"Master data! Master data! My supply chain for master data!"
With data quality and consistency becoming critically important factors in supply chain performance, companies will have to pay more attention to master data management. That may require supply chain managers to change the way they think about and utilize data.
"A horse! A horse! My kingdom for a horse!" screams King Richard III in Shakespeare's play of that name. At that point in the play, Richard, unhorsed and fighting on foot, is put at a disadvantage on the field of battle at Bosworth; as a result, he is killed by Richmond, who then succeeds to the throne as Henry VII. The point I am making here and with the title of this article is that the availability of a critical resource (like a horse, in Richard's case, or master data, for a supply chain) can be crucial for success and even for survival.
We at Gartner define master data management (MDM) as a technology-enabled business discipline in which business and information technology (IT) must work together to ensure the uniformity, accuracy, stewardship, semantic consistency, and accountability of an enterprise's official, shared master data assets. Supply chain performance is dependent on consistent definitions of customers, products, items, locations, and other master data objects. When data is poorly governed and inconsistent, supply chains become less competitive because more time and money is spent on managing information between systems and trading partners, and less is available for innovation. Good data leads to efficient supply chains, allowing resources to be spent on innovation rather than on coping with problems.
Master data has always been necessary, but the importance of its consistency in supply chains is growing. There are three main reasons for this. First, supply chain performance is coming under an increasing number of pressures. These include global and local competition; legal and regulatory demands; and social responsibility-minded shareholders, to name just a few of many possible examples. Underlying them all are today's fragile economic conditions.
Second, there is a growing emphasis among many organizations on knowing their customers' needs. More than this, organizations are seeking to influence the behavior of customers and prospects, guiding customers' purchasing decisions toward their own products and services and away from those of competitors. This change in focus is leading to a greater demand for and reliance on consistent data. For any supply chain leader, the path to meeting those demands leads back to master data.
And third, given the level of attention that IT is placing on data consistency, as well as companies' growing focus on collaboration with trading partners and their need to improve business outcomes, data consistency—especially between trading partners—is increasingly a prerequisite for improved and competitive supply chain performance.
As data quality and consistency become increasingly important factors in supply chain performance, companies that want to catch up with the innovators will have to pay closer attention to master data management. That may require supply chain managers to change the way they think about and utilize data. With that in mind, here are four topics that should be on the joint information management and supply chain agenda for 2013.
1. Business outcomes trump data quality
I am playing with words here. Of course data quality is important. But how important should it be? That is, how much cost should you incur to improve data quality, and what business value will you realize from that effort? Programs like master data management are most successful when they have a clear line of sight to a specific and measurable business outcome. By contrast, organizations seem to struggle when they focus on data quality and metrics related to the data itself as measures of success.
An example of a "bad" (ineffective) MDM metric would be "the number of de-duplicated records per month." This is of little interest to the user of business information, and it does not help the business understand why changing the way it uses information would improve outcomes. An example of a "good" (effective) MDM metric would be "net new revenue per first six weeks of new product introduction." This information will be relevant to the business user—the word "revenue" will make sure of that. Moreover, there is a specific time frame; the metric is bounded so that it can be measured. The number of de-duped records is not irrelevant, as de-duping would improve the quality of the data being used. But adopting the "net new revenue" metric will, rightly, keep the focus on the relationships between various activities and the outcomes of the work taking place, rather than on the data itself.
2. Information governance: Less about control and more about information value
Organizations are making progress with master data management and other information governance programs, but we are still seeing great resistance to these efforts. One reason for that resistance is that users often misunderstand what "information governance" means. Many organizations equate governance with rules, regulations, and "Big Brother" (management that exerts excessive control) limiting flexibility in how the business handles data and what it can do with it. However, a more informative interpretation of information governance recognizes that the focus should be more on identifying what data is most useful to the business and its desired business outcomes, and on designing processes that are as flexible as the business needs them to be.
When asked what the term means to them, however, business users we regularly speak with have offered many different definitions, including: security, access, control, rigidity, limited flexibility, IT managers or "Big Brother" watching, extra work, "something focused on data that IT needs to work with," "something we are doing wrong (apparently)," and "not related to what we do in the business." (The very word governance, which implies control from above, may be partly responsible for those attitudes. Replacing it with terms such as "stewardship" or "custodianship" might help to allay any fears users have in that regard.)
These responses reflect a dated, negative view of what information governance is about. Today, a much different approach is called for. No one, for instance, should design a governance process that is rigid; instead, the process and supporting organization should be as flexible as the business needs them to be. Security and access, moreover, will be policies of interest to the work being done, but they should not be the only or even the main focus of governance. Instead, they are now secondary or tertiary concerns.
