AI Strategy: Transformation | Innovation | Disruption
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Companies are using more real-time analytics, because of the pressure to increase the speed and accuracy of business processes — particularly for digital business and the Internet of Things (IoT). Although data and analytics leaders intuitively understand the value of fast analytical insights, many are unsure how to achieve them.
Every large company makes thousands of real-time decisions each minute. For example, when a potential customer calls the contact center or visits the company’s website to gather product information, the company has a few seconds to figure out the best-next-action offer to propose to maximize the chance of making a sale. Or, when a customer presents a credit card to buy something or submits a withdrawal transaction request to an automated teller machine, a bank has one second or less to determine if the customer is who they say they are and whether they are likely to pay the bill when it is due. Of course, not all real-time decisions are tied to customers. Companies also make real-time decisions about internal operations, such as dynamically rerouting delivery trucks when a traffic jam forms; calling in a mechanic to replace parts in a machine when it starts to fail; or adjusting their manufacturing schedules when incoming materials fail to arrive on time.
Many decisions will be made in real time, regardless of whether real-time analytics are available, because the world is event-driven and the company has to respond immediately as events unfold. Improved real-time responses that are informed by fact-based, real-time analytics are optional, but clearly desirable.
Real-time analytics can be confusing, because different people may be thinking of different concepts when they use the term “real time.” Moreover, it isn’t always simple to determine where real-time analytics are appropriate, because the “right time” for analytics in a given business situation depends on many considerations; real-time is not always appropriate, or even possible. Finally, data and analytics leaders and their staff typically know less about real-time analytics than about traditional business intelligence and analytics.
Find the certain concept of “Real Time” Relevant to Your Business Problem
Real-time analytics is defined as “the discipline that applies logic and mathematics to data to provide insights for making better decisions quickly.” Real time means different things to different people.
When engineers say “real time” they mean that a system will always complete the task within a specified time frame.
Each component and subtask within the system is carefully designed to provide predictable performance, avoiding anything that could take longer to occur than is usually the case. Real-time systems prevent random delays, such as Java garbage collection, and may run on real-time operating systems that avoid nondeterministic behavior in internal functions such as scheduling and dispatching. There is an implied service-level agreement or guarantee. Strictly speaking, a real-time system could take hours or more to do its work, but in practice, most real-time systems act in seconds, milliseconds or even microseconds.
The concept of engineering real time is most relevant when dealing with machines and fully automated applications that require a precise sequence and timing of interactions among multiple components. Control systems for airplanes, power plants, self-driving cars and other machines often use real-time design. Time-critical software applications, such as high-frequency trading (HFT), also leverage engineering real-time concepts although they may not be entirely real time.
Use Different Technology and Design Patterns for Real-Time Computation Versus Real-Time Solutions
Some people use the term real-time analytics to describe fast computation on historical data from yesterday or last year. It’s obviously better to get the answer to a query, or run a model, in a few seconds or minutes (business real time) rather than waiting for a multihour batch run. Real-time computation on small datasets is executed in-memory by Excel and other conventional tools. Real-time computation on large datasets is enabled by a variety of design patterns and technologies, such as:
Preloading the data into an in-memory database or in-memory analytics tool with large amounts of memory
Processing in parallel on multiple cores and chips
Using faster chips or graphics processing units (GPUs)
Applying efficient algorithms (for example, minimizing context switches)
Leveraging innovative data architectures (for example, hashing and other kinds of encoding)
Most of these can be hidden within modern analytics products so that the user does not have to be aware of exactly how they are being used.
Real-time computation on historical data is not sufficient for end-to-end real-time solutions that enable immediate action on emerging situations. Analytics for real-time solutions requires two additional things:
Data must be real time (current)
Analytic logic must be predefined
If conditions are changing from minute to minute, a business needs to have situation awareness of what is happening right now. The decision must reflect the latest sensor readings, business transactions, web logs, external market data, social computing postings and other current information from people and things.
Real-time solutions use design patterns that enable them to access the input data quickly so that data movement does not become the weak link in the chain. There is no time to read large amounts of data one row or message at a time across a wide-area network. Analytics are run as close as possible to where the data is generated. For example, IoT applications run most real-time solutions on or near the edge, close to the devices. Also, HFT systems are co-located with the stock exchanges to minimize the distance that data has to travel. In some real-time solutions, including HFT systems, special high-speed networks are used to convey streaming data into the system.
Match the Speed of Analytics to the Speed of the Business Decision
Decisions have a range of natural timing, so “right time” is not always real time. Business analysts and solution architects should work with managers and other decision makers to determine how fast to make each decision. The two primary considerations are:
How quickly will the value of the decision degrade?
Decisions should be executed in real time if a customer is waiting on the phone for an answer; resources would be wasted if they sit idle; fraud would succeed; or physical processes would fail if the decision takes more than a few milliseconds or minutes. On the other hand, a decision on corporate strategy may be nearly as valuable in a month as it would be today, because its implementation will take place over months and years so starting a bit earlier may not matter much.
How much better will a decision be if more time is spent?
Simple, well-understood decisions on known topics, and for which data is readily available, can be made quickly without sacrificing much quality.
Lastly, Automate Decisions if Algorithms Can Represent the Entire Decision Logic
Algorithms offers the “last mile” of the decision. However, automating algorithms requires a well described process to code against. According to Gartner, “Decision automation is possible only when the algorithms associated with the applicable business policies can be fully defined.”
Performing some analytics in real time is a goal in many analytics and business intelligence modernization programs. To operate in real time, data and analytics leaders must leverage predefined analytical models, rather than ad hoc models, and use current input data rather than just historical data.