Advanced Data Analytics: Crisis Pain Points - Pyramid Analytics

In 2021, global companies are in the midst of digital transformation. Many of these changes are continuations of crisis responses from 2020, when organizations rapidly established analytics capabilities to address COVID-19 challenges and prepare for the future. Even before COVID-19, business leaders prioritized advanced analytics, which McKinsey reports could offer up to $15.4 trillion in annual economic value.

This raises a critical question: As the world recovers from the COVID-19 crisis, how can business leaders bridge the gap between crisis response and realizing the opportunities advanced analytics promised from the start?

This article explores how Analytics and Business Intelligence (ABI) can provide data leaders and practitioners with tools and insights to quickly pivot to value-added strategic decisions. Drawing on direct quotes from a March 4, 2021, “Digital Boardroom” event hosted by the CIO Institute and Pyramid Analytics, it identifies modern transformation strategies that can guide companies toward long-term success.

Latent pain points emerged during the crisis

Despite its unique impact, the COVID-19 crisis brought several latent business pain points to the forefront. One major issue is the misconception that companies are “data-driven” when, in reality, most are not.

According to McKinsey, only 30% of organizations successfully align their analytics strategy with their broader corporate strategy. Pyramid Analytics’ research shows similar shortcomings:

• ​78% of decision-makers don’t trust the data they are presented
• ​72% of data analysis is done offline in desktop tools or Excel
• ​64% of users cannot access quality data and analytics

These issues negate much of the value business leaders hoped to gain from analytics investments and put company data at risk as users seek unregulated workarounds or make decisions based on bad information. Few organizations have achieved transformational outcomes (30%), and few executives believe their organizations are truly data-driven (24%).

There is a need for a new approach, different from the traditional top-down imposition of analytics tools. This new approach focuses on empowering individual frontline workers. As McKinsey notes, “For many organizations, such change … runs counter to deeply ingrained processes and business beliefs. However, during crisis-response efforts, organizations have readily empowered frontline employees with decision-making authority.”

Introducing the business decision supply chain

Optimizing analytics and ensuring its adoption must begin by aligning analytics strategies with real decision-making processes at all organizational levels. Bill Balnave, Vice President of Global Solutions Engineering at Pyramid Analytics, introduced the concept of the business decision supply chain:

In this model, “data consumers” can communicate new needs effectively so that “data producers” can deliver insights in a contextual way that data consumers can understand. With these business assets delivered or accessed in near real-time, data consumers can make quicker decisions about rapidly changing market conditions.

“This is how you get that transformation,” said Balnave. “We think this is the opportunity to think differently about how you look at your data assets and to have people use it to turn it into business (assets) decisions.”

Improving data accessibility—and data governance

A Director of IT EMEA Enterprise Platforms at a leading research-based biopharmaceutical company highlighted the complexity of data at enterprise scale. In commercial analytics, joining together many data sets—internal, external, and purchased—is essential for business context.

This director emphasized the critical balance between governance and empowering frontline workers to leverage insights for decision-making. “The essential part is not the technology,” she said. “It’s what you govern and what you keep centrally locked down … the rest of it, you get out of the way and you give access and let people do what they need to do.”

Participants echoed the need to enable business users while maintaining governance in environments that converge data from multiple sources for complex decision-making.

A Chief Information Officer at a global power distribution and protection manufacturer identified trust deficiencies that arise when data producers are not accessible or responsive to consumers needing data-driven insights. “Our biggest challenge was, we were too strict—IT was the only one delivering reports and we were too slow,” he said. “We couldn’t verify it and neither could the people presenting it.”

Technology can play a critical role in preventing data producers from wielding too much power and ensuring consumers have enough access. The importance of developing a successful operating model was discussed, where complexity is not a barrier for everyday data consumers:

“There is a lot of ambition in the business to do stuff with the data, [but] the skills required to do some of this stuff is beyond the average analyst,” said the IT director. “Instead of IT sitting on it—which is a dependency—we need to offer out the service; and if it gets complex, we help [frontline employees] get the necessary skills.”

In this way, decision-makers have the support they need, but “they don’t take the data offsite and expose it to hacking or other risks,” she said. “That is critical.”

Connecting data excitement with data capabilities

Across the modern enterprise, there is no shortage of “data-excited people” among data consumers, Balnave points out: “These become your de facto leaders in raising awareness and ability.” What’s missing is a deeper understanding of the value and context of data on the part of data consumers—and more accessibility, without surrendering necessary governance.

Self-service capabilities that enable data consumers to access insights on their terms make this possible. Optimizing the business decision supply chain means enabling data producers to serve up insights to users with minimal effort; it also means supporting understanding, consistency, and collaboration.

To find out more about Pyramid Analytics and their approach to the business decision supply chain, contact one of their analytics experts or request a free demonstration.

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This post originally appeared on the Global CIO Institute blog.