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Things are getting smaller and smaller.Not only in gadget miniaturization, medical nanotechnology, increasingly complex industrial electromechanical units and so-called shrink and expand This results in our candy bars being thinner or shorter for the same price, and the stats – the stats are getting smaller.

Data is getting smaller in two key ways: a) we are breaking down the components of application data flow into smaller containerized elements to work across similarly partitioned and containerized application services – b) The time window events in which businesses need to react to data are decreasing.

The latter time constraint on data certainly makes us understand the reality of real-time data and the need to be able to use it.

No real-time data, really

Real-time data is a tautology in terms of how the space-time universe we live in actually works, that data always requires a time fee to exist. Data may travel at the speed of light, but it’s still a speed. When we talk about real-time, we mean that the data is transmitted fast enough that a human cannot perceive any time delay happening. Thus, real-time expresses human perception of time, not machine perception or definition.

It’s all important because we should embrace a Industry 4.0 world Our factories are run by AI-enhanced intelligence and intelligent automation.However, manufacturers may not be ready for Industry 4.0 if they are facing complex data issues caused by production bottlenecks caused by disparate information systems within the organization, many of which still require human intervention – from manually entering sensor readings into databases to inefficiency clear build Status (ie readiness) monitoring and lack of integration with enterprise resource management (ERP) systems.

Keen to write something wrong in this space is Palo Alto-based KX. Known as KX and KX Systems, the company is recognized for its work on high-speed real-time data streaming analysis within intelligent systems that can also simultaneously take on tasks related to historical data workloads.

Analyzing the Maturity Curve

Given the current speed of industrial data processing and the need to achieve a Nirvana state for its personal fast streaming data-intensive analytics, KX refers to any given company’s evolutionary state as a point on the data “analytics maturity curve.” While marketing drives the naming attempts, KX does have a view that the commercial window for creating differentiated value for organizations in every market and sector is shrinking. So logically, the faster they can act on the insights derived from the data created on the fly, the better the results.

As KX CTO Eric Raab said said before“The opportunity for streaming analytics has never been greater. In fact, according to my firm’s research, 90% of companies It is believed that in order to remain competitive in the next three years, they will need to increase their investment in real-time data analytics solutions. Whether it’s a financial institution that needs to adjust customer portfolio settings based on changing stock prices, a utility company that monitors grid throughput, or an e-commerce website that needs to generate monthly reports, data accuracy is a huge challenge. “

What kind of data analytics can we get from an enterprise software platform that can perform at this speed? Discovering (and taking action on) anomalous data will be a key use case, KX said.

Often defined and interpreted as data points, events, or observations outside of the normal behavior of a dataset, anomalous data can be a key sign and indicator that alerts a business that something has caused (or could cause) a problem in the business.

“The ability to quickly detect and respond to abnormal events is critical, especially since the ability to get real-time responses can limit the cost of abnormal events. In addition to preventing problems from lingering in the business, employing real-time data can improve process efficiency. Positive kind of [advancements and innovations possible here include] Faster service, higher sales, better product quality, and lower prices—show how far-reaching and diverse the impact of real-time data can be,” KX noted in its Business Value Research report.

The company maintains that using a real-time data system can improve productivity by reducing the man-hours spent processing and managing data.This type of platform enables users to automate complex workflows that would otherwise be time-consuming, making it possible to use tested machine learning (ML) models that provide a level of actionable insight to guide business action

The road to microsecond business

If we go through this argument together and agree (even by a percentage point) that we need to pay more attention to real-time data and analytics technologies capable of handling complex high-speed information sources, then we may be in the process of implementing platforms such as KX and/or its competitors. .

KX isn’t the only fruit on this orange tree. Today’s list of notable dataflow experts might include Confluent for its fully managed Kafka service, Tibco for its Tibco Spotfire offering, Amazon Web Services Kineses, Microsoft Azure’s IoT offering, and, of course, Apache Kafka itself for open source purists. That’s not to say there’s nothing special about KX, it just highlights and possibly validates the company’s place in a well-defined technology discipline that addresses key needs.

Any business in an industry vertical that implements this level of technology is on the path to what we might soon call “microsecond business operations,” a term that’s likely to stick.



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