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Fundamentals of Real-Time Analytics

By   /  September 7, 2017  /  No Comments

real-time analyticsWith the emergence of multi-structured data, Big Data, Internet of Things (IoT), and streaming data, business owners and operators are now faced with the challenge of managing petabytes of data arriving at tremendous speed from various data pipelines across the enterprise. Thus, Real-time Analytics, or the technological capability for “processing data as it arrives” has assumed supreme importance in recent times.

In the past, competing businesses used advanced Business Intelligence (BI) capabilities to differentiate themselves from their competition. Now, Real-time Analytics can play the same role of a game changer in the competitive Business Intelligence landscape. In today’s business climate, the businesses which know how to extract insights from streaming data and use it judiciously for enhanced performance will win the race. Real-Time Analytics will play a crucial role in separating the winners from the followers or losers. The article titled Enabling Real-Time Analytics for IoT includes the technical justification of deploying real-time analytics platforms.

The journey into the world of Real-Time Analytics is covered in these Forrester slides, which initiate the new entrants into data technologies to the world of connected devices. Soon, more and more global businesses of all shapes and sizes will depend on Big Data, Hadoop, Cloud, and AI-driven “smart” Analytics systems to derive insights from gigabytes of data emerging from connected devices for operational excellence.

With cheaper hardware, economical hardware architectures, affordable data storage platforms, and hosted “data Centers” being available to businesses at every corner of the globe, the modern businesses have no excuse for delaying Real-Time Analytics platforms for faster, better, and more accessible data-driven solutions.

The Transition from Traditional to Real-Time Analytics

The biggest change in the transition from historical Data Management to real-time Data Management is that businesses now have to make quick and accurate decisions while facing customers on a long queue. In other words, Data Management has moved from a back-end, closet activity to a highly visible, front-end business engagement.

In modern businesses, especially in digital businesses, data-driven, decision management has to be perceived as a separate discipline like Data Management or process management. The blog post titled How to Move Analytics to Real Time claims that between 2016 and 2019,  companies were in a rush to invest in Real-Time Analytics platforms as the pressure on businesses to make immediate and accurate decisions (mostly with customers at the other end)  increased. In order to find the appropriate Real-Time Analytics solutions, the businesses should first assess their real-time decision-making needs, then explore and find a matching solution with aligned goals and objectives, and finally tune the analytics platform to handle the speed of decision making required in the businesses.

In this context, also review the following Gartner news flashes:

1.     The news flash titled Harness Streaming Data for Real-Time Analytics discusses complex architectural approaches to accessing streaming data for Real-Time Analytics.

2.     The news flash titled Four Steps to Successful Real-Time Analytics explains how frequent monitoring and updation of analytics models will help achieve the desired business outcomes.

Agile Decision-Making in Real Time

Real-Time Analytics promises a wide range of solutions to a wide variety of industry sectors—from recommending alternative products, validating card-card transactions, to helping airlines customers during flight delays.

All these mentioned activities are carried on in real-time, necessitating the businesses to use real-time analytics to find quick and accurate solutions. The article titled Real-Time Decisions and Analytics teaches readers how to view real-time analytics from different standpoints, how to match the speed of execution with a particular need, and how to differentiate between the different types of real-time decision processes.

As an example of real-time decision-making, the author of Manage Deploys MemSQL for Real-Time Analytics  talks about Manage, which  provides mobile marketing campaigns through “programmatic marketing and advertising” tools to clients like Uber and Amazon. This company uses MemSQL to power its real-time analytics activities.

Businesses today have to make countless, agile decisions during their operational hours simply to remain competitive and provide the best services to the customers. The Forbes blog post titled Real-Time Analytics: Six Steps for Fast and Precise Decision Making states that Real-Time Analytics support agile decision making through cost-effective, accelerated, completely automated, and auditable systems. As enterprises invest in such data-drive, automated systems, they will begin to pursue Decision Management as a separate business practice.


The Role of Big Data in Real-Time Analytics

Many industry sectors have plunged head on into Real-Time Analytics for some time now, but the most glamorous beneficiary of this new technology is marketing. The Forbes blog post titled How MTV and Nickelodeon Use Real-Time Big Data Analytics to Improve Customer Experience explains how the entertainment behemoths like MTV or Nickelodeon uses audience behavior data collected from connected devices and networks, and then conducts Real-Time Analytics on that data  to enhance viewer experience and increase customer retention. Real-Time Analytics on Big Data also helps these companies uncover which technologies are working and which are failing to meet viewer expectations.

