Data Revolution – Almost every company is now in a race of data revolution, call it madness or an ideology but this is one of the ways to survive and lead to the future. Every business relies on its resources (human expertise and infra) to tap the data at its full potential, but how much we really do that’s another question anyways. Data is needed for almost every machine learning model i.e. be it self-driving cars or stripping down information from thousands of pictures to asses who is in the image or for insurance claims processing for its damage. Oh yes, data has already become a new oil.

The Data Revolution

We have tons of data and it’s increasing every second and millions of tools but how to harness the power of modern data and related delivery tools, is still a question. It’s even more difficult now in the era of GDPR or similar regulations. Reducing the cost and simplifying data intelligence infrastructure going to be our next biggest challenge at least for the year 2020. Few questions at hand like below

  • How to decide and identify key success factors when scoping data models for any project.
  • What will be those factors to determine whether the source data will lead us to success or not?
  • How to select correct tools i.e. data models, machine learning algorithms to best fit for the project.
  • How to label unlabelled datasets so that chosen algorithms can learn and perform as designed to give required results.

So if Data is the new fuel of today’s time then we must accept a pool of data scientists as oil refineries. At the same time, data tools can be considered important ingredients that help to refine and produce desired results. Cognitive Analytics provides a 360-degree view to make the correct decision and the right time.

Real-Time Analytics – On the Edge

Organizations which are vulnerable and always on the radar of hackers/attackers require real-time analytics to assess and decide on high-velocity data. The confluence of big data, big strategy, big computing powers and a big hunger for new businesses can lead artificial intelligence into a gold mine. Having a system that studies intelligent methods, and learns from data science and data environment is best placed for this business. Examples include computational intelligence and metaheuristics.

For real-time data, analytics business needs,  the ability to process high-volume, high-speed and fastest streaming data from different data sources is extremely critical. To allow the business to gain insights and make quick decisions there is not much time to send data to a central data repository and then wait for results. Thus in such cases, meaningful analytics to make impactful decisions promptly becomes even more crucial.

The most important and critical success factor of any business of today is Data. Social media sites, IoT devices, financial institutions, retail shops, etc are working as data generation factories. The data generated by these factories is used for cross-selling,  improving user experience, and increasing customer buying rates. The analysis is also used for driving and creating new products and use cases. The actions from big data analytics can be business transformative.

Certain market dynamics determine the growth of the data and its related analytics. That’s where Data Intelligence’s adaptive dynamics come into play to assess the factors driving the organisation to adapt their existing, profitable lines of business. This helps them to stay relevant in the future of the rapidly evolving world and enormous helper for Blue Ocean’s shift strategy.

The Edge Analytics – Data Revolution

Edge analytics or a fashionable name for automated analytics for data points from sensors etc. The approach for collecting, analysing and making a decision on data in an automated manner with analytical computation methods. This is performed at network nodes, sensors, social media data pipes or banking switch gates, etc instead of the usual process of sending data to a centralised data store location. 

Cases, where decisions are needed in real-time like in case of actions on CCTV footage, aircraft condition, or any other situation where losing time, means disaster or huge business loss edge analytics comes in handy. The primary key success factors in such cases are reduced latency of data analytics. and the best scalability of analytics.

Behavioural biometrics data intelligence helps to learn consumer behaviour by tracking certain patterns. When behaviour changes it raises an alarm and detects subtle shifts in the underlying data then revises algorithms accordingly. Edge analytics works as a key factor for scaling the processing power and analytics capabilities by moving away from traditional centralised to isolated sites where the data is actually collected.

Transformation of data into Intelligence (DataIntelligence) is the highest stage that many services-providing organisations ever reach in the data pyramid. Data is collected from many sources, refined, and finally turned into valuable reports that are used internally to measure performance. In addition, it also incorporates social and other external data sources to enrich and enhance information-based management.

Why Edge Analytics?

Some things are best learned through real-world experience. Whether you are training a self-driving car, detecting animals with drones, or identifying car damage for insurance claims, the steps needed to effectively train a computer vision model at scale remain the same. Edge analytics brings the benefits of smart, faster and on-the-spot decision-making capabilities. Industries like retail outlets, smart cities concepts, and energy distribution & generation, etc.

Smart sensors and connected devices collect data on the state of art kind of infrastructure with the best available software and storage. Prepare data models for training and learning. In the end, choosing the correct algorithm to massage the data remains the most critical part. Computing power stress gets reduced in the case of edge analytics. Uses cases which support and advocate edge analytics are

  • Behavior analytics – monitoring passwords with keyboard usage or controlling shoplifting.
  • FinTech – Fraud detection and prevention at ATMs or mobile payments transactions
  • Monitoring fleet movement to analyze any unusual behavior

To answer why we edge analytics, we can answer it with the benefits it brings in like reduced latency or faster data analytics at a low scale of infra & low bandwidth such as behaviour analytics, live CCTV footage analytics or any such situation where speed is the key for analytical decision. Which means a low cost or helping the overall cost around the whole analytics game.

Machine Learning (ML) - Everything You Need To Know

Conclusion – From the post above, now we can make out. When edge analytics should be considered? and How can we leverage machine learning and data science techniques to get the most out of the in-hand data?

To get a close-up look at the essential products and solutions to help you accelerate your digital transformation. Edge analytics really require cutting-edge thought processes and technologies or in short itsit’sit’s not an overnight task. This requires a full end-to-en end architecture and frame from analytics model creation to deployment and finally execution. Decision logic and starting to pare points what is needed from edge analytics and what should still be done at the central model.

Points to Note:

All credits if any remains on the original contributor only. We have covered all basics around edge analytics for real-time data analytics and basics around data which are all about quality data, computing power and algorithms to look for information. In the next upcoming chapters will talk about implementation, usage and practice experience for markets.

Books + Other readings Referred

  • Research through open internet, news portals, white papers and imparted knowledge via live conferences & lectures.
  • Lab and hands-on experience of  @AILabPage (Self-taught learners group) members.

Feedback & Further Question

Do you have any questions about AI, Machine Learning, Data Science or Big Data Analytics? Leave a question in a comment or ask via email. I will try best to answer it.

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Posted by V Sharma

A Technology Specialist boasting 22+ years of exposure to Fintech, Insuretech, and Investtech with proficiency in Data Science, Advanced Analytics, AI (Machine Learning, Neural Networks, Deep Learning), and Blockchain (Trust Assessment, Tokenization, Digital Assets). Demonstrated effectiveness in Mobile Financial Services (Cross Border Remittances, Mobile Money, Mobile Banking, Payments), IT Service Management, Software Engineering, and Mobile Telecom (Mobile Data, Billing, Prepaid Charging Services). Proven success in launching start-ups and new business units - domestically and internationally - with hands-on exposure to engineering and business strategy. "A fervent Physics enthusiast with a self-proclaimed avocation for photography" in my spare time.

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