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Time for data analytics to come of age

It is one of the most valuable services an organisation can have today, but a shortage of qualified people hampers its development

"It's not the data you've got that matters, it's the data you haven't got" - potentially valuable data not yet to hand is the key job a data analyst must address.

We know about data. We use it all the time. Tables of stuff, but you get used to looking for the bottom right-hand corner where the real information is. 'What's the bottom line?' you ask anyone in earshot as your motivating mantra. In fact, you suspect that your nickname is 'Bottom Line'. Not bad for a relatively new CEO, eh? Sadly, we have news for you. You are not using your data properly. In fact you are not really using it at all.

Data analytics has more to do with people than profits

You may think it has only to do with profits. You actually know it has more to do with people. Look at it this way. What was the biggest problem for an advertiser when commercial television first started? Communication, of course. This expensive medium needed a lot of money for every second of airtime.

But nobody knew what value to place on a unit of time. Was one minute long enough to communicate a message? Probably not. In the slower-paced days of print media and movies with long establishment shots, we reckoned on more than that.

So the first TV commercial on British Independent Television lasted for three minutes, if I recall correctly. And cost the earth. It seemed like the business equivalent of War & Peace.

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It wasn't until Television Audience Measurement and Nielsen started monitoring the audience that we had a reasonable idea of what worked. Longer advertisements meant viewers went to make a cup of tea, rather than stayed in front of the television. Advertisements with sound could be heard even if the viewer walked away.

This led to the tuneful Murraymints ("the too good to hurry mints") being one of the first jingles to catch the imagination of the British public. In a different vein but equally effective, "Guinness is good for you" struck a chord of permission to "have a couple" that suited most building site navvies.

Market research was then, and is still intended to be, predictive. It achieved that with limited success. Ask people about their smoking, praying, car driving, exercising or spending habits and you get some weird answers. So we started to relate predictions based on surveys to what actually happened. That gave us a way of modifying what people said with what they did. It was slow and cumbersome.

The data deluge of a generation ago

The scene changed 180 degrees when online purchasing started in earnest, about 25 years ago. There had always been some such buying. Now, massive amounts of data became available and weighty algorithms appeared, capable of fast and vast analysis. It was suddenly possible to target individual buyers, understand and exploit their preferences and make use of relatively cheap social media thus saving millions of dollars in promotional costs.

The data mine was what all online traders, especially those dealing with Internet usage, collected in the course of their daily work. Once this data was available to be bought from the social media as well as those using the Internet in a wide variety of ways, it was possible to inspect, clean, transform and model the data. Data analytics had become possible.

This started to be especially valuable to businesses involved in finance, distribution, logistics and retailing - both online and high street.

Poor adoption of data analytics by big business

However, many large companies are not making use of the wealth of information available to them because data analytics development has been hampered by five major problems.

There is a shortage of people qualified to handle data analytics and this is a handicap to its development. Companies that have an eye to the future would do well to select and sponsor those especially talented in math and data handling at an early age to fast track them for well-scoped employment. Once a team is built, care needs to be taken if it is not to be poached.

Businesses often consider that they do not have the time and money to equip themselves with data processing capability. Initially, and inevitably, a lot of groundwork has to be done if the available datamines are to be assessed and ordered to provide maximum useful data.

The initial investment is not only money but the time of senior people who know where the data are. Some seniors will regard this as second to their day-to-day jobs.

Reluctance to renege on established connections and systems

Business connections with advertising agents and media are often long-established. People are reluctant to break these connections. The 100-year established business of advertising has been slow to change and even slower to adopt social media since these media do not provide the agency with the income they got from commission-paying media.

A reluctance to substitute judgment with data is part of the reason for slow adoption of data analytics. More established, experienced people earn their living from guessing "better than average". They have a natural disinclination to see that expertise challenged and possibly proved wrong. They sometimes adopt a Luddite-like approach to data analytics.

Once data analytics is accepted as a necessary part of the operational and promotional aspects of the business, there is much work to be done on existing and available data.

However, if the analysts confine themselves to mining this, they are missing what may be the most valuable part of their resource. Their work is best described as "it's not the data you've got that matters, it's the data you haven't got". Potentially valuable data not yet to hand is the key job a data analyst must address.

The role of data analytics in today's businesses

Valuable data analytics must be predictive, prescriptive, and geospatial. Limiting it to a task-defining brief fails to get what is there out of it. The best way to introduce data analytics into your business is to assign a few of the brightest, most open-minded people to devote 80 per cent of their time to helping the analysts do the preliminary exploration of what is available and what more is needed. By doing so you will start to build your own team, a valuable protection against possible loss of the more professional analysts..

A collegial approach between external data analysts and your own best people will make your day-mining experience useful and productive. Data analytics is one of the most valuable services an organisation can have today. Most companies are still operating in the relative dark when it comes to knowing about themselves. Data is not there to organise the company but it is there to inform it.

  • The writer is Founder & Chair, Terrific Mentors International

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