What the C-suite should know about analytics

Five key areas - and how to prevent analysis paralysis

Case study after case study confirms the value of analytics across a wide range of business functions, including pricing, demand prediction, targeted marketing, supply chain optimization, customer relationship management and HR. In my view, analytics is something much more than a technology with an ROI; it's a transformational phenomenon that will fundamentally change how business discourse will be conducted and decisions made. Here are five key areas to focus on:

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Kishore S. Swaminathan, Chief Scientist and Global Director, Accenture Technology Labs' Systems Integration Research

1. High analytical literacy

Data is a double-edged sword. When properly used, it can lead to sound, well- informed decisions. When improperly used, the same data can lead not only to poor decisions but to poor decisions made with high confidence that, in turn, could lead to erroneous and expensive actions. Let's consider some specific examples.

When one has access to real-time data, it's tempting to make real-time decisions. For instance, if you are a retailer and you have real-time access to sales data from cash registers from all of your stores and the inventory in your warehouse, you could be tempted to run sales promotions on the fly and manage your supply chain in tandem to support your real-time promotions.

This is unlikely to work because three types of events – your decisions, the ensuing customer behavior and supply chain events – operate in different time frames, so making decisions faster than the slowest-moving event could be useless at best and dangerous at worst.

Another problem with data and analytics is that they give you very fine-grained visibility into your business processes, and you could be tempted to over-optimize the processes. Highly optimized processes – just-in-time inventory as an example – are very fragile because circumstances beyond your control could arise, and there is little room for error.

A third problem is known as "oversteering," or making a decision when none is needed. For example, your data could tell you that a project is behind schedule, which, in turn, may lead you to berate the project manager or tell your stakeholders that the project will be delayed. Neither of these actions may be necessary if the project has contingency built in, if the status update has a different frequency from your sampling frequency, or if perhaps the employees who are aware of the project delay will put in more work time to get the project back on schedule.

2. Volatility

Businesses thrive on stability and repeatability. Stable and repeatable processes justify large-scale capital expenses and large-scale employee training. That stability also reduces cognitive overhead because those processes and decisions do not change, hence their rationale does not have to be explained repeatedly. By contrast, an analytically based enterprise of the future will have to be designed around volatility rather than repeatability.

When you have fine-grained visibility into your processes, customers, suppliers and competitors, you have the ability to make fine-grained decisions. In fact, your decision rules can capture subtleties such as "stock more beer on Sunday nights in locations where the home football team is on a winning streak." Such decisions are highly context-sensitive and can change as rapidly as the fortunes of the football team.

Volatility – or rapidly changing decisions that are context- and time-sensitive – will be a big challenge for enterprises. Decisions are no longer easily explainable and capital investments cannot be based on mass repeatability, but must cater to endemic volatility.

3. Integrated awareness

Today's enterprises have more information than they can act upon because the information is siloed in so many ways: technologically (data in different systems that cannot be brought together), organizationally (data in different governance units that cannot be brought together) or by ownership (inside versus outside the enterprise). The enterprise of the future will be (or will be forced to be) conscious in the sense that it will know that it must integrate everything it has access to.

As an extreme example of integrated awareness, let's consider the pharmaceutical industry, which has traditionally relied on clinical trials data to establish the efficacy and side effects of a drug.

A pharmaceutical company today can legally and morally claim immunity from adverse effects of a drug that were not revealed during clinical trials – in other words, any information that it did not explicitly collect as part of a clinical trial protocol. But in a world of blogs and social networks, where people share this information unprompted and in public, it will become both a responsibility and an obligation of pharmaceutical companies to monitor public sources and integrate the public information with their own clinical data.

"I should have known" (either for regulatory or competitive reasons) will be the new normal, replacing the "I did not know" or "I could not have known" approach to awareness and information integration.

4. The end of analysis paralysis

In the future, businesses will likely be run by managers and leaders who are no-nonsense empiricists; they won't move a finger until all of the relevant data has been gathered and analyzed. A recipe for organizational analysis paralysis? This is not an unreasonable fear. Though it may seem counterintuitive, an empirical enterprise with high analytical literacy is less likely to fall prey to this malady than today's enterprises.

There are three very distinct ways that organizations can fall into the analysis-paralysis trap. One is a managerial tendency to "over-fit the curve" – a statistical term that refers to the diminishing value of additional data once a pattern (or curve, in the graphic sense) has been found. Data collection has a price, inaction has a price, and an analytically literate organization will clearly understand the cost of over-fitting.

The second cause of analysis paralysis is waiting for data that simply does not exist, which reflects an inability to design experiments to generate the needed data. An analytically literate organization will be characterized by a clear understanding of data gaps and the value of experimentation to break the logjam.

The third cause of analysis paralysis is that most companies do not know their risk tolerance and are much more likely to penalize failed action than inaction. As a result, many managers do not act unless there is enough data to assure them of successful outcomes. An analytically literate organization will have a firm grasp of its risk tolerance. With guidelines and models for action under uncertainty, it will restore the symmetry between how it treats failed action and inaction.

5. Intuition's new Pulpit

Empiricism and analytics sound a death knell for such vaunted business traits as intuition, gut feeling, killer instinct and so forth, right?


Science is purely empirical and dispassionate, but scientists are not. Science is objective and mechanical, but it also values scientists who are creative, intuitive and can take a leap of faith.

Data, by itself, can be interpreted in many ways. Imagine a physical or business phenomenon that produces the following sequence of data: 1, 2, 6, 24, 33. Perhaps it's a factorial sequence with 33 as noise or a sequence where every fourth term is twice the multiple of the previous three. Or perhaps every fifth term is the sum of the previous four.

All are correct. To prove or disprove any theory, you need the next several terms of the sequence. A good scientist knows when there is enough data to warrant a theory, when there isn't, what new data to gather and how to design an experiment to gather the right data.

The late Steve Jobs, Apple's former CEO, was known to explicitly discount the value of surveys and focus groups for designing new products. How do you explain his apparent anti-empiricism?

One explanation is that, much like a creative scientist, people like Jobs recognize when there is not enough data or the right kind of data to form a theory. They recognize that, for completely new lines of products that will change a user's experience or behavior, the only useful data is experiential data, not commentary and reactions from those who have never used the product.

Jobs and people like him are akin to scientists who recognize what type of data is needed to support a theory (in this case, whether a product will succeed), recognize that such data cannot be gathered through focus groups (one type of experiment) and boldly design new types of experiments (release the product and gather experiential data).

It should be noted that some products – in Apple's case, it was the Newton – do not succeed and are terminated. Intuition, creative leaps and clever experimentation are not incompatible with empiricism; in fact, the value of these traits will be even better understood in the future enterprise by analogy to theoretical and experimental scientists.

The enterprise of the future, based on empiricism and analytical decision making, will indeed be considerably different from today's enterprise.

Bio: Kishore S. Swaminathan, Chief Scientist and Global Director, Accenture Technology Labs' Systems Integration Research


Read more:

10 Characteristics of an Analytic Leader

  1. Communicates well with others.
  2. Sets the expectation that decisions will be based on data and analysis.
  3. Hires smart people and gives them credit for being smart.
  4. Leads by example, using data and analysis in decision making.
  5. Sets strategy and performance expectations.
  6. Looks for incremental achievements.
  7. Demonstrates persistence over time.
  8. Builds an analytical ecosytem of industry leaders, external analytical suppliers and business partners.
  9. Works along multiple fronts with a portfolio of projects.
  10. Knows the limits of analytics.

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