Tag Archives: IoT

The Internet of Things, So Mom Can Understand

Dear Mom,

IoT

The Internet of Things, So Mom Can Understand

It’s great to hear that you’re feeling pretty hip at your ability to explain data, metadata and Big Data to your friends. I did get your panicked voicemail asking about the Internet of Things (IoT).   Yes, you’re right that the Internet itself is a thing, so it’s confusing to know if the Internet is one of the things that the IoT includes.   So let’s make it a bit simper so you can feel more comfortable about the topic at your bridge game next week. (Still shocked you’re talking about data with your friends– Dad complains you only talk with him about “Dancing with the Stars”).

First let’s describe the Internet itself.   You use it everyday when you do a Google search: it’s a publicly accessible network of computer systems from around the world.     Someday the Internet will hopefully allow for sharing of all human knowledge with one another.

But what about knowledge that’s not “human”?   It’s not only people that create information.   The “things” in IoT are the devices and machines that people, companies and governments rely upon every day.  Believe it or not, there are billions of devices today that are also “connected” – meaning they have the ability to send information to other computers.  In fact, the technology research firm Gartner says there will be 4.9 billion connected devices, or “things” in 2015.  And by 2020, that number will reach 25 billion!

Some of these connected devices are more obvious than others – and some have been around a very long time.  For example, you use ATMs all the time at the bank, when you’re withdrawing and depositing money.   Clearly those machines can only access your account information if they’re connected to the banks computer systems that hold your account information.

Your iPhone, your “smart” thermostat, and your washing machine with the call home feature are all connected “Things” too.  Mom, imagine waking up in the morning and having your coffee brewed already. The coffee machine brewed because it knew from your alarm clock what time you were waking up.

IoT will also help make sure your oven is off; your lost keys can be easily found and your fridge can check how many eggs are left while you’re standing in the grocery store. The possibilities are limitless.

And it doesn’t stop there.   Medical devices are communicating with doctors, jet engines are communicating with their manufacturers, and parking meters are communicating with city services.

And guess what?  There are people watching the computers that collect all of that information, and they’re working hard to figure out what value they can deliver by using it effectively.   It’s actually this machine data that’s going to make Big Data REALLY Big.

So does that mean your espresso maker, your cell phone and your car will be conspiring to take over the house?  Probably not something we need to worry about this year (Maybe you want to keep an eye on the refrigerator just in case).   But in the short term, it will mean people like me who have dedicated our careers to data management will have our work cut out for ourselves trying to figure out how to make sense of all of this new machine interaction data from devices. Then how to marry it with the people interaction data from social networks like Facebook, LinkedIn and Twitter. And then how to marry all of that with all of the transactional data that companies capture during the normal course of business.    As I’ve said before, data’s a good business to be in!

Happy Mother’s Day!

Love, Rob

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Big Data Crashes Twitter Earnings

twitter-fail-whale- Big data

Unmanaged Data may Result in Loss

The unintended consequences of big data, real time data and loose data security all showed up for the Twitter Q1 earnings release this week and showed how not having good control of data can cause some bad things to happen.

What happens when information is released because it is accidentally made public? In this case the damage might seem small, only 18% shaved off the share price of Twitter, but this amounts to about a $5 billion dollar drop in valuation and millions changing hands for some investors. Given earnings were a miss maybe the drop in stock value would have happened anyway but the surprise element may have increased the impact of the news.

This is a great example to anyone wondering about data security and what it means to properly manage public or private data. This episode will pass but it will leave a blemish on Twitter’s reputation and should lead them to take a closer look at how they are managing data that is accessible publicly or meant to be secured.

In this case the data leak was caused when a company, Selerity, who provides real time content analytics that specializes in financial market data and sentiment, picked up the Twitter earnings release in a PDF posted on their public investor relations website with one of their web crawlers about an hour before Twitter pushed out the earnings release. They simply reported the information that was on a hidden URL but public information since it was not secured. (A great lesson for many non-technical marketing and PR people)

My favorite quote from Selerity comes from The Verge. “Any time a company’s earnings are due for release, we check the website periodically to see if the earnings are available. In this instance, I am assuming that Twitter mistakenly posted the earnings to the website early. But they did make the earnings available on the website.”

