Monetizing IoT Data with Blockchain

IoT Data

“Data is the most important asset class of current generation”. In Internet of Things (IoT) era, with increasing device proliferation in hyper-connected world, humongous collection of sensor data can facilitate the conversion of incredible ideas into value-adding services. In creating value with data explosion, Blockchain Technologies can play a critical role creating a peer-to-peer marketplace providing IoT sensor owners an opportunity to monetize data and simultaneously enable data consumers with a decentralized market to buy IoT sensor data.

According to Allied Market Research (AMR), the global market of sensors is poised to grow with a compound annual growth rate (CAGR) of 11.3 percent until 2022 when the market would reach $241 billion. The data resulting from such vast reach of IoT sensors is for the primary usage of the sensor owner or it is enhanced with value-added insights and reselling. In both the scenarios of either for primary usage or for enrichment and re-sale, the data remains unacceptably under-utilized and the utility if hindered away in organizational silos. Blockchain can provide a marketplace for IoT sensor data connecting data owners with 3rd party data consumers directly by externalizing the data outside primary silos.

The upside potential arrives from expected growth of todays 10+ billion sensors deployed globally to reach 40+ billion by 2020. Blockchain technology can help monetizing data by creating a marketplace offering a fully built financial ecosystem with a very minimal fees compared to a traditional fiat payment processors who typically charge between 1 and 3% for transactions. Also with creation of data utility tokens offers possibility to use small fractions of the token combined with very low fees making micro-transactions feasible. As well decentralization with blochchain backbone enables a very large numbers of participants in a trustless environment transacting with each other.

As shown in the picture above, a perfect ecosystems can be built for monetizing IoT data with Blockchain technology backbone. The players include sensor owners, data lakes gets created, network providers, blockchian data broker framework, data processers/enrichers, and data consumers / buyers. Sensor owners get an opportunity to monetize their data recovering some of their investments in IoT sensors. Network operators can win-back their enterprise accounts gaining scale and speed in the adoption of their network. This creates new types of buyers offering ease of access to data. Alongside data processors gain an eco-system to sell their services to the right people.

The use cases for such monetization of IoT data can be numerous covering multiple industries. A few examples of described below.

Use case

Pragmatic solution for data-age

predictive1

300 hours of video are uploaded to YouTube every minute! Almost 5 billion videos are watched on YouTube every single day. This is one example of the pace of data growth and overall size of data is piling into zetta-bytes faster. Where am I going with this is relevance of rapid analysis of data measured in hours or days rather than the stereotypical months of traditional data mining. The result is an opportunity to derive meaningful insights to business with more emphasis on predictive analytics.

Predictive analytics is nothing new. Predictive analytics is a way to identify the probability of future outcomes based upon historical data. For example, from customer perspective, companies can predict a likely lifetime customer value or the probability of either loyalty or churn. Let us look at few use cases to examine the relevance and importance of predictive analytics in the world of enormous data.

A fashion retailer story: Predictive analytics helped in analyzing the campaign spends and predicting incremental campaign impact of spend. This helped the retailer in understanding where not to spend: Having a close look predictive analytics helps in understanding the relationship between customer segments and the marketing campaigns being interacted with. This retailer predicted the probability of a particular channel influencing online or offline purchases within specific customer segments, and while this was enormously useful in understanding how to spend budgets targeting more personalized digital campaigns, it was equally insightful into identifying spend that wasn’t contributing to incremental value. The power of predictive analytics came in determining, should the retailer pay for this ad, or will a sale happen organically through another channel or communication that might cost nothing or next to nothing? This allowed the marketing team to choose the right channels to most effectively and efficiently reach different groups of prospects and customers, and second, it provided the information required to personalize by sending the right content and message to the right segments at a very granular level.

Preventing hospital readmissions: Hospitals are turning to predictive analytics as they began to feel the financial pinch of high 30-day readmission rates. Real-time EHR data analytics helps hospitals cut readmissions by five to seven percent. This demonstrates how predictive analytics in real-time can analyze EMRs data to automatically identify and target patients at the highest risk of readmission early in their initial hospitalization when there is a lot that can be done to improve and coordinate their care, so they will do well when they leave the hospital. Notably, Kaiser Permanente has been working to refine its readmissions algorithms in order to better understand which returns to the hospital are preventable and which are not, a crucial distinction for value-based reimbursements.

