The Amara’s Law and the  Anatomy of Business Use Cases in IoT

Last week we had an interesting debate on what use cases we need to work on and across which industries . In the animated discussion, someone asked a quiz question – “What is Amara’s Law?”

It turned out that American Scientist Roy Amara came up with an interesting view and an easy to understand law –“While we overestimate the short term effect of technology, we underestimate the long term impact “. I feel in the world of IoT this law is fascinatingly relevant .

The hype surrounding IoT puts pressure on managers to monetise the investments being made in IoT in organization. The buzz descends on  coming up with business use cases which could be winners by honing their effectiveness on an  IoT Platform build or bought. The linkage between the identified use case and their deployment on the chosen IoT platform is a significant one to shorten the time to market.

For the sake of completeness, a Business Use Case is essentially a business process  flow or sequence of steps of a business activity which impacts the organization within and the exco-system it works in . Most of the time the Business Use Case is the precursor to build an IT solution  ( an IoT solution in this post !!)

Why are IoT based solutions are distinctive and diverse?

IoT solution design is quite different from typical IT solutions in that it bridges ( through communication media , internet ) the Physical Computing (also termed as Operations Technology (OT) with sensors, actuators and communication devices, and the Digital Computing  with data, analytics, workflows, and applications( also terms as Information Technology ( IT) . The diversity of use cases and operational requirements creates an array of IoT Endpoints, communication protocols, data management, and analytics technologies, as well as corresponding deployment topologies.

The real value which can be derived from a select Business Use Case in IoT comes from turning data into insight, and making it actionable to drive smarter operations.

Despite the diversity, there is a level of commonality across use cases that can illustrate the anatomy of IoT solutions.The use cases essentially fall into four broad areas of Monitoring , Control , Automation and  Analytics leading to predictive management . Taking a layered approach in describing the anatomy helps identify relevant services and technologies from the things-level all the way up to IoT apps.

How do we decide on the appropriate use case?

I have attempted to bring out a few key parameters to be assessed as we decide on this. This will help us go beyond the IoT hyperbole, but when technology pundits claim that the Internet of Things (IoT) can change everything and help us to sift out the frivolous gimmicks like Wi-Fi enabled toothbrushes from the consideration sets of the Use Cases . So let us discuss the following :

  1. What makes industries  IoT Use Case Friendly ?
  2. What type of Data Availability would enrich an Use Case to be pursued ?
  3. What is the expected Business impact and Return of investment?

What makes  Industries  IoT User Case Friendly ?

Industries which are  inherently Sensor Driven –This  not hard to guess .These would be   the first ones to be IoT user case friendly . Inputs from Intel and Cloudera could be referred to which document that Automotive , Energy (Utilities) ,Healthcare ,  Manufacturing , Retail , Buildings , Homes  and Transportation are the leaders in making IoT work in core business processes  . Given that the key capability of Machine to Machine communication (M2M ) already exists as something native to these industries and M2M communications rely on sensors within the device itself, and the networks that connect devices together makes M2M IoT enabled .

So what are the major omissions? Media, Finance are the two major industries. I would rate Homes and Healthcare to be a “median” case .

Industries which are  less sensitive to Data security and Privacy-  This is a major challenge that is not set to go away any time soon. Network security is a huge bone of contention when it comes to data security and data protection. For example, one of the advantages of the IoT for healthcare is the ability to collect medical data in real-time from devices such as wearables, to monitor patients’ health – particularly those with ongoing conditions such as diabetes or hypertension. This medical data is therefore extremely sensitive, security needs to be at a level where external threats are not able to access and steal data records within the network It can be said about the Finance industry as well on the similar lines. It is not a surprise that Financial sector will be a late adopter of IoT based initiatives. ( reports from Deloitte )

What type of Data Availability would enrich a Use Case to be pursued ?

The promise of IoT is derived from the potential access to data from a variety of sources . These would be massive volumes of data of intermittent data stream generated from a variety of sources and the data is  predominantly tume series .Data could be in real time streams or in batches , diverse in data structures and schemas . Culling out the signals from the noise of data is something which makes IoT program an effective one .

Hence  it is imperative that the marriage of physical computing and digital computing should be a near perfect one . To gain the benefits from the IoT Program the need to make use of the usage data ( from users )  , telemetry data ( from sensors and remote end points )  , contextual data ( from enterprise applications like ERP , CRM ) and ambient data ( weather , traffic etc ) to work on concert to provide the insight to act upon . Hence the selection of a right business use case is imperative for the success of the IoT program and if the use case can work on the standard layers of a typical IoT platform that would be an ideal situation to be in.

ToT programs are expected to essentially help address business functions which are in the realm of Monitoring , Control , Automation and last but not the least in bringing in predictability in actions through analytics . If we believe that the real value of ioT would be generated though predictive analytics and with it the attendant benefits , then the use case should be decided based on the following five questions which we need to pose ourselves with respect to data :

  1. What kind of data is needed ? The question which we need to ask something specific like: “I want to know whether the device would fail  will fail in the next X days if the temperature remains at Y degree centigrade .
  2. What are the measures we care and what data can provide that ? If we want to predict things such as failure at the component level, then we have to have component-level information. If you want to predict a door failure within a vehicle , we  need door-level sensors and  and the data aggregated from them . It’s essential to measure the data that we care about.
  3. Is the data accurate ? It’s very common in predictive maintenance that we want to predict a failure occurring, but what we may be actually predicting through the data may not a real failure. For example, it may be predicting fault. If we have faults in the dataset, those might sometimes be failures, but sometimes not. So we  have to think carefully about what we are modelling, and make sure that that is what we want to model.
  4. Is the data connected enough? : If we have significant usage information—say maintenance logs— but we do not  have other identifiers that can connect those different datasets together say from sensors, context ( from enterprise apps )  and ambience ,  then we are not doing justice to the analysis .
  5. Do we have enough data ? In predictive maintenance in particular, if we are modeling device   failure, we  must have enough examples of those device  failing, and the context and circumstances they are failing in .

What is the expected Business impact and Return of Investment?

It is a general expectation that once we embark on an IoT project IoT based insights  is expected to provide new view of  functionalities , revised capabilities and feature based differentiations .These could result in creating the right business impacts. Certain use cases could be more compelling  with a higher financial payback than others .Use cases focussed on Fuel , Energy and Labor savings have shorter payback periods and provide significant financial paybacks .

So as use cases are decided  the benefits could be defined to fall under :

  1. Internal Benefits – which help the internal organization operations . These use cases would focus on Safety and Security , Asset Optimization , Resource Conservation and expenses reduction .
  2. External Benefits – which contribute to the ecosystem in which the extended organisation works in .The Use cases would be providing improvement in well being, enhancing customer service and engagement , identifying new revenue streams .

From our experience , as IoT based project implementation would have to go through a life cycle , and its success would breed bigger success , it would pay to look at internal benefits in the early days and then branch out to the external benefits . The number of variables to control is lesser in the former and the chances of success are brighter .

Concluding Notes:

So to get the necessary short term effect to justify the investment in IoT and to keep an eye on the long term impact in the organization the right selection of Business Use  Case could be a critical one .Amara law has its notable influence on the Anatomy of the Business Use case in IoT !!

 

 

 

 

 

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