分析理论是个强大的工具，借用蜘蛛侠的话，“能力越大，责任越大”(with great power comes great responsibility)。企业应谨防三类常见的数据误用。
本文作者基肖尔•斯瓦米纳坦(Kishore S. Swaminathan)是埃森哲的首席科学家，以及埃森哲技术实验室(Accenture Technology Labs)的系统集成研究全球总监。
A few months ago, I received a memo saying that employees in my facility at Accenture must keep their offices clean, subject to regular inspections. As it happens, I am fairly tidy, but I wanted to understand if there was any data to show that clean offices lead to higher productivity.
Not surprisingly, my request was sidestepped, and I was told, "Kishore, clean offices leave better impressions with visiting customers."
That sounded reasonable, so I asked if there was any data to show that our customers are more likely to buy our services or view us more favorably after visiting our clean offices. Now I was wasting people's time on what should be obvious, and a few colleagues even suggested that I move on.
In today's highly competitive global business environment, how you should use data to support your decisions -- large and small -- is exactly the kind of conversation that organizations should be having. And with advances in business analytics, there is every reason to make well-informed decisions since supporting data is, in many cases, readily available at your fingertips.
Your company now can easily gain access to several years of data about your customer's buying patterns and the movement of goods through your supply chain. And your employees, your customers, your competitors, as well as the employees and customers of your competitors are all talking, blogging and tweeting, providing potentially useful information for your business. Today's technologies -- such as data and text mining and machine learning -- allow you to analyze all this data, and cloud computing allows you to examine this information at a scale that was not possible just a few years ago.
Most business leaders now demand empirical data to support important decisions. With advances in analytics, we are nearing the point where every executive at every level will have to subject even the most mundane business decision to the following question: "Do we think this is true, or do we know this is true?"
As more organizations move in this direction, though, they ought to be aware of the potential opportunities and challenges that go along with using data to guide more of their decisions and actions:
1. Avoiding the misuse of data
Analytics places tremendous power in the hands of its users, and to borrow from Spiderman, "with great power comes great responsibility." Organizations should watch for three common misuses of data.
First, just because you have access to real-time data doesn't mean you can or should make real-time decisions. Different types of data have different time scales: for example, your cash register reflects your sales the moment they happen, but your supply chain data can only reflect the last time an order was placed or a truck carrying your order was dispatched. Best decisions are made with all the data at hand, so you can only make decisions as fast as your slowest moving event.
Second, analytics enables you to optimize your business processes to minimize redundancies and inefficiencies. However, be careful not to overly optimize your business processes to the point that there is no room for error. Highly optimized processes -- just-in-time inventory or keeping a very small inventory and constantly replenishing it based on demand being an example -- are very fragile because circumstances beyond your control could arise, and there is little room for error.
Finally, watch out for making decisions where none are needed. Having good data does not mean you always need to act on it.
2. Preparing for a rapidly changing information world
A company that bases its actions on data can make very specific, fine-tuned decisions. In fact, your decisions can be based on subtleties such as "stock more beer on Sunday nights in locations where the home football team is on a winning streak." But these kinds of decisions are highly sensitive and can change as rapidly as the fortunes of a football team.
3. Making sense of a ton of data
Today's enterprises have more information than they can use or act on because many difference pieces of information are often isolated from each other. The enterprise of the future will need to devote a lot of time and energy toward integrating the useful information it has.
Pharmaceutical companies, for example, have traditionally relied on clinical trials data to establish the efficacy and side effects of drugs. If a problem didn't come up in clinical trials, they could claim legal or ethical immunity from adverse effects of their drugs. But with the advent of the Internet and social media, they must now monitor public sources and integrate that information with their clinical data. "I should have known" will be the new normal, replacing the "I did not know" or "I could not have known" response to a company's unexpected problems.
4. Avoiding paralysis by information overload
With access to so much data, the business manager of the future could easily fall into a trap of putting off decisions until everything has been analyzed, which may never happen. Look out for three warning signs of analysis-paralysis.
First, beware the managerial tendency to "over-fit the curve" -- a statistical term that refers to the diminishing value of gathering additional data once you find a pattern. Data collection has a price. Not taking action also has comes at a price. And a data savvy organization must understand the cost of over-fitting.
Second, do not fall into the trap of waiting for data that just does not exist. Data savvy organizations understand information gaps and how experimentation can break these kinds of logjams.
Finally, know what level of risk your organization is willing to tolerate when they take action. If you penalize employees more for failed action than for inaction, most employees will prefer to not take action rather than mess up. Having solid guidelines for how to treat failure versus not acting at all can help.
5. Intuition isn't dead
Relying on data does not mean that there is no room for intuition. Yes, it is true that science is empirical and dispassionate. But scientists are not. Most respected scientists blend objectivity with creativity, instinct and risk taking. It's a good model for organizations.
The enterprise of the future, based on analytical decision making, will be considerably different from today's enterprise. All of this goes back to that original scenario I painted about clean desks, efficiency, clients and whether there was any data to support a rather mundane policy decision. In this case, none was provided. But I keep my desk a littler cleaner just in case.
Kishore S. Swaminathan is Accenture's chief scientist and the global director of Accenture Technology Labs' systems integration research.