Want to become more data-driven in your business? That’s great, because it’s no longer a choice – it’s a necessity.
Data inundation is on the rise, doubling in volume every two years. As the web, social networks, sensors and smartphones stream new digital data, your company’s goal should be to glean insights, from the data.
“When I told a store manager who believed that most of his business was derived from local residents that, in fact, half of his business was coming from residents that lived in a town located 10 kilometres away, his eyes went wide and he said, ‘How do you know that?’ So we shared the data with him,” says Judy Gounden, a group marketing executive at Iliad Africa Ltd., referring to her use of Market Edge, a commercial data service provided by Nedbank.
It’s the era of data-driven transformation. But, yielding benefits such as better decision-making, lower cost of operations, better customer marketing segmentation requires one vital component before embarking on the transformational journey: Responsiveness.
1. Reinvent from the core foundations
Why do you need to be more data-driven? Well, data-driven organisations are 23 times more likely to acquire customers, 6 times likelier to retain those customers and 19 times more probable to be profitable as a result, Forbes reports.
“It’s easy to say that a company should adapt to this new reality, but the complexities of engineering that transformation are deep and wide,” says Tim Cholvat, CPG analyst at SAS. “To become a data-driven company, firms must consider reinventing the operations from the core foundations to the smallest details to have an effective impact on the final customer engagement.”
The first logical step is to revisit your company’s missions and principles. If customer service is your focus, consider the effect of instantly accessible data on your business goals. Are you delivering value and making customer’s lives better, easier or more fulfilling? How is your corporate mission evolving to meet their data-driven world?
2. Simplify the process for adaptability
Lengthy setup times and change-overs slow down your business’s responsiveness to changes in the corporate environment. A data-drive company is more agile, nimble and responsive to market changes.
“With the presence of data silos between the operating units, there is a difficulty with sharing information. At times, there can be no connection of data between customer interactions and operations,” notes Cholvat. “As a result, the business agility is slow, costly and often based on a limited data-set and therefore decisions are based upon intuition and best guess work.”
Prevailing cultures thrive in a legacy environment where intuition is valued or there is a lack of accountability. Just 19% of employees say that decision-makers are held accountable for their decisions in their organisation. It’s important to understand that data’s place in business today isn’t to undermine existing decisions, but to help inform better choices in future.
Cholvat suggests you consider simplifying product and manufacturing design to gain the advantages of adaptability, and create and strengthen the connection between the customer engagement side and the internal operations.
A recent report from EY reveals that whilst 81% of organisations support the notion that data should be at the heart of everything they do, the vast majority continue to keep data in silos, thus strangling their efforts at birth.
3. Conduct a data inventory
As a large company, there are mostly likely historical reasons as to why data is siloed. You may have acquired data systems through company acquisitions, thereby resulting in additional independent systems.
“Conducting a simple data inventory is often a job that executives are loath to embark upon, resulting in a bunch of data left to rot away in silos,” says innovation consultant and author Adi Gaskell. “Doing this work also allows you to focus on the prickly issue of security and compliance. Regulators on both sides of the Atlantic are cracking down on companies who hold personal data, with potential penalties of up to 5% of global annual turnover.”
Some tasks are tedious, but someone has to do them – especially when neglecting them affects the security of your business’s and client’s information.
4. Focus on your talent pool
Being data-driven isn’t just about acquiring the data, but sorting through it effectively and actionably. This is where getting the right people on board matters most. If they can analyse data well enough for the entire company to comprehend, that’s half the job done.
“Companies need to take the time to ensure they are hiring the right individuals to appropriately manage the business intelligence functions within their organisations,” Gaskell says. While having trained analytical professionals is a core part of the process, building a team with actual business experience is equally critical.
Analysis of the data into actionable insights not only guides the decision-making process, but helps in understanding the cause and effect of all factors surrounding a business – from how it operates internally, to the market conditions and how both intersect to the consumer.
5. Generate a glossary
You will need a data dictionary to know what the data fields and metrics you’ve collected mean. This is an aspect that trips up many organisations, according to Carl Anderson, product researcher at WeWork and Michael Li, founder of The Data Incubator.
“Knowing where to get the data, and providing quality data, is only one ingredient. When you don’t have a clear list of metrics and their definitions, people make assumptions – ones that may differ from colleagues. Then the arguments ensue,” say Anderson and Li.
Generate a glossary with clear, unambiguous and agreed-upon definitions, they suggest. Discuss these with all key stakeholders along with business domain experts. You’re going to need buy-in to those official definitions, because if not, you’re risking teams going rogue with their secret version of a metric. Remember that the goal is to collapse multiple similar metrics into a single common metric.
Data-driven culture is part of a multi-step process, which requires: