Adopting a microsecond mindset for faster decision making

The global pandemic has perhaps provided the clearest example yet of the value of having access to and acting on insights gained from high-quality, real-time data. For countries across the globe, data is guiding us on our routes back to normality; from vaccination strategies to the re-opening of shops, services and public transportation, data is playing a key role in every critical decision.

And it’s not just Governments and global health bodies that appreciate the value of being able to act on insights gleaned from the most up to date information – often combined with historic data for even richer context. Recent cross-industry research shows that 90% of firms plan to increase their investment in real-time analytics solutions over the next three to five years. Additionally, nearly two-thirds (64%) of firms believe that having access to real-time data is critical to making smarter business decisions, while over three-quarters (78%) say real-time data and insights are creating a competitive advantage for their business.

But the same study also shows that 69% of businesses think of real-time as over a second or longer with 45% of that group defining real-time as anything upwards from an hour! This suggests that businesses could be missing out on extracting the full value from their data by not thinking fast enough. 

No matter whether you’re in finance, manufacturing, utilities, telecommunications or other sectors, taking decision-making from minutes to microseconds can be a game changer in terms of operational performance and competitive advantage. Here are five steps to taking real-time analytics to the sub-second level to enable an operating model of continuous business intelligence.

1. Assess and understand

While not all firms need to be operating at the sub-second level, all firms create data in real-time. Having an understanding of how and where faster analysis could lead to better operational and commercial performance is always useful. 

To do so, businesses must consider whether they have a clear understanding of the value of the data that flows within a business and, critically, the rate at which that value diminishes once created. Also, being aware of whether there is a data-led culture, and ensuring the right tools and processes are in place so the people and applications can extract full value from that data. Finally, setting up realistic expectations and goals, and determining how success will be measured ahead of the platform integration. 

There is broad agreement on the value that an investment in these transformative technologies brings to business. Equally, access to the right technologies and having the right people with the right skills are common challenges. Having a clear understanding upfront of the data landscape, culture and goals is vital.

2. Ensure your data is in shape

Put simply, the better a firm’s understanding of where data resides, its format and its history, the better placed they’ll be to shorten the decision-making window. The most common types of datasets businesses often seek to bring together include those generated internally and externally, streaming data, data at rest, and both structured and unstructured data. Each is a discrete, distinct set and the relationship between them can be complex.

It is important to recognise that some data and data sources are more valuable than others. For example, time series data is one of the most valuable sources, particularly when generated in the IoT market. Highly-structured, machine-generated, and sent with timestamps between many thousands of devices at very high frequencies, it is relatively new to most organisations but prized. And you need a complete strategic solution for utilising it well.

3. Think in microseconds

Once a robust, core real-time analytics system is in place, teams should be challenged to think faster by testing and learning.

The best real-time analytics platforms enable sandbox environments where data scientists can build models and test outcomes rapidly without the worry of affecting critical systems running in parallel. This is the bedrock for unearthing news insights that allow for the iterative development of new capabilities, constantly adding value over time.

4. Be prepared for challenges

The primary data challenge, and opportunity, that many organisations face is no longer that of volume, but of speed. Streaming analytics solutions need to work with existing and new datasets, and so must interact with many existing technologies. Subsequently, interoperability can be a challenge.

Additionally, IT teams can be small, stretched, or simply battling for the right talent in an increasingly competitive environment. Implementing a new technology can be a daunting task. But it also presents an opportunity to upskill the workforce in areas that will be vital to a business future success.

5. Find a suitable partner

Once the decision is made to increase investment in real-time analytics, the next step is to find a suitable partner. There are many questions to consider when doing this, including:

• Ask about a typical engagement. Streaming analytics goes beyond capturing data to report in quarterly meetings. Always look for a provider with clear and demonstrable experience in helping businesses to make those sub-second decisions.
• Ask about iteration and future flexibility. Since advanced, streaming analytics is a developing area, your provider should be able to clearly demonstrate how they plan to keep adding value over time. That will, of course, mean iterating their solution and offering regular updates and upgrades to match market demands.
• Look for a partner, not a provider. Businesses benefit from technology when vendors think beyond the initial sale. And likewise, organisations should be looking for a collaborative, strategic partner over the years, rather than a one-time buy.

The volume, velocity and variety of data that organisations have to manage continues to grow exponentially. The operational and commercial performance benefits that can be realised through adopting a microsecond mindset and implementing technologies like streaming analytics can be considerable, but only for firms who challenge themselves to think and act faster.