Published: Sep 30, 2019

data democratisation for the data-driven organisation

Roused to action by catchphrases such as ‘disrupt or die’ and ‘data is the new oil’, companies around the world have invested billions of dollars into digital transformation. Asia-Pacific alone is estimated to spend US$376 billion on digital transformation-related activities this year, and global spending is expected to reach US$2 trillion by 2022, according to reports by IDC.

But for all the money poured into the effort, many companies are struggling to realise value from their digital transformation investments. In a survey of 1,000 business leaders in Singapore, cloud application provider Workday and IDC Asia-Pacific found that 76 percent of their respondents feel that their organisations have yet to achieve any major return on investment (ROI) on digital transformation spending. Even more worryingly, more than half of these said that they have not achieved any measurable ROI at all.

What this suggests is that before companies rush headlong into deploying data analytics, they need to ensure that what they implement can be scaled across the entire organisation. To benefit from digital transformation, companies need to first collect and organise the data that they have, a considerable challenge in and of itself that we dealt with in our previous article on going from proof-of-concept to production. But the crucial next step—one where, most companies fail—is connecting that data to decision making to become a truly data-driven organisation.

Data democratised

When companies first realised that data was valuable, a common approach was to assign specialist data scientists to collect and manage the data, then present their findings to management to aid in their decision making. Over time, organisations began to realise that this siloed approach was preventing them from reaping the full benefits of their data, as data scientists and business leaders often had different priorities when it comes to interrogating and making sense of the data. The hybrid role of business analyst was then introduced to bridge the gap between the technical and business teams.

The problem, however, is that organisations cannot call themselves data-driven if only a small subset of employees are data literate. For the full impact of digital transformation to be felt, small proof-of-concept studies have to be translated into company-wide processes. In other words, every employee needs to be comfortable with working with data.

Being data literate not only means being able to read and analyse data, but importantly, being able to argue with data, challenging what the data means and using data to support a hypothesis. Data literacy is important not only for data scientists and CEOs but also for every staff member on the ground because they are the people who understand the context of the data collected and can come up with unexpected insights into how it should be used.

To facilitate data democratisation, companies are increasingly adopting a ‘self-service’ approach to analytics. Unlike a more traditional approach reliant on data scientists or business analysts, self-service analytics tools empower employees to probe and analyse data on their own. By giving employees access to data across silos on a user-centric interface, self-service analytics help users to find the information that they need to make better decisions.

Closing the data-decision loop

While reading, writing and arithmetic are systematically drilled into students from a young age, data literacy is something that many adults learn in a piecemeal and incidental way. While on-the-job training and skills upgrading must be made a priority, companies can also tap on technology to lower the barriers to entry and help their employees make the most out of the data at their disposal.

This is where we see data democratisation moving: towards hybrid cloud environments with easy-to-use data analytics tools and technologies. Firstly, these platforms will need to be hybrid, giving companies the flexibility of the cloud while retaining certain data on site for security purposes. Once employees have access to the data, data visualisation is a powerful way of turning the raw data into actionable information.

There are also more and more tools today that are geared towards the “citizen data scientists” – business analysts who may not be armed with PhDs but who are just as capable in delivering actionable insights from a combination of enterprise and external data. Citizen data scientists are a direct result of the data democratization movement and they are aided by an array of AI-driven tools and technologies at their disposal.

Need to clean and prepare data for analysis but don’t really know any coding language to transform and wrangle the data? There are now various data prep tools that one could use that requires little to no coding. Need to build predictive models but don’t really know machine learning? There are now automated machine learning tools that helps you build algorithms with minimal knowledge of data science. Need a more intuitive way to interrogate data? The current trend is towards Natural Language Query that uses Google-like search to automate the creation of data visualisations. All these tools and technologies are designed to simplify the process of uncovering insights within the data.

At NCS, we believe that such simplicity is what will ultimately drive adoption and help companies see the fruit of their digital transformation efforts. Our vision of a truly data-driven organisation is one where every individual—from the CEO to junior staff members—has data in their hands to close any data-decision loop that may exist in their role.

Speak to us today to find out how to make data democratisation work for you. Visit for more information.



1. Worldwide Semiannual Digital Transformation Spending Guide

2. Here's why corporate digital transformation plans are failing

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