Using Agile data stacks to enable flexible decision making in uncertain economic times

by Janice Allen
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Eric Yau, COO, Precisely.

We live in turbulent times. And no matter how hard we try to deal with what’s to come, it remains unpredictable. Global disruptions such as the Covid-19 pandemic and hostilities in Ukraine have created uncertainty worldwide. The brewing of economic challenges, fueled by a changing workforce and inflationary pressures that we have not experienced in 40 years, will only create new problems in the future.

Business managers are faced with charting the optimal course in light of these evolving events. How organizations adapt to these risks and uncertainties will have a lasting impact on future fortunes.

While global economic disruptions as a whole are not unique, today’s challenges are like no other. Data is ubiquitous and technology supports much faster decision-making. In fact, at the start of the Covid-19 pandemic, some technology vendors did well as they were driven by the need for remote work, while segments like travel, leisure and retail took a turn for the worse. The current disturbances and uncertainty present different obstacles, and new types of disturbances are driving different reactions and results at an accelerating pace.

One way to manage in uncertain times like these is to create multiple potential scenarios and track real-time data to determine which scenario is most likely to occur. For example, a company can envision the best outcome scenario, a middle ground scenario, and a worst case scenario. As the data begins to reveal that one scenario is more likely, managers can adjust their strategy to mitigate risks and optimize opportunities. Many successful organizations adopt this approach.

With each new disruption, new stats are needed to track each scenario. For example, during the Covid-19 pandemic, cash flow was a major concern and outdated accounts were tracked with a new level of control to enable the company to respond quickly. But because the weakness is unevenly distributed, organizations need data from across their organization to understand what’s happening in different segments of their business and the broader market.

Metrics that have generated revenue in the past could be very different after an unforeseen global economic shock. As trend indicators shift from traditional metrics to something new, executives need to consult analytics and dashboards much more often. Having the data two weeks faster and the right analysis to support strategy adjustments can have a significant impact on the future. And finally, leaders need to be confident that the data and trends they see are real and current. Without this confidence, coming up with effective strategies becomes much more difficult, if not impossible.

Evaluate whether your tech stack is dynamic enough to thrive in turbulent times

The importance of a robust and dynamic data strategy is especially important when the business environment is changing rapidly. Data tech stacks need access to extensive data sources so that complete data is available when and where it is needed. Adding third-party data to an organization’s first-hand data can also provide more context and enable greater insight. But to really trust the data, it must be accurate, consistent, and its role in the business must be well understood.

Consistency: data that is fast and fresh

To make agile decisions to respond to changing scenarios, leaders need real-time visibility into business performance. This requires access to data from different business systems when they need it. Data silos and slow batch delivery of data are not enough. Outdated data and inconsistencies can distort the perception of what is really happening in the business, leading to uncertainty and delay.

Pipelines must have robust data integration capabilities that integrate data from multiple data silos, including the extensive list of applications used throughout the organization, databases, and even mainframes. Changes in one database must also be reflected in real time in another database. Robust, real-time data pipelines that can stream data from various business systems, even complex mainframe data, to dashboards and analytics platforms are essential.

Accuracy: data that can be used with confidence

In difficult times, the environment is much less forgiving, so the margin of error is very small. The trust of a manager in the data is paramount. And this trust requires data quality controls that include machine learning models to recognize whether the right rules are being applied to your data and flexible data management tools that can be adapted to different business scenarios.

The ability to support innovation at the core and at the edge of your data infrastructure is another key factor for flexible and dynamic governance tools. Data observability is also vital to ensure data anomalies are identified quickly before they affect downstream business applications. Knowing how your data pipeline functions before decision makers consume the data helps analysts identify and fix problems before they reach end users.

Context: data that is complete and enriched

To deliver the new insights needed to track changing trends, adding third-party enrichment data to the data pipeline also provides more context to help decision-makers understand which scenario is most likely. While the context required varies by organization, insights into dynamic weather patterns, population movement, demographics, and spatial relationships can deepen your understanding of possible scenarios and solutions, revealing insights that would otherwise be invisible to an executive team.

Adapt and thrive with data integrity

As the waters become more turbulent, the adaptability will improve your position against your competition. Trust in decision-making leads to a motivated team that leads to decisive action. Accurate, consistent contextual data is essential to managers’ ability to run an agile organization.


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