Author Archives: thedoctor

Good Data Will Not Guarantee Good Decisions

It was great to see this recent article in the Harvard Business Review on how “Good Data Won’t Guarantee Good Decisions” now that we are in the age of “Big Data” (which, in business, is Bullshit in Guise) and everyone is diving into their OLAP and reporting tools without even a clue as to what they are (or should be) looking for. There’s data. There’s information. There’s knowledge. There’s the intelligence required to understand it. And then there’s the wisdom to choose the right course of action.

As the authors state, investments in analytics can be useless, even harmful, unless employees can incorporate that data into complex decision making.

In the article, the authors summarized the result of a study by the Corporate Executive Board that evaluated 5,000 employees at 22 global companies. The study separated the employees into three groups:

  • Unquestioning Empiricists
    who trust analysis over judgment
  • Visceral Decision Makers
    that go exclusively with their gut and
  • Informed Skeptics
    who effectively balance judgment and analysis with their strong analytic skills and willingness to listen to others’ opinions but dissent.

Only the latter group are equipped to make good decisions, and, not unexpectedly, only 38% of employees and 50% of senior managers fall into this group.

And when you consider that their analysis also found that the functions where the employees had the highest average scores performed 24% better than other functions across a wide range of metrics (including effectiveness, productivity, employee engagement, and market-share growth), this is not a good thing.

Why is this the case? The researchers identified the following four problems that prevent organizations from realizing better returns on their data investment:

  • analytic skills are concentrated in too few employees
    just because you have a few experts doesn’t mean that the analytics skills will trickle down
  • IT spends too much time on “T” and not enough on “I”
    IT is used to working with functions where business needs are clearly defined, stable, and relatively consistent … when the needs become less defined, IT becomes less able to support them
  • reliable information is hard to locate
    many organizations lack a coherent, accessible structure for the data they’ve collected; the authors found in their survey that fewer than 44% of employees say they know where to find the information they need for their day-to-day work
  • business executives don’t manage information well
    (at least when compared to capital and brand) — they focus on more physical or traditional assets

While not addressed, and possibly not covered, by the study, I’d also add the following to the list:

  • No Good Roadmaps Exist
    Analytics skills are not enough — a guide on how to find the needle in the haystack is also required; take Spend Analysis for example … how many practical guides exist? (At least one.)
  • Lack of EQ
    as an employee needs to not only understand where to look, but when something is worthwhile to chase and when it’s not as there is a need to understand the business impact of what the data might be suggesting which requires understanding the business needs as well as the data

So what can an organization do to get more informed skeptics?

Invoking Innovation In Your Organization Internally

Supply Management magazine recently ran a great piece on innovation from the head of SRM at Best Buy Europe (where they might have it together better than Best Buy USA where you are not likely to get a Best Buy Experience) on “Creative Industry” where he described the difficulty of jump-starting an innovation initiative in an organization which has not been innovative in a (very) long time.

In the article, he detailed and exemplified an eight step process which is a good starting point for anyone trying to get in an innovative mindset.

  1. Lose the Fear
    Of being judged. Of disappointing others with your idea. Of just plain doing something different. Jamie says to be childlike in your approach and embrace the initiative with excitement. And if that don’t work, and it’s not against your religion, start with martini hour. Inhibitions are bad for innovation.
  2. No Idea is a Bad Idea
    It might not be the right idea for the organization, but it doesn’t mean it’s necessarily bad. In different circumstances, it could be a great idea. All ideas should be captured, and explored, at the right time, in a search for a better idea.
  3. Understand the problem.
    What is the issue? What is the objective? It’s the measurement stick for any idea you come up with.
  4. Diversity is King
    Have both experts and novices in the room. Make sure the novices are not afraid to ask “why can’t we do this”. Sometimes opposition is just knee-jerk. When there is no rebuttal to the question, you’re on the right track.
  5. Get Visual
    Draw. Illustrate. Sculpt clay if you have to. Make a prototype out of cardboard and play-doh. Whatever gets people thinking differently enough to actually innovate.
  6. Safe Environment
    Everyone is equal. No idea is bad. Freedom to speak up and speak out during the brainstorming process. Keep it out of management offices where positions of authority are implicitly conveyed.
  7. Subdue the subconscious
    It has default knee-jerk reactions to everything and default knee-jerk visualizations for every concept and pre-assigned meanings to every word. This gets us through the day, but is not always good where innovation is concerned. (Of course, if you start with martini hour, this may not be much of a problem. 😉 )
  8. Be Committed.
    Almost to the point where a conservative middle manager (who doesn’t understand the importance of relentless innovation) wants to have you committed. It takes a lot of effort to get an innovation project rolling, and even more to keep it rolling until the first positive, revenue-producing, output is produced.

This is a really great starter list and Jamie’s article on “Creative Industry” is really good. Take 5 minutes and read it end-to-end. It’s worth your time.

Can You Detect Greenwashing?

Can you detect when a supplier is Greenwashing their product or service? Are you sure? Did you take part in Earth Hour? You did? Guess what! You probably can’t detect greenwashing!

Earth Hour, another stupid marketing gimmick supposedly designed to get the message out about responsible energy usage, actually encourages the exact opposite by telling everyone to stop using power — at the same time — for one hour. Then, at the end of the hour, everyone — at the same time — turns their lights, tvs, and washing machines back on. This is the kind of action that can bring down a power grid, and, guess what, cost more energy then is saved in some cases. (Very little energy is actually saved, by the way. Less than an hour’s worth of lights or TV. You know why? When the hour is over, people are still going to do their laundry. They are still going to cook in their ovens. And they are still going to use whatever power hungry toys they have in their house.)

