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The importance of streaming analytics and capturing insights for real-time business decision making

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In an increasingly data-rich and technology-driven world, the role of data analytics has never been more important. From automotive to oil and gas, pharmaceuticals to telecommunications, the demand for data analytics is showing its biggest growth in businesses to unleash deeper data insights.

Empowered with the most effective analytics systems, organizations can make decisions that drive profitability, grow customer numbers, set new trends and futureproof operations for years to come.

Access to such data has become so important that it is estimated that by 2022, more than half of major new business systems will incorporate continuous intelligence. This is where organizations bring together real-time and historical data for real-time analysis, resulting in faster and smarter decision making. These decisions can range from what products to pursue, which target markets to engage with, and can be the difference between success or failure.

Different sectors are at various stages of adoption and maturity with regards to the streaming analytics platform they deploy. What we are seeing now is that companies in a range of sectors are looking to implement a streaming analytics solution to gain greater insights from the data that flows through their organization.

Benefits of streaming analytics

One of the primary drivers for implementing a data analysis platform is that organizations have historically risen (and fallen) based on the collective efficiency of the decisions they make each day. These decisions can range from what products to pursue, which target markets to engage with, and can be the difference between success or failure. Enterprises who adopt a streaming analytics platform will benefit from faster, smarter decision making, no matter what sector.

For example, in financial organizations this can mean a dramatic reduction in fraud, or for Formula 1 teams this can result in tenths of a second being shaved off lap times due to better understanding of wind tunnel data. Deploying streaming analytics means enterprises will be able to solve not only the problems they know about, but also gain unearth insights from their data that lead to other significant process improvements.

Additionally, businesses can gain insight that will lead to customer retention. As real-time analytics provide real-time insights on customer data like what they are buying, their preferences, likes, and dislikes to create customer profiles. In turn, this gives companies an advantage to retain customers as well as generate extra revenue, for example through tailor-made up-selling and cross-selling bundles.

Finally, streaming analytics capabilities can be used where a business sees fit - on edge devices in the cloud or on on-premises servers. The data can then be analyzed and filtered before being passed in to the back-end for additional processing. Businesses that use the same streaming analytics engine on the cloud all the way to the edge, for example, can develop a streaming analytics app once and deploy this everywhere.

Total analytics

To make the most out of streaming analytics, business and IT leaders must start thinking about a data environment as being one single entity rather than separate solutions. By adopting a strategy that goes beyond simple data management, and instead unites and analyzes both real-time and historical data in real-time, businesses will be able to extract high-value insights that can drive growth, efficiency and profitability. 

From machines in precision manufacturing that need to improve yield and reduce waste, to equipment monitoring in remote areas, optimizing 4G and 5G networks in real-time, or even improving vehicle performance for F1 racing teams, historical data can be used to inform and shape large volumes of incoming data – as that data is created – so that real-time information is immediately placed in the context of what a business knows already. This allows for faster, smarter decision-making and moves us beyond the age of data management and into the era of continuous intelligence.

Another key point to be addressed in ensuring that maximum efficiency and value is gained from your data, is providing a single platform that can address the needs of both your data scientists and your production applications. These two functions are entirely symbiotic; the output from data science, whether machine learning models or new calculations and analytics, turn into business value when they’re implemented as part of your application stack.

Therefore, having a single streaming analytics platform that can serve both the high performance and high availability requirements of your applications, as well as providing sandbox and iterative development features for your data science and development teams is imperative.

Businesses that implement the right streaming analytics platform gain access to total analytics; this means that not only are they enabled to serve applications in real time, but gain better overall business intelligence through improved data warehousing and access to historical data outputs. This in turn enables the business to support the flexible and dynamic workflows demanded by Data Science and Quantitative Research.


Ultimately there are three questions that businesses need to focus on when it comes to making a decision. What outcomes will this decision have? What data sources do we need to make it happen, and what else can we do to make the customer/end user happy? To address these, organizations need a streaming analytics platform that integrates within its existing application architecture, and has the ability to crunch data and compare this insight with information that has already been stored.

With businesses often only analyzing a fraction of their data, adopting a streaming analytics platform is paramount to provide evidence-based, forward looking insights and assurance that support decision making. With this, the ability to work faster and stay agile will give organizations a competitive edge they did not have before.

Dan Seal, SVP, Kx Streaming Analytics

Dan Seal is the SVP of Kx Streaming Analytics.