Earnings have been accidentally released many times before and this will not be the last. The good news is there are any number of simple ways stop or control data so it is not accidentally or purposely made publicly accessible by both using data publishing best practices as well as data management and security products. This is a good reminder to companies to review their data publishing, management and security practices and policies. Do nothing and your company could be the next one being talked about.

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Will the Apple Watch & Wearables change Technology Usage?

apple_watch_hero

Apple Watch

It is hard to miss all the commentary, commercials, ads and reviews on the soon to be released Apple Watch. It got me to thinking about how much has changed over the last 15 years when it comes to how people perceive and use technology and how the Apple Watch may just signal the next shift in technology usage. Yes, we have had wearables now for some time but when Apple does something new they have proven to be able to tap into the broader market conscious and in doing so take us to new places.

The iPad just turned 5. The iPhone was released just over 8 years ago. The iPod that started it all was released in 2001. (I have one of these and my kids think it is ancient. They always ask why it does not have a touch screen)

Apple iPod Generation 1 (circa 2001)

iPod

iPod in 2001

 

There were no touch screens or “apps for that” in 2001. In fact the touch wheel at the time for navigation was at best quaint. It ended up helping to change the entire music industry and set the stage for Apple and others to continue to innovate on this new technology platform for years to come.

What happened over the next 15 years were some really interesting trends that may be completely changed by how the Apple Watch adds to the discussion of wearables.

Some of the big trends pushed by the iPod-> iPhone-> iPad have included

1. Increased access to technology at a entry to medium level price point. As the these devices became more powerful and open platforms developed for applications and internet access the average person had access to ever increasing information and tools. While Apple products tend to be more on the higher price points of the market they did help create opportunities for other vendors to enter at lower price points.

2. Task based applications. An “app for that” mentality has grown and is very engrained in both the consumer and enterprise market. This mentality is very much at odds with the traditional monolithic application and stack world and has created many opportunities for specialized applications and services. Even where a software vendor continues to offer a platform or stack they are forced to think about the architecture and API access that would support smaller and more mobile applications.

3. Mobile first. While this is a bit of chicken and egg question it is worth giving credit to the explosion of personal devices for helping drive a mobile first approach on many consumer and enterprise solutions. Of course we also have seen a huge explosion in access to reliable (mostly) Wi-Fi access in public locations but it is reasonable to believe the user demand for access is more what has driven so many places from airports to McDonalds to offer free public Wi-Fi.

4. Social media. Really would all the Twitter, Facebook, Instagram, Snapchats and others of the world big as big as they are today without the huge increase in access from more mobile devices? Most likely the answer is no.

Ok, so why is the Apple Watch a possible shift in these usage patterns and not just the continuation?

1. Device Driven Attention Deficit Disorder (DDADD). Yes, I just made that term up but it is a real problem and we all either have it at times or we know people that suffer from this issue. Unless your actual job involves doing social media posts it is not really reasonable (or polite) to be posting away on your device every 5 minutes all night long. The watch/wearables may just provide a way for some people to strike a balance by streamlining interaction with all those applications on their larger phone or device. The review in the WSJ today really hit up on this point of the smartwatch being able to drive user efficiency by only bringing specific tasks to the watch. It is too early to tell but that sure sounds like a good thing.

2. Form and function. Just as the laptop, smartphone and tablet markets have changed the overall computer market (just ask those companies that sell desktop PCs how that is going) the smartwatch over time may do the same. This seems especially the case if the smartwatch can in some cases be a replacement to another device in addition to being used in conjunction with another device the way the Apple Watch and iPhone are used together.

3. New applications and services. These are coming and it is not easy to guess how much change is coming. In some ways the wearables market seems much harder compared to the micro-applications market but that could just be because all things new are hard to predict.

