How to digitally transform information into insight

As the coronavirus pandemic unfolded, companies around the world coped with the challenges posed by the crisis in different ways. Some were particularly well positioned to meet the unprecedented issues they were facing thanks to their foresight and early adoption of digital transformation; while others struggled to cope with the rate of change and took far too long to respond to critical issues, resulting in severe financial losses, not only to them, but to their supply chain as well.

While responses to the crisis, and their success, were varied, a common theme emerged – business leaders need an effective way to capture, receive, interpret, and act on information, and to add predictive power and agility to their organisation, says a report in PwC’s strategy+business magazine.

By ensuring streamlined data availability and using advanced analytics in combining new sources of data to develop proprietary insights, leaders can reveal crises before they wreak havoc, uncover competitive pressures before they threaten market share, and surface opportunities before they become someone else’s advantage, it adds, asserting that now is the time for companies to invest in and enhance these data and analytics capabilities as part of their broad digitisation efforts.

A PwC study conducted between October and November 2019 which surveyed more than 1,600 supply chain executives and decision makers in 33 countries, found that companies have recognised the urgency of investing in and enhancing their data and analytics capabilities.

“63 percent of Digital Champions — companies ahead of the curve in supply chain excellence — reported that they had already implemented an AI and advanced analytics platform, and another 24 percent said they were piloting such software. (It is worth noting, however, that Digital Champions made up just 9 percent of the overall sample.)

“Our own discussions with senior executives at several major companies have revealed that even as they recover from the more immediate effects of the pandemic, leaders are rethinking their competitive strategies and how they will reconfigure their businesses to be more resilient in the future. As they adapt to a new reality and cope with the immense challenges of asymmetry, disruption, age (demographic pressures), polarisation, and trust, they will need the ability to manage and draw insights from their information,” the report says.

At the same time, the advancement and affordability of key technologies has dramatically enhanced the transformative power of information – for example, the proliferation of connected sensors and infinitely scalable computing and storage capabilities at price points are compelling for a number of new use cases, coupled with the development of increasingly accessible machine learning and artificial intelligence tools. 

The technology is there and companies are already using it – in manufacturing organisations, IoT solutions that include facial recognition are being used to eliminate ‘badge swapping’, which puts unapproved people in a tool crib or uncertified operators at the controls of a machine, for example.

“Companies are now getting more real-time signals than ever before from their products, operations, and customers. This real-time information, combined with advanced AI technologies, helps companies gain critical insights to enable more efficient ways of doing business," Sanjay Ravi, general manager of Microsoft's automotive industry, is quoted as saying in the report.

However, building high-calibre data and information capability isn’t accomplished overnight – the coronavirus crisis has made it clear that many companies have gaps in their information value chain which could cause serious problems during challenging times.

Therefore, in order for companies to begin their journey towards data and analytics excellence, they should look to build their proficiency in six critical organisational elements:

1. Business decisions and analytics: Prioritise analytics insights that fuel the business strategy, not those that just report what’s happening.
2. Data and information: Let the data tell a story through the flexible integration of multiple data types, rather than forcing the data into a predefined model.
3. Technology and infrastructure: Build tools that support the analytics ecosystem, including AI, and democratize insights through analytics-as-a-service (AaaS).
4. Organisation and governance: Establish an operating model to empower the use of governed data and analytics.
5. Process and integration: Ensure insights are rapidly integrated into decisions through an aligned, agile process.
6. Culture and talent: Instil a data-driven culture that blends business knowledge and analytics insights across all levels.

With a more sophisticated approach to data and analytics, companies can adapt to change before it happens. The data transformation journey can be measured against an ‘information maturity scale’, which can be defined as such:

Stage 1:

Opinions matter most:

Designated experts within a company react to situations with decisions based primarily on opinion, rather than data

Stage 2:

Foresight with limited reach:

Company leaders have the foresight to respond proactively to new pressures, but the information doesn’t have the reach to lead to different actions

Stage 3:

Decisions that shape outcomes:

The integration of data and advanced models enables people throughout the organisation to see the world differently and make decisions that shape the future, rather than react to circumstances.

“In the first stage, companies generally begin to accumulate significant quantities of data and information, but it is sporadic and requires labour-intensive manual processes to stage and validate. Data is siloed and analytics is limited to historic performance — the insights don’t help shape future performance. Moreover, the data doesn’t always tell a consistent story across the enterprise (or worse, it tells outright conflicting stories),” the report says.

“The experiences of the first stage often lead executives to view the road to information maturity as too onerous to travel, and despite having some pockets of data-focused activity, they still rely more on expert opinion to make decisions. The data and analytics system as a whole is not yet delivering business insights.”

When companies move to stage two, they begin to ensure that the insights produced are aligned with their business strategy, and that there is a clear connection between business decisions and analytics. To support this transition, the need for organisation and governance becomes clear – companies should start defining processes that will deliver cleaner data more effectively. 

By the end of this stage, companies typically have a centre of excellence that delivers enterprise-wide insights to all business stakeholders across a harmonized data infrastructure, the report adds.

Finally, in order to move the needle, organisations must focus on process and integration and culture and talent. This will lead them to the third stage, in which data and insights are shared transparently across the company, and any questions concerning data ownership are resolved and leaders view the business as a “knowledge company,” with analytics and insights firmly embedded in decision-making processes. They have created a culture that is ready to take advantage of the insights, cultivating talent to support data optimisation.