Today, any vehicle data coming in and out of consumer vehicles go through OEMs, making the OEM the data’s gatekeeper and giving the OEM control over who has access, how the vehicle data is accessed and how much access costs – not the vehicle owner.

RUMBLE IoT and the Auto Care Association support a solution that gives the vehicle owner control over their vehicle data and which provides security, privacy, choice, safety, and a level playing field for the marketplace. We support a solution that enables an open infrastructure, where the vehicles of the future can “talk” to infrastructure components such as roads, traffic lights, emergency vehicles and other vehicles, which results in safer and more efficient roadways. 

The Auto Care Association hired RUMBLE to develop a solution that can provide vehicle owners that freedom of choice and control over their data.

Why is Vehicle Data Important?

As you drive, your car gathers data about your location, driving behavior, vehicle health, and more.

“A car can generate about 25 gigabytes of data every hour and as much as 4,000 gigabytes a day, according to some estimates.”

This can be used in a variety of useful ways, from knowing if your car needs maintenance to connecting to a smart streetlight.

In reality, you don’t own your vehicle data today.

71% of consumers assume vehicles owners have direct access to their vehicle’s data. They’re Wrong.

Only car manufacturers control who sees your vehicle telematics data and how it’s used.

The data trove in the hands of car makers could be worth as much as $750 billion by 2030, the consulting firm McKinsey has estimated.

Not controlling our car’s data could cost you money in repairs and maintenance. Because auto manufactures control the telematics data on each vehicle, they could limit where you can take your car for service, leaving you with limited, inconvenient, or more expensive options.

RUMBLE Develops a Secure Vehicle Interface (SVI)

The Auto Care Association approached RUMBLE to help solve this problem by creating an application called Secure Vehicle Interface, or SVI. Working with Auto Care, RUMBLE leveraged our expertise managing disparate types and sources of data to construct the first prototype SVI application. The Secure Vehicle Interface application provides a standardized, secure design for vehicle data to be shared with third parties at the owner’s discretion.  The SVI translates the vehicle’s data into a common language understood by third parties and creates a secure system to share that data. This application would give consumers the power over the data that they desire.

Take Ownership of your Vehicle Data. Sign the Petition.

But this isn’t available yet. Join the petition to get control of your vehicle data. 

The Auto Care Association’s campaign Your Car. Your Data. represents the interest of individual car owners and the independent shops that support them.

Anyone who feels you should own your car’s data should join RUMBLE IoT and the Auto Care Association by signing the petition today.  

Overland Park-based RUMBLE IoT applied its expertise in gathering data across disparate sources and types to extract telematics from large trucks including firetrucks, ambulances and other public emergency vehicles.  The solutions initial deployment is in St. Louis for use on public safety vehicles. 

As part of a new program targeted to boost public safety for communities across the USA, AT&T FirstNet, Fleet Complete, Cradlepoint, and RUMBLE IoT joined forces to develop the end-to-end FirstNet Ready™ In-Vehicle Network Solution.

The telematics will be delivered to the Cloud using AT&T FirstNet. FirstNet is the public safety communications platform available in the United States and provides secure, efficient communications to public safety organizations nationwide. 

The solution provides near real-time insights into critical fleet and in-field first responder activities, their location and vehicle status information. These insights provide improved situational awareness, efficiency and safety for first responders and public safety fleets.

With this solution, first responders get a holistic solution that helps them stay better connected to their workers and vehicles while keeping communities and citizens safer.  For instance, dispatchers gain first-hand visibility if a fire truck breaks down on the way to a call or if an ambulance is involved in an accident and delayed or disabled. 

Once installed, public safety agency administrators can remotely track vehicles in the field, obtain live engine diagnostics, get accident notifications, track driver behavior and safety and check sensors to monitor maintenance needs, fuel consumption and more.  Additionally, the solution provides in cab coaching and feedback to drivers to improve their safety performance.

RUMBLE IoT delivers solutions that allow real-time decisions to be made where the work is happening – on the edge – in transportation, utilities, manufacturing and healthcare.  For more information, contact us, visit or call 800-926-0556.