Applications by App Stores Explosion (originally appeared at app figures)4. Concerns over data and personal data. The data aspects of this are complex as we get into both usage data and personal data both of which are valuable and highly regulated. It’s hard to say how the wearables market changes things other than to put a bigger spotlight on the need for industry solutions that put the user in charge of their data.

In summary while the Apple Watch may or may not be a commercial success it seems like we could look back in 5, 10 or 15 years and see this as yet another huge shift in the way people perceive and use technology.

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Perceived Value Framework for Data Centric Solutions

A lot of my time is spent discussing enterprise and end user value of software solutions. Increasingly over the last few years the solution focus has moved from being first about specific application and business processes to being data centric. People start with thinking and asking about what data that is collected, displayed, manipulated and automated instead of what is the task (e.g. we need to better understand how our customers make buying decisions instead of we need to streamline our account managers daily tasks). I have been working on a mental model for how to think about these different types of solutions and one that would give me a better framework when discussing product, technical and marketing topics with clients or friends in the industry.

I came up with the following framework as a 2×2 matrix that uses two main axis to define the perceived value of data centric solutions. These are the Volume & Complexity of Data Integration and the Completeness & Flexibility of Data Analytics.

The reason for these definitions is that one very real change is that most clients that I work with are constantly dealing with distributed applications and business processes which means having to figure out how to bring that data together either in a new solution or in a analytics solution that can work across the various data sets. There is no single right answer to these issues but there are very real patterns of how different companies and solutions approach the underlying issue of growing distributed data inside and outside the control of the company.

Data Centric

Perceived Value Framework for Data Centric Solutions

 

Solution patterns:

1. Personal Productivity. These are solutions that collect and present data mostly for individual use, team data sharing and organization. They tend to be single task oriented and provide data reporting functions.

2. Business Productivity. These solutions usually span multiple data sources and are focused on either decision support, communication or collaboration.

3. Business Criticality. Theses solutions provide new value or capabilities to an organization by adding advanced data analytics that provided automated response or secondary views across distributed data sources.

4. Life Criticality. These solutions are a special subset which are aimed at either individual, group or social impact solutions. Traditionally these have been very proprietary and closed systems. The main trend in data-centric solutions is coming from more government and business data being exposed which can be integrated into new solutions that we just never could even do previously let alone think up. I do not even have a good example of a real one yet, but I see it as the higher level solution that evolves as at the juncture of real-time data meets analytics and distributed data sets.

Some examples of current solutions as I would map them on the perceived value of data centric solutions framework. Some of these are well known and others you probably have never heard. Many of these new solutions were not easy to create without technology that provides easier access to data from distributed resources or compute power for supporting decision support.

Data Centric

Perceived Value Framework for Data Centric Solutions

 

What I really like about this value framework is that it allows us to get beyond all the buzzwords of IoT, BigData, etc and focus on the real needs and solutions that are needed and that cross over these technical or singular topics but on their own are not actual high value business solutions. Feedback welcome.

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IoT Case Study: Fresh Powder = Fresh Data

EpicMix is a website, data integration solution and web application that provides a great example of how companies can provide more value to their customers when they think about data-ready architecture. In this case the company is Vail Resorts and it is great to look at this as an IoT case study since the solution has been in use since 2010.

The basics of EpicMix

* RFID technology embedded into lift tickets provide the ability to collect data for anyone using one at any Vail managed. Vail realized they had all these lift tickets being worn and there was an opportunity to use them to collect data that could enhance the experience of their guests. It also is a very clever way to collect data on skiers to help drive segmentation and marketing decisions.

* EpicMix just works. If any guest wants to take advantage all they have to do is register on the website or download the mobile app for their Android or iOS smart phone and register. Having a low bar to use is important to getting people to try out the app and even if people do not use the EpicMix website or app Vail is still able to leverage the data they are generating to better understand what people do on the mountain. (Vail has a detailed information policy and opt out policy)

IoT Case Study: Fresh Powder = Fresh Data

Figure 1: EpicMix Public Dashboard

* Value added features beyond data visibility. What makes the solution more interesting are the features that go beyond just tracking skiing performance. These include private messaging between guests while on the mountain, sharing photos with friends, integration to personal social media accounts and the ability for people to earn badges and participate in challenges. These go beyond the generation one solution that would just track performance and nothing else.

