August 17, 2022


Things get smaller. Not only in terms of instrument miniaturization, medical nanotechnology, increasingly sophisticated industrial electromechanical units and the so-called process shrinkage Which leads to our candy bars being thinner or shorter for the same price, but the data too – the data getting smaller too.

Data shrinks at two main levels: a) we decompose the component parts of application data that flow into smaller items in containers to work within similarly packaged, fragmented application services – and b) the time windows through which the work needs to interact with the data events decrease.

This last time constraint on data of course leads us to the reality of real-time data and the need to be able to work with it.

No real-time data, really

In terms of how the space-time universe we live in actually works, real-time data is a bit of a padding, i.e. data always has some time to push for its existence. Data may travel at the speed of light, but it’s still speed. When we talk about real time, we mean data transmissions that run fast enough for a human to not be aware of any time delays. Thus, real time expresses human perception of time rather than machine perception or definition.

These are all important things because we are supposed to adopt now Industry 4.0 Our factories are powered by AI-enhanced intelligence and smart automation. But manufacturers may not be ready for Industry 4.0 if they sit on the complex data issues caused by production bottlenecks caused by disparate information systems within an organization, many of which still require human intervention — from manually entering sensor readings into databases to inefficiencies. clear to build Status (ie ready to go) Monitoring and lack of integration with ERP systems.

Keen to write some errors in this space is KX Palo Alto headquarters. The company, known as KX and KX Systems, is known for its work in high-speed, real-time data flow analytics within intelligent systems that can also handle tasks related to historical data workloads at the same time.

Analytics maturity curve

Given the speed of today’s industrial data processing and the need to achieve its own personal nirvana state of fast, data-intensive streaming analytics, KX invokes the state of development of any given company as its point on the data analytics maturity curve. Despite the label’s demanding marketing effort, KX has a view, that is, the business window for creating differentiated value is narrowing for organizations in every market and sector. Logically, the faster they act on insights from the data generated at the moment, the better the outcome.

Like KX CTO Eric Raab mentioned before“The opportunities for streaming analytics have never been greater. In fact, according to my company’s research, 90% of companies We believe that in order to remain competitive over the next three years, they need to invest more in real-time data analytics solutions. Whether it is a financial institution that needs to adjust client portfolio settings according to ever-changing stock prices, a productivity monitoring tool across the power grid or an e-commerce site that needs to generate a monthly report, data accuracy quickly is a major challenge. “

What kind of data analytics can we get from enterprise software platforms that can perform at that kind of speed? KX says finding (and acting upon) anomalies will be a major use case.

Generally defined and explained as data points, events, or observations outside the normal behavior of a data set, anomalous data can be a key sign and indicator to alert a business that something has actually caused (or is likely to cause) a problem somewhere in the business. .

“The ability to quickly detect and respond to anomalies is critical, particularly because gaining the ability to respond in real time can reduce the cost of anomalies. In addition to preventing problems from slowing down within the business, adoption of real-time data can also To improve process efficiency.Types of positive [advancements and innovations possible here include] Faster services, increased sales, better product quality, and reduced pricing — show how far-reaching and diverse the impact of real-time data can be,” notes KX, a research report on its speed on business value.

The company insists that using real-time data systems brings productivity gains by reducing the hours people spend processing and managing data. This type of platform enables users to automate complex workflows that may be time consuming and thus use tested machine learning (ML) models that provide a certain level of practical action insights to guide business actions

The path to microsecond business

If, collectively, we go through this argument and agree (even by one percentage point) that we need to focus more on real-time data and analytics technologies capable of working with complex, high-speed information sources, we may be on a way to implement platforms like KX and / or its competitors.

On this orange tree, KX isn’t the only fruit. The list of popular data stream specialists today might include Confluent for fully managed Kafka services, Tibco for Tibco Spotfire product, Amazon Web Services Kineses, Microsoft Azure’s IoT offering and of course Apache Kafka itself for open source owners. This is not to say that KX is not special, it just confirms and perhaps underscores the company’s position in what is clearly a specific technical discipline that resolves a critical need.

Companies in any vertical sector that apply this level of technology are on their way to what we might soon call “microsecond business processes,” a term that may remain consistent.



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