In addition, information governance should only be undertaken when a business has both a desire to first, govern data for the express purpose of realizing business value, and second, a willingness to change its business processes that create, enrich, approve, or otherwise use data, so that it can extract that value. For example, business users who had previously been reluctant to participate in the governance of customer data would be willing to do so if it would help them achieve their own, measured objectives.
Unfortunately, people do not always recognize the potential benefits of information governance. Consider the example of an employee like "Fred." You all know who Fred is. He has been with your supply chain group for years; he does not "like" IT and IT does not "like" him. But when it is 4:45 p.m. on a Friday and the information system will not allow you to ship an order, you go to Fred to find out how to get that order out the door. Fred knows that if you enter "00" in the field that is at fault, it will override the system and allow the order to ship! Fred is the authority and the informal steward of information. He and his like are governing information every day. But information governance today is not focused on stopping what Fred is doing. Instead, it is focused on understanding what is wrong with a process and its supporting application, and on changing them to enable better outcomes—such as shipping orders complete and on time.
As this all-too-common example suggests, we need to avoid the emotional inhibitions related to terms and concepts like "information governance" and just get the job done.
3. Information as an asset (balance sheet) and information value yield (profit and loss)
The growing hype about "big data" analysis is leading organizations to ask themselves: Is there any way we can monetize our information? Can we use information not only in our own business but actually sell some aspect of it to others?
Some companies are already doing this. A few years ago Gartner published a case study about how Best Buy was, at the time, selling access to application programming interfaces (APIs) that published product-attribute information for use in marketing aggregators' online shopping sites. This data originally was provided, in part, to Best Buy by its suppliers. Those same suppliers, meanwhile, were working on ways to monetize their own product-related data. Thus, while manufacturers and their retail partners may primarily sell products like televisions and Blu-Ray players, they can also create an additional revenue stream by selling some of their information.
The Best Buy example and more-recent stories, such as how Netflix is able to mine insights from when and how frequently customers pause their video players, illustrate how information can be accounted for as an asset. This concept is beginning to attract more attention. But information is not yet considered to be an acceptable intangible asset for accounting purposes, so the monetary value of a company's unique customer master list remains unaccounted for.
If you accept, however, that your organization's information assets have financial value, then a host of questions will open up. Which information asset should you invest in most? Which information assets and information management or exploiting programs will yield the greatest returns? Should you keep information assets on the assumption that they will pay you a higher return later? Do you invest in enterprise resource planning (ERP) or business intelligence (BI) systems, and in which order? What about master data management? These are hard questions to answer. But it is these questions your IT group must be able to answer—and does so (perhaps informally) as it communicates what its priorities are in support of a particular business goal.
4. Making information governance "stick"
To address the issues discussed above, companies finally are starting to have more down-to-earth conversations about data governance. Many leading and next-leading organizations are appointing or hiring "data stewards" and are establishing business process and business data owners and data governance bodies. They may subsequently adopt master data management technology, perhaps coupled with a business process management (BPM) initiative. The scope of such initiatives, moreover, is often dictated by a broad and strategic focus on supply chain performance. That's what has been happening in 2012 and 2013. Why, then, are we not hearing more about successful MDM projects?
In fact, there are MDM success stories, but not every implementation is going as well as everyone would like. One scenario we have been seeing recently is what I would characterize as being unable to make information governance "stick." The situation typically looks like this:
Implementation is complete.
Applications have been integrated; data is flowing.
We hired data stewards (within the business, in fact).
There was or is a governance board; they met a few times—we think.
Now it's three months since "go live" and the project team has disbanded.
Exceptions are emerging in the data, and the business users are coming to IT for resolution. IT does not know what to do with the exceptions, and business users can't understand the language of the messages.
There are two reasons companies find themselves in this kind of situation. The first is that some organizations are struggling to get sufficient buy-in for the new roles and responsibilities required for governance (policy setting) and stewardship (policy enforcement). They initiate the necessary work as part of the implementation but do not seem to carry through with it day to day.
The second is that too many so-called master data management software vendors and their tools are not mature enough to adequately support business-led data stewardship. When I worked in industry (in consumer goods, industrial manufacturing, and white goods) in the days before information governance had been formally defined, I figured out how to use product data to do my job better. Sometimes that meant discovering what kind of data exceptions could be used to override the system. But the tools I used were rudimentary, even manual. Today's master data management solutions would not have been useful to stewards of supply chain product data like me, or to my current-day counterparts. Too much emphasis is being placed now on data quality, matching, integration, and modeling. And too little is being placed on the monitoring and problem-solving tools that business data stewards need in order to carry out their day-to-day work.
The role of data steward, by the way, should not be an onerous one. In fact, it should not be a full-time job. If it is, then the organization is focusing on the wrong things. Problem solving for business process outcomes that are held back by data problems that the IT group cannot handle should take no more than a few minutes each week. How many minutes may differ for each organization—it might be 15 minutes, or 20, or 10. The number is not the point; the point is that this responsibility should take up a very small amount of time compared to the rest of a business user's work.
One other important point is that data maintenance is different from data stewardship. Too many users and vendors do not understand that these roles, and the work associated with them, can and should be separated. Who actually creates the data is not so important; that work could be done as a shared service, or it could even be outsourced. But the role of steward—that is, the chief problem solver—cannot be outsourced or removed from the line of business that is affected by the data problem.
Winning with data management
The four issues discussed in this article are the largest and most notable of the trends related to master data management and information governance that will play out in supply chains across the globe. There is one important point I must re-emphasize. The supply chains that will win in the next few years won't come out on top simply because they have the best information. All of them, I believe, will do something more with their data: They will successfully tie their information management disciplines to specific and measurable business outcomes.
As trading partners continue to deepen their collaborative relationships, seek to better understand their customers and end consumers, and focus on ever more demand-driven supply chain strategies, the consistency of the data that resides within corporate systems and is shared with partners will become even more critical than it is now. Businesses will need to govern their information to a degree that will ensure the integrity of their supply chain strategies—and master data management is where this is taking shape.
Just 29% of supply chain organizations have the competitive characteristics they’ll need for future readiness, according to a Gartner survey released Tuesday. The survey focused on how organizations are preparing for future challenges and to keep their supply chains competitive.
Gartner surveyed 579 supply chain practitioners to determine the capabilities needed to manage the “future drivers of influence” on supply chains, which include artificial intelligence (AI) achievement and the ability to navigate new trade policies. According to the survey, the five competitive characteristics are: agility, resilience, regionalization, integrated ecosystems, and integrated enterprise strategy.
The survey analysis identified “leaders” among the respondents as supply chain organizations that have already developed at least three of the five competitive characteristics necessary to address the top five drivers of supply chain’s future.
Less than a third have met that threshold.
“Leaders shared a commitment to preparation through long-term, deliberate strategies, while non-leaders were more often focused on short-term priorities,” Pierfrancesco Manenti, vice president analyst in Gartner’s Supply Chain practice, said in a statement announcing the survey results.
“Most leaders have yet to invest in the most advanced technologies (e.g. real-time visibility, digital supply chain twin), but plan to do so in the next three-to-five years,” Manenti also said in the statement. “Leaders see technology as an enabler to their overall business strategies, while non-leaders more often invest in technology first, without having fully established their foundational capabilities.”
As part of the survey, respondents were asked to identify the future drivers of influence on supply chain performance over the next three to five years. The top five drivers are: achievement capability of AI (74%); the amount of new ESG regulations and trade policies being released (67%); geopolitical fight/transition for power (65%); control over data (62%); and talent scarcity (59%).
The analysis also identified four unique profiles of supply chain organizations, based on what their leaders deem as the most crucial capabilities for empowering their organizations over the next three to five years.
First, 54% of retailers are looking for ways to increase their financial recovery from returns. That’s because the cost to return a purchase averages 27% of the purchase price, which erases as much as 50% of the sales margin. But consumers have their own interests in mind: 76% of shoppers admit they’ve embellished or exaggerated the return reason to avoid a fee, a 39% increase from 2023 to 204.
Second, return experiences matter to consumers. A whopping 80% of shoppers stopped shopping at a retailer because of changes to the return policy—a 34% increase YoY.
Third, returns fraud and abuse is top-of-mind-for retailers, with wardrobing rising 38% in 2024. In fact, over two thirds (69%) of shoppers admit to wardrobing, which is the practice of buying an item for a specific reason or event and returning it after use. Shoppers also practice bracketing, or purchasing an item in a variety of colors or sizes and then returning all the unwanted options.
Fourth, returns come with a steep cost in terms of sustainability, with returns amounting to 8.4 billion pounds of landfill waste in 2023 alone.
“As returns have become an integral part of the shopper experience, retailers must balance meeting sky-high expectations with rising costs, environmental impact, and fraudulent behaviors,” Amena Ali, CEO of Optoro, said in the firm’s “2024 Returns Unwrapped” report. “By understanding shoppers’ behaviors and preferences around returns, retailers can create returns experiences that embrace their needs while driving deeper loyalty and protecting their bottom line.”