IoT Case Study: Fresh Powder = Fresh Data

Figure 2: Phone Application EpicMix view

This is the type of solution that qualifies as a IoT Personal Productivity solution and a Business Productivity solution.

  • For the skier it provides information on their activity, communication and sharing information on social media.
  • For Vail it allows them to better understand their guests, better communicate and offer their guests additional services and benefits and also how to use their resources or deploy their employees.

The EpicMix solution was made possible by taking advantage of data that was not being collected and then making it useful to users (skiers & guests). Having used EpicMix and similar performance tracking solutions the added communication and collaboration features are what sets it apart and the ease of use in getting started make it a great example of how fresh data can come from anywhere.

In the future it is easy to imagine features being added that streamlined ordering services for users (table reservation at the restaurant for Apre-ski) or Vail leveraging the data to make business decisions to provide more real time offers to guests on the mountain or frequent visitors on their next visit. And maybe we will see some of the new ski oriented wearables like XON bindings be integrated to solutions like EpicMix so it is possible to get even more data without having to have a second smart phone application.

Information for this post comes from Mapleton Hill Media and Vail Resorts

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Internet of Things (IoT) Changes the Data Integration Game in 2015

Data Integration

Internet of Things (IoT) Changes the Data Integration Game in 2015

As reported by the Economic Times, “In the coming years, enormous volumes of machine-generated data from the Internet of Things (IoT) will emerge. If exploited properly, this data – often dubbed machine or sensor data, and often seen as the next evolution in Big Data – can fuel a wide range of data-driven business process improvements across numerous industries.”

We can all see this happening in our personal lives.  Our thermostats are connected now, our cars have been for years, even my toothbrush has a Bluetooth connection with my phone.  On the industrial sides, devices have also been connected for years, tossing off megabytes of data per day that have been typically used for monitoring, with the data tossed away as quickly as it appears.

So, what changed?  With the advent of big data, cheap cloud, and on-premise storage, we now have the ability to store machine or sensor data spinning out of industrial machines, airliners, health diagnostic devices, etc., and leverage that data for new and valuable uses.

For example, the ability determine the likelihood that a jet engine will fail, based upon the sensor data gathered, and how that data compared with existing known patterns of failure.  Instead of getting an engine failure light on the flight deck, the pilots can see that the engine has a 20 percent likelihood of failure, and get the engine serviced before it fails completely.

The problem with all of this very cool stuff is that we need to once again rethink data integration.  Indeed, if the data can’t get from the machine sensors to a persistent data store for analysis, then none of this has a chance of working.

That’s why those who are moving to IoT-based systems need to do two things.  First, they must create a strategy for extracting data from devices, such as industrial robots or ann  Audi A8.  Second, they need a strategy to take  all of this disparate data that’s firing out of devices at megabytes per second, and put it where it needs to go, and in the right native structure (or in an unstructured data lake), so it can be leveraged in useful ways, and in real time.

The challenge is that machines and devices are not traditional IT systems.  I’ve built connectors for industrial applications in my career.  The fact is, you need to adapt to the way that the machines and devices produce data, and not the other way around.  Data integration technology needs to adapt as well, making sure that it can deal with streaming and unstructured data, including many instances where the data needs to be processed in flight as it moves from the device, to the database.

This becomes a huge opportunity for data integration providers who understand the special needs of IoT, as well as the technology that those who build IoT-based systems can leverage.  However, the larger value is for those businesses that learn how to leverage IoT to provide better services to their customers by offering insights that have previously been impossible.  Be it jet engine reliability, the fuel efficiency of my car, or feedback to my physician from sensors on my body, this is game changing stuff.  At the heart of its ability to succeed is the ability to move data from place-to-place.

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Will the Real IoT Please Stand-up

IoT

Will the Real IoT Please Stand-up

Lately I have been thinking a lot about what is real and just marketing fluff with the Internet of Things (IoT). From all the stories written and people that I talk to it seems I am not alone. One day there is news of what at best is a communications company receiving +100M in funding and the next there is what amounts to a re-skinned mobile app claiming to be the real IoT.

