Unlocking the Potential of Digital Evolution

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.

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 on driving data literacy.

Doing Data Democratisation Right

Although a crucial pillar of digital transformation, data literacy often gets the short end of the stick. This dismissive attitude can fuel challenges with data, especially as it evolves to increasingly come from multiple sources, while rapidly becoming more voluminous and varied.

With the rise of new business intelligence (BI) dashboards and other 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. But 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 to accelerating the acceptance of analytics. As more people outside of 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. In a recent report, IDC predicts that about 65% of Asia-Pacific organisations will embed AI across business technology categories by 2026, using AI to improve outcomes without reliance on technical talent. A talent gap will drive 55% of IT organizations to invest in AI skills by 2023 to automate IT operations and support business users. 

Making Strides in Data Literacy

One way organisations can beef up their data literacy is to set up formal training programs for it. Organisations should train employees in how to think innovatively and continually innovate using technologies. An industry report indicated that 24% of Malaysians lack training opportunities and support even though 85% of survey respondents said that upskilling and re-skilling are important to them.

However, organisations would be gravely mistaken if they opted for a one-size-fits-all approach. Instead, programs 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.

By Hemanta Banerjee, VP of Public Cloud Data Services at Rackspace Technology

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