8 ways big data analytics can be applied by any CEO

By: Ken Wong, President Lenovo Asia pacific

How do big data and data analytics play into strategy development at the chief executive level?

Should they play at all, or should CEOs and peers delegate data analytics to the experts?

It’s a discussion played out in businesses around the world. A recent report by McKinsey (Making data analytics work for you—instead of the other way around) shines a light on the value of data analytics to CEOs.

Often viewed in the context of addressing challenges, data analytics should also be viewed in the context of discovering, assessing and taking action on new opportunities.

I share the McKinsey view that data analytics do need to sit with the CEO, management team or board of a company - because analytics provide the inputs and insights essential to choosing future directions. Often viewed in the context of addressing challenges, data analytics should also be viewed in the context of discovering, assessing and taking action on new opportunities.

In fact, seeking to redefine your organisation ahead of changes imposed by external forces is clearly the better option. Using data analytics anchors such considerations in firm foundations, to provide confidence in any decisions you make.

The role of data analytics

At the CEO level, data analytics can be defined as the establishment and identification of market threats and market opportunities. The data delivers raw inputs, and the analysis makes sense of this. With strategy setting the context, an open mind and ruthless objectivity are essential so boards and senior management can understand progress... and change direction as required.

Data analytics can expose the need to transform, identify new revenue streams, reveal new ways to improve operations or lower costs, or measure sales success rates. All are essential inputs on the CEO’s strategy dashboard.

Data analytics can expose the need to transform, identify new revenue streams, reveal new ways to improve operations or lower costs, or measure sales success rates.

We’ve had experience of this at Lenovo with our PC business. Rapidly becoming commodity devices, desktop PCs are seemingly a dying product category if you believe some market and media commentary. But the data indicates otherwise. When asked to overhaul our regional PC business a few years ago, I pulled a team together to analyse our product range and the needs of the market. We discovered some surprising insights which allowed us to redefine how we went to market and how we developed product. Even in mature markets, we discovered that customers had a range of needs and preferences for how they wanted to use PCs.

The market was more granular than we’d assumed, with requirements that included premium performance for business and gaming, varying quality levels, specific applications for high-end graphics and gaming, and more. Speed and price were not the primary decision-making criteria. What’s more, all of this was as true in emerging markets as in mature ones - another surprise.

We have been able to use these insights into our product development and marketing operations, and continue to maintain market leadership for PCs. The power of the data, and having the data analytics task linked explicitly to clear business objectives (in our case, how to grow a plateauing market and protect market share) allowed us to develop a meaningful strategy that worked.

Moving from blades of grass to understanding the countryside

There’s an argument that data analytics is too granular and specialist for the CEO or board to consider, and that there’s an associated risk of becoming preoccupied with the blades of grass so beloved of data analytics experts.

I don’t agree with this assertion. Instead of delegating the analytics task to the experts, CEOs and senior managers should embrace them - but set clear objectives designed to support the development of new strategies. The purpose of data analytics is then to make sense of the blades of grass so organisations can understand the landscapes in which they operate - where to plant and where to reap.

There’s an argument that data analytics is too granular and specialist for the CEO or board to consider ... I don’t agree with this assertion. Instead of delegating the analytics task to the experts, CEOs and senior managers should embrace them...

Data analytics and new opportunities: defining C-O-P

Data analytics is about searching, testing and discovering answers to new ways to drive business value - the responsibility of any CEO or board. As the CEO you have a responsibility to consider how your data can define new strategies for success, and you can drive this process by setting context, objectives and purpose - C-O-P.

In reality, any new strategic position will be a consequence - or natural development - of your current position and strategies, which serve to define C-O-P. Using advanced data analytics, you can benchmark current strategies (for example, around pricing, customer service, or product). C-O-P removes the risk of data analytics being an end in itself: as noted above, data analytics must always be a means to an end, leading to the creation of meaningful new strategies. As big data guru Sandy Pentland of MIT notes, “People need to understand what the data means, how people work, and then apply it intelligently.”

Data analytics and transformations

To close, here are eight data analytics suggestions that I have used as a senior regional business manager, and which I think can be applied by any CEO:

1. Data means knowledge - knowing that to turn left is the correct decision is important when there’s a wall on the right

2. Align objectives with the data you seek, to minimise the risk of non-stop data-fishin

3. Iteration is the norm - but set time limits. As a CEO you are paid to make decisions, and data analytics helps you make better decisions

4. As a CEO, you are probably already aware that your natural behavioural style may well be dominant - so create teams of people who will reflect and actually analyse the data. Choose people with complementary skills - but always lead the process and impose the strategic objectives

5. Make everything time-bound. In the Lenovo example above, we turned the business around in three quarters. The world, and your competitors, won’t wait

6. Make the insights relevant to those who should use them, those for whom they will be most useful, and those for whom they should be mandatory

7. As the CEO you need to lead, digest and analyse the inputs you get from your experts and the data: leading is the most-important of these

8. Be clear that data analytics is there to provide new insights, not confirm long-held positions - you know what you know, focus instead on what you don’t

As McKinsey authors Helen Mayhew, Tamim Saleh, and Simon Williams note, advanced data analytics is a business matter.

I would add that it matters greatly to business.