Discussing the value of data in the fight against the pandemic is a moot point, because even before the outbreak, it had become a catalyst to help organisations identify insights for immediate decisions. And working in tandem with AI, it was already accelerating the speed at which a business can operate.
The coronavirus has simply served to highlight how digitally mature organisations, have been able to innovate and adapt overnight in response to a sudden shift in market conditions.
Their ability to work faster, and stay agile, has required data of the highest quality, delivered at high speed and in considerable volumes. Which they already had in place before the turn of the decade, let alone the start of the outbreak.
Which is why it is imperative that before an organisation rushes into a reaction against the pandemic, it looks in the mirror to ask itself fundamental questions like- What is my organizational goal? Why should I be interested in data? Do I understand and acknowledge my organizational data maturity? How can I unleash the power of data to meet these goals?
Companies adopting advanced data technologies with equally advanced data analytics will thrive in the post-COVID era because they can pivot, quite naturally, in reaction to the pandemic. However, many companies are at relatively early stages of their journey — especially when it comes to combining data and analytics and driving business value from it. (See Exhibit 1 that compares data maturity between 2016 and 2019).
This prevents systems integrations that might have otherwise helped reduce costs, complicates changes to the supply chain that could have increased efficiencies and prevents new product design or services that would have answered a shift in customer demand.
Realising their data challenges should help senior executives understand and prioritise the importance of enterprise data and analytics as a key to unlocking their ambition and future-proofing the business.
Except, understanding an organisation’s data maturity and aligning it with the business rationale to evolve into a data-driven organisation will take too long in today’s turbo-charged environment. Indeed, BCG’s Data Capability Maturity Survey shows that data transformation is proving arduous for many organisations — and the speed of transformation is often overestimated.
Moreover, it is likely that extended digital transformation programs are halted or placed on the back-burner during an economic downturn when companies are fighting for survival rather than market dominance. In fact, according to eConsultancy and Marketing Week, nearly half of organisations with a turnover north of £50m are delaying planned technology and IT infrastructure projects.
This does not mean laggards and even companies that are in the development stage should concede defeat. On the contrary, there are plenty of short-term objectives that can still be met and will ensure that any organisation, regardless of where it identifies itself along BCG’s scale for data maturity (see exhibit 2), comes out fighting post-pandemic.
Which is why we have outlined what companies in each category can be doing right now to navigate the post-COVID era with the help of data.
Laggards — use the opportunity to start investing time and effort in data
In the short-term, these organisations should assess (or reassess) their data vision; what makes an organisation interested in data? Is the penultimate goal — improvement on what is already in play or a radical transformation and investment?
Either way, an organisation at the infancy of its journey needs to evaluate the business case with a focus on resource optimisation, cost efficiencies, and asset optimisation to identify the key post-COVID use cases.
These organisations should carefully choose the pilot programmes that focus on the critical parts of the value chain — projects that immediately start delivering bottom-line improvements.
Undertaking data management actions in parallel and deploying them for the pilots may seem daunting at first. But utilising out-of-the-box data solutions can significantly simplify effort, increase the speed of implementation, and help in deploying advanced use cases such as AI and ML when the time is right.
In the long-term, these organisations can apply the learning from pilot projects to further transformations, and use the extra value created to self-fund further data developments.
Concurrently, they should also utilise the time to start focusing on building a robust operating model, data governance, and analytical organisation while keeping their data ecosystem simple.
Developing organisations — use the opportunity to raise the stakes
During this time, and as highlighted by the eConsultancy research, companies with developed data processes and platforms will be challenged to do more with less. For example, developing organisations will have to compromise between predicting customer satisfaction and achieving production efficiencies. Therefore, it is critical organisations take the time to reassess their portfolio and reprioritise data initiatives.
This might mean pulling the plug on the data efforts with a low return on investment, milking the existing data investments which drive significant business value, and carefully prioritising use cases that drive substantial future value.
As with the laggards, developing organisations must also continue to focus on building their data foundation. But with developing organisations, foundational activities typically include metadata management, developing and governing quality KPIs, and the enforcement of data governance with a focus on long-term capabilities. Combined it will help create a comprehensive data management framework that can harness the real value of data for such an organisation. (See Exhibit 3)
Leading organisations — Outpace the competition
Organisations that have state-of-the-art data platforms and mature processes can be defined by three immediately recognisable behaviours;
- They are integrating historical data with data generated during the economic pause to identify long term impact on business. From remote working to reduced business travel and expenses, resilience of remote IT infrastructure to vulnerabilities in supply chain, these organizations are utilizing data to reinforce weaker links of primary value chain and supporting functions.
- They are analysing external data to help understand real-time changes in market demand. This allows them to react to the slightest change in customer behaviour, execute customer sentiment analysis, predict new channels, perform demand/supply simulations, and keep an eye on market recovery indicators.
- They are combining all their data and analytics with emerging technologies to predict the future and forecast customer demand. For example, integrating data from economic indicators and performing predictive analysis can generate ‘what-if’ scenarios that can be deployed depending on how the economy evolves. Advanced analytics, combined with AI, can also build resilience in supply chains through predictive maintenance and improved planning. Targeting the customers using a combination of AI and dynamic consumption patterns can help boost existing revenues channels while unlocking new ones. However, as a previous BCG analysis has pointed out — most successful use cases will seamlessly combine AI with human judgment and experience.
Successful companies have worked tirelessly in the past on simplifying and modernizing their IT legacy to build an industrial platform for data and digital technology. Such a platform addresses their need for rapid execution of incremental business use cases. Starting small and moving fast has enabled these organizations to scale up their data efforts at an industrial level, positioning them ahead of the competition during the crisis times.
Conclusion: Prioritise the data portfolio to take decisive action
The coronavirus is forcing change, but how much change an organisation forces through will be dictated by the maturity of its data to cope with any transformation.
Those companies who are already utilising advanced data techniques, such as data analytics, can make more immediate and informed decisions with confidence; decisions that are often against the grain but will set them apart from the competition. Conversely, companies that are still developing their data capabilities will be more conservative in their decision making because the level of insight they can obtain from their data is limited.
Either way, in adversity there is always opportunity. And in the current environment, it’s the realisation that data efforts are being prioritised to support cost-reduction, improve revenue, prevent fraud and optimise the workforce — thereby positioning any organisation continuing to improve its data maturity more competitively against those that are halting the progress of their digital transformation.