This is the first part in a series of posts where I am going to define a framework for identifying real IoT solutions and the value that they provide. In addition I will provide actual examples of companies and solutions that fit this solution definition framework.

My main issue with the entire IoT universe is that a lot of the focus in on things that do not exist or that have been around a long time and have just been re-branded. Neither of these actually do justice to the concept of IoT that is very interesting, which is using distributed data and events to deliver totally new or dynamically better solutions (think 10x or more) compared to what exists today. We are talking revolutionary and not evolutionary.

From my point of view real IoT solutions need to address one or more of the following solution areas and I will be using these and additional criteria to build out the framework.

  1. Personal productivity
  2. Business productivity
  3. Business critical
  4. Life critical

Have another point of view? Feel free to share. My next post will focus on the segment of personal productivity.

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Great Data Increases Value and De-Risks the Drone

great data

Great Data Increases Value and De-Risks the Drone

At long last, the anxiously awaited rules from the FAA have brought some clarity to the world of commercial drone use. Up until now, commercial drone use has been prohibited. The new rules, of course, won’t sit well with Amazon who would like to drop merchandise on your porch at all hours. But the rules do work really well for insurers who would like to use drones to service their policyholders. So now drones, and soon to be fleets of unmanned cars will be driving the roadways in any numbers of capacities. It seems to me to be an ambulance chaser’s dream come true. I mean who wouldn’t want some seven or eight figure payday from Google for getting rear-ended?

What about “Great Data”? What does that mean in the context of unmanned vehicles, both aerial and terrestrial? Let’s talk about two aspects. First, the business benefits of great data using unmanned drones.

An insurance adjuster or catastrophe responder can leverage an aerial drone to survey large areas from a central location. They will pin point the locations needing attention for further investigation. This is a common scenario that many insurers talk about when the topic of aerial drone use comes up. Second to that is the ability to survey damage in hard to reach locations like roofs or difficult terrain (like farmland). But this is where great data comes into play. Surveying, service and use of unmanned vehicles demands that your data can answer some of the following questions for your staff operating in this new world:

Where am I?

Quality data and geocoded locations as part of that data is critical. In order to locate key risk locations, your data must be able to coordinate with the lat/long of the location recorded by your unmanned vehicles and the location of your operator. Ensure clean data through robust data quality practices.

Where are my policyholders?

Knowing the location of your policyholders not only relies on good data quality, but on knowing who they are and what risks you are there to help service. This requires a total customer relationship solution where you have a full view of not only locations, but risks, coverages and entities making up each policyholder.

What am I looking at?

Archived, current and work in process imaging is a key place where a Big Data environment can assist over time. By comparing saved images with new and processing claims, claims fraud and additional opportunities for service can be detected quickly by the drone operator.

Now that we’ve answered the business value questions and leveraged this new technology to better service policyholders and speed claims, let’s turn to how great data can be used to protect the insurer and drone operator from liability claims. This is important. The FAA has stopped short of requiring commercial drone operators to carry special liability insurance, leaving that instead to the drone operators to orchestrate with their insurer. And now we’re back to great data. As everyone knows, accidents happen. Technology, especially robotic mobile technology is not infallible. Something will crash somewhere, hopefully not causing injury or death, but sadly that too will likely happen. And there is nothing that will keep the ambulance chasers at bay more than robust great data. Any insurer offering liability cover for a drone operator should require that some of the following questions be answered by the commercial enterprise. And the interesting fact is that this information should be readily available if the business questions above have been answered.

  • Where was my drone?
  • What was it doing?
  • Was it functioning properly?

Properly using the same data management technology as in the previous questions will provide valuable data to be used as evidence in the case of liability against a drone operator. Insurers would be wise to ask these questions of their liability policyholders who are using unmanned technology as a way to gauge liability exposure in this brave new world. The key to the assessment of risk being robust data management and great data feeding the insurer’s unmanned policyholder service workers.

