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Real-time Predictive Analytics

What is Real-Time Predictive Analytics?

In simple words, Real-time Predictive Analytics is the process of obtaining useful information from diversified data sets in real-time. Business owners use Real-time Analytics to predict the future outcomes of a given situation. These predictions are based on ” if analysis ” which helps to fast-pace the decision-making processes. With the right prediction, the organizations get the first-mover advantage in the highly competitive world.

Here, the Predictive Model is built with a stand-alone tool where the data has to be exported in the consumable format and further streamlined with the operating analytics platform. This deployment of data into a Machine Learning environment helps to achieve a constant run-time prediction in real-time.

Use Cases of Real-Time Predictive Analytics

Real-time Predictive Analytics is incredibly useful today for businesses around the globe. The paradigm shift of business towards the fast-paced technology has given a big boost to the concept of Predictive Analytics and Modeling in real-time.

Use CaseDescription
Tracking Customer DataThe time-sensitive customer data need immediate attention as they are highly dynamic in nature. Thus, Real-time Predictive Analytics help to understand the behavior of the customers and optimize the decision to meet their satisfaction.
Developing Cost EfficiencyReal-time Analytics focuses on improving the profitability of the company by ensuring cost-effective processes. The areas like employee engagement, hiring, retention, reducing workload, etc get the acceleration from this technology.
Improve Response TimeThe market fluctuation is a big opportunity for businesses to get ahead in the competitive world. Putting Real-time Analytics to use is the right practice to take the first-mover advantage and be the money maker.
Facilitate Real-time testingIn the product-oriented market, Real-time testing can forecast the outcomes with better optimization. The split–testing or A/B testing is carried out to ease the process of decision-making more refined.

Examples of Real-Time Predictive Analytics

Several business houses across the world have brought Real-time Predictive Analytics to their operations which led to significant improvement in their process implementation. Some of the top names in the lists are as follows.

McKinsey – Personalised ExperiencesToday this company is known for delivering personalized experiences to its clients and customers across the globe. This became possible with the use of Real-time Predictive Analytics for content recommendation, shopping cart promotions, etc which in turn brought better customer engagement.
Ritual – PersonalisationAs a health-meets-technology company Ritual managed to monetize its new product lines effectively by running personalized banner ads, email campaigns and targeted bundled offers.
Accenture – Security AnalyticsWith the increasing cases of Cyberattacks, Real-time Predictive Analytics has emerged as the silver lining for all. By utilizing the features of this technology the company has reduced crime detection and response time followed by a 72% cost reduction.
UPS – Fleet ManagementUsing the UPS over 21 million packages are delivered every day and this is only possible with effective fleet management. With the investment in ORION, and its route optimization technology UPS reduces traffic accidents and saves 100 miles per year. 
Rumble – LeaderboardThe concept of Leaderboard is not just exclusive to gaming but also contributes to promoting the behavior and social connections in the field of Finance, Fitness, etc. Rumble which is a health and wellness company use a Real-time Analytics based Leaderboard  to count step by tracking the progress of users and unlock awards on goal achievement.,

Tools used for Real-time Predictive Analytics

There are several tools and software that have been introduced to the ecosystem by industry experts. These entire analytical platforms are considered among the best ones and most preferred by organizations across the globe.

Altair PanopticonIt is the renowned product of Altair that offers a suite of knowledge-driven solutions. It comes with patented Decision and Strategy trees that enhance the process of Predictive Modeling, in-database analytics, data preparation, etc. Furthermore, it allows users to export and import data using languages like R and Python.
Amazon Kinesis It is a unified stream and batch processing tool that offers server-less This product is owned by Amazon Web Services (AWS) that allows customers to collect process and analyze the streaming data in real-time. It comes with the flexibility to choose tools for specific situations and ingest data like video, audio, website, clickstreams, etc.
Google Cloud DataflowIt is a unified stream and batch processing tool that offers server-less infrastructure and automates resource processing and provisioning. The auto-scaling feature of the platform maximizes resource utilization with the Apache Bean SDK.
IBM StreamsThis IBM product is known to evaluate the range of streaming data and combine its capabilities to build required models. The massive data explosion can be combated with it in real-time. Some of its key specifications are development support, analysis, prediction, and data provisioning.
Rapid Miner StudioIt is a data science platform that allows users to build and operate AI solutions across the enterprise. Its extended lifecycle includes data exploration, preparation, developing & deploying models, and performing model operations. Thus, the process of understanding and streamlining the complex data becomes extremely smooth.
SAP HANA Streaming AnalyticsSAP is famous for offering a broad range of BI solutions and products that support self-service visualization and make the data easily discoverable. SAP’s other tools like Lumira are also developed to ease real-time data functioning. 
SAS Event Stream ProcessingIt is an advanced analytics and data science product that automates data preparation, lineage, and model management. Its Machine Learning algorithms are potent to generate insight for common variables in the model.
TIBCO StreamingThis streaming analytics software is based on Applied Learning algorithms that are automatically embedded into applications and supports quick decision–making. It is one of the enterprise-ready products with high performance, extensibility, usability, and availability.

Challenges for Real-time Predictive Analytics

To stand by the expectations of the competitive world Real-time Predictive Analytics shall focus on increasing availability and lowering the response time. With the data explosion in almost all industries, the need for such advanced technology is becoming a matter of deep concern. As a result, handling the growing quantity and diversity of data is becoming a growing challenge for industry experts across the globe.

Precisely, the technology requires specialized appliances in the form of hardware and software to leverage the full power of the system. The high cost of these appliances is still a battle to fight for making Real-time Analytics more affordable. Another key task is to make the software user-friendly to make it easily accessible and reduce dependency on technical experts. Hence, the environment of Real-time Predictive Analytics has a lot to address to ensure its wider adoption.

Final Words  

The solution architecture of Real-time Predictive Analytics is gaining momentum, especially in the Media and Communication industry where a massive data explosion happens every minute. Furthermore, Healthcare, Finance, Fitness, Gaming, and several other industries are adopting these advanced analytical technologies at a faster pace and boosting their complete ecosystem. 

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