CUTTER BUSINESS TECHNOLOGY JOURNAL VOL. 30, NO. 2

Defining Our Terms

Information superiority is an easy-to-understand concept that long predates IT, and it’s no surprise that the phrase, when capitalized as an IT buzzword (“Information Superiority”), came from the US Department of Defense. Knowing better than your enemy where your own and their troops are on the battlefield enables a nimbleness that can offset disadvantages in numbers and weaponry, and it has since the Trojan Wars. What wise generals have always done intuitively has been codified by today’s military into the “OODA Loop,” where the acronym stands for Observe, Orient, Decide, and Act.

“Digital capital” is a newer phrase and a newer concept. Obviously, it includes tangible assets like hardware and software that show up on balance sheets, but in today’s environment, it can and should include so much more — if not explicitly on a GAAP-approved balance sheet, then at least in how we think about investments. An article in the McKinsey Quarterly provides an expansive view of what constitutes digital capital:

• “The unique designs that engage large numbers of users and improve their digital experiences"
   
• “The digital capture of user behavior, contributions, and social profiles"
   
• “The environments that encourage consumers to access products and services"
   
• “The intense big data and analytics capabilities that can guide operations and business growth”
   

It would be difficult to think of a better example than Amazon of an enterprise that manages all of these supremely well.

I would suggest that information be cited very explicitly as digital capital, but not just any information. To count as digital capital (i.e., an asset, however intangible), information must be:

• Accurate
   
• Up to date
   
• Immediately available anywhere, in easy-to-use form, and to anyone who can use it for the benefit of the enterprise
   

Information that meets these standards¹ represents the nexus between digital capital and information superiority, the linked topics of this issue. Such high-quality information can be used at the operational level (such as for customer service), at the managerial level (to spot problems and support short- and intermediate-term decisions), and at the strategic level (to inform decisions about longer-term direction).

In OODA terms, Observe means to collect and make available relevant data on a timely basis, and Orient means to put it in a form in which it can be understood in context by the person who can use it to Decide and Act for the benefit of the enterprise.

Perspective

In the preindustrial era, when most manufacturing work was done in an artisan’s shop, information was easy to get. You knew your customers, and you could know everything you needed to know about the operation by just looking around: your inventory of supplies, what employees were doing, the state of completion of orders, and so on. You had complete, accurate, line-of-sight, real-time information. In your well-run shop, you had information superiority over the artisan across town whose chaotic shop featured misplaced tools, scattered inventory of supplies, and lots of stalled work in process. You probably did better than he did because you delivered products faster and more predictably and had lower costs.

The Industrial Revolution complicated things. Large factories in multiple sites, global customers, and global suppliers all required the creation of a whole information and control infrastructure to deal with the complexity and lack of anything like real-time, line-of-sight information. Official information existed only on paper — what else was there? — and the movement of paper through (mostly?) value-adding steps became a form of industrial process in itself. Not surprisingly, the information adhered to none of the standards cited above. Its paucity and poor quality necessitated multilayer reporting structures to maintain some semblance of control, relying on people with experience and seniority to exercise judgment and make educated guesses. Over nearly two centuries, all this became such a way of life that people thought of it as almost divinely ordained.

But no more!

For a few hundred dollars, a laptop or even a tablet can provide global visibility as good as the 18th-century artisan’s eyes. The challenge is to leverage today’s (and tomorrow’s) technology to achieve this preindustrial simplicity and economy in a global enterprise, scraping away the accumulated barnacles of the Industrial Age. In the early 1990s, the reengineering movement started the intellectual process, but results were too often limited, usually due to lack of imagination, but also because the technology still did not have power to realize the theoretical potential. Today the global Internet, broadband telecommunications, and Moore’s Law make that an ever less valid reason for falling short of information superiority and well-managed digital capital.

Getting to Information Superiority

The purpose of this article is to identify and describe critical steps and considerations for achieving infor­mation superiority and to offer practical suggestions for each.

Vision

Establishing a vision is the most critical step for identifying options and setting priorities. This step must not be shortchanged, but neither should it be allowed to become too abstract and drawn out. Critical questions include:

• What does information superiority mean in our industry/competitive space?
   
• What would information superiority look like for our enterprise? What could we do differently/better? What might we do that we otherwise could not even attempt?
   
• To what extent is information for the customer a critical component of the product or service (e.g., online forums for customers to ask questions and share ideas for more satisfactory use)?
   
• What more could we know about our customers that would help us serve them better or sell them more?
   

