“Facts are stubborn things, but statistics are pliable” – Mark Twain
Humans are terrible with numbers. They just don’t fit in our brains. It’s why scientists can hammer us with statistics about global warming but we will stop believing in it as soon as it gets cold where we live. It’s not our fault — the human brain just isn’t built for this, and quite frankly, we don’t really need to process huge numbers to get by in our everyday lives. You don’t have to understand long term data trends in order to change a goddamn light bulb.
But there are some basics that everyone should know. Each of them sounds incredibly simple when it’s explained, yet each of them will fool you again within days of reading this article. So try to keep in mind …
Here’s a shocking statistic: The average income of the world is around $10,247 and the Indian average is $1,582. If your income is below the global level, reading that is quite a kick. You thought you were actually doing pretty well for yourself, but now you’re tempted to get a second job outside India just to bump your net worth up to what the guys in USA are probably making. What’s their secret, damn it? Are they all cooking meth?
The Problem Is …
The popular use of the term “average” is way different from the mathematical term, but they get used interchangeably. That’s why we’re so often shocked at how the “average” person is richer/fatter/taller than us. In everyday language, we use the word “average” to mean “most people,” or the most representative person (as in, “The average person doesn’t read classic literature” or “The average Joe can’t afford to dress like Prince”). But then when they start using the word “average” to talk about statistics, you get weird results, like the fact that 89 percent of people in the India make less than the “average” global income, when India forms one fifth of the global population. So how the hell can “average” mean “most people” when most people aren’t average?
Well, we all learned in school how to calculate an average: You take all the values you’re averaging, add them up, and divide them by the number of values. This is fine if what you’re trying to average is pretty uniform — the average of 1, 2, 3, 4, and 5 is 3, right there in the middle. The problem is that averages are absolutely useless if a minority of numbers are unusually high — the average of 1, 2, 3, 4, and 40 is 10, which doesn’t help anybody know shit about anything.
And that’s the problem with the “average income” statistic — a few rich people are skewing the shit out of the number. If you’re earning less than the average global income, it’s not because your job is screwing you or the country you are born is bad, it’s because you live in the same world as Bill Gates, Mukesh Ambani, Mark Zuckerberg, and whoever owns Coke or whoever.
So it is so stupidly obvious when explained, but create more myths by the day. Their growth rate is 175% more than the average (not talking about many insurance companies, and a new Ayurvedic FMCG). Statistics is deceptive. What do you do as a business to use the numbers and their power to take appropriate decisions that will really give actionable results that are economically progressive for the company? This is a million dollar question in the minds of many managers.
Most of the times, the business leaders would love to see the deceptive statistically correct picture of their business. A sure recipe for failure. Are we talking about Kings of the Good Times. Yes, we are.
How do you handle this?
A life insurance CMO I met recently told me that they have done away with physical forms. Every application and document has to be submitted in an electronic format. No exceptions.
So, that is rule # 1 — discipline your data capture mechanism. If you are a legacy industry, invest some effort in data capture/enrichment. There are have agencies that can do this for you on a per record basis. This will save you serious dirty labour down the line.
Rule # 2 — Jot down how you will make use of the data. Who all will be involved, which other departments need to be co-ordinated, who will close the loop. You don’t have to be 100 per cent certain, but should have some sense about it.
Almost four-five years ago, one of the Top-3 private sector banks adopted the practice of having a dedicated department servicing the data/analytical needs of all other departments, called a BIU — short for Business Intelligence Unit. This ensured that the department making the request was thinking through what it would “do” with the output.
The processing department, over time, knew how the data was put to use across the organisation and what were the emerging data/analytical needs. The BIU also became the driver for putting in the data discipline mentioned in rule # 1 above.
Next — start with data integration. Integration comes from the Latin word integer, meaning whole or entire. Just replace people with data and society/organisation with store or base. The idea is to develop a common base from which you can make sense of the data. So that the analysis is reliable, trustworthy. It is not based on some person’s laptop data, or another one’s individual definition of accounts receivable.
The last step is realising the value of the above three rules through analysis — What happened? Why did it happen? And, what will happen? Or in our terms— “hindsight” and “foresight”.
The first part is answered by the standard reports you get from your enterprise resource planning (ERP)/operational systems — monthly production/consumption, 30-days outstanding, inventory position at each depot. The second one requires you to adopt a BI (business intelligence) tool that will help you seamlessly manoeuvre the entities/data/reports/key performance indicators (KPIs), etc. Compare sales with same period last year, across territories/SKUs, what-if I changed my plan by 5%, increased ad spend by 2% and the likes.
The last one — what will happen — is a science in itself and deserves a column all by itself. Simply put, business and technology come together with statistical skills to predict the future outcomes — which customers are likely to buy this new product? Which distributors are likely to reduce their business with me? Which stores are more price-sensitive than others?
Get these four parts together and your journey of making data actionable will start yielding results … and money as a bonus escape from the average deluge every business suffers when they fail to respect the above four steps.