3 Ways to Avoid Data Failures in the Time of Crisis


Glenn Rabie, CEO of Yellowfin, a business intelligence and analytics software provider talks about how the world, as it confronts its greatest challenge in decades, finds that data in all its forms is more important than ever. Mishandling or misinterpreting it leads to poor decisions and even chaos – its time to relook at how we use and understand business data.

When global emergencies strike, people seek answers—and answers require data. The biggest risk, however, is misinterpreting the very data that is so essential for good decision-making.

The problem during the COVID-19 pandemicOpens a new window isn’t acquiring data- as a society, we’re flooded with it. But drawing the wrong conclusions can lead to miscommunication, poor planning and, potentially, catastrophic results.

Leaders in public and private organizations are currently wrestling with all kinds of issues both old and new. From supply chain managementOpens a new window and procurement to contact tracingOpens a new window and economic modeling, managers and officials must make clear assessments based on dataOpens a new window that are accurately and consistently understood.

Without such clarity there is only confusion and contradiction. We see it today in the conflicting messages sent from state and federal agencies. We see it in the reactionary media environment. We see it from essential businesses and entities wherein one group wants to respond in a certain way, and another pushes for a different direction.

There are massive lessons to be learned right now about how to use data. If we can correctly understand the information at hand, put it into the appropriate context, and then build cohesive and broadly-supported narratives, we will experience better outcomes not only in public policy but also in all forms of organizational management.

1. Speed and Specialization

As the business world grapples with hugely consequential problems, CEOs are demanding more from the data they receive. They want high-quality data, they want to know its source, and they want to remove the bias. How is this all accomplished?

Amid all the noise data can generate, arriving at a cohesive view of reality requires both speed and specialization. Generic BI platformsOpens a new window are increasingly losing favor as the go-to alternative. Analysts, managers and C-level executives all want the same thing: to be able to flip the switch on a solution and get answers quickly. Elaborate platforms that try to be all things to all people rarely fit the bill.

Interestingly, AI has also struggled to prove its worth during the pandemic. AI and machine learningOpens a new window have been heralded in the past as transformative technologies—but they have their limitations because they learn from historical precedent. Our world is in uncharted territory.

Instead, organizations are increasingly seeking analytics with a vertical focus and quick ramp-up. Prepackaged analytics solutions for procurement, sales, marketing, supply chain and HR are popular, largely because users don’t have twelve or eighteen months to wait as someone implements a huge enterprise-class system. They want to get going.

Another major trend is automated analytics. Interest in automationOpens a new window is surging because, once again, there isn’t time for data analysts to create tools, models and dashboards in order to draw conclusions. People want to know what’s happening at a really low level in their organizations very quickly, so they can spot trends and take advantage of opportunities. If a certain product line is growing, for example, and everything else is dropping, decision makers need to understand why. They want to amplify that insight, rather than having it remain hidden.

Learn More: Guide to Supporting Remote Employees With a Data Catalog in Coronavirus EraOpens a new window

2. The Need for Narrative

One of the greatest keys to gaining clarity, however, is collaborationOpens a new window . With most employees working from homeOpens a new window and further apart than ever, modern BI tools must allow professionals to collaborate with the goal of shared understanding.

By coming together, teams can—and should—build relevant narratives or “data stories” that allow users at all levels of the organization to get a clear picture of what the data is saying. Now more than ever, data requires context. Without it, leaders are left with simply telling people what to do, without providing a framework for action. Data can simply become numbers—or worse, create anxiety and confusion. With context, however, organizations can have rational discussions about cash flow, strategic alternatives, change management, best practices and so on, deliberately and with greater focus.

Collaborative stories built around data create trust. This form of narrative encourages transparent and consistent communication all across the enterprise. Moreover, when multiple eyes have looked at the numbers and agreed to their implications, it inspires confidence. It helps people form their thoughts and ideas.

Learn More: 3 Questions To Drive Better Data GovernanceOpens a new window

3. Time to Re-examine

The COVID-19 era has caused people to ask more questions about the value of data than ever before. Taking the time to actually look at data and consider its impact on decision-making is almost a lost art. Today it’s making a powerful return.

After nearly a decade of favoring results over process, it’s time for those responsible for data to stop and reexamine their assumptions. It’s time for silos and departmental agendas to end. It’s time to interlink individuals, disciplines and whole organizations to create a cohesive viewpoint. If someone from another part of the enterprise is able to read the narrative and empathize with the shared story, they see issues in a different way.

This is a time when companies, industries and even whole segments of society are fighting for survival. There is a fundamental shift in how data is viewed and used. Business and people are both complex—which means BI technologyOpens a new window , and the data that drives it, must amplify the strengths of both. If we can increase data literacy—the ability to understand its reality, impact, and implications—we will be able to solve the problems that confront us, quicker and more efficiently than ever before.

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