Master class in analysing big data

David Court is passionate about marketing and data, and believes together they can create game-changing business tools to drive revenue.

His feel for numbers came from statistics during a business degree at Queen’s University, Ontario. Marketing expertise was acquired at Procter & Gamble, handling brands such as Joy and Cascade detergent.

“I know more about cleaning dishes than any man should know,” jokes Mr Court, who also met his wife Alice at P&G.

“Understanding the basics of marketing and getting a wife is as good as you can get for a first job,” says the Canadian, whose 32-year career at management consultants McKinsey & Company began after a Harvard MBA.

The combination of being customer driven while understanding statistics informs his most recent passion – helping large corporations embrace the opportunities of big data while avoiding the many pitfalls.

The statistics are impressive. “Companies that use big data and analytics effectively show productivity rates and profitability that are 5 to 6 per cent higher than those of their peers. The companies that succeed aren’t the ones with the most data, but the ones that use it best,” he argues.

For a salesman, he believes, decisions never change – how do you identify your best prospects, what’s the most effective pitch and how to handle pricing? It’s just that big data, which makes a lot more data available in real time, gives you an edge.

“It’s the same decisions, but the fact that you have all this data in real time allows for better and better decisions,” says the consultant who has also run McKinsey’s worldwide functional, as opposed to geographical or industry, practice from its Dallas office.

The companies that succeed aren’t the ones with the most data, but the ones that use it best

He reckons he can predict which corporate approach is likely to benefit more from big data.


A company that says we have lots of data, now let’s find opportunities to use it, is unlikely to have long-term success. Instead you start with the business issues and decide how data can drive the business and make more money.

To start assemble external and internal data, making sure data from different internal systems, which often do not talk to each other, is properly integrated.

You then build a predictive model or algorithm. It took Amazon ages to be able to say if you bought that book, you will like this one. Now building the software can take as little as three weeks.

“You build the individual software and then you tailor it to clients. The really tricky thing is getting the front line to use it. This is where most companies fail. They have a sophisticated model, but nobody uses it,” explains Mr Court, whose long McKinsey career included leading the sales and marketing practice.

To get sales staff to use the data predictions they must be offered options rather than being told what to do by “a black box”.

Training is also vital. Many companies spend 95 per cent of the cost of moving into big data building a great model and the rest on training, when the split should be 50-50.

“You are keeping empowerment; you are just making better decisions, that’s what data analytics does,” claims Mr Court, who argues that big data has opened up opportunities in every industry McKinsey has looked at, although advantages can come from different areas.

In business-to-business, the key is getting the pricing of goods and services right, while for large retailers the importance of getting the right match between sales and inventory is huge.

But where should the big data function be located in a company? Fewer companies are choosing the IT department, while small pockets of isolated analytics workers leads to poor staff retention of these highly desirable professionals.

McKinsey has concluded the answer is “a hub and spoke” approach, a smart model-building group at the centre working with analytic specialists in corporate divisions, who are close to the business issues.


When he talks to marketers, Mr Court reflects the new reality that marketing is shifting towards more personalised messaging. Advertising is far from dead, but the Holy Grail is customer engagement, something likely to be achieved by giving consumers useful and relevant information, when they want it and how they want it.

“Analytics is how you make this happen without blowing your brains out from a cost perspective,” says the executive, who has worked for McKinsey in London, Chicago and Sydney, as well as Toronto and Dallas.

The move to more personalised data increasingly raises problems over privacy, a trend that led the European Parliament in March to vote overwhelmingly for stricter data protection rules.

Mr Court is optimistic the solution can be found within industry through a form of “depersonalised” personalisation and a reliance on consumer segments.

“I have to reassure the regulators and the public that I am not giving out information I do not need. I do not need to go out and analyse what David Court specifically wants and send David Court a specific message,” he explains.

Models are so sophisticated that there is little commercial difference between marketing directly to individuals and targeting depersonalised segments.

“What companies need to do is avoid violating people’s concerns by dealing with it through segmentation. The better companies understand the concerns and are moving in that direction,” he says.

This 54 year old is, however, happy to reveal that, as a member of National Geographic’s Advisory Council, his private passion is travel and in August he went to Zambia on holiday with Alice. Put that in your database.