How operational intelligence eliminates decision dead zones

In today’s interconnected world, decisions and strategy are driven by terabytes of data and insights. Increasingly this requires closing the gap between analytics and action to make agile, confident decisions in the moment. 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, a McKinsey report found. These so-called ‘decision dead zones’ cost time, efficiency and money, which is why access to operational intelligence can be the difference between reaching a confident decision in the moment or letting the opportunity slip by.

Operational intelligence is the next level of big data analytics and can be applied across industries. It combines the power of blended data, interpreted through machine learning and artificial intelligence to provide real-time insights that improve business agility. It is used to anticipate, detect and monitor anomalies in real-time and provide actionable insights that enable faster decisions. It can require integration of multiple, relevant data sets, domain expertise, big data skills, advanced computing ability, and management commitment to act.

By leveraging operational intelligence, businesses can reduce decision-making dead zones, respond quickly to changes in the environment, and improve their operation or economic position in near-real time. To take advantage of operational intelligence on their digitalisation journey, most businesses must take action in the three main areas detailed below to optimise business outcomes.

Eliminate siloed data

Companies lose up to 30% of potential annual revenue due to inefficiencies — with siloed data as a primary cause, an IDC study found. Siloed data contributes to decision dead zones, preventing functional teams from collaborating toward the most effective outcomes and limiting leaders’ abilities to activate optimal decisions. These dead zones hinder scaling key growth areas, or, worse, it creates risk.

Connecting systems and integrating internal and external data streams facilitates operational intelligence, supporting shared institutional knowledge and providing the kind of better-contextualised insights that are becoming increasingly critical for any operational process. Working from common and consistent data helps to improve efficiency, safety and sustainability.

Integrate external data

It is at the intersection of internal, external and timely data where businesses can best engineer and model potential decisions to eliminate decision dead zones.

Weather forecasts, for example, are modelled on insights that can change minute by minute. When combined with industry-specific data, the resulting insights are optimised for better decisions.

For road managers in the U.K. making critical decisions about crew and gritting resources for winter roads requires hyperlocal weather forecasts integrated with road network conditions, sensor data, and additional temperature impacts (such as trees and buildings). Weather forecasts blended with agricultural data helps farmers make strategic planning and harvest decisions that can increase yields. Although some businesses may find it a challenge to combine internal and external data, APIs are helping to fill in gaps and incorporate timely information.

Avoid data noise

Siloed data is one problem, but too much irrelevant data is another. More data doesn’t equate to more data-informed decisions. In fact, Forrester reports that between 60% and 73% of all data within an enterprise goes unused for analytics. Too much data without the right context can create a high signal to noise ratio, which can distract or cause decision-makers to ignore key inputs or lead to analysis paralysis.

Businesses who work backwards by first defining the insights they need to make smart decisions, either in the moment or forward looking, and then identifying the required data points are better positioned to identify signals and patterns among vast amounts of complex data streams. Operational intelligence helps enable this process by integrating the defined relevant data streams into one decisioning environment, with contextual insights that are being surfaced and delivered faster than ever before.

How industries use operational intelligence in the real world

As global businesses step into their digitisation journeys, operational intelligence is the next evolution in big data that will influence strategic decisions. Tapping into insights immediately after the data enters a system allows for better contextual analysis of a situation or opportunity. The combination of the right data, delivering intelligence at the right time — or operational intelligence — doesn’t only drive in-the-moment decisions but also guides the organisation’s long-term prosperity.

Utilities

Electric utilities rely on multiple data streams for evaluating storm risks and anticipating load demand. Large utilities use operational intelligence to better prepare and respond to storm-related outages, by collecting diverse sets of information. These include grid performance, infrastructure integrity, service areas, topography and vegetation management. In addition, the utilities receive historical and real-time weather analytics to deliver improved intelligence on potential outage risks or spikes in demand. Blended data can inform the design of demand response programmes and provide real time data to make adjustments as the weather influences electric power utilisation. This dynamic approach sorts through noise and isolates information as it evolves in the models. Agile, confident decisions can reduce strain on transmission services, balance the grid, improve outage response and restoration which help utilities avoid asset losses, regulatory fines and negative public sentiment.

Shipping

Using operational intelligence to optimise vessel routes provides the captain and onshore operations team with real-time routing intelligence based on wave heights, surface winds and updated weather forecasts that can affect vessel safety and speed decisions. Timely information can inform route changes while at sea. Real-time integrated analytics also allow for optimised fuel consumption, reduced carbon emissions and reduced voyage costs within the constraints of evolving environmental regulations that drive operational boundaries.

Offshore

As more oil and gas companies move from static platform operations to drilling ships in the deep sea, real-time weather insights, combined with operational analytics, positioning data and custom evacuation protocols have become even more critical for crew and equipment safety. Due to their size and connection to the sea floor these vessels can’t reposition quickly when adverse weather is imminent. Every positioning decision – stay or evade – has safety and significant long-term financial impacts such as lost production time and revenue, potential equipment and environmental damage, possible regulatory fines and reputational costs. With real-time operational intelligence these companies can access critical information as it is evolving to make the best strategic move.

Sports and safety

Integration of real-time weather analytics is imperative for teams, fans and crew safety. Blending weather intelligence with other types of data such as ticket sales, food and beverage inventory, climate control and custodial costs can help improve efficiencies and profit margin. Outdoor events that are broadcast require additional, hyperlocal data to help deliver a high-quality telecast. Turf management also benefits from operational intelligence. The agronomists who maintain golf courses, football pitches and other professional playing fields need to have optimal application windows for water, mowing and other treatments to keep the turf in top condition for play.

Refined fuels

Operational intelligence is also essential in the downstream oil and gas industry as it begins to digitise and capitalise on more timely insights. Access to real-time inventory levels and BOL information, bridges the gap between fuel buyers and sellers, is more efficient and time-saving, which is transformational in this fast-moving trade environment and can increase revenue. Forecasting and allocations are notoriously difficult, but with the right data at the right time, fuel suppliers can minimise credit, supply risk, and improve their margins.

These are just a few examples of how operational intelligence can transform a business or an industry. As organisations continue their digital journey, the ability to gather and access ever-increasing volumes of data will become more evident. For companies to glean clarity from this complexity, it is important to move toward operational intelligence. Delivering specific, contextual insights in the moment they are needed helps eliminate decision dead zones, allowing for better planning, reduced risk, improved efficiency, and earlier identification of new opportunities and markets.