Saturday Essay: Why Data-driven Reinvention Will Be Key To Business Growth, Post-pandemic
Image by Gerd Altmann
“It is a capital mistake to theorize before one has data.”
This phrase might be attributed to one of the most famous fictional detectives in history, but business leaders and HR managers might owe some of their success to Sherlock Holmes when conceptualizing their post-pandemic data strategies. Put simply, without ample data to back up an idea or concept, it is difficult to drive smart business decisions.
Image by Gerd Altmann
This has been especially important throughout the pandemic. Given the fact that many businesses have been forced to operate remotely and adapt their services in line with social distancing restrictions, company offerings and internal operations have required a substantial rethink.
But beyond the effects of COVID-19, businesses must continue to innovate and pivot if they are to stand the test of time. Indeed, most sustainable businesses have had to reinvent their organization several times over to remain relevant.
So, how can organizations put data at the helm of their plans for reinvention?
Reinvention begins within
First and foremost, organizations must understand that any transformation must begin by looking inwards. By that, I mean that it is vital to build the right organizational culture, and train staff so that they have the knowledge required to effectively harness data, refreshing the business from the inside out.
Up until now at least, it is important to note that organizations have not been particularly confident when it comes to doing this. According to a recent survey from Soffos.ai, one third (33%) of businesses say they lack the tools and knowledge to effectively support their remote staff where training is concerned. Employees were in agreement, with the same number (33%) also stating that online learning solutions have been too generic to help their professional development throughout the pandemic.
As such, businesses would do well to invest in innovative learning management systems (LMS) that utilize sophisticated data analytics to understand the specific needs of their employees. In doing so, administrators will be able to identify particular knowledge gaps amongst teams and individuals, so that they can tailor their training incentives – a far cry from the traditional en masse, ‘one size fits all’ approach commonly employed today.
At the click of a button, learning leaders will be able to see which members of staff are engaging with training resources, what questions they are asking, and when. This should be a great help when it comes to optimizing training strategies, while slowly putting data at the front and center of the way they do business.
The transformative power of artificial intelligence (AI)
Image by Gerd Altmann
Once organizations recognize the power of data to support employee satisfaction, this should spark something of a culture change – one that privileges the insights that data has to offer to the wider business. From here, business leaders can apply a data-driven strategy to their operations more generally, whether this means collecting more information about their customers and clientele to personalize their offerings, or using this information to make way for new ventures.
As a next step, businesses should look to migrate their data to the cloud. Moving storage and IT resources to the cloud will enable organizations to easily scale up if the growing volume of data requires it, and only pay for what they use. It is also the first step to breaking down data silos and making information readily available to everyone who needs it.
Organizations should also put more concrete plans in place to make business decisions based on the data collected. In particular, tools that utilize artificial intelligence (AI) and machine learning (ML) technologies will simplify this process, and provide business leaders with the firm foundations needed to make informed choices – especially if these changes require substantial investment.
Although many people might use ‘the more data the better’ as a guiding principle, there is naturally a limit to the amount of data that any one analyst can manage on their own. Sometimes businesses can run the risk of being overwhelmed by a sea of data, without really knowing what to do, or what questions to ask of it. This is where analytics tools powered by AI and ML technologies come in handy.
A subset of AI, the main objective of machine learning is to effectively process and make use of mounds of complex data that is sourced from various data points. Whether it is determining customer trends, predicting buying behaviour, or detecting fraud, AI-powered solutions empower businesses to spot and maximize opportunities, and ultimately make the most of developments that would otherwise escape the human eye.
In many ways, the onset of COVID-19 and the dawn of hybrid working has been a great reminder that organizations must continually reinvent and strategize to survive. When faced with difficult business decisions, additional clarity and evidence will never go amiss, so business leaders would do well to rethink their data strategies in the months and years to come.
Nikolas Kairinos is the chief executive officer and founder of Soffos, the world’s first AI-powered KnowledgeBot. The platform streamlines corporate learning and development (L&D) to deliver seamless professional training for employees. You can follow him on LinkedIn and Twitter.