Facing an evolving supply chain landscape in 2025, companies are being forced to rethink their distribution strategies to cope with challenges like rising cost pressures, persistent labor shortages, and the complexities of managing SKU proliferation.
1. Optimize labor productivity and costs. Forward-thinking businesses are leveraging technology to get more done with fewer resources through approaches like slotting optimization, automation and robotics, and inventory visibility.
2. Maximize capacity with smart solutions. With e-commerce volumes rising, facilities need to handle more SKUs and orders without expanding their physical footprint. That can be achieved through high-density storage and dynamic throughput.
3. Streamline returns management. Returns are a growing challenge, thanks to the continued growth of e-commerce and the consumer practice of bracketing. Businesses can handle that with smarter reverse logistics processes like automated returns processing and reverse logistics visibility.
4. Accelerate order fulfillment with robotics. Robotic solutions are transforming the way orders are fulfilled, helping businesses meet customer expectations faster and more accurately than ever before by using autonomous mobile robots (AMRs and robotic picking.
5. Enhance end-of-line packaging. The final step in the supply chain is often the most visible to customers. So optimizing packaging processes can reduce costs, improve efficiency, and support sustainability goals through automated packaging systems and sustainability initiatives.
That clash has come as retailers have been hustling to adjust to pandemic swings like a renewed focus on e-commerce, then swiftly reimagining store experiences as foot traffic returned. But even as the dust settles from those changes, retailers are now facing renewed questions about how best to define their omnichannel strategy in a world where customers have increasing power and information.
The answer may come from a five-part strategy using integrated components to fortify omnichannel retail, EY said. The approach can unlock value and customer trust through great experiences, but only when implemented cohesively, not individually, EY warns.
The steps include:
1. Functional integration: Is your operating model and data infrastructure siloed between e-commerce and physical stores, or have you developed a cohesive unit centered around delivering seamless customer experience?
2. Customer insights: With consumer centricity at the heart of operations, are you analyzing all touch points to build a holistic view of preferences, behaviors, and buying patterns?
3. Next-generation inventory: Given the right customer insights, how are you utilizing advanced analytics to ensure inventory is optimized to meet demand precisely where and when it’s needed?
4. Distribution partnerships: Having ensured your customers find what they want where they want it, how are your distribution strategies adapting to deliver these choices to them swiftly and efficiently?
5. Real estate strategy: How is your real estate strategy interconnected with insights, inventory and distribution to enhance experience and maximize your footprint?
When approached cohesively, these efforts all build toward one overarching differentiator for retailers: a better customer experience that reaches from brand engagement and order placement through delivery and return, the EY study said. Amid continued volatility and an economy driven by complex customer demands, the retailers best set up to win are those that are striving to gain real-time visibility into stock levels, offer flexible fulfillment options and modernize merchandising through personalized and dynamic customer experiences.
Geopolitical rivalries, alliances, and aspirations are rewiring the global economy—and the imposition of new tariffs on foreign imports by the U.S. will accelerate that process, according to an analysis by Boston Consulting Group (BCG).
Without a broad increase in tariffs, world trade in goods will keep growing at an average of 2.9% annually for the next eight years, the firm forecasts in its report, “Great Powers, Geopolitics, and the Future of Trade.” But the routes goods travel will change markedly as North America reduces its dependence on China and China builds up its links with the Global South, which is cementing its power in the global trade map.
“Global trade is set to top $29 trillion by 2033, but the routes these goods will travel is changing at a remarkable pace,” Aparna Bharadwaj, managing director and partner at BCG, said in a release. “Trade lanes were already shifting from historical patterns and looming US tariffs will accelerate this. Navigating these new dynamics will be critical for any global business.”
To understand those changes, BCG modeled the direct impact of the 60/25/20 scenario (60% tariff on Chinese goods, a 25% on goods from Canada and Mexico, and a 20% on imports from all other countries). The results show that the tariffs would add $640 billion to the cost of importing goods from the top ten U.S. import nations, based on 2023 levels, unless alternative sources or suppliers are found.
In terms of product categories imported by the U.S., the greatest impact would be on imported auto parts and automotive vehicles, which would primarily affect trade with Mexico, the EU, and Japan. Consumer electronics, electrical machinery, and fashion goods would be most affected by higher tariffs on Chinese goods. Specifically, the report forecasts that a 60% tariff rate would add $61 billion to cost of importing consumer electronics products from China into the U.S.