Analytics and the lost plane

A plane gets lost. What do people who can do something do?

They look for data. Where do they look for data? Everywhere.

In the case of Malaysian Airlines Flight 370, people looked at, among other things, radar records from various countries, satellite archives, passport records, criminal files, maps of all kinds, airplane manufacturing records, histories of missing planes, even bedrooms of crew members.

Experienced pilots, safety experts, counter-terrorists, oceanographers, even families of those aboard were interviewed.

Countries talked to each other, coordinated their efforts and shared their findings, ensured that their airplanes and ships would not bump into each other, that sort of thing.

In other words, there was so much information that no human being – no matter how brilliant – could have pieced it together without the help of today’s most accommodating helper – the computer.

Fortunately for the human race, many human beings, using many computers, can create order out of the chaos.

This is essentially what Analytics does. It looks at huge amounts of data and tries to make sense out of them. It looks for patterns. It discovers woods while staring at trees.

Doctors use Analytics in treating cancer patients. Farmers use Analytics to get better results. Governments use Analytics to prepare for disasters, particularly weather disturbances.

Take business organizations. To make a major business decision, a boss has to look at all sorts of data to try to see what they all point to. If the boss had people she or he could call on, life would be much simpler and less risky. Those persons would be Data Scientists, people who use Analytics.

As CHED Memorandum Order 11, s. 2013, describes it: “Business analytics is essentially about providing better insights, particularly from extensive use of operational data stored in transactional systems, statistical and quantitative analyses, explanatory and predictive modeling, facts-based management to drive decision making for optimal results.”

Why is Analytics so important for Filipinos? Because we are intelligent. Because we are at home with computers.

Explains CHED Memorandum Order 12, s. 2013: “This is a big opportunity for the Philippines to take the lead in analytics. The Philippines could be the global center for analytics due to its English communication skills, low costs, growing technical skills and proven success and experience in business process outsourcing. The Philippines will be home to top consulting, technical and support skills for the sales, solutioning and delivery of advanced business analytics globally and will be a core point of knowledge and responsibility for business analytics where best practices will be developed and implemented.”

To make that clearer, let me give another example, a hypothetical one.

Suppose you want to know if you want to hire a certain applicant in your own company. You can yourself figure out how much it will cost you to have this applicant in your company for, say, the next ten years. Compute his or her salary, add the expected increases per year due to inflation and promotion, figure out how many other people you have to hire (if any) as his or her staff, and so on. That is the easy part. You could do that all by yourself with Excel.

The hard part is figuring out if, ten years from now, when it will be very difficult to let go of this person, it will still make sense to have him or her around. How will you know what your company will look like ten years from now? Or even five years from now? Or even just a year from now?

In the past, you would rely on your experience and wisdom. You might attend a conference or two, talk to your friends about the prospects of your business, or read or surf a lot about trends in your industry. But all you would have would essentially be your own guess, no matter how intelligent.

If you could ask an expert in Analytics, you would have hard data on which to base your decision. The Data Scientist would be able to put together data from all sorts of available sources and come up with a scenario that would be a much more intelligent guess than yours.

Even if you had a really small company (say, a small hardware store), there are so many things you have to think about that you do not have time for. For example, how much land is available nearby? How possible is it that a giant mall will be built next to your small hardware? How much will your land be worth in five or ten years? How many start-up companies are doing things that would render the goods that you sell obsolete? How real is your dream that your little neighborhood hardware store will grow to become a giant hardware chain?

Fast forward to the future, when you indeed already have a giant hardware chain. You now need Analytics more than ever, because you now have much more data than you can handle alone.

Analytics, as one commercial company describes it, is “the ability to probe, prod, and pull data from every perspective, to quickly examine anything from the largest trend to the smallest detail, to discover new insights hidden in big data.” (To be continued)