What is Edge Computing ?

The name edge computing signifies the corner or edge in a network diagram at which traffic enters or exits the network.

Edge computing pushes computing power to the edges of a network, so instead of devices like drones or smart traffic lights needing to call home for instructions or data analysis, they can perform analytics themselves on streaming data and communicate with other devices to accomplish tasks.

In edge computing, the big data analytics happens very close to the IoT devices and sensors. Edge computing thus can also speed up the analysis process, allowing decision makers to take action on insights faster than before.

For organizations, this offers significant benefits. They have less data sent over their networks, which can improve performance and save on cloud computing costs. It allows organizations to discard IoT data that is only valuable for a limited amount of time, reducing storage and infrastructure costs. Further edge computing improves time to action and reduces response time down to milliseconds, while also conserving network resources.

In Industrial Internet of Things, applications such as power production, smart traffic lights, or manufacturing, the edge devices capture streaming data that can be used to prevent a part from failing, reroute traffic, optimize production, and prevent product defects.

Coca Cola free style dispensers are using edge computing to quickly understand the consumer behavior and help to be more responsive to needs.

GE locomotives take advantage of edge computing by gathering and processing real-time data about railway conditions, train maintenance, and even crew morale to help railroad companies move trains through crowded railway corridors in as safe and efficient a manner as possible.

With Digital Transformation and emerging technologies that will enable “smart” everything – cities, agriculture, cars, health, etc – in the future require the massive deployment of Internet of Things (IoT) sensors while edge computing will drive the implementations.

Watch these Movies for Big Data Analytics & Machine Learning

Business analytics & Big Data has not only got the business and technology industry excited, but have influenced many movie-makers across the last few decades. It would be a big miss for data scientists and business analysts alike if they don’t know these good references to their field of work and passion.

Here are some interesting movies with Big Data, predictive analytics, and machine learning embedded in the story line.

Ex Machina :

Plot: Caleb, a 26 year old programmer at the world's largest internet company, wins a competition to spend a week at a private mountain retreat belonging to Nathan, the reclusive CEO of the company. But when Caleb arrives at the remote location he finds that he will have to participate in a strange and fascinating experiment in which he must interact with the world's first true artificial intelligence, housed in the body of a beautiful robot girl.

21 :

Plot: The movie is based on a true-story of five highly intellectual Ivy League students who get trained by their professor in card counting at blackjack. Using a combination of code talk and hand signals, these students manage to win hundreds and thousands of dollars at casino after Casino in Las Vegas.

Moneyball :

Plot: Oakland A's general manager Billy Beane's successful attempt to assemble a baseball team on a lean budget by employing computer-generated analysis to acquire new players.

With the use of historical data and predictive modeling to build a champion baseball team, this true story is sure to delight both data scientists and sports enthusiasts.

I, Robot :

Plot: In 2035 a technophobic cop investigates a crime that may have been perpetrated by a robot, which leads to a larger threat to humanity.

A super computer called VIKI, which stands for Virtual Interactive Kinetic Intelligence, uses truckloads of data and computational powers to take control of the world’s robots. VIKI and its evil plans are eventually defeated by the protagonist with the help of a friendly robot.

Minority Report :

Plot: In a future where a special police unit is able to arrest murderers before they commit their crimes, an officer from that unit is himself accused of a future murder.

Crucial to this movie’s story are the PreCogs, a small team of humans who can see into the future to predict when the murder will be committed, by whom it will be committed, and who will be the victim.

The PreCogs are the source of future data for the PreCrime police unit, who are really the super-smart “data scientists” doing the hard work. It’s down to this team to solve the time-bound challenge of sifting through visual data and piece together information that nails down the final details in order to prevent the next crime. 

It is fun watching these movies while learning the use of Big Data Analytics & Machine Learning.

Using Data Science for Predictive Maintenance

Remember few years ago there were two recall announcements from National Highway Traffic Safety Administration for GM & Tesla – both related to problems that could cause fires. These caused tons of money to resolve.

Aerospace, Rail industry, Equipment manufacturers and Auto makers often face this challenge of ensuring maximum availability of critical assembly line systems, keeping those assets in good working order, while simultaneously minimizing the cost of maintenance and time based or count based repairs.

Identification of root causes of faults and failures must also happen without the need for a lab or testing. As more vehicles/industrial equipment and assembly robots begin to communicate their current status to a central server, detection of faults becomes more easy and practical.

Early identification of these potential issues helps organizations deploy maintenance team more cost effectively and maximize parts/equipment up-time. All the critical factors that help to predict failure, may be deeply buried in structured data like equipment year, make, model, warranty details etc and unstructured data covering millions of log entries, sensor data, error messages, odometer reading, speed, engine temperature, engine torque, acceleration and repair & maintenance reports.

Predictive maintenance, a technique to predict when an in-service machine will fail so that maintenance can be planned in advance, encompasses failure prediction, failure diagnosis, failure type classification, and recommendation of maintenance actions after failure.

