How to monetize sensor, device and machine data

I want to start this week post by thanks to Jaspersoft and Mongo DB for invite me to the event in Madrid “JasperWorld the European Tour”.  Considering the complexity of current landscape of Big Data, M2M and Cloud-IaaS vendors, and my doubts about use Open Source software in mission critical business, this event allowed me to check the level of information/confusion among customers, software vendors and systems integrators around these technologies that according with Gartner’s 2013 Hype Cycle for Emerging Technologies Maps will be source or enormous revenues but also disillusionment in the next coming years.

I agree with Brian Gentile about the importance that embedded BI will have in the future. Jaspersoft´s CEO is convinced like many other Business Intelligence vendors that companies need to democratize BI tools and embed real time Business Analytics in Operational systems to improve business process.

The other topic related with the previous one that I discussed with other attendants was the monetization of sensor big data and the recommended Big Data architecture and technology infrastructure to allow companies create new data driven business models that help companies in different sectors to reduce operational cost, increase sales and improve customer experience.

This is not the first time I discuss this topic. I have been talking for months with several colleagues and some Mobile Operator about new business models and how to monetize Big Data and M2M. Under pressure to seek new revenue streams mobile carriers are now carefully mining, packaging, and repurposing their subscriber data and sensor/device data to create powerful statistics about how people and things are moving about in the real world. Nobody doubt that Wireless operators as such as Over the Top Internet companies and technological companies like Google, Facebook, Twitter, Apple, Microsoft, Cisco, Intel, Samsung, etc have access to an unprecedented volume of user information and device/machine/sensor information.

Nevertheless, I expect to see soon several new Big Data Service Providers entry into the Digital Business industry offering new embedded business analytics services and real time analytics services. My point of view is that in the same way that the OTT companies like Google, MSFT, Facebook, Whatsapp, take advantage of Telco network infrastructure, these BDSP will be able of aggregate data captured from sensor data from public cloud, social networks, and third-parties without the need for carriage negotiations and without any huge infrastructure investment. We expect millions of sensors providing data about where people live, work, and play, but also about many other environment parameters and generating Petabytes of data. A new business model where Big Data Service Providers will be able to store sensor data, aggregate with companies internal data, social network data, cloud services and outside parties data to offer Business Analytics services that will allow organizations optimizing the value of all assets including machines and physical systems or that will made possible the launch of its Precision Market Insights division will be a reality soon.

Why I think that will be a reality soon?. I recommend you to read the article: “How Wireless Carriers Are Monetizing Your Movements” to check what US operators are doing and analyze the examples like AirSage or Streetlight.

Nevertheless the journey will not be easy for Big Data Service Providers. They must wrestle with the challenge of conveying what “anonymous” and “aggregated” sensor data means. Today there is a big concerns about personal data privacy and the concerns about making sensor/device data available might be cross-referenced with other data sources to reveal unintended details about individuals or specific groups.

Big Data Service Providers will need to evangelize people and enterprises about their privacy concerns and the benefits of making their sensor/device data more available. In spite that research and experience suggest that in practice most people don’t mind, or don’t care as much as they think they do about privacy, it is mandatory to write effective Safe Harbor agreements among the  US-EU and add other important countries like Russia, China, India or Brazil to guarantee the privacy of people and smarts assets and avoid incidents like NSA Surveillance.

The evolutionary journey of add sensor data analytics to current business analytics reflects the trend moving from analyzing historical perspective into capturing business foresight, from operational perception to customer insight; from predicting what will happen to step further: What should you do upon it; or put simply, from traditional BI into advanced analytics; and from predictive analytics to prescriptive analytics. For those of you less familiar with the concept of Predictive Analytics vs. Prescriptive Analytics I recommend to read the following blog http://futureofcio.blogspot.com.es/2013/10/predictive-analytics-vs-prescriptive.html#!

There is a challenge for Big Data and Business Analytics vendors to create new tools that allows companies to capitalize on their own data plus aggregate sensors data gathered from sensor networks, public and private clouds and provide embedded predictive and prescriptive analytics services to support the CEOs, CFOs, CMOs, Sales & Support in crucial decision processes and reinforced their ability to monitor the company’s financial performance, to keep the costs down, improving their results and increase customer experience.

According with his web, Glassbeam provide a Big Data platform that allow companies see the hidden value in machine logs data accelerating diagnostic capabilities for their support, sales, marketing and engineering groups. I will try to get information from the company about success histories of customers that could troubleshoot faster, build better products and increase revenues by mining machine data and their plans to offer this as a service.

The other technology that BDSPs need to follow is wearable technology such as Google Glass, Nike+ FuelBand and the reported Apple iWatch. These products will not only enhance consumers’ lives, but will also provide a new source of commercially exploitable data.

As conclusion of this post, I want to focus in a couple of ideas and a recommendation:

  • An advanced analytics scenario must include sensor data and embedded actionable BI prescriptive and predictive analytics tools.
  • Business analytics should be provided by Big Data Services Providers in a Business as a Service model on top of IaaS vendors.

Finally, my recommendation is that in spite thinking and intuition will always be required to do both predictive and prescriptive analytics, is key that you and your company count on the right people that understand the business problems and can ask questions whose answers are potentially actionable.

For additional information please contact: francisco.maroto@oies.es

Una respuesta to “How to monetize sensor, device and machine data”

  1. Crossmedia Says:

    Very good article, entertaining and current and interesting information.

    Me gusta

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