Democratising the Data Analytics Landscape

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In May 2017, The Economist published an article proclaiming “The world’s most valuable resource is no longer oil, but data”, paraphrasing a quote frequently attributed to mathematician and entrepreneur Clive Humby.

In his original quote, Clive says that data “if unrefined cannot really be used. [Oil] has to be changed into gas, plastic, chemicals, etc to create a valuable entity that drives profitable activity; so, must data be broken down, analysed for it to have value.”

It is estimated that between 2020 and 2025, the global Big Data Market will grow to reach a value of $229.4 billion; driven largely by the mainstream adoption of data analytics by companies seeking to enhance their internal processes, drive value, identify new opportunities and improve operational efficiency.

From corporate real estate to human resources, more and more organisations and industries have sought to adopt data analytics as part of their operations; understanding the unique opportunity that it presents.

Bridging the Gap

As the adoption of data analytics grows, so too does the demand for data science skills. A report from The Royal Society found that between 2013 and 2018, demand for “Data Scientists” and “Data Engineers” rose by 1287% and 452% respectively (compared to an increase of just 36% for all types of workers during the same period).

Despite this growing demand, significant skills shortages still exist. In their 2019 CIO survey, professional services provider, KPMG, found that skills shortages are at an all-time high, with 67% of respondents stating that they are unable to find the right talent; citing Big Data and Analytics amongst the most scarce skills.

Further to this; in their 2019 – 2020 “Top Insights for the C-Suite” report, global research and advisory firm Gartner outlined that despite 70% of chief auditors stating that they are more likely to act on recommendations based on data-driven insights, just 4% of auditors currently employed in large metropolitan areas possess data analysis skills.

Organisations hoping to benefit from data analytics are faced with a problem. How can they realistically hope to bridge the gap between their data analytics aspirations and the reality of the skills market? For many organisations, software is the solution.

Democratising the Data Analytics Landscape

In a recent report, Gartner identified the “Democratisation of technology” as one of ten strategic technology trends for 2020, outlining its ability to provide organisations with “easy access to technical or business expertise without extensive (and costly) training”.

Gartner goes on to illustrate how “democratization would enable developers to generate data models without having the skills of a data scientist. They would instead rely on AI-driven development to generate code and automate testing.

“Democratised” solutions utilise low-code/ no-code user interfaces that forego the need for technical expertise and experience.  Gartner estimates that by 2024, “75% of large enterprises will be using at least four low-code development tools for both IT application development and citizen development initiatives”.

At Analytics Engines, “Democratisation” is something that we understand very well. For example, Perspective, a solution which we developed for the National Gallery, has helped to “democratise” and improve their reporting process by unifying disparate sets of data within a single platform.

The solution is able to present users, regardless of their data analytics abilities, with intelligence and insights that can be easily understood and actioned. Since deployment, the solution has been adopted across the organisation and has helped to improve transparency, communication and teamwork.

The New Oil?

Clive Humby understood that much like Oil, data is most valuable when it is refined, shaped and productised. If data truly is “The New Oil”, then “Democratised Data Analytics Systems” may well be the Ford Model T – the means by which mass adoption of data will occur.

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