I’ve often promoted the idea that data-driven decision-making shouldn’t be a “top-down” thing. Companies that prosper in this era of digital transformation will be those that enable staff at all levels to access data and make customer-centric decisions.
Now, this has been quantified by new research, finding that businesses that empower and equip frontline staff to make informed decisions are experiencing higher growth than the "laggards" that do not.
The report – created by Thoughtspot and Harvard Business Review, shows that over a third of organizations classified as "leaders" in this respect – with data-driven decisions being made by frontline staff – experienced revenue growth between 10 and 30 percent. Additionally, 16 percent of these leader organizations experienced growth above 30 percent.
These leader companies accounted for just 20% of the report’s respondents. Another 43% are classified as “laggards” – having taken few steps to equip frontline staff to make data-driven decisions.
A key finding was that, while 90 percent of businesses understand the need to enable data-driven decision making at all levels, just seven percent have so far made this a reality. So what are the hurdles that are stopping this from happening? I spoke to Thoughtspot CEO Sudneesh Nair, who told me that there are two main blockers – leadership and culture - you can watch the full interview here:
"When we ask people why they aren't doing it, they say 'oh, because we don't have the data," Nair tells me. However, this is really just a symptom of the problem, rather than the cause.
"The real number one reason is leadership – if leaders are not on board, nothing is going to happen. They [c-level executives] are happy to talk about data, but actually, they are often afraid of data, and they would rather lean on gut [instincts] – and find data that justifies the decisions they have already made."
In fact, the study revealed that at "laggard" companies, staff were 10 times more likely to report that senior management does not want frontline workers making decisions (42 percent) than at "leader" companies (four percent).
The second major obstacle is a company's existing culture. "Culturally, they are not set up [for data-driven decision-making] … they are afraid this is a zero-sum game – they believe that if my data becomes more useful than your data, then I win and you lose – so they start hoarding data, they create silos."
Those two hurdles, Nair believes, hugely hinder the ability of many organizations to start generating useful insights from data, before they even start thinking about what data they have, and what tools they need to collect, store, analyze and act on it.
Good examples of companies getting it right can be found in the retail sector. Giving sales staff the ability to work with customer and inventory data on the shop floor, face-to-face with the public, means they can quickly direct customers to items they are interested in. If what they want to buy isn’t immediately available, then they can suggest alternatives, or check stock levels at other nearby branches and arrange home deliveries. Nair names Walmart and Nike in particular as retailers that have invested in leadership, culture, and technology, and reaped the benefits.
“If the customer walks in and asks for a product – simply saying ‘we don’t have it’ is one way of doing it … but if we can do these other things at the frontline, the customer is going to walk away with a much better experience,” he says.
Perhaps unsurprisingly, response to the study from technology and telecoms businesses indicates that these sectors have the highest number of “leader” to "laggard" organizations, when it comes to enabling frontline staff to make data-driven decisions. They are followed by financial services, while manufacturing, government, education, and healthcare industries trail behind.
That last one, in particular, is concerning, Nair says. "In healthcare specifically, it's common to blame the government and governance … 'this is patient data, so we can't do that’”.
However, companies following this line of logic leave themselves open to challenges from disruptive startups that are building their business models around finding new ways to leverage data.
"The cost of healthcare as the population in the western hemisphere ages is uncontainable – in that context, we now need more than ever to be able to cut costs and increase efficiency – which is life and death in the world of healthcare – and data analytics can play a huge part in that," Nair tells me.
“If you go to a doctor or pharmacy and look at how much time they spend actually talking to you compared to how much time they spend entering data into a computer or putting pills into bottles, you can clearly see that it’s inefficient – it’s a pity we’re stuck here.”
There are several steps companies can take to solve these problems and make better decisions at all levels and across all of their operations.
One is to make sure they have someone whose job involves spending "all day, every day," breaking down the barriers that are stopping it from happening. This means taking a strategic overview of all of the data that is available and ensuring it can be accessed wherever it is needed, rather than being left in silos to go to waste.
Another is to foster a culture that encourages change. Nair says, "You cannot be the old-school data analyst, [who believes] my job is to understand databases – SQL – I don't care what the business does.
“Those days are gone. There is no data analysis without understanding business priorities – you cannot be oil and water – if you’re building a report without understanding what the report is used for, you’re failing.”
Another essential element is the ability to work with and understand governance requirements and regulations. Particularly when working with artificial intelligence (AI), it's essential that business leaders – and everyone else in the business – has full awareness of the restrictions and responsibilities that they have to work within.
"AI is going to play a huge part in the democratization of data insight – all the way to the frontline, as the decision-making has to be enabled and enhanced by AI, “ Nair says. “That requires some level of governance and control from government.
“So that fact that business leaders are thinking more tech-first – talking like Silicon Valley innovators, working with governments and leaders, is pretty important when … you start doing more with AI.”
It's certainly true that innovation within an organization doesn't have to come from the top down. When an organization is filled with smart and capable people, game-changing insights are just as likely to come from those on the frontline, dealing with customer issues day in, day out, as it is from the board room. Enabling front-liners to understand and act on the data they generate while carrying out their responsibilities means intelligence can be built into the company at every level, leading to better outcomes and stronger growth.
Here, you can read the full research report: Meet the New Decision Makers
Bernard Marr is an internationally bestselling author, futurist, keynote speaker, and strategic advisor to companies and governments. He advises and coaches many of the world’s best-known organisations on strategy, digital transformation and business performance. LinkedIn has recently ranked Bernard as one of the top 5 business influencers in the world and the No 1 influencer in the UK. He has authored 16 best-selling books, is a frequent contributor to the World Economic Forum and writes a regular column for Forbes. Every day Bernard actively engages his almost 2 million social media followers and shares content that reaches millions of readers.