Resistance Is Futile

Any serious Trekkie would immediately recognize this title. But I am not talking about the Borgs, who are coming to assimilate us into their hive-minded collective. I am talking about a rather benign-sounding subject — and my profession — analytics.

Any serious Trekkie would immediately recognize this title. But I am not talking about the Borgs, who are coming to assimilate us into their hive-minded collective. I am talking about a rather benign-sounding subject — and my profession — analytics.

When you look at job descriptions of analytics leads in various organizations, you will often find the word “evangelization.” If every stakeholder is a believer of analytics, we would not need such a word to describe the position at all. We use that word because an important part of an analyst lead’s job is to convert non-believers to believers. And that is often the hardest part of our profession.

I smile when I see memes (or T-shirts) like “Science doesn’t care about your beliefs.” I’m sure some geek who got frustrated by the people who treat scientific facts as just an opinion came up with this phrase. From their point of view, it may be shocking to realize that scientifically proven facts can be disputed by people without any scientific training. But that is just human nature; most really don’t want to change either their beliefs or their behaviors.

Now, without being too political about this whole subject, I must confess that I face resistance to change all of the time in business environments, too. Why is that? How did activities of making decisions based on numbers and figures became something to resist?

My first guess is that people do not like even remotely complicated stuff. Maybe the word “analytics” or talk of “modeling” bring back all of the childhood memories of their scary math teachers. Maybe that kind of headache is so bad that some would reject things that could actually be helpful to them.

If the users of information feel that way, analysts must aspire to make analytics easier to consume and digest. Customers are not always right, but without the consumers of information, all analytical activities become meaningless — at least in non-academic places.

An 80-page report filled with numbers and figures dumped on someone’s desk should not even be called analytics. Literally, that’s still an extension of an unfiltered data dump. Analysts should never leave the most important part of the job — deriving insights out of mounds of data — to the end-users of analytics. True, the answer may lie somewhere in that pile, but that is like a weather forecaster listing all of the input variables to the general public without providing any useful information. Hey, is it going to rain this morning, or what?

I frequently talk about this issue with fellow analytics professionals. Even in more advanced organizations in terms of data and analytics infrastructure, heads of analytics often worry about low acceptance of data-based decision-making. In many instances, the size of the data and smooth flow of them, often measured in terabytes per second as a bragging point, do not really matter.

Information should be in nugget-sizes for easy consumption (refer to “Big Data Must Get Smaller”). Mining the data to come up with fewer than five bullet points is the hardest part, and should not be left to the users. That is the primary reason why less and less people are talking about “Big Data” nowadays, as even non-data professionals are waking up to realize that “big” is not the answer at all.

However, resistance to analytics doesn’t disappear, even when data are packaged in beautifully summarized reports or model scores. That is because often, the results of analytics uncover an inconvenient truth for many stakeholders — as in, “Dang, we’ve been doing it wrong all of this time?”

If a person or a department is called out as ineffective by some analytical geeks, I can see how involved parties may want to dispute the results any which way they can. Who cares about the facts when their jobs or reputations are at stake? Even if their jobs are safe, who are these analytics guys asking us to “change”? That is not any different from cases where cigarette companies disputed that smoking was actually beneficial in the past, and oil and gas companies have an allergic reaction when the words “climate” and “change” are uttered together in present days.

I’ve seen cases where analytical departments were completely decimated because their analytics revealed other divisions’ shortcomings and caused big political hoopla. Maybe the analysts should have had better bedside manners; but in some cases I’ve heard about, that didn’t even matter — as the big boss used the results of analytics to scold people who were just doing their jobs based on an old set of rules.

You can guess the outcome of that kind of political struggle. The lesson is that newly discovered “facts” should never be used to blame the followers of existing paradigms. Such reactions from the top will further alienate analytics from the rest of the company, as people get genuinely scared of it. Adoption of data-based decision-making? Not when people are afraid of the truth. Forget about the good of the company; that will never win vs. people’s desire for their job security.

Now, at the opposite end of the spectrum, too much unfiltered information forcing decisions can also hurt the organization. Some may call that “Death by KPI.” When there are too many indicators floating around, even seemingly sound decisions made based on numbers and figures may lead to unintended consequences; very often, negatively impacting the overall performance of the company. The question is always, “Which variable should get higher weight over others?” And that type of prioritization comes from clearly defined business goals. When all KPIs are treated to be equally important? Then nothing really is. Not in this complex world.