Lifetime value analysis of a subscribers of communication service providers: CSPs are lately realizing that not all customers are the same. It is important for CSPs to assign a quantifiable dollar value to each customer, in order to prioritize various sets of customers. The Lifetime Value of a subscriber provides the predicted yield from each customer over the customer life. This helps CSP in offering high priority customers loyalty bonuses, preferential treatment through personalized service, better credit norms etc.
We are living in a world of data overloading. Predictive analytics can be a true human partner from elections to sporting events to the stock market on what the future will bring. Predictive analytics elevate human kind from an educated guess to data backed decision.

Trends in Edge computing – Ease & secure access of Industrial data

computing-edge-default-logo-450x198

In IIoT ecosystem, IT and OT meet at the edge. The edge is where data is sampled and collected from the environment by instrumented things or devices. Edge-Centric Architectures extend elastic compute, networking and storage across the cloud through to the edge of the network. The following are few of the trends for maintaining security and uptime with edge computing while streaming data securely.

  • Cloud offloading to address communication latency: Handling more processing at the network’s edge reduces latency from the device’s actions. Use cases that are highly time-sensitive and require immediate analysis of, or response to, the collected sensor data are, in general, unfeasible under cloud- centric IoT architectures, especially if the data are sent over long distances.
  • Encryption on endpoint to safeguard data security: By and large, sensitive and business-critical operational data are safer when encrypted adequately on the endpoint level. Unintelligent devices transmitting frequent and badly secured payloads to the cloud are generally more vulnerable to hacking and interception by unauthorized parties. Additionally, many enterprises may need to secure and control their machine data on the edge level for compliance reasons.
  • Sensor fusion: Combining data from different sources can improve accuracy. Data from two sensors is better than data from one. Data from lots of sensors is even better.
  • Sensor hubs: Developers increasingly experimenting with sensor hubs for industrial internet devices, which will be used to offload tasks from the application processor, cutting down on power consumption and improving battery life in the devices.
  • Analytics on the Edge leading to low cost-of-ownership and secured data: Reduce potentially huge cloud-computing costs (because of the sheer volume of 24/7 sensor data) by allowing “fog computing,” where the processing would be done right at the collection process combining real time analytics, with only the small amount of really relevant data being passed on to a central location.
  • Gateway-mediated edge connectivity and management architecture pattern: As the widespread acceptance of modern, open-field protocol standards has reduced the need for traditional gateways in the field, the IIoT has created a need for a new breed of intelligent gateways that unlock the full potential of interoperability among diverse real-world devices and industrial Internet systems

How global economies embracing Industrial Internet (IIoT)?

Industrial Internet (IIoT) incorporates machine learning and big data technology, harnessing the sensor data, machine-to-machine (M2M) communication and automation technologies that have existed in industrial settings for years. The driving philosophy behind the IIoT is that smart machines are better than humans at accurately, consistently capturing and communicating data. This data can enable companies to pick up on inefficiencies and problems sooner, saving time and money and supporting business intelligence efforts. In manufacturing specifically, IIoT holds great potential for quality control, sustainable and green practices, supply chain traceability and overall supply chain efficiency.

A major concern surrounding the Industrial IoT is interoperability between devices and machines that use different protocols and have different architectures. The nonprofit Industrial Internet Consortium, founded in 2014, focuses on creating standards that promote open interoperability and the development of common architectures.

How Manufacturing embracing IIoT:

The high level of interest and hype surrounding the Internet of Things is driven by the proliferation of everyday objects with an Internet connection — everything from kitchen appliances and household electronics to clothing, vehicles and retail goods. This transition is truly amazing, and developing faster than anyone could have imagined. But in the world of manufacturing, our own version of IoT, the Industrial Internet of Things (IIoT), is a logical extension of automation and connectivity that has been a part of the plant environment for decades, primarily in the area known as machine-to-machine (M2M) communication.

The IIoT movement is, of course, growing and expanding at least as fast as the Internet of Things (IoT) in the outside world because smart devices and connected sensors are proliferating in the plant as well. But the transition from M2M and plant networking to full IIoT presents interesting challenges that manufacturers must address before the technology gets out of hand and threatens, rather than enhances, the benefits that it promises to provide.

The connected factory as it exists today is a relatively closed environment, designed to communicate within the plant network and not necessarily with the outside world, with or through the Internet. Most companies are quite comfortable with that; the Internet can be a scary and threatening place. So one of the first decisions companies face when considering IIoT is whether the benefits are sufficient to overcome the risk of making all that detailed company information accessible through the Internet and leaving internal systems vulnerable to hacking, viruses and destructive malware. Despite the fact that Stuxnet was developed and deployed with the best intentions, its very existence is a wake-up call for any company thinking of opening internal systems up to the Internet.