Time will tell all the great and imaginative things that will take place with this new technology. One thing is for certain. Great data management is required in all aspects from amazing customer service to risk mitigation in operations.  Happy flying to everyone!!

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Wearables Hackfest: What to do with that Data?

I had the opportunity to participate in the Boulder edition of the “Where are your Wearables” Hackfest last week hosted by Quick Left.

34420_large_Under_Armour_Wearable_Concept_FP_Wide

With over a 100 people showing up and lots of first time hackers we saw eight teams form up with a mix of talents. Being a non-coder myself I simply picked a group that was not too big and offered my product talents in talking through concepts. More on what we built in a moment.

First a little bit about the hackfest: We were provided access to the Fitbit API,UnderArmour API and had Sparkfun LilyPad kit with the goal to build something in 3 hours.

Observations

Hackfests almost always create issues when it comes to integration. Unless someone on the team already knows a lot about an API the first issues are getting setup.

  • Connecting to data. Most teams used the Fitbit API only or a couple had another device they brought and then used that as a data source. Most teams spent time just getting access to the API setup, OAuth working and then dumping their data into a client for the app (if they got that far)
  • Security. OAuth was a requirement for the Fitbit API which is pretty standard these days. The team I worked on had some issues that slowed us down getting this to work correctly.
  • Clean data & real-time data. Given most teams had a basic scenario we were all only working with a single data set, but still getting access to the data in real-time for the client app added complexity to the solution. In the real world for most wearables to break through the basic health examples we see today they are going to need to blend multiple data sets to provide value to the end user.

A few thoughts on the solutions that were built

Most of the teams just tried to get one integration working and then add a secondary calculation or evaluation that would provide the value to the user. It’s only 3 hours and the KISS principle was generally followed by the best examples of the night.

In no order these were some of the solutions that stood out to me.

WaterGoals: Wearable integrated: Fitbit. This seemed like a very practical solution. The idea was that a lot of people are not able to read the display screen on the typical watch if they are exercising so why not integrate the data and add visuals that give them data like heart rate and water consumption. For example, the data could be integrated into the clothing they are wearing and either a single visual element or the entire garment could change color based on the person being below or above their hear rate goal range. Another example was adding a touch pad that would add a quantity of water consumed – say 1 cup – when the person pushed it while they were exercising because it’s not easy to fumble with your wearable.

Fitbeer: Wearable integrated: Fitbit. I worked on this team and the idea was just to provide an easy way for someone to track consumption of any liquid, but we used beer for the example. By tracking an activity type on a Fitbit and identifying arm movement the goal would be to track the number of times a person picked up their glass to consume and track real-time the calories being burned by the activity and the calories being consumed. In addition we planned an integration to Twitter so someone could share their results as a social component.

Where’s the damn remote: Wearable integrated: Myo armband. This team used a Myo to integrate to an Apple TV so they could do selection of shows/movies/music with hand gestures. This was interesting since they had to define the hand gestures and then do the integration to map them to get the desired action in the Apple TV. This was the most real demo of the night in terms of fully working.

My main take away from the event was that people are still searching for the way that wearables can be dead simple for the user and provide lots of value. Most of the generation 1 solutions provide some type of health measurements and now also provide access to the Internet (e.g. Google Glass) but finding ways to combine multiple data sets to provide a life changing solution are what will let us know when we are starting to see generation 2 solutions. And for the enterprise IT area it still seems wearables are a long way off as they remain very much a consumer oriented solution today with some things to work out before we see anything but very early experimentation for enterprise IT.

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Ready for Internet of Things?

internet_of_thingsData has always played a key role in informing decisions – machine generated and intuitive.  In the past, much of this data came from transactional databases as well as unstructured sources, such as emails and flat files.  Mobile devices appeared next on the map.  We have found applications of such devices not just to make calls but also to send messages, take a picture, and update status on social media sites.  As a result, new sets of data got created from user engagements and interactions.  Such data started to tell a story by connecting dots at different location points and stages of user connection.  “Internet of Things” or IoT is the latest technology to enter the scene that could transform how we view and use data on a massive scale.

