Meta Analysis

I’ve been attending business launch presentations for twenty-some years, and it’s stunning for me to see the trivia that passes for innovation these days. If I hear one more entrepreneur crowing about an iphone app to remind me to remind Grandma to take her medication, my brain will implode. Well no, it won’t, but I will be tempted to stand up and shout, “A mobile phone app is not innovation!” Apps use technology, but they don’t push it forward.

Not every business needs to be innovative. Most do not! Take, for example, Hooters. They have built an empire on the time-tested fundamentals of beer, burgers and breasts. No news there, but they make a lot of money. They prove that a business can be successful with nothing more innovative than chicken wings and hot sauce, served by an attractive woman – the oldest value-add in the business world. If your business isn’t innovative, that’s A-OK, but let’s not kid ourselves, about it, shall we?

When I heard a VC pitch about flat screen technology in the 80s, that was innovation. It was so new and expensive that the only commercially viable applications were military, like use on a submarine, where space is precious. When I heard a prototype interactive voice recognition system just a few years later, that was innovation, though today I might wish to do without it. And let me tell you, this stuff wasn’t developed by college dropouts working in a garage with a little seed money from Mom. It was the result of concentrated teamwork by people with serious formal education.

Now let’s talk about some of the ultimate in modern innovation – the transistor, the vacuum tube and information theory, as well as thousands of other remarkable works of invention, all came out of one great center of innovation – Bell Laboratories. If you want to know a little something about how serious, full-strength scientific and commercial development happens, you must read The Idea Factory: Bell Labs and the Great Age of American Innovation. What a page-turner for business and technology fans. You’ll never look at a tech pitch the same way again.

The Nation reports, in a March 7, 2012 piece titled “The Genius of Cecile Richards,” that

Planned Parenthood, at Richards’s direction, collaborated with Catalist, a national progressive voter file, to build the country’s first model of support for choice. Thanks to this tool, every woman voter in the country has a score between one and 100 indicating her likelihood to be pro-choice, and there is a way to reach her. It’s an efficient way to mobilize pro-choice women to vote or even persuade them to canvass for Planned Parenthood–endorsed candidates.

Cecile Richards is Planned Parenthood’s President, who took the job in 2006, already an experienced political activist and former deputy chief of staff to Nancy Pelosi.

This story is not unique. Very few candidate or issue campaigns have the expertise or resources to perform analytics independently. But there is an industry of political consultants who have the knowledge, and because they support many clients, collective buying power extends access to a wide range of campaigns. These service providers specialize, focusing on a single party, or positioning themselves for either progressive or conservative issue politics.

Political consultants draw on many resources to develop their lists and models. Make no mistake, we’re all included. Unless you live way off the grid, many, many political campaigns know you exist, and they are making a serious effort to assess how much or little you sympathize with their cause.

Read “The Genius of Cecile Richards.” It puts a load of context around the use of predictive modeling in politics.

An EMC industry survey on data science indicates that 34% of respondents believe the best new source of data science talent is students studying computer science, and 12% believe the best source is today’s business intelligence professionals. Think about that – nearly half of the respondents feel that the best source for new experts in data science are people who typically have no exposure to statistical analysis.

Who are these respondents? The methodology section describes them as “497 data scientists and business intelligence professionals from around the world… pre-screened for information technology decision making authority”.

Pre-screened for IT decision-making authority? Since when are IT decision makers experts in drawing meaningful insight from data?

How about the other half of respondents? Most expected the best source of new talent to be either students (24%) or current professionals (27%) in in fields other than computer science. No details on which other fields.

There was a tweet floating around yesterday, that said a load about analytics. I couldn’t pin down the originator of the tweet, but it appeared to be a quote from Avinash Kaushik speaking at Strata. The message was…

You don’t need real time data if you can’t take action.

Says a lot, doesn’t it?

For all the buzz around Big Data, you’d think we were already taking full advantage of small data, which we’re not. Not even when we have all the right resources at our fingertips.

Here’s a dirty little story a colleague shared in hushed tones. His client runs digital ads. The ads are all tested, all the time. And the client gets a report on the performance of each ad, on a regular basis. The end.

Wait, that can’t be the end. What happened to the part where the client drops some of the ads that perform poorly? And the part where the client creates some alternative ads and runs tests to see if the alternates are more effective than the controls? Nothing happened, because the client never did any of that, just kept running ads and getting reports, never changing anything.

It would be nice if this were just a rare, odd, laughable little tale. But it isn’t funny, because it isn’t rare. It’s a terrible waste of resources, and of course it does no good for the client’s business.

I’ve got nothing against Big Data analytics, but the business community could stand to squeeze a lot more mileage out of the little stuff first.

Way back when, I worked with two very competitive managers. One was my boss at the time; the other ran a group worked closely with ours. The two men were about the same age, they had similar education and experience. They even had the same first name! [Let’s leave the real name out of it, but for this post, we’ll call them Bob1 and Bob2.] These were company men, both planning to spend a lifetime at one company, and both eying the same rungs on the career ladder. Every encounter between the two Bobs was a competition.

One day my boss, Bob1, came to me. He had just returned from a meeting with Bob2 and upper management. Bob2 had made some bold, confident statements about the effect of pricing on purchasing behavior. Upper management was very impressed, and they were running with it. Bob2 was a star! Bob1 was not happy, not happy at all.

Bob2’s remarks were rather remarkable. For one thing, his group didn’t do the type of research that would be needed to back up his claims. That was Bob1’s arena. On top of that, the stuff Bob2 had said sounded flat out wrong to Bob1. So he came to me. Bob1 repeated what Bob2 said. It sounded wrong to me, too, very wrong.

It appeared Bob2’s statements had come straight from his own misinformed imagination, but the C-level guys loved it, and they were going to run with it. Bob1 was fuming. This was war.

We had research, the best research available on the particular issue at hand. We had statistical analyses based on historical data. We had a good business understanding of the customers in question and the factors driving product demand. To top it off, I interviewed a big batch of our people in the field to see whether any of them had encountered the behavior patterns that Bob2 had described. Every single one of them laughed in my face; several actually chortled.

So, the story should end with happy ending for Bob1. He presents his case, backs it up with summaries of qualitative and quantitative research, and becomes a star in the eyes of upper management. The next promotion goes to Bob1. Bob2 slinks away in disgrace. But that’s not what happened, and often it is not. The big guys liked the first story, and they were sticking with it. It’s much easier to sell claims that people like.

I’ve written a lot about how to communicate about analytics, how to make a case, how to get and hold attention. But we all need to understand that even great research and great communication don’t always get us what we want. Still, it’s worth sticking with the program. Today, I did a little research. Bob1, it turns out, is still with the same company, and in a pretty influential role. Bob2? Couldn’t find him. Maybe the execs got the message, after all.

Executives Don’t Like Analytics: Why Business Isn’t Data-Driven

Crossing the Language Chasm: Extracting Information from Foreign-Language Text

Text Analytics WIIFM (What’s in it for Me?)

No Smokescreen Area: Tips for Hiring Analysts

Was Edison “Agile”? Extracting New Value from Old Techniques

The CEO Wants Analytics! Now What? [article]

The CEO Wants Analytics! Now What? [slide presentation for business analysts]

Analytics, Schmanalytics! How to Evaluate an Analyst

Why IT Doesn’t “Get” Analytics (and Why the Time is Right for Change)