It’s conceivable that you can obtain many of the benefits of IIoT without that outside connection. Upgrading or changing the internal network to Internet Protocol (if not already TCP/IP compatible) should allow a company to install and use the new devices and sensors in an internal IIoT and that may be perfectly adequate.

 Taking advantage of IIoT:

Why would a company want to enable the Internet connection? A connection to the Internet allows access to IIoT data and supporting applications from virtually any device, any time, from any place in the world. Functional managers can check on specific machines, schedules, inventories, etc. at any time, in full detail, no matter where they are. Executives can drill down to study situations and analyze performance and results when at home or on the go. More importantly, perhaps, IIoT with Internet connectivity can provide hands-on visibility and control capabilities for remote locations, subcontracted manufacturing plants or suppliers’ factories.

Although much intra-factory communications takes place over an Ethernet network, many existing devices use proprietary protocols and many are not Internet enabled. Can these existing devices play a role in a connected IIoT enterprise? The short answer is maybe — with limitations. The bigger question is whether they all have to be replaced by fully Internet-enabled IIoT devices. Again, the answer is not a simple yes or no.

Strategy and tactics for implementing IIoT must align with a company’s goals and concerns. An all-in commitment means the eventual replacement of non-compliant controllers and devices so that all detailed data is available to the network and authorized remote users. This strategy requires the most attention to security and access control and the most vigilance on a continuing basis. It’s possible that some of the existing equipment can be upgraded or modified to fit in with an IIoT implementation and not have to be replaced.

Taking a part-way approach to IIoT:

A part-way strategy leaves more options for continued use of some or all the incumbent equipment and also may alleviate some security concerns. The existing internal network can be preserved and even enhanced with the addition of more sensors and devices while remaining a closed system not attached to the Internet. This is arguably a corruption of the IIoT ideal but can be a practical approach to additional tracking and visibility. IIoT-like data management and analytics can be a part of the enhanced internal network structure so many of the IIoT benefits become available, just not from remote access. This approach provides what technologists call an “air gap” that separates internal data and controls from direct connection to the Internet, and is commonly done for security reasons. In this case, the motivation goes beyond security to include practical and monetary considerations as well.

This part-way strategy doesn’t fall under the definition of IIoT and certainly doesn’t deliver the “anytime from anywhere” access benefits. A possible workaround would be to pass the data to a database accessible from the Internet. Although this approach doesn’t provide real-time data, it does restore some remote availability benefits with reduced security concerns — hackers could access the data but not the controls themselves and any damage would be done to the copy in the accessible database, not the source data that remains in the internal network.

Whatever the approach or extent of commitment to IIoT, the availability of more connected sensors, controls and devices undoubtedly provides the opportunity for a level of visibility and control unimaginable just a few years ago. Companies have the opportunity to keep closer track of everything going on in the plant, at subsidiary and remote plant locations, at subcontractor and suppliers plants, at remote warehouses, and on goods in transit anywhere in the world. New and evolving analytics and data visualization tools are making the IIoT’s “big data” usable and beneficial as we continually strive for more efficiency, responsiveness and agility.

Any technological evolution will cause disruption, cost and strategic realignment but it’s always done with the promise of improved operational performance. IIoT may just be the current step in that evolution but it is a significant one. We can already see glimpses of what tomorrow’s factory will look like and IIoT will clearly be its eyes and ears.

Industrial IoT riding M2M advantage to next stage

Industrial IoT nonetheless needs better connectivity, business models.Proponents of the much-hyped Internet of Things (IoT) are entering the practical phase of figuring out exactly how to turn ideas into reality.

To be continued…

Making sure manufacturing teams up for challenge and the reward with IIoT

While the Industrial Internet of Things (IIoT) has the power to significantly improve reliability, production and customer satisfaction, taking full advantage of its capabilities requires that companies re-think the people, processes and technologies involved in all
aspects of operations.

I was part of a panel of industrial manufacturing leaders in the field of IIoT to discuss the necessary requirements for this transformation, and the best practice approaches for realizing maximum value.

We produced a report that provides a clear roadmap, important examples, and actionable insights for any manufacturing and operations team in the manufacturing sector. It also offers fresh perspectives for IIoT leaders. You can learn more from the report here: bit.ly/28JwfvM