Misguided interpretation of numbers leads to distrust in analytics. Just because someone quoted an interesting figure within or without proper context, that doesn’t mean that there is just one version of an explanation behind it. Contextual understanding of data is the key to beneficial insights, and in the age of abundant information, even casual users of analytics must understand the differences. Running away from it is not the answer. Blindly driving the business just based on certain indicators should be avoided, as well. Both extremes will turn out to be harmful.

Nevertheless, the No. 1 reason why people do not adopt to analytics is many have gotten burned by “wrong” analytics in the past, often by the posers (refer to “Don’t Hire Data Posers”). In some circles, the reputation of analytics got so bad that I even met a group of executives who boldly claimed that whole practice of statistical modeling was totally bogus and it just didn’t work. Jeez. In the age of machine learning, one doesn’t believe in modeling at all? What do you think that “learning” is based on?

No matter how much data we may have in our custody, we use modeling techniques to predict the future, derive answers out of seemingly disjointed data and fill in the gaps in data — as we will never have every piece of the puzzle nicely lined up all of the time.

In a case of such deep mistrust in basic activities like modeling, I definitely blame the analysts of the past. Maybe those posers overpromised about what models could do. (No, nothing in analytics happens overnight). Maybe they aimed for a wrong target. Maybe they didn’t clean the data enough before plugging them into some off-of-the-shelf modeling engine. Maybe they didn’t properly apply the model to real-life situations, and left the building. No matter. It is their fault if the users didn’t receive a clear benefit from analytical exercises.

I often tell analysts and data scientists that analytics is not about the data journey that they embarked on or the mathematical adventure that they dove into. In the business world, it is about the bottom line. Did the report in question or model in action lead to an increase in revenue or a reduction in cost? It is really that clear-cut.

So, dear data geeks, please spare the rest of the human collectives from technical details, and get to the point fast. Talking about the sample size or arguing about the merits of neural net models – unless the users are equally geeky as you — will only further alienate decision-makers from analytics.

And the folks who think they can still rely on their gut feelings over analytics? Resistance to analytics is indeed futile. You must embrace it — not for all of the buzzwords uttered by the posers out there, but for the survival of your business. When your competitors are embracing advanced analytics, what are you going into the battle with? More unsolicited emails without targeting or personalization? Without knowing what elements of promotions are the key drivers of responses? Without even basic behavioral profiles of your own customer base? Not in this century. Not when consumers are as informed as marketers.

One may think that this whole analytics thing is overly hyped-up. Maybe. But definitely not as much as someone’s gut feelings or so-called business instincts. If analytics didn’t work for you in the past, find ways to make it work. Avoiding it certainly isn’t the answer.

Resistance is futile.

Don’t Hire Data Posers

There are data geeks and there are data scientists. Then there are data plumbers, and there are total posers. In this modern world where the line between “real” and “fake” is ever-blurrier, some may not even care for such differences.

data poserThere are data geeks and there are data scientists. Then there are data plumbers, and there are total posers. In this modern world where the line between “real” and “fake” is ever-blurrier, some may not even care for such differences.

Call me old-school, but at least in some fields, I believe that “the ability to do things” still matters. Analytics is one of those fields. When it comes to data and analytics, you either know how to do it, or you don’t know how to do it. The difference is as clear as a person who can play a musical instrument and one who is tone-deaf.

Unfortunately, there is no clear way to tell the difference in this data and analytics field. It’s not like we can line up contestants and ask them to sing and be judged here. Furthermore, “posers” often have louder voices — armed with fancy visuals and so-called automated toolsets.

I’ve been to many conferences and sat through countless presentations in my lifetime. It may sound harsh for me to criticize fellow data players and presenters, but let me just come out and say it: A great many presenters and panelists at conferences are posers.

How do I know that? Easy. I asked them. For example, when I stalked some panelists who preached about the best practices of personalization after the session, the answers were often “Well, it is not like we do all those things for real …” Sometimes I didn’t even have to ask the question, as I could tell something is seriously broken in their data and promotion chain by observing their marketing messages as a customer.

The bad news for the users of information — and for consumers, for that matter — is that it takes a long time to figure out things are not going fine. Conversely, we can all tell who is tone-deaf as soon as a singer opens her mouth. It is so hard to tell the difference between a data scientist (i.e., an analyst who provides insights and next steps out of mounds of data) and a data plumber (i.e., supposedly an analyst who moves big and small data around all day long and thinks that is his job), that I admit it sometimes takes a few months — generally after some near meltdowns — for me to figure it out.