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Transforming Information into Insight with Data Analytics
Key takeaways
- Companies without a strong data and insights capabilities have found themselves at a disadvantage in the world of fast-paced change brought by coronavirus.
- Business leaders need to be able to master their data, understand its insights and act on that information to remain agile and competitive.
- Organisations can think of their data and analytics journey as progressing along a maturity scale in three stages.
As the coronavirus pandemic unfolded, one leading global engineering and technology services company found itself well positioned to meet unprecedented challenges. The firm had kicked off a digital transformation project the year before, including investing in a data and analytics program equipped with advanced tools.
A few weeks into the crisis, it became clear that in several major countries in which the company operated, engineering and construction were likely to be considered essential services. The CIO began scenario planning. He determined that supporting remote work would be critical in the weeks and months that followed and asked regional leadership and IT teams to accelerate training programs for remote working and digital tools, performed a cyber security assessment to highlight vulnerabilities and risks, and asked the procurement and telecommunications teams to contract for additional bandwidth. As a result of these moves, employees were able to seamlessly deliver sophisticated infrastructure and architectural designs to their customers.
Other companies struggled to keep pace with the rate of change. It took the leaders of one global automotive components manufacturer nearly a month to obtain the information and insights needed to make critical staffing changes at various facilities. Elsewhere, consumer goods companies lacked the ability to read signals about changing consumer preferences during lockdowns, such as increased demand for kitchen tools and home gym equipment.
Although responses to the crisis and the success of those responses have been varied, a common theme has 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.1 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. Now is the time for companies to invest in and enhance these data and analytics capabilities as part of their broad digitisation efforts.
The advancement and affordability of key technologies has dramatically enhanced the transformative power of information. Consider, for example, the proliferation of connected sensors and infinitely scalable computing and storage capabilities at price points that are compelling for a number of new use cases, coupled with the development of increasingly accessible machine learning (ML) and AI tools. The technology is there, and many companies are already using it.
But bringing this vision to life by building a high-caliber data and information capability can’t be accomplished overnight. In fact, if there is one thing that the coronavirus crisis made clear to many companies, it’s that their information value chain had gaps that could create serious problems during challenging times — gaps not easily overcome. What may not be as clear, then, is how companies should begin their journey to data and analytics excellence.
Infographic: Opto Design / James Yang © 2020 PwC. All rights reserved.
Organisations can think of their data and analytics journey as progressing along a maturity scale. At each stage, companies build their proficiency in six critical organisational elements:
- Business decisions and analytics: Prioritise analytics insights that fuel the business strategy, not those that just report what’s happening.
- 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.
- Technology and infrastructure: Build tools that support the analytics ecosystem, including AI, and democratise insights through analytics-as-a-service (AaaS).
- Organisation and governance: Establish an operating model to empower the use of governed data and analytics.
- Process and integration: Ensure insights are rapidly integrated into decisions through an aligned, agile process.
- Culture and talent: Instill a data-driven culture that blends business knowledge and analytics insights across all levels.
Certain elements will play a more visible role at different points along the maturity scale, but ultimately, excellence in all six is required to realise the full value of a data and analytics transformation.
In the first stage, companies generally begin to accumulate significant quantities of data and information, but it is sporadic and requires labor-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). Companies at this first stage are likely to have developed some of the technology and infrastructure needed to support the analytics ecosystem, but usage is typically limited.
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 are able to move to stage two, it is because they are beginning to ensure that the insights produced are aligned with their business strategy — there is a clear connection between business decisions and analytics. To support this transition, the need for organisation and governance becomes clear. Companies 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 harmonised data infrastructure. Company leaders have gained the foresight to see disruptions or competitive pressures coming, and to make data-driven decisions to prepare for them — but this is happening only at the highest level.
At this point, company leaders ask themselves: Why can’t we make better decisions given everything we’ve done so far? To move the needle, organisations must focus on process and integration and culture and talent. Doing so leads them to the third stage, in which data and insights are shared transparently across the company, and any questions concerning data ownership are resolved. Leaders view the business as a “knowledge company,” with analytics and insights firmly embedded in decision-making processes. They create a culture that is ready to take advantage of the insights, cultivating talent to support data optimisation: Companies need a digitally upskilled workforce prepared to adopt the new tools and technologies.
In addition, when companies have reached peak maturity, many types of data are brought together. This is true for information generated within different business units — the analysis is cross-functional. But it is also true with regard to data from outside sources, such as market data, supplier health data, and customer behaviour data. Moreover, companies at the third stage use a rich variety of algorithms and AI/ML tools to put all this data to work, and extract insights beyond the capacity of the human brain to tell stories that would otherwise remain hidden.
In other words, through the three stages, companies move from the “what” (accumulating information that illustrates what happened), to the “so what” (contextualising insight that explains why it happened), to the “what next” (asking what they should do to rewrite the script, either to capture opportunities that are not yet obvious or to prepare for challenges that have not yet hit). They don’t just see the factors that could impact their performance — they see how to change those factors in ways that shape their market.
The coronavirus pandemic should be a wake-up call for companies that have yet to start their information value chain evolution or that have been idling at an early stage. Whether leaders thought they could wait, or thought they could get by with less sophisticated capabilities, or were unsure how and where to start, they now see that transforming information into insight will be key to reinvention — both during recovery and in the post-crisis world.
This is a truncated version of an article originally published by strategy+business on 9 September, 2020.
References
- https://www.weforum.org/agenda/2020/07/3-ways-covid-19-is-transforming-advanced-analytics-and-ai/
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