The scope is broad. Some of the examples below, while old and well-known, illustrate basic principles:

• What opportunities are there to enhance the value, usability, and/or customer satisfaction with existing information? One example is providing access to very detailed assembly or troubleshooting information, including video, which was not economical or practical in a one-size-fits-all user manual.
   
• Could we use information created by products in the field to improve the customer’s experience (i.e., Internet of Things). A pre-Internet example of this was Otisline, a creation of the Otis Elevator Company that attached its elevators’ self-diagnosing capability to a telephone that would launch a service call to fix a condition before it caused a breakdown.
   
• What is our customer really buying? Is it our product, or is it information that could be delivered by other means (e.g., recorded music that one could buy over the Internet, bypassing the physical medium)?
   
• Could we enhance the customer experience in purchasing and using a product or service? For example, frequent flyer programs began when airlines took data previously discarded after a flight (i.e., the customer’s identity) and used it to reward loyalty with free flights, upgrades, and better ground service.
   
• Reengineering done right can improve the speed, quality, and cost of processes all at once. The sidebar “Same-Day Claims Processing?” describes a situation where a bit of imagination could short-circuit years of incremental change.
   
• Managerial decisions can be made better and in a more timely manner. The sidebar “Fast Fashion” shows how large, high-risk decisions were turned into small, low-risk decisions.
   
• Information byproducts of no clear value could be leveraged in other parts of the enterprise or even sold outside the enterprise.
   
• Big data and analytics can take advantage of super-cheap storage and processing power to analyze data in new and creative ways that help identify trends and patterns.
   

When crafting a vision, it’s critical to keep the horse before the cart, thinking through what to do and why before going too far with technology. It is also important to recognize that the vision will evolve.

Assessment

Now comes the reality check. Having developed ideas for where we want to be, we need to look at where we are and what we have to build on. First, the applications portfolio:

• What information do we produce and keep?
   
• What information do processes and products create that isn’t being stored, let alone used, because nobody saw its value?
   
• How fully are we utilizing and leveraging enterprise suites like ERP, CRM, and supply chain?
   
• Out of all this, what information do we have or could we easily gather that’s relevant for achieving the vision?
   
• Does the information we have meet the standards for digital capital? That is to say, is it timely, accurate, and accessible enough to support the vision? What’s missing?
   

The technology infrastructure (processing, storage, communication, and software) may not be up to meeting the “immediately available anywhere” standards for digital capital. Big data and its analysis require tools and techniques plus people — business analysts and data scientists — who know how to use them to good effect.

Barriers

The other side of assessment addresses what can keep you from achieving the vision. Some barriers are “merely” technical (i.e., they can be overcome by spending money):

• Organizations, functions, and processes have legacy systems never designed to interact with one another or share data. Almost inevitably, this means incompatible hardware, applications, data definitions and structures, and coding schemes. There are copious details to sort through where the devil can lurk.²
   
• Even enterprise software packages can be customized in different and incompatible ways. Version releases can get out of sync.
   
• The “available anywhere” standard means exposing new surface area for hackers, necessitating enhanced security.
   

Cultural barriers are also there, particularly in established enterprises trying to transform themselves into digital players:

• Organizational silos die hard. Loyalty to business units, departments, or functions may override loyalty to the enterprise as a whole. Internal rivalries and distrust are common.
   
• Budget battles for resources are usually a zero-sum game where losers may sharpen their knives, especially if they feel that office politics trumped a solid business case for funding.
   
• Subtle differences in data definitions may have their logic, making them difficult to resolve without a diktat, another form of the zero-sum game.
   
• Even the best information isn’t a productive asset if people don’t use it or don’t know how to use it to maximal effect. Case files of IT projects are rife with examples of functionality installed with inadequate preparation, orientation, training, and follow-up. This is primarily true of information systems (rather than transaction processing), where the job has been and can be done without the new information, though not as well. But people resist change, a reality that must be dealt with.
   

Far too many IT initiatives have disappointed or gone astray because cultural factors were ignored or scanted. IT managers tend to do that by nature, and too often the business people don’t feel enough ownership of the intended benefits to make the necessary effort to ensure they’re realized. (And guess who gets blamed?)

Planning and Phasing

No big bang!

IT-related fiascos and mere disappointments have many causes, but their negative effect is greatly exacerbated when expectations have been raised about the coming transformation. Creating information superiority is a journey, not a destination. It’s never finished, because competitors can and will catch up if the innovation has merit.³ Digital capital should grow over time, as new and better ideas and technologies emerge.

