What does data-driven mean in business?

As data has become a more important asset, the term ‘data-driven’ has become something of a buzzword.  But what does data-driven mean in business in practical terms? In order to be meaningfully data-driven, businesses have a number of criterias to meet and must have a solid data management strategy in place. 

There’s a key difference between having access to quality data and using this to properly lead business decision-making. New technologies such as AI may also play a role in the mix when it comes to data-driven decision-making – the business benefits of AI-powered analytics are numerous – but at its core this is a strategic issue.

A data-driven business is one that is “constantly capturing and analyzing data to optimize its processes” says Edgar Randall, UK managing director at Dun and Bradstreet.

“For instance, when it comes to demand generation activities, inefficient businesses regularly adopt a ‘spray and pray’ approach, one which is very generic, wastes effort, and has low conversion rates and high costs. On the other hand, a data-driven business would use insights derived from datasets to ensure they target the right audience at the right time. This would in turn lead to higher conversion rates and a stronger return on investment."

What are the benefits of data-driven decision-making?

The benefits of data-driven decision-making are widespread, ranging from greater insight into customer decisions realized through data analytics to detailed understandings of one’s supply chain resilience and business trajectory through big data analytics and predictive analytics. 

Edgar gives some concrete examples of areas where being data-driven is advantageous for businesses: “Extending credit to a customer, assessing regulatory, compliance and ethical issues with suppliers, deciding where to focus investment effort in order to grow a business and avoiding bad actors or fraud."

A business that is using data to its fullest will be able to answer nuanced questions about its processes and strategy and getting this right can be important to business survival. Waseem Ali, CEO at management consultancy Rockborne and former chief data officer at Lloyd’s of London, puts it bluntly: “History tells us that those organizations that haven't kept up to speed with the market or with the relevant external data have not succeeded.” 

How to spark data-driven decision-making in business

Data-driven businesses get the best outcomes when the data they use is relevant, accurate, and appropriate. So how do businesses ensure this?

Leadership is key, Ali says, adding that simply wanting to see regularly produced management information is a “very transactional and reductive way of thinking”.  Instead, a leader should aim to understand their customer’s needs, wants, and concerns. Customer interests will be visible in data obtained through data analytics to an extent it won’t be in simple balance-sheet-based reports. For example, data-driven decision-making is can inform the channel to help fiorms better serve their customers and give leaders projections for the wider economic picture.

Using the right kind of data is essential here as Greg Hanson, GVP EMEA at enterprise cloud data management firm Informatica, warns. Without preparing data correctly for data-driven decision-making, he says firms can experience to inefficient marketing campaigns, inability to cross-sell and upsell, and decreasing productivity due to inaccurate business processes. 

The first two of these are about knowing your customers and ensuring that messaging is on point. Accurate, reliable, and plentiful data is only part of the solution. A business also needs to know how to interrogate that data, interpret what it reveals, act on what is learned, and evaluate actions to inform decisions for next time. 

Hanson says one key action in this respect is to implement data quality standards to establish a baseline, “Define the criteria for accurate, complete, and consistent data. Regularly assess and monitor data against these standards to identify and rectify any discrepancies promptly. This can involve creating data quality checks, validations, and procedures.”

Getting to this point may require an earlier exercise, led by roles such as data scientists or data analysts. Randall proposes “a data audit to understand how and where data is currently used within the business and to assess the quality, reliability, and consistency of that data. Following this, reviewing core tech infrastructure including customer relationship management (CRM), enterprise resource planning (ERP), and marketing technology (MarTech) to understand how data enters those systems and how well it flows between them will be essential.” 

The outcome of all this will be a core data governance strategy that encompasses managing the provenance, accuracy, appropriateness, and reliability of current data as well as the collection of future data.

Randall stresses  that “with the right foundations in place, attention can shift to the most efficient use of data, achieved through analysis of process, AI and automation”.

Data-driven business, not data-dominated business

Ali makes it clear that data should never be relied upon to do the heavy lifting: “When we speak about data-driven business, what we’re really talking about is data-aided businesses. No business is entirely driven by data because there are other elements that play a part in business decisions. 

“There will always an element of ‘gut’ decision-making involved. The data is there to aid and inform the process, not to control it.”

In other words, while data helps people make decisions and better data helps people make better decisions, high-quality decision-making will always take other factors into account as well. If businesses ensure that their data is put together well, regularly audited and being processed with the best-in-class tools for the job, they can expect to make meaningful gains and keep up with the competition.