How Operational Intelligence Eliminates Decision Dead Zones

Good decisions don’t happen by accident, but too little data can erode confidence, too much can cloud the big picture, and both scenarios often trigger decision dead zones. A recent Gartner report found that 65% of senior executives indicated decisions are more complex than five years ago — and yet there is more data than ever. With interdependent marketplaces and efficiency at a premium, agile decisions are essential. Access to Operational Intelligence can be the difference between making a confident decision in the moment or letting the opportunity slip by, lost in the decision dead zone.

With interdependent marketplaces and efficiency at a premium, access to Operational Intelligence can be the difference between making a confident decision in the moment or letting the opportunity slip by.

Digital-centric organizations can capitalize on Operational Intelligence by harnessing available data and constantly incorporating new, relevant data as it is generated to help uncover hidden opportunities and eliminate uncertainty — the kind of uncertainty that creates decision dead zones. Most businesses must address three main areas on their digitization journey to optimize actionable insights.

 

1. Eliminate siloed data

All businesses collect, store, and can process multiple sets of data, from high-level inputs, such as performance, efficiency, and revenue, to the daily operational inputs that collectively impact the bottom line. That data is worth far less when it is unavailable to decision-makers.

According to TechTarget, a data silo is a repository of data that’s controlled by one department or business unit and isolated from the rest of an organization. It is often unintentional, typically the result of an older process or a quick solution to meet a team’s functional goals, but in today’s dynamic world, siloed data often does more harm than good. An IDC study found companies lose up to 30% of potential annual revenue due to inefficiencies — with siloed data as a primary cause.

Siloed data contributes to decision dead zones, preventing functional teams from collaborating toward the best outcomes and leaders from making optimal decisions. It inhibits scaling key growth areas — or worse — creates risk. But disconnected data is, in fact, an untapped asset.

Siloed data contributes to decision dead zones, preventing functional teams from collaborating toward the best outcomes and leaders from making optimal decisions.

After the harvest, grain literally sits in silos — but compartmentalizing information about a commodity, like grain, holds a business back. Connecting how much grain is likely to become available, with how much is in storage, with grain trading prices that change minute-by-minute, can uncover spot deals and optimal moments to sell at a better margin or make more advantageous buys.

Connecting systems and integrating internal and external data streams facilitates Operational Intelligence, providing better-contextualized insights that are becoming increasingly critical and can lead to improved efficiency, safety, and sustainability.

 

2. Integrate external data

Data analysis is fundamental to every decision, but data itself isn’t enough. Insights — the ability to quickly gain an accurate and deep understanding of a topic — are required. It is at the intersection of internal, external, and timely data where businesses can engineer and model their data to eliminate data dead zones and begin capitalizing on Operational Intelligence. Evolving people, process, and technology are filling in the gaps, creating new opportunities to surface more insights that can more specifically inform operational, risk, and sustainability decisions across every industry.

Weather forecasts are modeled on insights that can change minute by minute; when blended with industry-specific data, the resulting insights are optimized for better decisions. Weather forecasts blended logistics data can provide insights and enable a company to better manage and track supply chain carbon emissions. Weather forecasts blended with agricultural data help farmers make strategic planning decisions and increase yields.

Integrating new data sets is becoming easier with APIs that help fill in gaps and incorporate timely information. As industries begin or continue to digitize, the capability to ingest relevant internal and external data sets is becoming easier. The combination of data can surface industry and operationally-specific insights to help make better decisions. Modeling the data with industry-specific, scenario-specific, and timely inputs can surface the Operational Intelligence required for the planning, efficiency, and risk mitigation decisions that all companies need to prosper.

 

3. Avoid data noise

Siloed data is one problem, but too much data is another. By 2025, global data creation is projected to grow to more than 180 zettabytes. Too much data can create data noise, which can distract or cause decision-makers to ignore key inputs and lead to analysis paralysis — another decision dead zone. Knowing what information is important to the situation and relevant to the industry helps cut through the data clutter. Data engineering is critical for gathering the right data, adjusting to constantly updated inputs, and modeling it in a contextual way to detect market and operational signals among the noise.

New technologies, like digital twin technology and artificial intelligence, are two ways contextual insights are being surfaced and delivered faster than ever before.

New technologies, like digital twin technology and artificial intelligence, are two ways contextual insights are being surfaced and delivered faster than ever before. Machine learning also helps to sort through data to isolate the decision intelligence that matters most. All three approaches can quickly ingest new inputs in the right context to adapt and deliver better decision insights in near real-time.

Electric utilities rely on vast amounts of data for evaluating storm risks. Large utilities implement artificial intelligence and machine learning platforms to collect and coalesce diverse sets of information, including grid performance, infrastructure integrity, service areas, topography, and vegetation management, along with historical and real-time weather analytics to deliver improved intelligence for potential outage risks. This dynamic approach sorts through the noise and isolates information as it evolves in the models so that utilities can tap into the latest insights to make decisions quickly and confidently around response and restoration.

 

Operational Intelligence eliminates decision dead zones

The costs of decision dead zones are more than missing opportunities and failing to act quickly. Annually, it costs time, efficiency, and potentially millions of dollars. A McKinsey report recently estimated that ineffective decision-making accounts for about 530,000 days of managers’ time lost each year for a typical Fortune 500 company, equivalent to some $250 million in wages annually.

With ever-increasing volumes of data generated every second, the ability to create clarity from complexity comes from an approach that can quickly surface relevant data with scenario-specific insights. This kind of Operational Intelligence can change the way decisions are made, from reducing risk and improving efficiency to identifying new opportunities and markets.

Explore how Operational Intelligence from DTN helps eliminate decision dead zones and support prosperity at organizations with complex supply chains.