Analytics Used in Agile Product Development

More and more, traditional R & D setup and product development functions in enterprises are gearing up for sensor-driven, agile product development practices, which claim to reduce time, cost, and efforts involved in typical product development endeavors.

The Forbes blog post titled Real-Time Feedback for Agile Product Development at Walmart depicts one such agile product development story, where the company’s Data Science team is transforming the current product development practices with iterative, Real-Time-Analytics-Enabled, “development, testing, instant feedback, and re-development cycles.” Further, Real-Time Analytics is used to monitor the performance of developed products for corrections.

Analytics and the Internet of Things

Real-time Analytics is immediate and involves quick response time. As digital businesses run on a 24/7 basis, gradually all businesses will need continuous Real-Time Analytics, which typically includes a Hadoop environment powered by an NoSQL database.

NoSQL, with its immense capability for supporting a wide variety of Data Models, is supremely positioned to handle real-time data. With more and more enterprises depending on connected devices for routine operational efficiency, the business data landscape is suddenly facing avalanche of data emanating from powerful sensors across their organizations. The wide majority of IoT-ready enterprises, according to a recent survey, agree that this new data avalanche is more of a golden opportunity to improve existing operations rather than a threat.

An Infographic: Real-Time Analytics and the Internet of Things from DATAVERSITY® explains how millions of businesses are already prepared to handle data floods coming from connected devices. Most organizations view the Big Data enabled, Real-Time Analytics as a big opportunity for improving business outcomes.

Also review Invoca Integrates IBM Watson for Real-Time Voice Analytics, which discusses an innovative use of IBM Watson Cognitive Computing technology in a voice data-analytics solution. You may also want to review another article titled NoSQL and Real-Time Analytics: What You Need to Know to get the full story on Hadoop, NoSQL, and Real-Time Analytics.

Real-time Analytics Use Cases: At a Glance
Healthcare

In the healthcare industry, The Power of Real-Time Analytics at the Point of Care has been around for some time now. Analytics enabled diagnostic and treatment facilities cuts across geographies and infrastructure setups to provide high quality care at affordable cost without compromising regulatory requirements. The process of automating the acquisition of patient care data, treatment records, and medical research data, assists the healthcare providers or medical experts to make collaborative treatment decisions across thousands of miles just when it is needed.

Real-Time Analytics helps to collect and organize massive volumes of medical data kept in disparate data repositories, and then to derive medical insights from the data in real time for fast and accurate decision making. Clinicians can now rejoice at the prospects of being able to access treatment histories from any location so long they are digitally connected to such data repositories.

All these technological solutions will collectively help to deliver higher quality healthcare services to patients of all economic groups in any location. Further, in complex cases, the healthcare providers can use data technologies and patient care data to analyze and compare which treatment method will be suitable in a particular case in real time. Very soon, the patient will become an informed, decision-making partner for the doctor and other healthcare providers rather than a passive recipient of complex treatment processes.

Retail: Mobile Analytics in Retail

Here are a few excellent articles depicting real-time, mobile analytics in the retail industry:


Big Data Analytics in Retail

  • Real-Time Analytics in a UK grocery chain: This article describes how a 3,500-units strong, UK-based chain grocery store uses Big Data, Iot, and Real-Time Analytics for operational efficiency leading to steep customer growth. Analytics has helped this huge business keep track of purchase and customer behavior data on an average of 40,000 products that each store stocks.

Certainly, Real-Time Analytics is here to stay, but many other allied technologies must be upgraded or tuned to get the maximum benefits of instant data analysis. The technology is likely one of the biggest hurdles to getting all the systems to work together, especially integrating older, legacy technologies into the newer Big Data, IoT and other similar platforms needed to leverage the benefits of Real-Time Analytics. The processes for growth have begun, but there is still so much work to do for the real advantages to occur.

 

Photo Credit: agsandrew/Shutterstock.com

About the author

Paramita Ghosh has over two and a half decades of business writing experience, much of which has been writing for technology and business domains. She has written extensively for a broad range of industries, including but not limited to data management and data technologies. Paramita has also contributed to blended learning projects. She received her M.A. degree in English Literature in 1984 from Jadavpur University in India, and embarked on her career in the United States in 1989 after completing professional coursework. Having ghostwritten and authored hundreds of articles, blog posts, white papers, case studies, marketing content, and learning modules, Paramita has included authorship of one or two books on the business of business writing as part of her post-retirement projects. She thinks her professional strength is “lifelong learning.”

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