Business is picking up. In all stages one, two, three (and especially within the fourth industrial revolution, driven by artificial intelligence), the company has accelerated the pace at which it produces, creates, trades, operates and (in some cases where innovation does not work) fails.
This eternal reality may be one of the reasons why the tech industry has always been obsessed with moving from data to real-time data.
Death of the series
When data exists in environments and resides in applications, databases, web services, and other entities that move back and forth, its non-real-time status is usually characterized by the fact that it needs to be translated, parsed, manage, store and thus process. in batches at the end of the day or at another specified period. That’s why real-time advocates today like to talk about the so-called “end or death of the series”.
Knowing when and where data is moving in real-time streams is a matter of sufficient computing power and enabling the real-time intelligence of the software engine to allow a human user to perceive that a process has occurred instantaneously. In reality, even real-time processing takes a few milliseconds, so it’s not entirely “real” in terms of our existence on the planet, but that’s a philosophical argument for another day.
A committed protagonist of real-time technologies is Hazelcast. The company is known for its open source in-memory data networking technology that shares information between computer clusters to enable fast data processing. But the company is realistic, it says, that true real-time (the ability to act on information in an instant) is difficult to achieve, especially when we’re talking about when, during and before an event, not after.
“Many vendors still claim to deliver real-time while waiting for databases and/or other services, which causes latency and post-event data processing and misses the benefits of real-time,” explained Senior Solutions Architect at Hazelcast Johnson Noel , to the company’s technical blog.
“True real-time involves continuously collecting, analyzing, canonicalizing and performing functions (such as processing according to rules or machine learning models) on data that is ‘inflow’ outside of a database or any other service – in other words, the entire process it should run in memory with very low latency and be delivered to users or downstream processes long before it touches the database,” explained Noel.
Automation rules the economy in real time
Now to explain why automation is ruling the real-time economy, Hazelcast draws on one of its own implementation stories to explain how fast the business world is moving and, there’s that word again, accelerating.
Imagine that a person can get a loan while standing at an ATM. The customer didn’t know they needed it before they got there. But they quickly realized that they wanted more money than they had available. Armed with data on the client’s credit risk, profile, investments and financial flows, the bank immediately performs a risk calculation and decides that it is a good risk. Customer A (let’s call her Michele) walks away with the extra money she needs.
Not a single human was involved in this interaction, well, no one but Michele.
Not a single slip was examined by human eyes. Instead, the technology analyzed new data—Michele wants $500 more than she has—and old historical data—Michele earns well and is expected to deposit her paycheck in two days, as she has done for 10 years, and she also has money in her account. mutual funds. The system then calculates in real time that extending the loan offer is a good business decision.
This is the real-time economy in action – and (arguably) powered by the real-time economy that shapes
“In order for automated real-time economic actions to happen at the right time and for the right reason, companies need to combine fresh insights from new streaming data and complement it with the historical context of data/insights from the past. The longer or more general the reaction to an insight based on fresh + historical data, the lower the business value of the answer. This is where automation will decide the winners and losers in the real-time economy,” said Kelly Herrell, CEO of Hazelcast.
This whole process is something Hazelcast likes to call today “the end of the wait-and-see period”. As you can see, the evangelists of real-time computing have a somewhat cheerless, almost dark obsession with sounding the death knell for the technology platforms and tools that came before them, it’s just one of those side quirks of the tech industry.
As CEO Herrell bluntly stated on Techzine.EU: “Success in the real-time economy requires immediate calculation on new and historical data, something traditional databases cannot do. After years of building our reliable, low-latency data store, we are focusing on convergence with real-time data to enable businesses to take a new approach to improving customer satisfaction, generating new revenue and reducing risk.”
How fast is real time?
Returning one last time to our ATM example, we can ask how fast is real time really? If the marketing team is behind the broader promotions Michele could offer based on business logic, can we get a sense of how fast things are moving within the internal mechanics of the software system that serves the ATM screen?
As stated in the description of the real-world implementation, “processing completes in less than 120 milliseconds, giving the marketing team [business logic programming inside the software system] more than enough responsiveness to quickly respond to customer needs. In most cases, promotions may have business logic to deliberately delay communication with customers by a few seconds to ensure delivery at the ‘right’ time.”
Hazelcast enables this technology with its existing IT stack base and the beta launch of its ‘codeless connectors’ technology. These are components of software code designed to perform aggregations of data streams in real-time computing environments. They act as declarative method units of technology that declare an objective result of the existence and operation of program code and currently support AWS Relational Database Services (RDS) for MySQL and PostgreSQL.
We will not see breakfast business TV presenters like him again The BBC’s (always deeply probing) Sally Bundock start talking about the real-time economy to any extent, or is the real-time economy really some new paradigm or principle that the technology sector is enabling now, but it’s only going to be a little while before that happens.
In the meantime, let’s make tea and head to the ATM.