Part 1 of a multi-part series on the “New Age of Master Data Management”
by Julie Hunt
The ‘New Age’ of Master Data Management
It’s about time that master data Management (MDM) enjoyed a ‘new age’. The creation and use of master data for Business purposes has always been a good idea. The goal has been to make sense of all data that touches the organization: clean it up, add context, enrich it, and put it to work strategically as well as operationally.
Unfortunately, MDM has been plagued with certain problems from the very start. This stems mostly from the fact that MDM lands hard on some of the messiest aspects of an organization: data silos, unconnected systems and processes, management turf wars, and organizational roadblocks to disparate people working effectively together for common purpose.
Why Does Master Data Matter?
As the importance, value and sheer volume of data assets grow, so does the critical need for trustworthy, up-to-date information that is ready-to-go for any purpose. MDM processes consolidate data from numerous sources in meaningful ways. Master data also provides context and reveals data relationships for sources like big data that often exist as fragments without useful referential connections.
Master data has a large and growing role in areas that matter to organizations: strong brand presence, multichannel customer interactions, right-fit content and information, and highly variable buying journeys.
Analytics, particularly of the real-time variety, produce more realistic and trustworthy results when utilizing master data. Master data is often a key part of predictive analytics in support of: supply chain processes; personalization for improved customer experiences; logistics; operational optimization; and safety and preventive procedures.
A core element of MDM is data quality. Improved data quality impacts endless aspects of the business for reasons that should be obvious. But all too often, business management fails to comprehend how much poor data quality impacts profitability, productivity and even perception of the brand. Real world incidents that have been repeated at far too many organizations include:
- Inconsistent data of poor quality has led to extensive quantities of items in warehouses to “disappear” from systems records, never to be sold or consumed by the business. This can amount to millions of dollars of losses.
- Contradictory and confusing product lists and catalogs that deter purchases by customers and make sales staff look stupid – again with no purchases resulting.
- Costly pricing errors that persist undetected over time can have breath-taking negative impact on the bottom line.
Roadblocks, Detours, Dead Ends
Over time traditional approaches for MDM implementations have faltered for several reasons, many of which echo the failures of traditional ERP installations:
- Implementations take too long and cost too much.
- Project scope is often much too large.
- Business and IT teams are poorly coordinated for achieving the desired end state.
- Results fail to meet important business needs and / or show value in an obvious and timely fashion.
- Traditional approaches lag in keeping up with new technologies.
- Solutions have delayed including new data sources like the many varieties of big data.
A continuing obstacle is the lack of involvement of upper management with such important activities. If organizations want to tap into more data that is trustworthy and business-ready, it requires buy-in from upper management on down. Such buy-in is shown through clear commitments, upper management champions, and real data strategies – basically understanding the high value of data and what it takes to achieve it.
The Reality of MDM: People and Politics
What many people get wrong about MDM is thinking that it’s all about the technology. In reality, it’s much more about people, processes, and (unfortunately) politics. The people and politics of the organization can throw up significant obstacles to successful MDM programs. This is more likely to happen in organizations where upper level management has failed to provide its leadership and involvement in MDM implementations.
Silos permeate organizations from top to bottom, and not just in terms of software systems or digital assets. Collaboration between people is often limited. Internal politics can bring on secrecy or “turf wars” when it comes to the ownership of data and business processes. Resistance to change, lack of trust, desires to “hoard” data and information all come into play. Orchestration and collaboration of business and technical teams have always been critical to MDM success, but can be derailed by internal silos and politics.
Of course, technology provides an important means to achieve MDM implementations. But many vital activities aren’t technical. For MDM and data governance to be successful, distinct changes must take place in organizations to eliminate the many silos that prevent the generation of reliable and up-to-date master data.
Not surprisingly, when things go wrong, organizations want to blame MDM technologies for the failures, instead of facing up to what really ails the organization. However, traditional MDM solutions have had significant limitations and often don’t do much to help with the “people problem”.
Wanted: Improved Approaches
A classic approach to MDM, the “bottom up” method, has not worked well simply because unsynchronized teams are trudging separate long roads to achieve the “desired end state” and often don’t ever get there. It’s easy to imagine the many ways that this method can be derailed. Instead, businesses benefit most from MDM approaches that work the way the business and its employees need to function to achieve real goals and objectives.
One of the most important changes to MDM approaches has organically grown out of the fact that much of MDM and data governance revolves around business use cases and business users. So it’s gratifying to see more overtly business-defined approaches that not only delineate the approach but are enabling changes to the technologies that support them. These business-defined approaches reflect the complex, sophisticated needs of many organizations:
- Working from the desired end state and / or from the requirements of complex business problems to create master data management processes.
- Choosing contextual and analytic MDM solutions over traditional MDM tools that can handle new approaches for multi-dimensional and complex hierarchical data, including social and commercial graphs that underlie business use cases such as customer-responsive interactions across digital channels.
- Utilizing new MDM platforms that simplify the development of industry-focused business applications that take advantage of master data to solve business problems.
- Transforming model-driven approaches to be more flexible and agile in order to work with existing business processes as the starting point (instead of creating new processes), to deliver business value more quickly. IT and business teams can then transition to optimizing various processes for additional value.
In a real world example, a pharmaceuticals company has utilized a new MDM solution to uncover significant information that improves the success of promoting drugs to individual healthcare organizations. Such information includes:
- Which insurance companies or payers accepted by the healthcare organization actually cover the drugs
- And for those payers, which insurance plans provide coverage
- What contracts for the pharmaceuticals company are already in place
- What other sales teams in the same company are already promoting to the healthcare organization
- Other important influence and decision-making factors that derive from complex hierarchies and networks of relationships
Running through such an analysis based on master data before approaching a healthcare organization helps the sales team determine if any business opportunities exist, and reveals better ways to approach contacts in the organization.
Master Data Management Rebooted
In the next five posts, I’ll present and analyze what is changing for master data management approaches and solutions, including new technologies, and the evolutionary involvement of business roles in MDM and data governance.
One of the final aspects that I’ll explore is this: is it time to quit calling it “master data management” and devise a name that better fits MDM Rebooted?
Image Source: hbr.org
Julie Hunt is the editor of Hub Designs Magazine and co-founder of the Hub Designs MDM Think Tank. Her “day job” is as an independent B2B software industry solution strategist and analyst. She provides consulting services for vendors to help develop successful strategies for buyers, customer and user experiences, solutions, go-to-market, and future direction.
Filed under: Best Practices, Big Data, Master Data Management, Politics, Strategy Tagged: Best Practices, Big Data, featured, Master Data Management, MDM, Strategy