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Given the speed at which markets shift and customer sentiment changes, it is easy for executives to despair at how long it takes to track, filter and uncover new business insights. For most companies, there is a constant fear of being behind the curve with regard to the data that pours into the business each day.

In today’s volatile business environment, the need for rapid, fact-based decision-making is acute. For many executives, there is widespread frustration about the limitations of current analytics systems and the speed at which they operate. In today’s ultra-competitive marketplace, business leaders need easy, immediate access to real-time data to unlock the insights that will drive their corporate success.

However, corporate data is usually stored in specific applications, in different parts of the business, which typically rely on disk-based facilities. As a result, traditional analytics systems running on traditional databases typically hit a wall when trying to handle the real-time data required for immediate insights.

But help might be at hand: The emergence of in-memory technology is promising to lift the threat of needing to base decisions on outdated data.

For example, consider Yodabashi, a major Japanese electronics retailer. By using in-memory technology, the company cut the processing time of calculating incentive payments to its 5 million loyalty program members from three full days to just two seconds. This gargantuan performance gain allows the firm to be more customer-centric, by delivering rewards to customers while they’re still in the shop, while also calculating what other offers might be suitable based on their transactions being processed.

In-memory technology presents two major advantages over traditional disk-based technologies. The first is speed. By storing all relevant data in its main memory, rather than on disk, new calculations can be performed on the fly, as illustrated in the example. This also gives executives a live picture of their business operations by replicating the company’s real-time ERP data. The second advantage is flexibility. Because a specifically structured data layer no longer needs to be built for a given query – which has long been a key part of most BI deployments – in-memory provides greater scope for analytics freedom, widening the range of what executives can query.

Over the last 18 months, we’ve seen the technology mature. The most recent and prominent example is SAP HANA, which puts in-memory database technology to even more powerful use. Its approach to data storage – using columns rather than rows – allows data to be queried at a higher velocity. HANA allows business analytics to be applied in real time to transactional data, such as data in CRM or ERP systems, for instance. The company has already launched more than 30 different planning, reporting and analytics tools on HANA and also will offer its full SAP Business Suite on the platform.

In-memory technology has the potential to deliver real-time insights in every area of the business. Capabilities range from basic operational questions around daily turnover, profit margins and working hours to the effects of competition on pricing; from risk management across multiple products and services to performance assessment at the most granular level; and from inventory management to error analysis. It also enables management teams to conduct sophisticated “What if?” modeling to test out new business ideas before implementing them in the marketplace.

Although these benefits will be hugely valuable to companies in any sector, early adopters of in-memory technology are industries with very high transaction volumes and a pressing need to rapidly generate analytics from this data. They include the utilities sector, telecommunications, retail and financial services.

In the electric power industry, smart meter technology generates increasing amounts of  real-time usage data on individual customers and entire neighborhoods. In-memory technology can process that data in order to make quick, well-informed decisions about buying and selling power, or to offer customers applications that control their home appliances in the context of the changing price of electricity.

In the consumer sector, fast-moving consumer goods companies are beginning to use in-memory systems to analyze retailers’ point-of-sale data in order to predict continually changing levels of demand for individual products. This ensures that stock levels never run low, even during aggressive sales promotions.

And for marketing functions, in-memory technology  enables companies to conduct real-time analysis of social media feeds. This allows for trend detection, tracking and forecasting, which can result in powerful assessments of customer sentiment at different times and in different locations

The in-memory data systems will be transformative. For the first time, management teams can ask ad hoc questions of their businesses’ databases, with the confidence that they’ll get answers within seconds, based on real-time information, and that they’ll be able to use those insights to make immediate decisions.

So what needs to be done? While IT practitioners at leading companies are now actively exploring the potential of in-memory technologies for the enterprise, they are faced with some practical challenges. Where can these solutions drive value? What is the cost and impact of deployment? How do you articulate that value in order to redeploy investment dollars? Can these technologies speed application and solution development? What is the value of greater speed of decision-making and action to the enterprise, and can the company’s processes actually handle that?

In order to answer these questions, technology leaders need to start experimenting with in-memory systems and running quick pilots to understand what options exist and any practical issues that may be involved. Early successes here will build credibility and provide a platform for building out future capabilities.

The potential competitive advantages warrant this exploratory investment. Combining data analytics with in-memory processing will change the way business is done and raise the bar for your competition.

Narendra Mulani is the managing director of Accenture Analytics. Leading an integrated community of more than 15,000 management consulting, technology and outsourcing professionals who serve clients around the globe, he is responsible for driving Accenture’s strategic agenda for growth across business analytics. Mulani is a member of Accenture’s Global Leadership team. He graduated from Bombay University in 1978 with a Bachelor of Commerce. He received an MBA in finance in 1982 and a Ph.D. in multivariate statistics in 1985, both from the University of Massachusetts. Prior to joining Accenture, Mulani ran his own consulting company.