If you were a power engineer, you’d know that grids are designed to work where energy requirements are at rather constant levels. Basically, in laymen’s terms, whatever is put on to the grid has to be taken off, or the grid will blow. Similarly, if too much energy is taken off too fast, the grid can blow too. If power usage all of a sudden drops 50%, sometimes entire plants have to be rapidly taken off-line. (Which can be a problem if you have a nuclear plant in the mix. You just can’t shut one of those down on a whim!) This wouldn’t be a problem if it wasn’t for the fact that it takes a lot of time and energy to start up certain types of power plants. Water may turn turbines on its own when they are placed in a waterfall thanks to gravity, but sometimes a kick is needed to start a wind turbine. And coal burning plants don’t start up at the flick of a switch. It takes thousands of households turning off tvs and lights to equal the power required to start a small power plant. Imagine the power wasted shutting down and starting up a large power plant in one hour.

But I digress. As per the Sins of Greenwashing, maintained by TerraChoice, only 4.5% of products in 2010 were sin-free. Only 4.5%! The average claim is stretched so far from the truth that it’s only basis in reality is that the words used to describe it are part of the English language (most of the time). This is important to keep in mind with Earth Day coming up in two weeks and marketing folks getting ever more keen to tap into all the hoop-la that it entails. (Not that Earth Day is bad — just the marketers who try to sell non-green products and services as green.)

So how do you detect greenwashing? Familiarize yourself with the “10 Signs of Greenwash” developed by Futera and documented in this guide on Understanding and Preventing Greenwash co-developed with BSR. In brief, they are:

  1. Fluffy Language
  2. “Dirty Company”
  3. Suggestive Pictures
  4. Irrelevant Claims
  5. Best in Class
  6. Just Not Credible
  7. Jargon
  8. “Imaginary Friends”
  9. No Proof
  10. Out-Right Lying

There’s No Such Thing As BIG Data in Business

I’m getting sick of all this talk about “BIG Data” which is nothing but Bullshit In Guise. The marketing mania has gotten more ludicrous than Ludacris so I’m not sure where to begin at this point, but let’s start with the basics.

  1. Data has always been big
    We’ve always had more data than we can process in real-time, or even in the time window that we want it processed. ALWAYS. Since we’ve always been able to store more data in physical / external storage than we can store in memory, we’ve always had more data than we can process in “real” time. Quod Erat Demonstrandum.
  2. In Business, Big has always been meaningless.
    We’re not doing protein folding, climate modelling, nuclear simulations, supercollider data interpolation, cosmological computations, or even trying to beat Deep Blue at Chess. We don’t need Deep Thought, HAL, or even The Architect to solve an average business problem which can probably be solved on the iPad 3 most of you are now carrying around.
    The reality is that even though you may have 100 Million transactions in your ERP, you don’t need to analyze them all at once. Analyzing all of your spend at once is akin to comparing your DVD Player to the kitchen sink to an apple. Does that make any sense? (It doesn’t. And if it does to you, please seek professional help.) Real insight comes from analyzing heterogeneous and related data, possibly through federation, and not from throwing everything into a number cruncher to see what comes out. You wouldn’t ask for the average temperature on earth over the course of a year would you? (And that’s exactly what you’re asking for when you ask for the average transaction size across your business which will include a $4.99 ream of paper to refill the printer to a $200,000,000 acquisition of a new steel plant.)
  3. If you’re smart about your technology acquisition,
    you can already analyze and drill-down into tens of millions of transactions in real time on an average quad-core laptop with 8GB of memory. You will have to get a product coded in C/C++ to take full advantage of the 128 Billion Instructions per second you can get on an Intel Core i7 2600K (as something programmed in an interpretive language like Ruby on Rails will go through so many layers of translation that you’ll be lucky to get 128 Million Instructions per second dedicated to your computations), but the reality is that we now have way more speed than most platforms can take advantage of because, in our quest to not only build cross-platform apps, but teach people to code as quickly as possible, we’ve lost the art of lower-level coding and optimizing an analytics engine to be as fast as possible.

There are “big” data problems, but they don’t exist in business. They exist in some of the areas I mentioned earlier, and that’s why we’re working on exa-flop super-computers, but not in business. Furthermore, your data problems scale in comparison to the data problems coming down the pipe with DOME, a collaboration between IBM and Astron to build a next generation radio telescope, known as the Square Kilometer Array (SKA), that will collect more data in a day than currently exists across the entire internet, factoring in the massive amounts of adult entertainment and multi-media content hosted by public file-share servers. You don’t have big data problems, and if some analyst, consulting, or technology firm tries to tell you that you do, they are talking out of their donkey.

And just so you know, if you have data problems, the cloud, which is NOT a magic mirror (but still full of sweet fluffy dreams for those who choose to join Coleridge in Xanadu), is NOT going to solve your problem. Pushing your data out to random servers on the internet with no IO, no bandwidth, and no computational guarantees is only going to exacerbate your situation. (The Cloud is cheap because most of the servers have less power than the iPad 3 and most of the low-cost providers give you no performance guarantees. If you want performance, power, and pipe – be prepared to pay three to thirty times as much [relative to a server’s useful life] as just buying a high-end server and sticking it in the janitor’s closet where you just happen to have a fiber feed. [A surpising number of small and mid-size non-technical business have their network feeds going into what should be the janitor’s closet because they think it’s a fit place for a server. Why, I don’t know. But they do!])