The Dearth of Master Data is the Inconvenient Truth
You will find that your future holds a reality where data is everywhere, business relationships multiply, and everything assumes a programmatic manner and feel. But while this may seem like the epitome of progress, the inconvenient truth is that your commercial data infrastructure simply isn’t ready to support these coming changes. The melting ice caps of legacy models are raising the water levels of data management, and many established companies are seeing their strategies flooded with new challenges.
Like it or not, the very nature of your business climate is rapidly changing. As your environment transforms from analog to digital, everything you touch turns to data. The meteoric rise of artificial intelligence (AI), the Internet of things (IoT), and machine-to-machine (M2M) communication has highlighted the glaring need for a standard way for data across multiple systems to connect.
The broader picture is best characterized by Peter Lucas in his book “Trillions, Thriving in the Emerging Information Ecology" we are about to be faced, not with a trillion isolated devices, but with a trillion-node network: a network whose scale and complexity will dwarf that of today’s internet.” Although system integration is feasible, systems, both internal and external, don’t automatically speak the same data language. Without a dependable, standardized language for commerce that could be “spoken” across and between verticals, our business ecology is headed toward what I consider digital global warming.
With the additional concurrent megatrends of big data, globalization, and the API economy converging, data interoperability is now a universal and ubiquitous use case. This megatrend convergence will increase the need for clean, coded, standardized, expertly-governed master and reference data content that seamlessly integrate internally across methodologies, processes, workflows, and platforms--as well as externally between enterprises, value chains, and throughout market ecosystems. Does your company recognize this urgent need to reverse digital global warming?
In my global travels, I consistently find that most enterprises, both large and emerging, do not have their internal data management under control, much less enjoy the ability to interoperate with external processes and ecosystems. The classic internal challenge that continues to confound enterprises of all sizes is the use of multiple systems and workflows that create disparate data sources with differing definitions that lack consistent standards. In fact, the bigger the enterprise, the bigger the challenge. Simple terms like “customer” and “market” can have grossly different meanings for sales, finance, and marketing departments, affecting how the data collected by these departments is sourced, tagged, integrated, and used. Many companies are now feeling the same interoperability pain externally as they seek ways to connect systemically with other types of business relationships, including customers, vendors, partners, prospects, and distributors.
Hot Transformation Topics
At the heart of Machine Learning is “training data” which allows us to apply rules to new datasets to determine new outcomes. This Machine Learning is the fundamental basis for Artificial Intelligence. The operating theory of the Internet of Things (IoT) and the promise of the 4th Industrial Revolution is seamless connection. Everything needs to connect to everything else--when it should. It is the “should” that is the hardest to achieve. When machines talk to other machines, the conversation goes something like, “I have to find something, determine if I can trust it, and then connect to it.” The ideal response is, “I have what you need to find, you can trust me, and here’s how to connect.” But unfortunately, the response is often, “I don’t even understand what you’re looking for,” or “I think this is what you’re looking for, but I’m not sure.” During these machine and data connections, micro-data content enables search, identity validation secures trust, metadata structure facilitates integration; and the hope is that this data is synchronized and connected accurately. Enormous waste and inefficiencies are created when vertical processes fail to connect, and when siloed learning can’t be shared.
Master Data to the Rescue
To meet these challenges, you need master data. Wikipedia defines master data as common source of basic business data used across multiple systems, applications, and processes. Until recently, master data and master data management (MDM) were thought of primarily as internal clerical exercises. Companies would have a list of customers, vendors, partners, or brands in one department or system that would not match up with another department or system. When these lists needed to be merged, or if the company wanted to combine the information found in both, it would have to bang the two lists together. That is a messy and painful process. Multiply this issue by a typical global enterprise’s footprint across regions, go-to-market channels, software types (ERP, CRM, finance, media, etc.), and external third-party sources, and the situation rapidly spirals out of control.
A real master data solution goes well beyond the legacy terminology in the data management space. Concepts like “cleansing” and “quality” are important but they are hardly holistic. Most data cleansing exercises are ad-hoc campaign-based projects isolated to a siloed use case. These ad-hoc projects may cool down the system temperature temporarily, but they won’t alter the on-going business climate. There are broader, more fundamental political and cultural business changes needed if an organization wishes to fulfil the vision of a true digital transformation. We need to change the entire nature of how data is created, managed, curated, integrated, and aggregated to drive interoperability.
To work on your commercial data sustainability here are three ways to think about eliminating waste and protecting your business environment from digital global warming:
- Reduce – Knock Down Legacy Silos
Many companies and departments add new data sources believing they can clean it up and align it to internal data later. To avoid the data misalignment problem, you need to reduce the versions of the entity data coming into your system. Simple steps include searching existing data before creating new records and leveraging data standards, both within your industry and from external providers.
- Reuse – Find Relationship Centricity
Establish common definitions and unique identifiers for all external parties with whom you engage. Don’t limit your thinking to a single domain. External parties include customers, suppliers, vendors, partners, etc. Define the different types of “relationships” within your enterprise and create a common, single, or golden view for each party. This is crucial as you may have multiple relationships with a single party. Using the same vocabulary (unique identifiers and tags) will ensure the same meaning across the enterprise. Relationship Centricity can be achieved by creating a horizontal view of your relationships across the entire enterprise with standard definitions. The short-term rewards of internal alignment pale in comparison to the much bigger opportunities outside of your enterprise. (More on Relationship Centricity in a future post.)
- Recycle – Engage in Trust Networks
“In this new information ecology,” Lucas continues, “interoperability must become a sacrament.” Using the same standard data and definitions, or links to the same standard data and definitions, within your enterprise and across verticals and markets provides the basis for seamless integration. Consider that a sacred part of your quest for a seamless customer experience. Find ways to leverage data and syndicate your processes within Trust Networks to scale interoperability. (More to come on Trust Networks.)
“It's about the organizational structure to connect all the disparate data sources” says Bob Carrigan, Dun & Bradstreet CEO. Of course, Carrigan recognizes the foundational need for master data to scale business interoperability. Many other CEOs, however, are also talking about seamless integration with external partners, without calling out the need for master data. Look for clues in the use of data in your own Leadership’s vision and business strategies. Does your executive team tout your business opportunities using any of the data megatrends mentioned above? If so, they will need master data to drive their data vision or accelerate their business strategies. Most organizations are stymied by the ROI exercise. And while rigorous cost/benefit analysis is important for any investment, I would suggest the primary business case for master data is to scale the interoperability of the BUSINESS YOU ARE IN. As the nature of your business expands across ecosystems and markets, so does the nature of those who thrive in those markets.
The headlong leap into the future of a data-inspired world will lead to unimagined opportunities and consequences for everyone in business. Some will transform and gloriously provide heretofore unknown levels of value in spaces none of us ever thought existed, and many others will find themselves isolated as their shorelines and profits erode and they confront the possibility of extinction. How you manage your master data going forward will have a lot to do with which fate your company could face.