The Industrial Internet of Things or IIoT adoption is on the rise. Yet, according to a recent IndustryWeek survey, skepticism is among the factors that continues to hold back adoption, particularly in manufacturing.

“In the next decade, data—or rather, getting the most out of your data—could be the one thing keeping you in business.”
-IndustryWeek IIoT Survey August 2019

RUMBLE CEO, Terri Foudray, was interviewed by IndustryWeek to share why she thinks IIoT adoption isn’t running more smoothly.

We agree that IoT can be complex and frustrating for the subject matter expert trying to manage a transformation process with internal resources. According to Terri, having the right partners is key to success.

“An experienced IIoT System Integrator has the experience to identify resources required for a complicated integration, source those resources and organize a project that runs to completion and delivers the expected value. Manufacturers should expect smooth seamless project integrations that keep them involved and imbued with the sense of ownership that is critical for long-term success.”

The latest version of the Gartner Hype Cycle for Emerging Technologies, just released last month, shows that IoT integration has not peaked yet, which we think is consistent with the findings in the IndustryWeek survey.


In my previous post (The First Level of Analytics for the Data Driven Company), we defined the basic building blocks of descriptive analytics and introduced the first of three applied descriptive analytic examples to help drive value for your company and make your decision making better informed and more data driven.

The goal of increasing efficiency in a systematic matter, fundamentally, is a commitment to the philosophy and process of continuous improvement. Measure, analyze, respond, act, measure, analyze, respond. Repeat.

The first applied descriptive analytic we reviewed last post was The Reports Audit and Data Model Creation or Optimization where we focused on how “report creep” can saddle your organization with a costly drag on employee productivity. By establishing a data model that classifies and organizes your data elements, you can realize huge gains in FTE hours saved in your organization’s reporting infrastructure.

Here are two additional applied descriptive analytic examples for your review:

Applied Descriptive Analytic #2: Key Performance Indicator (KPI) Review and Testing

Real-life Example: We recently helped a company that provided social services to various states, primarily placing at-risk kids into home solutions (adoption, foster, family, etc.). We were discussing how analytics could help increase their efficiency and one of the clients voiced a particular problem they faced.

Periodically, they had to provide to the states they contracted with a summary of where the kids were in the system at a specific point of time (i.e. just entering, placed in care, receiving counseling or other services, existing the system). We were astounded to hear that it took two to three weeks to assemble the information needed to populate this report. Since the company depended on these state contracts, this report qualified as a KPI and was both essential and strategic for their future growth.

Think about this example – the company produced hundreds of reports to internal and external audiences but had to manually gather data to answer what many of us on the outside world would expect to be a reasonable and even common information request. These folks were simply not tracking the right KPIs.

Your organization’s KPIs should be testable – they should be able to demonstrate that they are predictive of the outcomes you’re trying to achieve. One common problem we see are KPIs created from the bottom up – in other words because a given metric is available on a report, it becomes de facto selected.  Useful KPIs should be created from the top-down. The executive and leadership team should be responsible for determining the strategic definitions  of success, and the metrics defining these conditions should be either selected or created. Again, having a defined data model gives you a leg up as the analytics’ team can test to confirm the metrics are statistically verifiable in terms of the outcomes you’ve defined as positive.

One last comment – KPIs are never fixed. They, like everything we’re discussing, are subject to a process of continuous improvement. We’ve worked with companies that quarterly review their KPIs and question what has changed in the make-up of the company since the last financial period? Do the critical KPIs still reflect the desirable strategic goals. If you’re not asking these questions, over time your KPIs will drift, and you’ll find yourself wasting time, money and hours monitoring the wrong metrics.

Applied Description Analytic #3: Attribution Analysis

Real-life Example: We worked with a client in Hong Kong that was spending millions of dollars on multiple on-line and off-line channels to sell a product. They wanted to know if they were allocating their funds to optimize their growth. We worked with their digital marketing team to examine the reporting streams associated with each of the digital channels and built an attribution model to measure contribution.

