McKinsey’s lessons in data analytics
Data, models and transformation are the key factors for successfully exploiting data analytics, according to McKinsey.
From his Dallas office, David Court leads McKinsey’s advanced analytic practice and has observed the changing world of business since joining the firm in 1982. Conversations with 100 key leaders in the sector have led him to conclude that success in data analytics boils down to three elements: data, models and transformation.
The data factor, he explained last month, means “the creative use of internal and external data” to give “a broader view” of trends in an organisation’s operations or its customer base. Workable modelling can then either help senior managers to make better predictions or optimise the data for the benefit of the business.
Transformation is the third and hardest part i.e. changing the company to take advantage of models and redoubling training so that staff understand and can confidently use data tools. The first, relatively straightforward, phase involves training front-line managers to use the model. The medium-term challenge is to “upscale [the] company to be able to do this on a broader scale.” Ideally, staff should be ‘bimodal athletes’ who can handle analytics but also have the business sense to make good decisions.
In process terms, two things need to be right: focus and simplicity. A pricing manager or a retail buyer may have 22 things on their ‘to do’ list but Court advises against changing all 22. “Try and change two or three things,” he suggests. “Focus on part of the decision and focus, therefore, where the greatest economic leverage is in the business.” The simplicity side relates to how people interact with the tool: “The moment you make it simple, understandable, then people start using it and you get better decisions.”
“Big data and analytics actually have been receiving attention for a few years, but the reason is changing,” he remarked. “A few years ago, I thought the question was: ‘We have all this data. Surely there’s something we can do about it.’ Now, the question is ‘I see my competitors exploiting this and I feel I’m getting behind.’ And in fact, the people who say this are right.”
In Court’s view, big data is “for just about everybody” and not just data-based companies such as Amazon, Google and Bloomberg.
Data analytics have a near-universal relevance and he encourages clients to focus on the big decisions in which they would make more money if they had better data or a better ability to predict or optimise those data.
Practical examples on the customer side include airlines optimising their flight prices for each day of the week or banks improving their customer care through the communication channels available to them.
Behind the scenes, data analytics can result in better shift scheduling for staff and a supply chain with a more efficient balance between transportation and storage costs.
David Court’s full interview is available at www.mckinsey.com/features/advanced_analytics
