Are You Ready For The Data Economy?


 In Brief

Everything holds the potential of becoming a piece of information, reportedly, by a source of information, be it primary, secondary or eavesdropping hearsay. The latter being inadmissible evidence by involuntary third person, altogether. In business, everything is about to change with IoT real-time information recording-reporting. A new era of data-driven economy is upon us, where business strategy and decision making is based on data patterns, or, recurring consumer behavior revitalized from a state of discombobulated cherry picking!


As big data becomes a familiar word dilly-daily, business operators start to pay attention. On the surface, machine learning guide users through enhanced online experiences to the right sources and products. Behind the scene, the emergence and exchange of data over the IoT creates new building blocks that hold the secret to business success and even optimal economic components. That makes it the keeper of much soughted after deltas that constantly redefines the nature and growth of the very business itself.

Digital data differs from all other sources of information and plays a different role than legacy applications that screen and arrange data and sequence of events to represent things in certain ways that management sees fit. It is traded in various forms for value as a force that can alter market structures, it be consumption behavior, customer needs or pertinent rules and regulations. Competition has rendered those with such information the power to set trends and rise to the top.

In 2017, the International Data Corporation (IDC), a marketing information consultant, event organizer and telecommunication services, predicted that by 2025 the digital universe, or the scale of archived data each year, will amount to 180 zettabytes. That’s a staggering 21 zeros. And, as has proven with its own digital platform, to process data properly, cloud computing and data refinery is a must.

The quality of data itself has also changed from the past, from unregulated data entry, mis-format settings and disconnected databases to standardized enterprise systems. Invaluable information is no longer simply a PC archive of personal profile with age, sex, income, and etc columns. Real-time analysis and processing of transactions, including images and video files, social network interfaces and data from live monitoring sensors, all prove vital to the big data oeuvre which is composed and recomposed endlessly by the millions of users with smart devices – the new primary source of big data information. Big data has to, unsurprisingly, increase exponentially as trillions of devices across the world are sensor equipped to communicate with each other and track digital traces everywhere they go. Facebook and Google initiated data collection from user activity, e.g., newsfeeds and posts Likes and Comments that eventually became potential personality indicators of the user. Now we have machine learning that enhances online experience for each unique user optimize digital marketing. The two giants have also been successful using such user data to test and create new algorithms every time a post is posted or a user’s account gets tagged. Facebook today can automatically tag user accounts for a range of things with 98% accuracy. The data also generates new revenue through sales to numerous third party manufacturers.

Uber may be known to many as simply a platform to call taxis at affordable prices in some locations and chi-chi chauffeur charges in others. From a logistics-transportation standpoint, uberization is one of the biggest game changer of the 20th-21st century, technologically wise, to one of the three core IoT pillars and therefore to major economic components that founds the new sharing economy. As the size of the data pool generated through such application is so vast, it naturally carries vital information pertaining to different customer segments that affect both the supply and demand side of the equation, e.g., the number of drivers and passengers available in strategic geographical locations vs. logistics requirements and fleet size. Data can be analyzed the same way Tesla customers indicate the demand for EVs with self driving features.

Applying a capacity from Nexar, on the other hand, may rescue Uber from the arch nemesis of double standard auto insurance through admissible evidence collected in real time. Continue reading: 

Nexar: optimized roadway security and insurance processing

Nexar is the startup company that created the AI dash cam app that tracks the driving behavior of the driver along the way while capturing traffic information in real-time through computer vision and sensor fusion technology to detect accidents, analyze road conditions and stream alerts to other vehicles on in the target network in real-time. The innovative AI dash cam app is regarded as a ‘claims processing game-changer’ as it optimizes insurance claim processing, proactively, with optimize fraud prevention. Vehicles in the same network where accident occurs can also generate admissible evidence from such data for insurance companies to use for collision reconstruction and claim investigation.

Nexar’s CityStream capability, on the other hand, digitizes the public infrastructure to stream vehicle-sourced operational data such as traffic congestions, traffic patterns, infrastructure defects, road hazards, and collision instances, among others. The vast amount of data generated can thus be applied as invaluable information for municipalities and transportation-ridesharing agencies for infrastructure management, traffic management, and roadway maintenance, according to Nexar’s official website.

Led by Alibaba’s Ibex Ventures, Nexar managed to raise a $30 million Series B fund at the end of January 2018. Participants also included Aleph, Mosaic Ventures, Nationwide Insurance, Slow Ventures, True Ventures and Tusk Ventures. Through a joint venture with Uber or Lyft, the company intends to focus the funds to an all important near future target: to construct V2V (vehicle-to-vehicle technology) framework, or, the technology to found smart city transportation of the future, in conjunction with V2P (vehicle to pedestrians), V2I (vehicle to roadway infrastructure), and V2N (vehicle to the network), thus collectively serving as smart building blocks that operate autonomously (follow Video City Stream by Nexar at the link Nexar launched the first application in 2016 and is currently serving more than 760 cities in 160 countries around the globe, the biggest markets being New York, San Francisco and Las Vegas. With the recent joint venture with Alibaba we can expect Nexar to commence operations in China, soon.

Toyota has, yet again, set the example as a major automaker that embraces change and evolve with game changing advancement. With the launch of Toyota Connected, Toyota is reorganizing with a focus on data science technology by integrating Toyota’s vast resources of innovation to capitalize on data-driven services, analytics and data center management. Toyota is not aiming to be simply a great car manufacturer, but the power that controls the information that revolutionizes automotive safety and experience. A connected car that links traffic data with insurance models according to observed driving styles is another example of the future unfolding before our eyes, today. 

Smart Pricing is a pricing model optimized by the data economy; big data and machine learning working in tandem. Two examples represent the epitome of 21st century sharing economy: Airbnb and Uber, both an American startup. Airbnb provides an online marketplace for home owners to offer hospitality services by renting or leasing out their idled space or property for short-long term lodging. Airbnb uses big data to help homeowners set the most appealing price options through Smart Pricing, an automated price reflecting demand and supply for similar listings in the same area that all fluctuate by the same travelling seasons and local events at any given time. In other words, it mirrors the relative market’s latest state of inflation or deflation. Homeowners can review daily reservation schedule for each month and determine whether the set price is competitive, or not. Similarly, Uber enables car owners to maximize the utilization rate of the most under used, yet, most expensive, resource consuming and global warming product in the world (unless the owner’s name is Prius); their idled vehicles, by offering ridesharing services to passengers in the close proximity. It offers smart pricing that takes into account the total number of passengers in need and available drivers in the area in peak and off-peak hours, city by city. Although metropolitans end up paying more than smaller-town passengers, smart data benefits the drivers, the passengers and obviously Uber all the same, tremendously, with appropriate value for money based on the state of affairs in real time.

It is predicted that by 2019, more than 90% of companies will have a Chief Data Officer to take on data management properly, i.e., define and prioritize data strategies, design data storage and find ways to take advantage of big data as well as identify underperforming capacities that affect the organization, directly and indirectly. The promise is a double dip, better decision making on top of minimized operational costs. Businesses cannot afford to snooze and lose the opportunity of a lifetime to build the platform for sustainable success through a data economy.


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