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Leveraging better data to combat decision dead zones

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Leveraging better data to combat decision dead zones

By Doug Bennett, Chief Operating Officer at DTN

In today’s digital world, the ability to make effective decisions has become increasingly difficult due to the growing complexity of business environments and the abundance of available data. A recent Fivetran study found that while 86% of respondents say their business needs access to real-time enterprise resource planning data to make smart business decisions, only 23% of responding companies have systems in place to make that possible. 

What’s more, according to a Gartner report, in spite of the exponential increase in data, 65% of senior executives acknowledged that decisions have become more intricate compared to five years ago. While an appropriate amount of data can instill confidence in business leaders when making decisions, an excess or shortage of data can result in decision paralysis or ‘decision dead zones’.

So how can business leaders avoid decision dead zones, which consume valuable time, hinder efficiency, and incur costs? The solution lies in leveraging operational Intelligence, which offers timely and context-specific insights precisely when they are required. By employing streaming data feeds and event analytics, businesses can develop and optimize models to uncover industry-specific, and often task-specific, insights that guide operational instructions, facilitate process adjustments, and reveal untapped opportunities. To overcome decision dead zones, businesses need to address three key areas.

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Remove siloed data once and for all

A data silo refers to data that is collected and accessible only to a specific group or unit within an organization. These data sets typically become isolated due to outdated processes and time constraints. According to a Zendesk report, only 22% of business leaders claim that their teams effectively share data. Data silos can hinder business progress by creating decision dead zones.

When data is not easily accessible to decision-makers, it obstructs collaboration across departments, allowing inefficiencies to persist and even posing additional risks. However, if companies integrate disconnected data streams into a centralized source of truth, this siloed data can transform into an untapped asset. This integration not only facilitates better-contextualized insights but also reveals new opportunities, risks, and efficiencies that were previously unnoticed for optimal decision-making.

Leverage external data to enhance decision-making

Relying solely on internal data is insufficient to gain a comprehensive understanding of a company’s operational opportunities. Therefore, businesses must recognize the importance of incorporating external data to enhance their understanding of operations, potential risks, and decision-making across the organization. By combining internal, external, and timely data, businesses can effectively acquire a more complete and accurate depiction of their operations and obtain valuable insights for informed decision-making.

As businesses increasingly embrace digitalization, the process of integrating data has become significantly more streamlined. By leveraging Application Programming Interfaces (APIs), companies can access and merge comprehensive operationally-focused insights to enhance complex decision-making. The ability to model data with scenario-specific inputs yields critical Operational Intelligence that optimizes planning, efficiency, and risk mitigation.

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For instance, integrating external and industry-specific data allows companies to optimize weather intelligence beyond the forecast to monitor and respond to weather impacts. In the shipping industry, this integration enables logistics companies to make better-informed decisions, such as managing fuel consumption. By utilizing real-time routing intelligence based on factors like wave heights, surface winds, and updated weather forecasts, combined with relevant weather data, companies can determine vessel safety measures, carbon emissions, and speed decisions.

Avoid data noise with emerging tech 

To avoid an overwhelming amount of data, which can be distracting, Operational Intelligence can help filter through data and identify the meaningful signals that are relevant to the organization.

Emerging technologies, such as digital twin technology and machine learning, play a significant role in organizing, processing, and contextualizing insights, enabling faster delivery than ever before. These approaches have the ability to quickly incorporate new inputs within the appropriate context, adapting and providing improved decision insights in near real-time.

For instance, utility companies heavily rely on diverse data streams to evaluate various weather impacts, such as service disruptions and load demand predictions. By leveraging Operational Intelligence, utilities can gather and consolidate a wide range of information, including grid performance, infrastructure integrity, service areas, topography, vegetation management, as well as historical and real-time weather analytics. This enables them to deliver enhanced intelligence precisely when it is needed. By gaining comprehensive insights into factors that can affect a utility’s power delivery in real-time, decision dead zones are effectively eliminated.

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As global data generation is projected to exceed 180 zettabytes by 2025, it becomes imperative for businesses to optimise their decision-making processes to avoid falling into decision dead zones. To achieve this, organisations need to develop an approach and embrace the appropriate tools that allow them to extract relevant data and obtain scenario-specific insights. By doing so, businesses can make well-informed decisions that are crucial to their operations. Operational Intelligence plays a vital role in enabling organisations to mitigate risks, enhance sustainability, and identify untapped opportunities and markets.

 

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