Toxic Environments for CI

November 7, 2017

In the past I have commented[1] on the fact that competitive intelligence cannot thrive in contexts where there is imperfect competition or an outright monopoly situation.

Now consider this observation from a recent issue of Time:

“[T]he leaders of other emerging powers – not just Russia but also democracies like India and Turkey – are following China’s lead in building systems where government embraces commerce while tightening control over domestic politics, economic competition, and control of information.”[2]

So, to the above environments where CI cannot thrive (or perhaps even function), add those situations where government is not only involved in controlling commerce, even without the presence of oligopolies and monopolies, but where it is also controlling much of the data, the raw material from which CI is developed.

“Withholding information is the essence of tyranny. Control of the flow of information is the tool of the dictatorship.” author Bruce Coville.

[1] See It Is What It Is and  Why No CI?

[2] “Advantage China”, Time, November 13, 2017, p 42.


Who’s worst?

October 12, 2012

The last several days have seen people in politics raise questions about the validity, or more generously the accuracy and consistency, of recent federal statistics on the unemployment rate, the number of people filing first claims for unemployment compensation, and related data.  Now what will happen, almost certainly, is that this most recent monthly data will be “restated” next month or the month thereafter, a continuous process with flash macroeconomic data in the United States.

Does this mean you cannot rely on US government data?  Almost certainly, yes.  But the US is not unique.  The US is probably just the best of a bad lot.  Consider The Economist’s recent discussions of data in China:

“With China so engaged in the global economy, there is a never-ending stream of data, often unreliable, to feed the appetites of economic-research firms, investment banks, hedge funds, short-sellers, political risk advisors, think tanks, consultancies and financial and military newsletters – not to mention legions of academics, journalists, diplomats and spies.”  Banyan, The Leader Vanishes, September 15, 2012.

“[N]o other important country is as murky [in terms of providing accurate, credible data] as China.” Schumpeter, The summer Davos Blues, September 15, 2012

That China is murky, with respect to data both at the government and company levels, does not excuse the way in which United States collects and processes econometric data.  However, for politicians, businesses, and others, to make decisions based on the movement of 1/10 or 1/100 of some monthly measure from US government statistics is also foolish.

There may be many iron rules about data, but for competitive intelligence, I would propose the following:

Data from only one source is not data.  It is conjecture – until it can be confirmed.

Data based on telephone surveys should be increasingly subject to question.  Our brethren in marketing have already come to this conclusion, given the demographics of the populations that have shifted from landlines to cell phone only service, especially when cell phone users are notoriously difficult to survey.

The smaller the sample is and the more quickly the data is collected, the more likely it is to be inaccurate, and inaccurate in an unpredictable manner.

Combining data sets that are individually unreliable does not necessarily make the conclusion more reliable.  I realize that there are those in the statistics world that would disagree with this, but I do not believe that such aggregations always contain the necessary mutually offsetting mistakes to generate a reliable whole.

Any data that has to be restated should not be relied on at all in the first place, or at least not until it is eventually restated.

Using short-term data to determine the presence and direction of a long-term trend is not forecasting; it is at best guessing and at worst irresponsible.

What is truly ironic that all of this is that the US government releases such statistics, upon which so many rely with so little reason, while it would never allow a firm going public,  such as Facebook[1], to get away with using similarly dubious data.


[1] Linda Sandler, Brian Womack and Douglas MacMillan, “Facebook Fought SEC to Keep Mobile Risks Hidden Before IPO”, Bloomberg, Oct. 10, 2012)

 


Go or no go?

August 22, 2012.

Originally, I was going to write about how to make a decision on how much to spend on a competitive intelligence project, whether hiring an outsider or just spending your own resources.  Given some of the mathematical complexities of this, and there actually is math involved, I decided to step back and consider when, if ever, you should not consider conducting a competitive intelligence project.

I have identified five types of such situations for you:

  1.  You do not have enough time to do it right.  By that I mean you do not have enough time to conduct research properly, and/or, you do not have enough time to properly define the problem.  This is best identified by the ever dreaded voice mail message to the effect that “I’m calling an emergency meeting at 1:30 this afternoon and I need to get a decision on this project at that time.”
  2. You do not have resources available.  I resources I mean both money to hire someone or to spend on an assignment, as well as your own time to put into it.  If you cannot participate in defining the project, even when you cannot actually carry out the research, then merely throwing money at it is a poor solution, at best.
  3. There are major issues with the targets.  What do I mean by that?  Well, for example, conducting competitive intelligence against Chinese targets in China is not only costly and lengthy, but very, very difficult.  Conducting competitive intelligence against several privately held companies that you also partner with may put those relationships, commercially important ones, at risk.  Think carefully about this before moving.
  4. You lack a clear target.  By this I mean one of  two things.  First, you’re not sure against what company or companies you are trying to collect what sort of intelligence.  This means you need to do some more thinking and some more work before you even get going.  Second, you lack a clear decision to be made or action to be taken.  Again, here, I’m returning to the “need to know” vs. “nice to know” dichotomy.  The solution is to determine what you will do with the intelligence, before setting out to, or setting someone else to, collect it.
  5. The cost of doing the work is disproportionate to the benefit to be received.  What do I mean here?  Let’s assume that the research will cost $15,000 in cash and your time.  What are you gaining from this?  If you are not gaining a quantifiable improvement in the decision-making process, then this may not be worthwhile.  What I mean by quantifiable?  Let’s assume that the project has at this point a 60% chance of success.  Will obtaining the actionable intelligence raise that probability of success to 75%?  If so, it may be well spent, depending on the size of the project.  Or will spending the money reduce the overall costs?  If so, by how much?  While the numbers here are not firm, the decisions must be made honestly.

If you pass these five steps, then CI, done properly, should be very valuable to you.  Happy hunting!