Speed of response is the next battleground in data. An enterprise that generates actionable insights from data faster than its rivals can outperform – but only if it quickly follows through.

Accenture’s latest “High Performers in IT” study found that at 46 percent of high-performing IT companies, CIOs say employees need real-time data to do their work. At other organizations, the figure is just 26 percent. This doesn’t mean every business needs to hurry toward becoming fully real time. Some processes simply don’t need it. Rather, the larger goal is to become a “right-time enterprise.” Basically stated, the concept is about being ready for interaction, not just with the customer but across the value chain.

Business at the Speed of Thought: Examples

There’s no doubt that a speed-oriented mindset constitutes a significant part of the equation. Much value is to be gained from expediting data-driven insights – and decision-making processes and operations – across the organization.

Consider the example of a water utility that formed as a result of a series of mergers and acquisitions over the past 20 years. As a consequence, it inherited dozens of different systems generating data. Also, the business’s different units each distinguish between plant (the infrastructure where water is stored and treated) and network (the pipelines through which water is supplied). And as is not uncommon in the water sector, the company has kept its business technology platforms separate from its operational technology estate.

In the past, bringing data from these disparate islands of technology in a single data store would have been unaffordable and working on them separately was too laborious to be worthwhile. Today, cheaper and better storage and big data solutions make that possible, and applying analytics tools to the data generates valuable and actionable insights for the business far more quickly.

One practical application lies in production management, where streaming data enables the company to exercise opportunities to reduce the risk to supply and save costs of operations by responding to the performance of its production systems in near real time. It is then possible to monitor in real time how well the theoretical plan works in practice. Furthermore, it can be constantly corrected as opportunities for further improvements are identified. For instance, predictive technologies with inputs such as weather forecasts can help prepare sewage networks for upcoming rainfall. Analytics capabilities also makes it possible to monitor assets remotely so the utility can anticipate equipment failures in advance or respond quickly to problems such as leaks. This can even happen before operations or customers have spotted the issue.

The benefits to all these are numerous. With more efficient operation, there will be less need for the utility to build expensive redundant capacity. Operational costs will come down and customer service should improve as problems are resolved before customers are affected.

Ireland’s Office of the Revenue Commissioners provides another good example of right-time enterprise. Before 2012, while the Revenue had processes in place designed to identify fraud and error on the part of taxpayers, cases were often flagged after refunds were paid or credits had been issued. By introducing real-time risk scoring and predictive analytics tools, the authority was able to speed up the red flagging of suspicious cases and improve accuracy.

In its first six months of operation, the project enabled the Revenue to block €2.5 million (approximately $3.48 million U.S.) of fraudulent or erroneous transactions before they were made. The number of false positives (flagged cases that proved benign) fell by 50 percent, improving customer satisfaction levels, and the authority saved on recovery costs, making fewer payments that subsequently had to be retrieved.

A third example comes from one of the world’s largest integrated energy companies, which has realized valuable benefits from a geospatial data visualization tool that is helping it to optimize its supply chain and improve its margins. The company’s oil and gas network supports outlets throughout North America, but assessing inventory needs was difficult given the scope of its operations and the vast amounts of data generated both inside and outside of the company because traditional reports obscured trends and variances.

Now the company uses a data visualization tool in Google Earth that enables it see real-time inventory levels at outlets on customizable maps. Advanced analytics and multidimensional images, charts, maps and overlays enable more accurate demand forecasting, identify previously hidden patterns and relationships, and ultimately facilitate faster decision-making to overcome supply chain constraints.

A Vision for Speed

Of course, enterprises wanting to exploit the potential of greater velocity face challenges:

As yet, little data is available on the return on investment in faster technologies, which is a challenge for making the business case. As a result, enterprises need to be bold in their vision but very pragmatic and systematic in their execution. An iterative approach to making the business case can help pave the way for expanding investments in real-time transformation. Consider laying out the whole journey and breaking it down into granular projects and pilots with clear before and after metrics.

Culture becomes the constraint for how fast a company will move in the direction of real-time decision-making and operations. The real-time enterprise will change the way executives and employees work, for example by enabling decision-making on the front lines. Sometimes the best decision may be even made by a machine, and allowing that recommendation to flow through the organization is a huge cultural change.

Despite the challenges, there’s no arguing that the window for competitive differentiation in all markets is getting narrower as we speak. It’s time businesses take advantage of time as a differentiator.