT along with this note – Combine Pace Layering and Bimodal IT to Modernize Your Information Infrastructure – and they can now develop a persistence, integration and API strategy. Whatever you do, don’t go off half-baked and agog with API-Economy and micro-services until and if you address the foundation of semantics and trust in your most important data and analytics assets!
Would love any feedback – and as a client – if you need help working your way through the material, let me know and we can get you moving via inquiry.