In the technology business, we are inundated with new things on a daily basis, and we spend a lot of our time learning about them, trying to predict which will gain traction, which will prevail and writing and speaking about them. Today, I am going to step back from big data, the “internet of things,” machine learning, artificial intelligence and software-defined infrastructure, among a hundred other things, and think about what it all means.

There was a time when computers were too slow to do more than bookkeeping and other back-office chores. Without machines to interfere in their interactions, people performed office work like a ritual. There were set hours, dress codes, rigid hierarchies, predictable tasks and very little emphasis on change from year to year. Nothing moved very quickly. Good companies were stable and planned thoroughly. That was then.

As computers slowly became more useful, necessity and market forces applied them to increasingly critical tasks. By the 1990s, Total Quality Management, Process Reengineering and headcount reductions driven by the brutal pressure of corporate raiders forced organizations to look at the efficiencies that could be gained by streamlining business processes. After more than two decades of tireless cost-cutting and pursuit of efficiency, the relaxed and personal office life depicted on television of the 1960s was gone forever. Today, we operate Netflix-style, on-demand.

The critical mass for an on-demand world is composed of information technology elements such as ubiquitous communications (the internet), open standards and easy access. In an information business, speed is king. For an organization conducting business, or an army managing a battlefield, or anyone monitoring the world’s financial markets, going faster means a shift from a reliance on prediction, foresight and planning to building in flexibility, courage and faster reflexes, catching the curls as they come and getting smarter with each thing you do (and making your partners smarter, too), ranking the contingencies instead of sticking to the plan no matter what.

The paradox of efficiency

But what exactly is speed?

Speed implies more than just doing something quickly. For example, being able to load and index 10 trillion records into a data lake in the blink of an eye is one measure of speed. But if the process has to wait until the middle of the night, or it takes another day to aggregate and spin out the data to other repositories before it can be used, or the results have to be interpreted by multiple people in different domains, then the relevant, useful measure of speed is the full cycle time.

In Six Sigma terms, cycle time is the total elapsed time to move a unit of work from the beginning to the end of a physical process. It does not include lead time. Measuring speed can be relative or absolute. Closing the books in three working days is absolute; being first to market is relative.

There is a paradox of efficiency: Investing in efforts to pare the time it takes to complete work steps can often lead to even longer cycle times. Consider the scheduling of aircraft: When a step is delayed or fails, there are people to consider – passengers and crew, for example. The efficiency of the solution vanishes when something doesn’t work. Perhaps the process is very efficient, but brittle, so when it breaks, all prior gains are lost.

The lesson is that speed can’t be measured by the speed of steps or by the speed of a sequence of steps. People are always involved, and often those people tangential to the process. The Concorde cut Paris-to-New-York flying time in half, a savings of three and a half hours, but in today’s congested surface traffic and extreme security, a 3.5-hour flight could still take (if the Concorde were still in service) eight or nine hours door-to-door, a savings of only 25 percent or less — possibly not worth the 300 percent fare increase except for the most extremely time-conscious.

Meet the enemies of speed

What are the enemies of speed? Today, much of analytics is a solitary effort with highly skilled and trained workers expending a significant amount of their time reconfiguring data or waiting for others to do it as details of the problem space change. While one group expends a considerable amount of time developing models, another group pauses to create reports and presentations. When information from analytic work is communicated to others, the results are often difficult to explain because they are conveyed in summary form, and usually in aggregated levels, statically. There may be no explanation or explicit model to describe the rationale behind the results.

These additional presentations about methodology, narratives about the steps involved, alternatives that were considered and rejected (or perhaps just not recommended) and a host of other background material — usually presented in a sequence of time-consuming, serial meetings that have to be scheduled days or weeks in advance — are the greatest enemies of speed today. They turn cycle time into cycle epochs.

The reason for all of this posterior explanation is the cognitive dissonance between the various actors. The result is that well-researched and reasonable conclusions are often not actionable because management is not willing to buy in thanks to their lack of insight into the process by which the conclusions were arrived.

The solution to this problem is an environment in which complex decisions that have to be made with confidence and consensus can gather recommendations, which then can be presented unambiguously and compellingly in the decision-making process. Speed has to be an organizational concept. Parties to the discussion and decision-making need to understand the priorities and not waste time trying to optimize things that aren’t important.

Gaining confidence

The late Peter Drucker said that information was “data endowed with relevance and purpose,” but it takes a human being to do that. Unfortunately, one person’s relevance is not necessarily another’s. The process of demonstrating to others what you’ve discovered and/or convinced yourself of can add latency and frustration to the process. The generation of mountains of fixed reports and even beautiful presentations of static displays such as visualizations cannot solve the problem.

Henry Mintzberg wrote repeatedly that strategy was never predictable; it was “emergent,” and based on all sorts of imperfect perceptions and conflicting points of view. The lack of confidence that each party has in every other party’s methods and conclusions is a serious enemy of speed. It is the cause of endless rounds of meetings, delays and subterfuge.

Conclusion

Cost-cutting will always be a useful effort in organizations because inefficiency will always find a way to creep back in, but the dramatic improvements are largely over. The real battlefield today is differentiation, distancing your enterprise from your competitors. And in an era when every company potentially has access to the same level of best practices and efficiency, the key to leaping ahead is speed.

Finding a new insight, a disruption or a discontinuity before anyone else, and being able to act on it, is the ticket to the show. Organizations need to be constantly on the lookout for new ways to streamline, to enhance revenue opportunities, to improve in a multitude of ways, to go faster. Faster decisions, faster to market, faster to understand the environment, faster to go faster.

The Red Queen Effect masks speed for progress. Making things go faster or better is rewarding, but giving time back to people is crazy fast. It’s supercharged. The one resource that is in shortest supply is the time and attention of your best people. Give some time back to them and you can change their world.

Image: John Tenniel from Lewis Carroll’s “Through the Looking-Glass”