Analytics

What connected car makers can learn from the IoT sector

Image Credit: Subaru

The rise of the Internet of Things (IoT) has caused stratospheric growth in the number of connected devices and sensors in enterprises across all industries. It’s estimated that more than 80 “things” per second are connecting to the Internet, and by 2020 there will be a whopping 50 billion things connected to the IoT. Industries such as manufacturing and retail are being dramatically transformed by the IoT, with enterprises adopting technologies like fog computing and advanced analytics, or deploying hundreds of thousands of sensors throughout every aspect of their supply chains to create efficiencies, increase productivity, and gain real-time insights about their customers.

The connected car market today is facing many of the same IoT-related challenges that enterprises in other industries have already encountered and overcome. Here’s my take on some of the best practices and lessons learned along the way — many of which can be applied to the connected car market to help automakers and their partners harness the full potential of the IoT. Because this is such an important topic with many lessons to be learned, we’ll explore it over the course of a two-part series.

In many ways, the connected car can be considered the ultimate “thing” in the IoT. These data centers on wheels are truly the epitome of everyone’s best hopes and greatest challenges when it comes to the IoT.

With often more than 100 onboard computers continuously monitoring location, component performance, driving behavior, and more, experts estimate that highly automated vehicles will generate four terabytes of data per hour! And, as our transportation systems become even more connected through vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) communication, the amount of data generated is going to shift into overdrive.

As a result, automakers and their partners are beginning to experience many of the same challenges that enterprises in other industries have, including managing an overwhelming number of connected devices and the huge volume of data they generate, as well as challenges related to security, pervasive connectivity, bandwidth optimization, and more.

Lesson #1: Managing mushrooming devices when traditional methods won’t scale

Not long ago, enterprise IT revolved around managing a few large mainframes. Then suddenly, new paradigms emerged, like client-server, distributed, and mobile computing — forcing enterprise IT to evolve. The trend of bring your own device (BYOD — allowing employees to connect their personal devices to the company network) created…

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How data analytics will help us understand chatbots

Bots can augment human interaction, create greater business efficiencies, and remove friction from customer interactions.

It’s also a market that’s attracting impressive investment dollars, with 180 bot companies raising $24 billion in funding to date. Industry leaders from IBM to Facebook are making big efforts to take advantage of this trend, spending significant resources encouraging developers to create new bots that enable more personalized customer interactions. In March of 2016, Cisco announced the Spark Innovation Fund, a $150 million investment in bots and developers who want to make new products for Cisco endpoints in offices around the world.

Some of the most obvious uses for bots revolve around communication, customer service, and ecommerce. Chatbots are at the center of the way people communicate today, with over 2.5 billion people worldwide using a messaging platform like WhatsApp, Facebook Messenger, or Telegram. Twitter recently rolled out a bot-like feature within its DM service to enable brands to interact more frequently with customers, with the goal of ultimately improving the customer experience. Facebook is testing a service to enable users to make payments on Facebook Messenger that are facilitated via the use of bots built on its platform. Gaming companies are using bots to help ward off trolls that might interfere with the natural progression of the game.

All this is happening while we create almost unfathomable amounts of data — data that is expected to reach 35 zettabytes by 2020. So how can companies outside ecommerce take advantage of bots to automate these new data sets and deliver smarter, faster analytics access in the process? Let’s take a look:

The concept of human to machine interaction via natural language processing can drive immediate analytics responses, rather than waiting on human analysis…

Adobe brings AI-powered Virtual Analyst to Analytics Cloud

Adobe will today announce the introduction of Virtual Analyst, powered by its Sensei AI.

The analyst runs 24/7 in the background to monitor data and detect and find the root cause of anomalies in online activity. This replaces the painstaking process of an engineer or data team manually searching analytics reports for insights, which can diminish in value over time.

“Insights we do believe have a shelf life and to have a system be automated and can handle these on its own is really key, I think,” Adobe marketing manager Nate Smith told VentureBeat in a phone interview.

Sensei was first introduced last fall as an artificial intelligence service trained by massive amounts of data gathered from Adobe Creative, Marketing, and Analytics cloud software.

Sensei can do things like auto-caption images, deliver data insights, or talk people through how to use Adobe software. Adobe ultimately wants the AI to also train novice creatives how to…

Dataiku Offers Advice on how to Create Data Team Harmony

Harmony and analytics are two terms not often found together, especially when executing data science chores using a team of diverse data professionals. A data science team is often made up of people from diverse backgrounds, with diverse skillsets – from the machine learning specialist to the master Python coder, to the beginning data analyst. To successfully build and execute any sized data science project requires harmony across all of the team members. Everyone needs to work effectively and efficiently, using the tools they know best.

GigaOM recently had the opportunity to discuss the dynamics of building data teams with Florian Douetteau, CEO of Dataiku. Douetteau offered some sage advice and was able to point out what challenges face those trying to build teams.

Douetteau said “one of the biggest challenges is finding, hiring, and keeping people with a background in machine learning. There is a high demand for experienced data scientists, meaning that it can cost a lot to hire one, especially considering the opportunities data scientist have.”

Douetteau added “yet so many of those data professionals are specialized in their area of expertise, meaning that it is difficult for a business to maximize the return on investment of hiring a data scientists. In other words, to build a team, businesses need to move beyond a single individual’s domain of expertise and hire individuals with different skill sets and enable them to work cooperatively and productively.”

The growing deficit of Data Scientists, along with the closed nature of many analytics tools have made building effective teams a near impossibility. Yet all is not lost. Douetteau said, “there exists a vast ecosystem of opensource tools that are available to the masses, which can help to level the playing field, and bring data analytics capabilities to professionals of all stripes.”

However, much like the cola wars of the 80’s, there is an almost infinite variety of flavors and formulas that drive tastes, at least when it…