Why Data Upskilling is the Backbone of Digital Transformation

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Why Data Upskilling is the Backbone of Digital Transformation

Why Data Upskilling is the Backbone of Digital Transformation

Data transformation underpins digital transformation

Over the past two decades, digital-first startups such as Uber, Amazon, Airbnb, and Stripe have disrupted vital industries such as transportation, commerce, travel, and banking. Organizations across all industries have recognized the need for digital transformation to compete in the new information economy. This is especially true of the COVID-19 economy, which has been accelerating the digitization of their processes and services (PwC). Despite massive digitalization investments, the painful truth is that approximately 70% of digital transformation initiatives fail to reach their stated goal (McKinsey).

While there may be many culprits for why digital transformation programs may fail, a key reason is not recognizing that having sustainable data transformation is a prerequisite for successful digital transformation. Gartner finds that fewer than 50% of documented corporate strategies mention data analytics as a critical lever for delivering enterprise-wide value.

Leaders need to look at data first to succeed in their digital initiatives, rather than treating them as an afterthought to help with ad hoc projects. —Mike Rollings, Research Vice President at Gartner

There are many levers for successful data transformation—from investing in data infrastructure, to data tools, to improved processes and agile organizational structures. However, none of these levers would be effective at ushering in data transformation without investing in organization-wide data fluency skills.

Many organizations have tried to cultivate data fluency skills by creating data science and analytics teams. But merely hiring data scientists isolates data science as a service center, and won’t usher in organization-wide data fluency. Moreover, as demand for data scientists grows, hiring one’s way out of addressing the data fluency skills gap is unsustainable.

In a data-driven organization, data science—and more broadly, data fluency—is an inclusive methodology for answering organizational questions where everyone is equipped to answer questions with data. The key differentiators between the disruptors and the incumbents is not technology-based but in their data-driven culture, the insights they draw from data while examining and iterating upon their services, and the data fluency skills they foster.

In short, the success of your digital transformation pivots on having the appropriate data fluency skills across the organization.

Addressing the data fluency skills gap

Indeed, many organizations have recognized the need to address their data fluency skill gaps. A McKinsey survey of over a thousand businesses from various industries found that the most pressing skill gap to be addressed was data analytics—with 43% of respondents believing it to be the most urgent priority when it comes to upskilling. Similarly, PwC’s 2019 annual CEO survey found that 34% of CEOs believe skill gaps in data analytics are the most crucial threat for their organization.

Forward-thinking organizations are already pouring in investments to upskill their people to compete in the digital age. For example, Marks & Spencer created a retail data academy to upskill over 1,000 employees. Amazon launched a Machine Learning University to equip their engineers with the skills needed to deploy machine learning at scale in their products and services. Airbnb developed its own Data University to provide every level of the organization with the skills to make data-driven decisions. AT&T embarked on a $1 billion, 10-year long project to upskill more than half of its 250,000 people workforce.

On aggregate, data fluency upskilling efforts are paying off. Another McKinsey study found that 70% of organizations that invested in upskilling efforts are reporting positive business impacts that exceed the initial investment in upskilling. For example, 48% of organizations have reported moderate to significant positive effects on bottom-line growth due to upskilling—and 73% of organizations have reported moderate to substantial improvements in employee satisfaction. A DataCamp survey of over 300 L&D leaders across industries found that companies that have invested in mature data fluency competencies are also outperforming their less data fluent counterparts in revenue growth, market share, profitability and more.

The challenges of L&D when building data fluency

Unlike traditional learning & development initiatives that often take the shape of one-off training for specific skills, data fluency is a methodology for answering business questions that organizations should hone over time. Learning journeys will vary depending on the level of interaction different individuals may have with data. For example, a marketing analyst who regularly works with Excel may need to learn R or Python to succeed at their job, while a manager or leader may only need to know how to make educated decisions using data. In short, there are no one-size-fits all when it comes to data fluency.

This is why a role-based, persona-driven learning journey is more effective at scaling data fluency training programs. Every persona has a different relationship with data and would need to acquire competencies in different tools, and grow different skills to thrive in the digital age.

At DataCamp, we have identified the different types of tools needed to succeed when working with data. More importantly, we have found 8 key data personas found in every data-driven organization alongside the different skills they need to possess, and the tools they most commonly use.

If you want to learn more about these data personas and how they could fit into your organization, download our white paper, The L&D Guide to Data Fluency.

Why Data Upskilling is the Backbone of Digital Transformation

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