I don’t know about your conclusion but how you arrived at it is flawed

It has become common place in the tech and other marketing blogs – popular bloggers  making bold predictions about the future of a firm or about a technology. They state it with utmost confidence without any uncertainty. All black and white, no room for gray areas.

They back it up with scant evidence which is mostly selectively chosen to support their preconceived notion. Or these evidences are anecdotal or manufactured based on their own biased reading of the market. They fail to seek any evidence to the contrary or refuse to ask questions on what evidence will disprove their theory.

On top of this poor evidence seeking behavior we see even poorer analysis. All set up to support their foregone conclusion.

The latest in that mold comes a blog post that boldly proclaims how an enterprise software business that

  • hauls in $35 billion in revenue
  • has $29 billion in cash
  • has almost limitless borrowing power
  • has a market capitalization of $142 billion

is really doomed.

So writes Sarah Lacy in her blog.

I am not going to argue with the conclusion. I do not know what the future holds. But let us raise some questions on her  analysis (if we can call that):

  1. What does doomed mean? $35 billion in revenue wiped out? Cut in half?
  2. What are the assumptions and preconditions for this outcome? Remember, a model is only as good as its assumptions.
  3. What time frame are we talking? 5 years? 10 years? 20 years? Did she consider how long will it take to lose any portion of the $35 billion revenue or any of the $29 billion cash? What other technology and market shifts could happen during that period?
  4. One way to make a prediction is to run a scenario analysis and state in what percentage of the scenarios (and under what conditions) a particular outcome is possible. An example of such an analysis is the one by Jeremy Siegel when he predicted Dow will hit 15000. Has she run through such an analysis? What are the alternative scenarios and how likely are they?
  5. Let us consider a case where the revenue drops by 10%. Will the decision makers stand still and let the slide continue all the down to 50% and finally to 0?
  6. How many millenials will go on to install what she calls as skunkworks implementations? And if they do, will the business in question standstill and not use their cash pile and borrowing power to buyout these smaller solution providers?
  7. The story  is titled,   “$142 billion market cap business is really doomed”, backed by anecdotes quoted from selective memory and adorned with flashy narratives sounds plausible, but is it probable?

If the analysis is flawed or if the analysis is just a figment of one’s imagination, if you and I cannot reproduce the same results with the data set (in this case there is no data set) we have to reject the conclusion regardless of how plausible it sounds or how popular the blogger is.

3 thoughts on “I don’t know about your conclusion but how you arrived at it is flawed

  1. it always prudent to have facts right before airing views on a particular issue. I believe that shifting focus from our selfish ambitions to the consequences of our acts goes a long way in setting the records straight.



  2. People love to prognosticate about the future as no one typically bothers to 1. check them on it when they post it, 2. follow up with them 5 years later when more of the evidence is in. Pendulums swing both ways, I am not sure the author gets that.


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