By expanding data literacy to non-data scientist roles, businesses can realise the full potential of digital transformation.
While Covid-19 winds down, the repercussions from the pandemic have not withered away just yet. The acceleration of digital transformation in that period was imperative to survival. Although still a core component of success, digital transformation takes on a different character for many organisations today.
Amid a race to innovate, improve processes, gain a competitive edge, and win market share, NTUC LearningHub finds that more than seven in 10 employees and business leaders in Singapore feel that a tech-savvy workforce is the top attribute of digital transformation.
The migration to the cloud of recent years has had something to do with this, as has the remarkable recent advancements in artificial intelligence (AI). This progress has given businesses added flexibility, cost elasticity, greater scalability, and better speed of deployment.
Naturally, however, organisations need to take the next step, and that rests in driving data literacy.
Doing data democratisation right
Although a crucial pillar of digital transformation, getting the organisation data literate usually gets the short end of the stick. This dismissive attitude can fuel challenges with creating a culture of data-driven decision-making, especially as competition becomes more global and business models increasingly dependent on data.
With the rise of self-service data discovery and dashboarding tools, along with the spread of capabilities that used to be available only in IT and business analyst roles, "citizen data scientists" are eager to explore data and develop predictive models on their own.
The expansion of data democratisation and self-service functionality is a shot in the arm for enterprises. However, tools alone are not enough to produce high-quality insights. The spread of intuitive, self-service tools and applications can lead many to overlook the critical role of data literacy.
Advancing organisation-wide transformation
At the crux of it, data literacy has two goals. The first is to enable users to analyse and interpret any data they are handling, as well as to share and communicate their insights. After all, large amounts of data and high processing speeds are for nothing without confidence in the insights derived or if they leave no impression on decision-making. The second goal is to increase the accountability of those who collect, integrate, prepare, and protect data.
These twin goals underscore the importance of data literacy in accelerating the acceptance of analytics. As more people outside the traditional data science roles are empowered to become "citizen data scientists", long-held assumptions will be challenged through data insights. Thus, it is counter-intuitive to impede critical thinking - as it is necessary to evaluate results, ask additional questions, refine analytics models, and most importantly, determine how insights uncovered affect business decisions.
The rapid maturation of AI/Machine Learning augmentation in business applications also highlights the importance of data literacy. With augmentation, the mayhem from a flood of alerts is replaced with prescriptive recommendations, delivered to users on business rules and data-derived insights in the context of their responsibilities and interests. Through smarter data-driven notifications, workflows and processes can be prioritised according to need.
Making strides in data literacy
One way organisations can beef up their data literacy is to set up formal training programmes. According to NTUC Learning Hub’s 2023 report, half of employees perceive the lack of expertise as the top challenge to implementing digital transformation initiatives. To drive home the centrality of data literacy to digital transformation, the same report found that employees were most concerned about getting training to work with new technology.
However, organisations would be gravely mistaken if they opted for a one-size-fits-all approach. Instead, programmes should be calibrated for individual backgrounds, experiences, and responsibilities. Another way to enhance organisational data literacy is to include data governance requirements in the subject matter. Data stewards, or those with expertise in data and who can oversee both defensive and offensive data governance, can provide mentorship that improves data governance accountability either through formal or informal teaching.
With AI and cloud computing adoption at the fore, organisations are entering a new data landscape. By moving past the traditional focus on system configuration and IT budgets, organisations can now tap into their data's full business potential.
However, to do so, organisations must match the power of AI and modern cloud platform providers with data democratisation and a workforce of citizen data scientists. By expanding data literacy to non-data scientist roles within the workforce, businesses will position themselves to realise the full potential of digital transformation.
Hemanta Banerjee is the VP of Public Cloud Data Services at Rackspace Technology