The best approach is incremental steps toward the vision, each delivering some tangible value, even if limited in scope or scale. Benefits should be measurable, whether in money, quality, and/or cycle time.

A bit of showmanship can pay off. It is better to ensure early phases do something visibly useful, even if it isn’t the technically optimal implementation sequence, in order to best maintain interest and support. Sink some easy shots, pick some low-hanging fruit — choose your metaphor — early on to build credibility. Too often IT programs get in trouble when an optimal implementation sequence is pursued that requires a lot of spending on infrastructure and preparation before any value is delivered. CIOs can find themselves on the defense, with the technically correct explanation sounding like jargon-laden excuses for slow or no delivery. (“There they go again.”)

Each step, particularly early ones, should be chosen for quick doability and because it delivers something of value. If it’s successful, a less than brilliant design can be made more robust and flexible. If it’s not, the project team should figure out why and try a different approach or move on to more fertile ground.

Steps taken toward the vision don’t have to occur one at a time. Two or more paths can be followed if they are independent enough of each other that a problem in one does not affect the other(s).

Evaluation and Learning

The one sure thing about the vision is that it will need to be updated to reflect new technology and changes in the business landscape. Every step in its implementation should produce not only value but learning. Inevitably, some ideas will not prove as brilliant as hoped. Failures can be even more instructive than successes, but only if we ask what happened and why without treating it like an inquest:

• If the benefits did not materialize as projected, why not? Did we not understand the situation well enough? What did we miss and how did we miss it?
   
• Were the intended beneficiaries insufficiently prepared or committed? How did that happen? If formal change management was used, why did it not work? If not used, could it have made a difference?
   
• Was the technology too much of a stretch? Not as good as advertised?
   
• Did the information end up falling short of the information superiority standards? What led us to believe incorrectly that the data was current and accurate enough?
   
• Can we fix the shortcomings, or should we move on?
   
• What should we make sure we do differently?
   

Success should also generate questions:

• What were the risks — technology, people, business — and how did we mitigate or overcome them?
   
• What could we have done differently to achieve even better results?
   
• What did we do that we didn’t need to do, and how could we have recognized that beforehand?
   
• Are there barriers — technology, people, business, quality of available data — to replicating the success in other parts of the enterprise?
   
• If this was a pilot project, what are the constraints to scaling it up?
   

In short, every step provides learning. Absence of mistakes and disappointments means you didn’t attempt enough. When mistakes do happen, learn from them and move on!

Continuous Improvement

There will always be better ideas, whether generated internally or thrust at us by a competitor we must match or exceed. The Lean approach, based as it is on successful Japanese manufacturing practice, makes a big deal of this, as it should for any part of a program aimed at information superiority. This is one way information systems differ from transaction processing systems, where “success” is more binary — either the transaction is processed correctly or it isn’t. In information systems, there is no such thing as “correct” when you can always do better.

Stewardship

The term “capital” in digital capital implies something of value that needs to be carefully nurtured and protected. In the subset of capital represented by information, the notion of stewardship applies in three ways:

1. Proprietary information of value to the enterprise must be protected from competitors and hackers.
    
2. Private data of importance to customers must be protected from hackers.
    
3. Information generated at little or no incremental cost by processes, services, and products in the field, but for which no immediate use is apparent, should be stored, indexed, and made retrievable. In an era of big data and cheap storage, why not? You may find a use for it later.
    

Privacy and security are the two areas where stewardship means that the “quick and dirty” approach suggested above needs to give way to “cautious and deliberate.”

Direction and Management

CIOs cannot drive the creation of information superiority and digital capital, though they must be responsible for implementation of agreed capabilities. When business heads commit to an initiative, they must be accountable for producing the projected benefits. That assumes IT delivers the agreed functionality and that the functionality has been specified through a thoroughly collaborative process. The Agile approach can be very helpful, and the CIO needs to insist that it be followed, at least in spirit.

It is also important to manage expectations; not every at bat yields a home run or even a single. We should remember that even though Babe Ruth hit more home runs than almost anyone not on steroids, he also struck out a lot more than most. That’s why focusing on early, visible value from information superiority successes is so important. The CIO must trade off between technically optimal sequencing and promptness in delivering business value.

Conclusion

To answer the question in the title, no, information superiority and digital capital are not synonymous. They are, however, intimately related. If you have information superiority, you perforce have digital capital. If you don’t have information superiority, your digital capital account will be meager no matter what tangible assets are on the books. More than ever, information superiority is what successful organizations need to build and nurture, just as successful generals have done for thousands of years.