Businesses have a lot to learn from Marie Kondo. The Queen of Decluttering has carved out a lucrative career as an “organising consultant” whose Golden Rule is to keep only the items that “spark joy”, writesIan Matthews, Data Evangelist at NGDATA
This is great advice for those who have allowed a lifetime’s acquisitions to clutter up their homes, but it’s equally useful for corporations. And the most important application of Kondo’s Golden Rule isn’t in slimming down bloated product portfolios or ‘rationalising’ employee headcount. It’s to do with data.
Businesses are waking up to the concept of “data capital”: that the information they hold is one of the most important determinants of their future success. Many, however, still labour under the delusion that more data means more value, as if each megabyte added to their bottom line. Wise organisations know that information has no intrinsic worth. Instead, it’s what each unit of data can do for you that makes it valuable – or, to paraphrase Ms Kondo: “Does your data spark joy?”
Too often, the answer is a resounding “no”. McKinsey found that businesses typically “leak” about 90 per cent of the potential value that analytics ought to deliver. The most important element of any analytics programme is taking the right actions from the insight provided. Data is obviously one of the key inputs to get the right insights and make good decisions. But businesses cannot ignore the importance of taking the right action from this insight and articulating the right use case and desired outcome to direct that action. It’s easy then to see that true insight is not about data volume, but having the right data to make informed decisions.
Gathering data is easy, but too much or the wrong sort of data can have grievous effects on an organisation’s ability to extract value. Like the hoarder whose house is cluttered with items “just in case they’re useful”, businesses risk piling up unnecessary information in various siloes, creating impenetrable data swamps and potentially damaging data protection risks – not to mention the cost of storing the stuff.
Businesses should therefore beware of analytics vendors whose whole pitch is based on pulling together data from an almost infinite number of sources, as if this were a goal in itself. As we’ve seen, this approach more often than not results in negative ROI while creating little or no useful insight.
We’re in danger of getting things the wrong way around, much like the aliens in The Hitchhiker’s Guide to the Galaxy, who built a computer powerful enough to answer the ultimate question of life, the universe and everything. The answer, of course, was forty-two, and gave them no direction in their quest for meaning and purpose, demonstrating the importance of asking the right questions.
We need to start with the problem to resolve or goal to achieve, and only then interrogate the data to find out what aspect of their operations they need to improve. Rolls-Royce provides a great example of asking the right questions. In 2017 the engineering firm launched its R2 Data Lab, which identifies the specific challenges that the company and its customers face, and then uses data to find out what solution will work best.
This approach isn’t right for every enterprise. In fact, one of the most common mistakes is to cloister data analysts in their own department where their insights aren’t readily available to those who need them most.
Business Insight: A Cross-Organisational effort
Today, almost every employee is a knowledge worker, yet far too few have access to the data they need to be effective in their jobs. NGDATA believes that data should be embedded in every team, whether it’s sales, product design or logistics. It ought to be obvious that viewing analytics and data science as a separate part of the business is self-defeating: imagine if only one team was allowed to use computers, or every phone call had to be made through the communications department!
Teams operating at the cutting edge of business can’t wait for data scientists to deliver dense analytical reports. They need data, dashboards and real-time insight to ensure they hit their sales targets, improve product design, or optimise customer service.
Building a business insight strategy begins with people and processes, not data. For example, employees who operate at the coalface of a business have much more valuable insight on the information that’s collected and what’s actually valuable. In day-to-day running. As a result, they need to be incorporated into the business insight process.
Surprisingly, retailers are often poor at gathering useful customer data. We expect them to monitor and analyse every in-store or online move we make, yet some can only able to identify their most valuable customers when they reach the checkout. They also fail to capture valuable data by having a web app that the user doesn’t need to log into. Similarly, customer service operatives can be starved of key insight into the customers’ they’re talking to – for example, lacking key information on previous interactions or details about their account. They also have to flit between multiple systems and screens to get a full picture of the customer, instead of having a single version of the truth on a single, easy-to-read dashboard.
Businesses need to be asking whether the data they are capturing is useful or not, and it’s the coalface employees who are often best placed to decide the question of what data will spark the most joy – both for themselves and their customers. The organisational insight strategy should allow every staff member to contribute their views on what data is useful; only then can the business take steps to capture it.
For example, marketing employees might highlight the need to capture customers’ address and date of birth to deliver personalised campaigns and offers. The business can then ensure that they require users to input this data when they sign up to use a service. Customer service staff, meanwhile, could talk about the frustration of lacking billing or other details that they need to complete calls satisfactorily.
Data is for Everyone
We need a new way of thinking about data and insight. For example, we shouldn’t let the data-gathering opportunities of the Internet of Things to blind us from the most powerful of connected devices: a customer using their mobile phone. Businesses need to work out how they can harness customer mobile application data to solve the specific challenges they face, such as increasing loyalty or harnessing upsell opportunities.
There’s no reason why data should only be used by data scientists. We live in a world awash with information, and we’re all becoming well-practised at filtering out the noise and focusing on relevant data. Businesses must expect the same of themselves, looking to all of their employees when building an insight strategy, ensuring that data brings joy to all.