What we found in the data was interesting. The digital team was seeing a much higher number of arrivals to their website from several social media channels compared to more direct digital marketing channels. The contribution of social to conversion was also higher than the direct channel options, even though the latter cost more. But why?

By examining the specific content in the digital channels using various listening platforms, we discovered a fascinating trend: the social chatter revolved around people discussing a series of advertisements the institution had placed on billboards affixed to city buses. The selection of male and female models and the clothes and accessories they were wearing had caught their attention which translated to sharing, more pull-through visits to their website and sales of the product.

In this example, the attribution model not only showed that the cheapest digital channel was contributing to more conversions, further analysis of the results uncovered the root cause of the increased conversions was due to an inexpensive series of traditional ads. And yes, the featured male and female models got more work! Thanks to the power of analytic attribution modeling. The client also benefited as they increased their marketing efficiency by realigning their spend to take advantage of this information.

These are just a couple more examples of how applied descriptive analytics can help drive value for your company and make your decision-making better informed and more data-driven. In our next blog series, we’ll shift gears and look into the realm of Predictive Analytics and how data builds on data as companies increase their depth and maturity in applying analytics to their business.

Article by Chris Schultz, a principal at Analytic Marketing Innovations (www.analyticmarketinginnovations) and a RUMBLE Strategic Partner. Their solutions delivery approach identifies executable steps and recommends both near-term and long-term courses of actions, helping your business leverage data insights for growth and transformation.

A recent Forbes article by Maciej Kranz ( points out that IoT adopton has been “more complex, costlier and riskier” than anticipated, making it a slower process than was originally predicted.  Kranz looks at four key differences between expectation and reality.  When IoT was introduced in 1999, and as it has taken shape over the last 10 years, people were promised an idealistic world of technology.  Many expected that the IoT industry would be further developed at this point.  Kranz details key predictions vs today’s reality and explains how businesses can prepare for “IoT’s continued evolution.”

Prediction 1: The IoT will be an overnight sensation

The first is the rate at which the IoT industry would grow.  Initially forecasted that there would be 50 billion devices by 2020, that number has since been lowered to 20 billion.  This is partially due to many companies encountering barriers when trying to implement IoT.  The primary barrier has been cost and speed of implementation.  Other roadblocks Kranz mentions is the recalibration of sensors, integration into legacy infrastructures, and the need for heavy customization.

Prediction 2: Vendors thought they could go it alone

Vendors expected to be able to build vertically and horizontally with sensors and software.  But they have had to refocus on their core capabilities and customers have now become the driving force as to how and why IoT is implemented.  The consumers have pushed various specialists to work together to deliver a solution. “IoT requires collaboration.”


Solutions built on data collected and analyzed through IoT devices are dramatically improving operations of many companies while enabling others to create new value propositions, new services, new revenue streams and new business models. Although some of the predictions of the IoT didn’t quite pan out the way we had envisioned, businesses must take note of the realities and adjust expectations and approaches accordingly.  –Maciej Kranz, Forbes Councils


Prediction 3: Iot technology would be seamlessly interconnected

When it was first introduced, IoT was an idealistic solution with billions of devices interconnected.  People did not expect to struggle with connecting the digital and physical worlds. Issues making connections led to vendor groups working together to set standards. In the industrial market, OPC/UA is becoming the common ground.

Prediction 4: Traditional security solutions would be enough

The final topic Kranz covered was security.  It was assumed that old OT security tools and generic IT tools could operate and secure new IoT technology.  Since, we have learned that an integrated architectural approach is the best security strategy. And to be even more specific, one flexible security architecture for the entire enterprise that is multi-vendor and developed jointly by customers and horizontal/vertical specialists. Security is still one of the greatest obstacles to IoT adoption.


The learning process has been slower than expected with implementation, but this is just part of the growing pains of a new industry. That being said, IoT is helping and enabling growth across many industries. The misalignment of expectations is an example of the overestimate effect in the short run and underestimate in long run. Kranz concludes that he “remains steadfast that the future (of IoT) will be incredibly bright.”