Another buzzword? 

Does IoT present a significant opportunity for companies to transform their business processes?  Internet of Things probably add an important awareness veneer when it comes to data.  It could bring data early in focus by connecting every step of data creation stages in any business process.  It could de-couple the lagging factor in consuming data and making decisions based on it.  Data generated at every stage in a business process could show an interesting trend or pattern and better yet, tell a connected story.  Result could be predictive maintenance of equipment involved in any process that would further reduce cost.  New product innovations would happen by leveraging the connectedness in data as generated by each step in a business process.  We would soon begin to understand not only where the data is being used and how, but also what’s the intent and context behind this usage.  Organizations could then connect with their customers in a one-on-one fashion like never before, whether to promote a product or offer a promotion that could be both time and place sensitive.  New opportunities to tailor product and services offering for customers on an individual basis would create new growth areas for businesses.  Internet of Things could make it a possibility by bringing together previously isolated sets of data.

Proof-points

Recent Economist report, “The Virtuous Circle of Data: Engaging Employees in Data and Transforming Your Business” suggests that 68% of data-driven businesses outperform their competitors when it comes to profitability.  78% of those businesses foster a better culture of creativity and innovation.  Report goes on to suggest that 3 areas are critical for an organization to build a data-driven business, including data supported by devices: 1) Technology & Tools, 2) Talent & Expertise, and 3) Culture & Leadership.  By 2020, it’s projected that there’ll be 50B connected devices, 7x more than human beings on the planet.  It is imperative for an organization to have a support structure in place for device generated data and a strategy to connect with broader enterprise-wide data initiatives.

A comprehensive Internet of Things strategy would leverage speed and context of data to the advantage of business process owners.  Timely access to device generated data can open up the channels of communication to end-customers in a personalized at the moment of their readiness.  It’s not enough anymore to know what customers may want or what they asked for in the past; rather anticipating what they might want by connecting dots across different stages.  IoT generated data can help bridge this gap.

How to Manage IoT Generated Data

More data places more pressure on both quality and security factors – key building blocks for trust in one’s data.  Trust is ideally truth over time.  Consistency in data quality and availability is going to be key requirement for all organizations to introduce new products or service differentiated areas in a speedy fashion.  Informatica’s Intelligent Data Platform or IDP brings together industry’s most comprehensive data management capabilities to help organizations manage all data, including device generated, both in the cloud and on premise.  Informatica’s IDP enables an automated sensitive data discovery, such that data discovers users in the context where it’s needed.

Cool IoT Applications

There are a number of companies around the world that are working on interesting applications of Internet of Things related technology.  Smappee from Belgium has launched an energy monitor that can itemize electricity usage and control a household full of devices by clamping a sensor around the main power cable. This single device can recognize individual signatures produced by each of the household devices and can let consumers switch off any device, such as an oven remotely via smartphone.  JIBO is a IoT device that’s touted as the world’s first family robot.  It automatically uploads data in the cloud of all interactions.  Start-ups such as Roost and Range OI can retrofit older devices with Internet of Things capabilities.  One of the really useful IoT applications could be found in Jins Meme glasses and sunglasses from Japan.  They embed wearable sensors that are shaped much like Bluetooth headsets to detect drowsiness in its wearer.  It observes the movement of eyes and blinking frequency to identify tiredness or bad posture and communicate via iOS and android smartphone app.  Finally, Mellow is a new kind of kitchen robot that makes it easier by cooking ingredients to perfection while someone is away from home. Mellow is a sous-vide machine that takes orders through your smartphone and keeps food cold until it’s the exact time to start cooking.

Closing Comments

Each of the application mentioned above deals with data, volumes of data, in real-time and in stored fashion.  Such data needs to be properly validated, cleansed, and made available at the moment of user engagement.  In addition to Informatica’s Intelligent Data Platform, newly introduced Informatica’s Rev product can truly connect data coming from all sources, including IoT devices and make it available for everyone.  What opportunity does IoT present to your organization?  Where are the biggest opportunities to disrupt the status quo?

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