Although many businesses claim to be data-driven today, siloed and inaccessible data remains the most common reason for analytics projects and new technology deployments to grind to a halt. This not only leads to diminished profitability, it also has a profoundly negative impact on brands’ relationship with their customers as well as employees, who may feel their expertise and time would be better-valued elsewhere. Indeed, 92 percent of data analysts – who were already in short supply before the Great Resignation shook up the job market – say they have needed to perform tasks outside of their roles, and on average, only spend half of their working hours actually analysing data. Much of their ‘wasted time’ is taken up by trying to make data accessible for analysis and searching for the most up-to-date data to report on – a tiresome manual task for which automation already exists.
If not even the specialists can easily make sense of a company’s data, what chance do other employees have at using this insight to make revenue-impacting decisions? The solution to this conundrum lies in data democratisation.
The role of the modern data stack
To democratise access to data is to recognise that the power of data analytics is often best-placed in the hands of those closest to the customer, to a bottle-neck or a use case. A marketer may only ever look at the low-level insights collected by their department and miss out on tell-tale customer behaviour patterns, such as churn, observed elsewhere in the organisation.
Achieving a state where all relevant stakeholders have access to relevant data starts with a much more fundamental process – creating a single source of truth in the data. The base requirement for this is a modern data stack, which consists of automated data integration, a centralised cloud data location and modern business intelligence software. Together, these technologies enable raw data from diverse and disparate sources to be transported into the heart of the organisation and made analysis-ready, without requiring manual untangling from data professionals. Data analysts can then add value where it matters: building dashboards for different business units, translating the insight for the C-suite and building AI and ML engines on top of the stack. Enabling them to focus on value-added tasks will not only improve their experience, it will also unlock new data-driven potential for the business as a whole.
With these best practices in place, customer-facing teams can achieve near-real-time visibility into where the customer experience suffers and where opportunities for up-sell or cross-sell occur. What’s more, they can trust that the data underpinning their decisions is both accurate and up-to-date. This is a crucial point, as globally, only 13 percent of companies report being able to derive value from newly-collected data within minutes or hours. For the rest, it takes
several days or up to a week. While the cost of these delays can manifest in missed customer opportunities and lost revenue, the opposite of it is true as well.
Case in point: World Fuel Services
World Fuel Services (WFS) is a Fortune 150 company that solves energy challenges by marketing, selling and delivering fuel around the world. Operating in the volatile energy market means that its teams have to make tough decisions in real time, as well as launch or scale campaigns quickly to take advantage of changes in supply and demand.
WFS has grown enormously over the past 10 years through more than a dozen acquisitions. These subsidiaries operate semi-independently and have their own clients lists that they service, which used to make it difficult to get a global view of customers across the entire company. WFS’s modern data stack – underpinned by Fivetran and Snowflake – now enables it to easily and seamlessly ingest data from dozens of sources from across the organisation to make data-based decisions that improve customer experience.
For example, teams are able to grab an updated customer list across business units every day to compile a master record that is then enriched by demographic data from Dun & Bradstreet. This master list is then used for lead generation purposes across subsidiaries and business units — leading to upsell and cross-sell opportunities. Because it has this global view of customers, WFS has generated more than 300,000 new leads. In addition, WFS has saved 200 hours per month by eliminating the need to manually create, manage and update data connectors.
Catalyse employee-led innovation
Once data management issues have been resolved and burdensome tasks eliminated, companies should go even further by building a strong culture around this data to help them harness the skills and ideas of the entire workforce.
Surprisingly, one issue that organisations face time and time again is that employees simply do not consider it their responsibility to look at data. This could not be further from the truth. One would be hard-pressed to find examples where a business decision was not underpinned by data, so it’s key that all relevant employees can interrogate, interpret and act on data in a self-service way. Data leaders should demonstrate the strategic role of data in decision-making to their employees and help them view data as if it was any other product they use and expect to get value from. The process begins with a simple conversation, but soon, every conversation will revolve around data.
In strong cultures, employees mobilise around customer-centric data, pooling their expertise and creative powers to resolve issues in the customer journey as well as in wider business operations. This is the golden ticket to driving sustained innovation on behalf of the customer and remaining agile in the face of new business challenges.