Busting big data

Most small to medium sized businesses don’t have a vast IT resource, work on tight budgets, have gaps in their technology and can’t call on a lot of in-house analytical talent. In such conditions, it is always going to be more straightforward to make a business case for an investment in a new wide-format printer, or some clever new design software, than in the kind of software and capability that can help you analyse your business, your customers or the marketplace.

The key here is not how much data you generate but whether you can get the right data to the right person at the right time to help them make the right decision. SMEs in other sectors have already used big data to discover lost inventory, save money by optimising delivery routes, streamline workflows by acquiring a detailed, accurate picture of how every machine is operating and generate more revenue from promotional events by focusing on particular customers.

Customer service could be the key to winning over sceptics, especially those in the finance department. Many of the large companies that buy print are already investing in big data to analyse consumer behaviour, tailor advertising and monitor the effectiveness of their marketing (and they need to – on a good day 97% of marketing messages are ignored, deleted or trashed).

In a debate with a client, gut instinct – no matter how credible or experienced you might be – is rarely going to triumph over data. Imagine, instead, if you could go to your next client meeting armed with unprecedented insight into their expectations, preferences and aversions, able to call on data so granular that you no longer had to guesstimate what they wanted.

The investment does not have to be massive or complicated. You don't have to hire a data scientist to make the most of software like IBM’s Watson Analytics and InsightSquared and they don’t cost the earth.

Few issues fit the Chinese cliché about a journey of a thousand miles beginning with a single step as snugly as big data. And when you make that first step, it’s not a bad idea to have a destination in mind.

To know where you’re going, it helps to have an accurate idea of where you are. In their book ‘Competing On Analytics: The New Science of Winning’, Thomas Davenport and Jeanne Harris, say that most companies fall into five categories of what they call “analytic maturity”.

1. Analytically impaired: companies that have little analytical skill and executives who have no interest in acquiring any.

2. Localised analytics: companies that have some analytical capability but it’s not coordinated or siloed in certain departments.

3. Analytical aspirations: companies that have good intentions but are making slow progress.

4. Analytical companies: businesses that make wide internal use of analytics.

5. Analytical competitors: companies that use analytics to gain competitive advantage.

Once you’ve identified at what level of analytic maturity you’re really at – and there’s no point in deluding yourselves or being over-generous when you assess your capabilities – it will be easier to take the next step. The secret is to start small but with something concrete. A focused trial programme with a quick win-win, where you can measurably demonstrate cause and effect, will encourage buy-in.

Don’t underestimate the cultural challenge of getting different departments to play nice. Sales staff live in the here and now where everything is possible if the customer wants it. Production staff live in a different, more constrained, kind of here and now. And as for IT, they’ve already got a hundred other projects to worry about.

Throw in a fair amount of what they refer to in Silicon Valley as “alpha-tech monkey behaviour” – a culture where everyone has a patent on their own knowledge – and you can see that persuading everyone of the need to behave – and think – in new ways is not going to be easy.

Writing in the ‘Harvard Business Review’, Jeanne W Ross, Cynthia M Beath and Anne Quaadgas, argue: “The biggest reason that investments in big data fail to pay off is that most companies don’t do a good job with the information they already have. They don’t know how to manage it, analyse it in ways that enhance their understanding and make changes in response to new insights. Companies don’t magically develop those competences just because they’ve invested in analytics tools.” Trying to bring big data into an organisation that hasn’t really accepted the principle of evidence-based decision making is, they suggest, like asking people who can’t do arithmetic to master algebra.

The key lesson as you invest is that it’s not how much data you have – you can always get more, but will it necessarily be relevant? – but what you do with it.

As Eddie Short, partner and lead for Data Analytics at KPMG in the UK, put it recently: “It’s not all about the numbers behind the strategy, it’s about the strategy behind the numbers. You need to understand the key drivers of value in your business, make sure you have a clear line of sight between data and your strategic priorities and make sure you have the processes you need to convert data into actionable insight.”

The people you need to ensure you can deliver these actionable insights may already be on the payroll. They may not be analytics experts but they should have a gift for posing analytical questions. If they’re already working for you, they will also know your business and the challenges it faces, and that gives them a head start over any external candidate with the requisite number crunching skills.

The right internal candidate will also be able to explain how analytics have been effective in terms that other managers can grasp easily. You don’t need a 60-slide presentation that will have managers going glassy eyed as you explore the minutiae of algorithms and data cleansing. You just need five slides that give concrete examples of the financial impact analytics have had on the company.

Finding the right advocate is vital because too many companies are understandably distracted by the technology and too many managers develop a counterproductive habit of screaming for more data. You need someone to ensure that departments really share data, spread the word about what analytics are doing in your business – and what they have accomplished elsewhere – and to ensure that the right data is put in front of the right people at the right time.

Big data doesn’t take decisions, but it should help your business make better ones. Gut instinct and experience still have their place. The data will often present you with a choice, not a solution. And not all data is created equal. For example, using big data to analyse your manufacturing costs is probably going to be more reliable than data about people, which may well be protean, contradictory and unreliable. Yet analytics can open up companies – and management mindsets – to the outside world, acting as a salutary reminder that not everything that succeeds in business is based on something you have done before.

As Nate Silver, the statistician who achieved instant fame after correctly predicting the outcome in all 50 states in the 2012 US presidential election, said recently: “It’s hard for any of us to recognise how much our relatively narrow range of experience can colour our interpretation of the evidence.”

Managers should not treat data as if it has come from a soothsayer armed with a computer. Nor should they kid themselves that their experience just proves the data wrong. As Hal Varian, Google’s economist, said: “Data is so widely available and strategically important that the scarce thing is the knowledge to extract wisdom from it.”