Business benefits of Data Science with predictive maintenance:

• Minimize maintenance costs - Don’t waste money through over-cautious time bound maintenance. Only repair equipment when repairs are actually needed.
• Reduce unplanned downtime - Implement predictive maintenance to predict future equipment malfunctioning and failures and minimize the risk for unplanned disasters putting your business at risk.
• Root cause analysis - Find causes for equipment malfunctions and work with suppliers to switch-off reasons for high failure rates. Increase return on your assets.
• Efficient labor planning — no time wasted replacing/fixing equipment that doesn’t need it
• Avoid warranty cost for failure recovery – thousands of recalls in case of automakers while production loss in assembly line

TrainItalia has invested 50M euros in Internet of Things project which expects to cut maintenance costs by up to 130M euros to increase train availability and customer satisfaction.

Rolls Royce is teaming up with Microsoft for Azure cloud based streaming analytics for predicting engine failures and ensuring right maintenance.

Sudden machine failures can ruin the reputation of a business resulting in potential contract penalties, and lost revenue. Data Science can help in real time and before time to save all this trouble.

Mobile enablement in Digital age

Gone are the days when we used to carry big fat wallet filled with cash, coins, multiple credit cards, business cards, travel tickets, movie tickets, personal notes, papers with names, numbers and the list can go on.

Mobile technologies have transformed the way we live, work, learn, travel, shop, and stay connected. More than 80% of time is spent on non-voice activities.

The rapid growth in Mobility, Big data, IoT and Cloud computing technologies has changed market dynamics in every industry and is changing customer behavior. Digital Transformation has become the norm.

Mobile is spearheading this transformation by putting businesses on the move and by connecting the enterprises with customers, partners, employees and machines.

Businesses are fast realizing that they need to offer their customers cutting-edge mobile applications that will help them engage with the brand and its services, in near real-time.

Some innovative use of mobiles in digitization:

• Payments - NFC payments, Biometric payments using finger scans, facial recognition, voice recognition, retina based check. Major players in this space are PayPal, Apple Pay, Android Pay, Samsung Pay etc.
• Virtual or digital currency
• Tsunami of Apps from Google maps, to zomato helping us throughout the day
• Retailers can use targeted mobile campaigns for customer acquisition, retention
• Live streaming apps like Meerkat and Periscope delivering targeted content to site-specific users which benefits both the consumer and the creator.

Impact of mobile enablement:

• With mobile enablement, a merchant can enhance your payment experience and boost operational efficiency
• Real time communicating with the customer, can be greatly enhanced through mobile enablement. Businesses can quickly respond to customer complaints or questions through social media, or the apps
• By analyzing the data generated by mobiles using Big data Analytics, businesses can give personalized experience to consumers
• Digital assistants like Google Now, Siri are helping everyone

Here are some well-known industry examples:

• Starbucks processes over 8 million mobile transactions each week, this data of mobile user behavior to customer preferences, is then analyzed by a team of data scientists for insights.
• Coca Cola is using mobile apps for field sales folks, equipment service teams and knowledge workers and commercials like get free coke on mobile
• The emergence of hyper-local startups like Jugnoo, Zopper, Grofers and PepperTap using mobile first strategy
• Virgin Atlantic, Bank of America, Delta, Chipotle have their industry leader apps for fantastic customer experience

As the penetration of smartphones and internet is increasing with 5G and beyond, along with the changing shopping behaviors, the mobile revolution is here to stay and impact the Digital Transformation further.

Customer 360º view in Digital age

In today’s digital age of customer hyper-personalization, organizations identify opportunities for real time engagement based on data-driven understanding of customer behavior.

Customers have taken control of their purchase process. With websites, blogs, Facebook updates, online reviews and more, they use multiple sources of information to make decisions and often engage with a brand dozens of times between inspiration and purchase.

It’s important that organizations collect every customer interaction in order to identify sentiments of happy & unhappy customers.

Companies can get a complete 360º view of customers by aggregating data from the various touch points that a customer may use, to contact a company to purchase products and receive service/support.

This Customer 360º snapshot should include:

• Identity: name, location, gender, age and other demographic data
• Relationships: their influence, connections, associations with others
• Current activity: orders, complaints, deliveries, returns
• History: contacts, campaigns, processes, cases across all lines of business and channels
• Value: which products or services they are associated with, including history
• Flags: prompts to give context, e.g. churn propensity, up-sell options, fraud risk, mood of last interactions, complaint record, frequency of contact
• Actions: expected, likely or essential steps based on who they are and the fact they are calling now

The 360º view of customers, also often requires a big data analytics strategy to marry structured data (data that can reside in the rows and columns of a database), with unstructured data (data like audio files, video files, social media data). 

Many companies like Nestle, Toyota are using social media listening tools to gather what customers are saying on sites like Facebook and Twitter, predictive analytics tools to determine what customers may research or purchase next.

What are the returns of Customer 360º:

• All customer touch point data in a single repository for fast queries
• Next best actions or recommendations for customers
• All key metrics in a single location for business users to know and advise customers
• Intuitive and customizable dashboards for quick insights
• Real time hyper personalized customer interaction
• Enhanced customer loyalty

Customer 360º helps achieve Single View of Customer across Channels – online, stores, marketplaces, Devices – wearables, mobile, tablets, laptops & Interactions – purchase, posts, likes, feedback, service.

This is further used for customer analytics – predict churn, retention, next best action, cross-sell & up-sell opportunities, profitability, life time value.

Global leaders in customer experience are Apple, Disney, Emirates.

A word of caution though - Focus & collect only that customer data, which can help to improve the customer journey.