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  1. Technology
September 23, 2022

Why all businesses must democratise data analytics

Alteryx’s Alan Jacobson discusses the need for greater data democratisation, a journey that begins with empowering one’s knowledge workers.

In virtually every industry, access to and understanding data analytics has never been more important. Organisations need to upskill their knowledge workers or risk being surpassed by competitors who have made data democratisation a priority.

Democratisation involves sustaining value creation and innovation at scale – and, rather than siloing analytics expertise, empowering as many stakeholders as possible to derive value from data.

data analytics
Increasingly, all enterprises must be data-driven, but analytics is too often left to small, specialist teams, detached from the wider business. (Photo by Song_about_summer)

But creating and maintaining such a culture is a continual journey with no fixed end-point. For many enterprises, it requires a significant shift in mindset. Investing in data does not equate to being data-driven – and digital transformation needs to be a collaborative, inclusive process.

In the eyes of Alteryx chief data and analytic officer, Alan Jacobson, many companies still approach analytics from the wrong direction, seeing it as the purview of a small coterie of experts. While individuals within an enterprise often recognise the need for greater data capabilities, the wider business – often driven by middle management – fails to adequately empower them to act.

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“This is not a question of technology or even data fluency,” Jacobson says. “The physical subject matter can be learned in weeks, if not days, and it doesn’t take much to become competent in the use of analytics in a relatively short space of time. The problem is that businesses need to realise that analytics is fundamental, and democratisation must become a top priority. If you don’t learn how to do this stuff, you’re not going to remain competitive.”

One metric companies should consider using to get an idea of where they are on this journey, Jacobson advises, is the percentage of knowledge workers able to operate at a competency level above spreadsheet management: “If it’s single digit percentages, you might want to consider making more concerted efforts to bring your staff onboard.”

A top-down approach for data analytics

So if not a technology challenge, what is it then? According to Jacobson, this is, at its core, an issue of change management and, as such, subscribes to well-established best practises and pitfalls.

“While the C-suite is becoming more interested in the prioritisation of data and analytics to drive future profitability,” Jacobson says. “That attitude still isn’t there at every level of management, and it needs to be. It’s the responsibility of C-suite, not only to pay lip service to greater use of data, but to implement top-level changes in a way that works for everyone.

“Whether by offering training or explaining the logic behind the introduction of analytics as a central tenet of the business, every train needs a conductor and that conductor has to think programmatically about how to bring everyone along on the same journey.”

Empowering knowledge workers

Democratisation comes down to giving knowledge workers the tools to not only make day-to-day tasks easier, but to collectively turn their organisations into data-driven centres of excellence.

Promoting analytics literacy benefits everyone, according to Jacobson, as it leads to more efficient long-term data handling and offers better security than hiring a handful of data specialists to work on projects in isolation from those at the actual business coalface.

“We see more companies putting chief data officers in place, or chief data and analytics officers, or chief transformation officers,” says Jacobson. “Unless these leaders are there to build analytics into the culture of the business, then success is going to be limited.

“What we see happening is pockets of innovation influencing behaviour within the wider business – more of a bottom-up approach. Where, for example, finance departments might decide to transform the way finance is done, and by introducing analytics within their own department, suddenly the word spreads over an entire organisation.”

Though Jacobson acknowledges that such bottom-up initiatives can engender real change, he also stresses that enterprise-wide change driven from the top is likely to make a bigger impact more quickly. He cites the need to identify analytic champions with the enthusiasm, experience and influence to secure wider buy-in and build momentum.

For driving democratisation, Alteryx suggests a four-stage approach: aware; prepare; execute and sustain. The first step involves ensuring executive buy-in, gauging where you are in your data journey, and identifying those aforementioned champions who will help drive democratisation efforts. The Prepare stage sees you define your vision and strategy, as well as identifying, mapping and prioritising those initiatives which can build the most early impact. Once that plan has been executed, efforts must be sustained through tracking impact and rewarding success. 

A journey, not a destination

Jacobson predicts that if organisations don’t master or at least grasp the importance of analytics quickly, they may not be here a decade from now. C-suite may implement the directive, but unless they can bring middle-management on that journey, the half-life of those organisations may be even shorter, since middle-management are ultimately the people who maintain the day-to-day running of things.

The good news, he says, is that change management is a well-studied discipline and, despite its seemingly complex nature, creating a data-driven culture should follow a similarly predictable playbook; the first step of which is to make employees aware of the changes being made and the logic behind embarking on that analytics journey.

From Alteryx’s perspective, Jacobson has noted significant changes for clients who have implemented analytics in this way; not as a one-off, but as a continuous, evolutionary journey.

“We’re seeing lots of our clients improving their bottom and top line impact percentages, improving revenue by as much as ten, 15, sometimes even 20%,” he says. “Aside from hard numbers, however, the truly transformative work is long-term, when average knowledge workers are able to use data and analytics in different and more mature ways.”

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