In just about every area of life, we are increasingly generating ever-larger volumes of data, and one of the most valuable uses businesses are finding for it is helping them to make better decisions.
This happens all the time and can be a manual process – for example, taking the time to review the LinkedIn profiles of job applicants to help make better hiring decisions. Or identifying markets where our products are popular in order to target sales resources. The most exciting applications of data, however, are automated and used to solve big problems that businesses are facing. For example, UPS made massive savings in fuel and wage costs and hugely reduced its energy footprint when it started using location data and traffic information, combined with artificial intelligence (AI) to route its network of delivery trucks. Similarly, retailers including Amazon and Walmart use customer purchase history to predict, with increasing levels of accuracy, what products customers want to buy. Netflix learns about its users purely from how they use its service, learning about what content they enjoy and what makes them switch off, in order to keep them hooked on its service. This happens automatically, without any human employee having to lift a finger!
Smarter decision-making means making decisions that are most likely to help companies move towards their goals. Traditionally, the driving force behind decision-making has been the experience and instincts of business leaders. And unfortunately, that’s one of the primary reasons behind the unsettling statistic that 90% of small businesses and start-ups fail. Experience and instincts are valuable, of course, but research confirms that businesses that base decisions on data – not instincts or experience – are 19 times more likely to be profitable.
There are lots of reasons for this – one of the biggest is that the world changes, as do customer expectations and behaviors. Our own individual beliefs and ideas, on the other hand, tend not to. That is, once we hit on something that works, we don’t expect it to stop working. And we can’t always trust that we will have the presence of mind and forethought to predict every disruptive event or competitor that could emerge and turn our world on its head. Think of Blockbuster Video turning down the opportunity to buy Netflix, or even Yahoo turning down the opportunity to buy Google’s PageRank algorithm for $1 million.
In both cases, and many more that happen every day, bad decisions were made because business leaders – successful ones with proven track records, who had taken their companies to new heights of success – based decisions on their instincts and experience.
Today most companies claim to be data-driven to some extent – it’s a very trendy thing to say. But I am sure many people reading this will have had the experience, at some point in their careers, of working for a company that says it is data-driven but is only really data-driven when the data happens to align with the beliefs or instincts of the leadership!
Becoming truly data-driven means looking to your data as the single point of truth when it comes to making decisions. This means all decisions, from high-level ones about strategy and objectives, right down to issues involving individual customers or employees. There are four key areas where data can help make better decisions. Those are:
Decisions Relating To Customers, Markets, And Competitors: This involves understanding as much as you possibly can about who your customers are and the choices that are available to them. This is how companies like Amazon, Walmart, and Tesco learn how to advertise particular products to particular people, how they should be priced in order for the business to be competitive, and how habits may change over time as the world changes and people move through different stages of their lives. Here, data means we can more efficiently meet customer expectations and stay ahead of rivals.
Decisions Related To Finance: This is where a business looks at sales trends, cash-flow cycles, revenue forecasts, and the movement of share prices in order to make decisions around budgeting and cost-saving measures. Being data-driven here means more accurately and efficiently balancing the books and driving growth.
Decisions Related To Internal Operations: This is where companies like UPS drive efficiency by automating planning of its delivery routing, and manufacturers reduce costs (and increase profits) by optimizing the operation of machinery and processes using AI, enabling paradigms such as predictive maintenance – knowing in advance when breakdowns will occur and repairs will be necessary, to minimize downtime and plan the distribution of replacements and spare parts. Being data-driven here means less waste and reduced operational costs.
Decisions Related To Your People: Making sure you have the right people in place to carry out the jobs you need them to do, ensuring that they are supported in all the ways they need, and sufficiently compensated for their time that they won’t be tempted to leave you for a rival, taking all their skills and expertise with them. A good example here is Google which set out to learn what they could from their data about how different qualities of their managers affected the performance of their teams. Using data, they were able to identify eight core qualities of managers- including “is a good coach” and “has a clear vision for the team” that correlated with successful teams. This helped them make better decisions about who should be promoted to managerial roles.
Shifting To Data-Driven Decisions
Becoming data-driven often involves an investment in technology – as we covered earlier, the most ground-breaking and valuable data initiatives are often automated, involve huge amounts of datasets (falling into the category we often refer to as “big data”), and advanced analytics capabilities like AI. This is a big challenge and requires a strategic approach, taking care that projects and initiatives are in line with overall business goals and priorities. Ensuring your organization has all of the necessary skills is also important – whether that means upskilling people, making new hires, or partnering with outside agencies.
Just as importantly, however, it often involves a cultural shift. In many organizations, employees are used to doing what they are told by their manager because they are their boss and not having to put much thought into it past that! Being data-driven means developing a culture where everyone is willing to put their hand up and say “actually…” when they uncover data or insights that suggest a different course of action might be more effective. Managers should expect their teams to bring data to the table when they ask for ideas and input, but likewise, employees should expect managers and bosses to back up their decisions with a clear line of data-derived reasoning.
This can be a sensitive area when it comes to making changes – as egos bruise easily, particularly if managers and leaders are used to things being done the way they want, “because I say so.” But it’s an important cultural adaption that any company has to undergo when it wants “data-driven” to become an operational reality, rather than just a trendy buzzword or box that has to be ticked.
Transitioning to data-driven decisions can be a rocky road for companies of any size, but it’s a particular challenge for larger organizations with deeply entrenched values and beliefs. However, in today’s climate of fast-paced technological change and digital transformation, it’s increasingly becoming the deciding factor in whether a company will rise to the top or get wiped out by more forward-thinking, digitally adept competitors.
originally posted on forbes.com by Bernard Marr
About Author: Bernard Marr is an internationally best-selling author, popular keynote speaker, futurist, and a strategic business & technology advisor to governments and companies. He helps organisations improve their business performance, use data more intelligently, and understand the implications of new technologies such as artificial intelligence, big data, blockchains, and the Internet of Things.