At the Annual Members Meeting of the Institute for the Study of Business Markets, there was considerable discussion of the new competencies that were going to be required of U.S. manufacturing firms.
This was not surprising, given the meeting’s focus on the most significant emerging challenges that businesses were likely to face. And, given the breadth of participants from industry and academia, it’s not surprising that perspectives and recommendations varied widely.
One manufacturing CMO noted in a discussion that his job had evolved to include a significant amount of “teaching new tricks to old dogs.” Another speaker offered the viewpoint that “If you find that you need to climb trees, hire some squirrels rather than trying to retrain the horses.” Animal analogies aside, my guess is that most of our firms will have to do some of both in the next few years to respond to those emerging challenges.
One of the areas in which new competencies are almost certain to be required involves the many initiatives that go under the umbrella heading “Big Data.”
In earlier IndustryWeek articles, I reported on some success stories involving manufacturing firms that had identified growth opportunities involving the newly-available wealth of data resources and suggested some approaches to thinking about such opportunities that drew upon the experiences of the information industry itself.
That industry provides many lessons that can guide both strategy and competency development, regardless of whether the approach to the latter involves retraining old dogs and horses or recruiting from the squirrel population.
Perhaps the most important concept builds upon a key foundation of information theory, namely that the way to measure the value of information is by quantifying the change in the quality of relevant decisions made with and without it.
Investments in Big Data initiatives, like any other investment, need to be focused on contributions that make a difference.
If the same decisions would be made with or without the information, it has no value. If a different decision, with a higher payoff, can be made as a result of having the information, then the payoff is real and you have a benchmark as to the level of investment that could be made in getting the information while still yielding a positive return.
Considering the Customer
Focusing attention on the Big Data business case is an important contribution. Like the case with other efforts at innovation, it is far too easy to focus instead on the technical challenges associated with the concept, rather than asking the questions “Who along the customer chain will be made better off if this information is made available to them?” and “What value will be created as a result of the better decisions that can be made with this information?”
These are very operational questions, ones that can ensure that Big Data initiatives and investments have the potential for value creation and capture.
The former of the two questions is often the more difficult one in business markets. Often, the answer involves a customer several stages down the customer chain, rather than the direct customer of the firm making the Big Data investment. In such cases, the challenge then becomes that of ensuring that the value potential is recognized and rewarded at all of the intervening stages of the customer chain.
Addressing the improved decisions that can be made with the information requires that you put yourselves into the shoes of the identified customer and, the more effectively that is done, the more likely it is that you’ll reach a high-quality assessment, rather than just a superficial perspective that overstates the value of the information.
In one application, the potential of avoiding certain maintenance activities was identified as a Big Data contribution. In looking at how this could be implemented by equipment operators at job sites, the insight was that there were three key data-based facts that were required in order to reach the decision to skip the maintenance activity.
The equipment manufacturer built registers into its equipment that displayed these data elements and “lit up” when all were favorable. Not only was the “better decision” opportunity identified in this case, it was made operational by the equipment supplier.
That example suggests a second concept relevant to developing Big Data applications. Your customer is already “information rich,” although perhaps not in the ways envisioned by your initiative. And, most likely, your customer has already established processes and patterns through which they source and assimilate information.
Passing the Progress Test
And, drawing upon the earlier analogy, if you can avoid having to teach your customers new tricks, their interest in the new information resources you are offering them is likely to be higher than if your offer requires them to change systems, processes, practices or themselves invest in training.
Turning the telescope around and looking at your Big Data concept from the perspective of the customers’ existing practices and approaches to decision making is a skill that can yield insights that raise the probability of success.
That concept doesn’t deny the numerous possibilities that have emerged to offer customers lowered costs or easier access to information through new technology.
Each of us can easily think of business applications that are now done through the use of hand-held tablets of some form or other to the delight of all, replacing past-generation technologies that were probably more costly, slower and less efficient.
But it is important to test whether a new technology passes the progress test, as your customers are more than likely to view change itself as a cost to be considered as part of their evaluation of your Big Data offering.
A third insight that should be part of your team’s Big Data toolkit involves the fact that information is a multi-dimensional concept. Any information resource has dimensions that go far beyond the concept being measured.
Accuracy, frequency of measurement, availability of history, linkage to other metrics and numerous other dimensions determine both the value and the cost of providing the information.
In one application, a Big Data concept went from being prohibitively expensive to economically attractive when it was realized that customers did not require a constant, real-time feed of information, that an event-driven feed in fact better matched their decision processes and needs.
Possibilities, with Limits
Today’s information technologies create extraordinary possibilities, but often the opportunity for value creation does not require pushing to the limits of what is possible.
And stopping short of those limits often allows significant costs to be avoided, both for your firm and for your customer.
Once again, thinking about how the customer can and will use the information, and about the investments imposed on them if you push to the limits along all information dimensions can enable solid decisions that will be applauded in the marketplace.
It is certain that more and more manufacturers will create value for their customers by enhancing the information content of their products.
There are already great success stories in industry after industry, and that roster will only grow over time. But there are also already unfortunate examples of Big Data investments that failed to gain traction in the market.
The competencies outlined here – emphasizing the business case contributions of Big Data investments through a focus on how customers can make higher-quality decisions, linking new information offerings to existing customer processes for acquiring and using data and understanding the dimensions of information that actually matter to customers – are ones that can help shift the mix of results towards success stories and away from disappointing outcomes.
George F. Brown, Jr. is the cofounder of Blue Canyon Partners Inc., a consulting firm working with leading companies on growth strategy. He is the coauthor of CoDestiny: Overcome Your Growth Challenges by Helping Your Customers Overcome Theirs, published by Greenleaf Book Group Press of Austin, TX. He has published frequently on topics relating to strategy in business markets, including articles in IndustryWeek, Industrial Distribution, Chief Executive, Business Excellence, Employment Relations Today, iP Frontline, Industrial Engineer, Industry Today and many others.