Digital transformation has evolved so rapidly in recent years that an “analytic divide” is now emerging. While successful digital players like Amazon and Netflix continue to distinguish themselves from competitors by using data analytics and automation to make accurate predictions about customer demands, others are still standing on the precipice of how to extract value from data to transform.
To move forward, organisations must push beyond this precipice and embrace business insights fuelled by data and driven by predictive analytics. Yet, a common misconception is holding many back - that they need to use complex code to build accurate predictive models. Over the past few years, much has changed in this arena.
The development of self-service, code-free predictive analytics and model scenarios means that organisations of all shapes and sizes can drive forward strategically and reap the benefits commercially. Those in possession of these advanced analytic capabilities are beginning to stand head and shoulders above those who lack the capacity to interpret, embrace and strategically utilise data and analytics.
Organisations accumulate vast amounts of information. If this data Is not utilised correctly, it tends to bury a wealth of transformational business insights, making it redundant. The solution? Leaders must cultivate a culture of data science and analytics from the top down. At the top of that pile comes data literacy.
Data literacy should be viewed as a crucial skill and, thankfully, it’s something that seems to be becoming more pronounced on corporate agendas, with 82% of C-suite executives planning to address the data skills gap in 2020. However, with the demand for specialist data skills tripling over the last five years to 231%, 47% of organisations are struggling to fill their data science roles.
There is a clear necessity to empower workers at all levels to work with data. In pursuit of evading the “analytic divide”, data must be democratised to enable ordinary people – not just trained statisticians – to solve complex data science challenges.
Last year, the UK Government invested £170 million in educational facilities across England to invigorate the UK’s data literacy, an insufficiency which, by the Government’s own admission, is costing businesses more than £2 billion a year. It has long been reported that organisations are burdened by significant barriers to up-skill their workforce in AI and data proficiency. In one recent survey, 73% of UK firms said they lacked the talent to complete AI and data science initiatives.
To fulfil the Government’s commitment to increase R&D investment to 2.4% of GDP by 2027, 2,500 traineeships have become available in enhancing data proficiency, with 1,000 scholarships specifically targeting female, BAME and disabled demographics. Although Government initiatives are useful, they also require organisations to shift focus, reconsider their existing timelines and re-evaluate the way they operate in order to bridge this “analytic divide”.
So, how does it work in practice? The first step in democratising data and analytics is providing access to one centralised platform which allows analytics to be transformed into an asset to inform business. An accessible data platform is essential for capitalising on the data economy because it supports analytics creation and consumption across an entire organisation. By democratising data access and discovery, every worker will be empowered to ask the right, business-relevant questions and obtain swift answers without relying on highly-trained data professionals.
Additionally, and perhaps most importantly, shareable analytics platforms will be critical to unifying the data, analytic processes and people within an organisation. By making all data work additive, and relevant insights seamlessly (and securely) accessible by relevant parts of the organisation, every business will fast-track its digital transformation journey.
These systems are already readily available to those with the foresight to adopt them (eg, the data scientists of the world). However, the next step is demonstrating the prowess data and analytics can have in revolutionising your organisation. Awareness stems from both Government and corporate initiatives promulgating the advantages of emerging technologies to the next generation of our workforce. It needs to be a joint effort.
By making data and analytics accessible to ordinary people, we empower them to harvest business critical insights to drive transformational change. Quality data insight is no longer a luxury - teams that understand both the business and what it takes to pull the right predictive and prescriptive insights are successfully evolving with the times and keeping one step ahead of their competitors. Thanks to the evolution of technology, we’re now seeing a wave of smarter, more accessible data systems that can be deployed by any organisation without the need for specialist qualifications or code.
In many ways, we’re lucky. The learning curve with the latest enterprise tools is much less steep than it has been with previous generations. Capabilities like self-service, drag-and-drop, code-free automation, along with built-in help and extensive community support, all make the road much easier to navigate. The data journey has never been shorter and, as companies strive to become more efficient during difficult times, there is ample motivation to embrace smart, data-driven decision-making.
The new category of analytic process automation (APA) is here to support this mission - it’s a software swiftly differentiating itself by accelerating the rate at which organisations can make critical, data-driven decisions. Through these platforms, methods that have historically required a high level of skill can now be executed by ordinary employees, thanks to code-free and assisted building blocks that can construct models with transparency and make data science learning and up-skilling easier.
Empowering your workforce with the right tools is vital. Using a platform with self-service tools that enable employees to automate analytic processes enables them to eliminate the manual work that slows down problem-solving. On the back of that, they can get the business insights they need in the most efficient way.
This democratisation ensures that anyone can go on the data analytics journey, whatever their level of expertise. The workforce is enabled to move beyond human decision-making and empowered to use predictive analytics to harness the hidden power within thousands of disparate data sources and processes to inform these decisions. They are able to uncover actionable insights and advocate business process automation.
It’s important to recognise the human element, too. Fostering a culture of analytics involves participating in the wider business community and finding out how other companies have used analytics to solve similar problems. If your company culture doesn’t embrace this human-centric approach, then no matter the technology and data you have, it’ll be difficult to cross the digital divide and remain competitive.
Despite the speculation about the future of the workplace not being entirely optimistic, adversity provides a moment of clarity - a moment to look ahead to data. The potential of automation and intelligence driving tangible, measurable business outcomes through amplifying ingenuity with humans in the driving seat. We can, for sure, expect a resurgence of smart, responsive organisations embracing data-driven insights to equip themselves for the challenges ahead.
Nick Jewell is senior director, product marketing at Alteryx. He will be part of a panel discussion on this subject at Big Data London.