It’s not enough to merely have access to the right data. Proper analysis of that data can often serve as the differentiator for companies. Accordingly, a wide range of products and solutions exist to make the most of the data at a company’s disposal, in the form of data analytics platforms.
In recent years, a number of trends have defined the ways these products are evolving. Those trends focus on, for instance, democratising access to data analysis across an organisation, allowing correct interpretation and data-aided decision making at all levels, and visualising data to make it less inscrutable for the layman. According to a report from Deloitte: “By investing in a modern business intelligence (BI) platform that complements existing business intelligence systems, businesses can expand their range of insight-driven capabilities. With this investment comes a shift in data ownership from IT to business groups, giving more users the power to answer any question, with any data, at any time.”
The benefits of making data more accessible to users throughout an organisation are manifold. According to Jai Gandhi, VP of Consulting, Ciklum, “providing more people with analytics brings [...] indirect benefits such as congruent decision making (as everyone uses a common set of data) and lower risk (as models are used to profile different scenarios).” On the indirect side of the equation, opening up data analytics can also engender trust in the benefits of data and demonstrate concretely how it can help employees.
But there are also more concrete rewards for companies that unleash the power of data analysis across an organisation. Commenting, Dan Sommer, Senior Director, Qlik, says: “A global study from Qlik, conducted by IDC, revealed that organisations that strategically invest throughout their data and analytics pipelines - from finding and accessing valuable data, to analysing it and taking action - are seeing significant bottom line impact. Three quarters of firms reported that operational efficiency, revenue, and profit increased by 17%.
“This value is only achieved when its benefits are distributed across the organisation. Innovation often happens at the fringes of organisations. Data has limited potential if only a few, highly technical people can access, understand and utilise a data source.”
One of the premiere ways for achieving that level of democratisation is data visualisation - we, after all, are highly visual creatures. As Helena Schwenk, Market Intelligence Lead at Exasol, explains while warning of potential pitfalls: “Data visualisation essentially turns raw data into a universally, consumable form. It’s a tool through which individuals can better consume and understand large volumes of data. When data is properly visualised, patterns become obvious – helping individuals to draw simple, actionable conclusions. Although it’s worth noting that the insights gleaned are very much dependent on the quality of data underpinning the visualisations and the expertise of the user extracting the information.”
Throughout the ongoing COVID-19 pandemic, people in all walks of life have become familiarised with data visualisation, perhaps elevating the general level of data literacy found in companies. According to Sommer, “In 2020 shared data, visualisations, and storytelling exploded in mainstream news, and has driven policy in most countries. Just think of “flatten the curve”. General audiences pored over data in sources like ECDC, Financial Times and Our World in Data. There has been a massive up-leveling in the conversation about data”. He cautions, however, that data is open to interpretation and getting to the heart of the matter requires debate. “We’ll also need to start building frameworks of agreeing on the common ground – and work on an etiquette for intellectual honesty in debating data. If we can get that ironed out, it will bring in millions more on the journey toward data literacy.”
Going forwards, the evolution of data analysis platforms is sure to be impacted by experiences of the pandemic, not least the rapid uptake of cloud solutions. “Where once there was reticence to investing heavily in cloud and other as-a-service solutions, now many are embracing the approach, benefiting from its scalability and elasticity, as well as the fast access to the likes of augmented analytics”, says Sommer. “ This trend is going to continue, with a greater migration of databases and applications from on-premises, legacy infrastructure to cloud environments. In turn, this will drive a need for technologies that can access, move and harmonise data from multiple places.”
Cloud adoption, therefore, is a catalyst for even more data democratisation, as Schwenk explains. “We can expect to see organisations do three things; increase access to consistent and secure data, incentivise data sharing, and make a more concerted effort to put insights into the hands of everyone – rather than just senior decision makers.
“The move to the cloud has been unstoppable and holds true for data and analytics organisations too, as they utilise its scale, performance, cost effectiveness, and ability to support access to distributed data stores.”
Where human analysis becomes infeasible, the slack is of course taken up by artificial intelligence, although this comes with its own set of risks. “Predictive models typically don’t work well when a critical input datapoint has never occurred,” says Sommer. “We saw this in the results of the A-Level exams in England, where an algorithm was used to determine scores, and cemented existing trends while locking out outliers. The lack of governance around the impact of outliers in this case led to many young people from disadvantaged areas initially receiving lower scores.”
“The trifecta of Technology, People and Processes will be critical in this next stage of advanced analytics adoption. Humans must proactively govern algorithms – and the ensuing scenario analysis to inform action when the unusual occurs – to ensure that their benefits are achieved at no cost to the outliers.”
Despite the risks, artificial intelligence can work alongside humans to derive even more value from data, as Schwenk explains. “We’ll see more and more of what we term collaborative intelligence this year – combining human expertise and AI as we shift toward deeper data-driven decision-making. While the concept of machines augmenting human activity has been with us for some time, a significant change in working patterns combined with increased pressure to support faster and higher quality decisions when adjusting to rapidly moving situations, means we expect to see further experimentation and investment in 2021.”