Releasing Software Developer Superpowers

Article is aimed at anyone looking to gain the edge in their software development team creation or advancement in the digital age. Concepts can be applied outside of sw dev at some level. Open to discussion – views are my own.

UX is not just for Customers

User Experience is an ever growing component of product development, with creating user centric design paradigms to ensure that personalisation and consumer/market fit is achieved. From a development team view, leveraging some of the user experience concepts in how they work can achieve operational efficiency, to accelerate product development. For example, how is the experience for each of the developer personnas in your team? How do their days translate to user stories? Can interviewing the development community lead to creating better features for your development culture?

Build Products not Technology

Super important. Sometimes with developers, there is an over emphasis on the importance of building features, a lot of the time for features sake. By keeping the lens on the value or “job to be done” for the customer in the delivery of a product at all times can ensure you are building what is truly needed by your customer. To do this, select and leverage a series of metrics to measure value for that product, along with keeping your product developent in series, and tightly coupled to your customer experience development.

Leverage PaaS to deliver SaaS

This sounds catching but its becoming the norm. 5 years ago, it took a developer a week of development time to do what you can do in Amazon Web Services or Azure now in minutes. This has led to a paradigm shift, where you being to look at the various platforms and tools that are available to enable the developers to deliver great products to customers. Of course, there will always be custom development apps, but you can help your developers by getting them the right toolkit. There is no point reinventing the wheel when OTS open source components are sitting there, right? Products like Docker and Spring and concepts like DevOps are bringing huge value to organisations, enabling the delivery of software or microservices at enhanced speed. Also, the balance between buying OTS and building custom is a careful decision at product and strategic levels.

“The role of a developer is evolving to one like a top chef, where all the ingredients and tools are available, its just getting the recipe right to deliver beautiful products to your customer.”

Create Lean Ninjas!

Evolving the cultural mindset of developers and the organisation toward agile development is super important. Having critical mass of development resources, plus defined agile processes to deliver business success  can really reshape how your organisation into one where value creation in a rapid manner can take place. However, its important to perform ethnographical studies on the organisation to assess the culture. This can help decide on which agile frameworks and practices (kanban, scrum, xp etc) can work best to evolve the development life cycle.

Implement the 10% rule

Could be slightly controversial, and can be hard to do. Developers should aim to spend 10% of their time looking at the new. The new technologies, development practices, company direction, conferences, training. Otherwise you will have a siloed mis-skilled pool of superheros with their powers bottled.

However, with lean ninjas and effective agile company wide processes, resources and time can be closely aligned to exact projects and avoid injecting randomness into the development lifecycle. Developers need time to immerse and focus. If you cant do that for them, or continously distract them with mistimed requests – they will leave. If you can enable them 10% is achievable.

Risk Awareness

We are seeing an evolution in threats to enterprise all over the world, and in a software driven and defined world, getting developers to have security inherent design practices prior to products hitting the market can help protect companies. Moons ago, everything sat on prem. The demands of consumers mean a myriad of cloud deployed services are adding to a complex technology footprint globally. If they know the risk landscape metrics from where they deploy, they can act accordingly. Naturally, lining them up with business leaders on compliance and security can also help on the educational pathway.

Business and Technology Convergence

We are beginning to see not only evolution in development practices –  we are also seeing a new type of convergance (brought about by lean agile and other methods) where business roles and technology roles are converging. We are beginning to see business analysts and UX people directly positioned into development teams to represent the customer and change the mindset. We are seeing technology roles being positioned directly into business services teams like HR and finance. This is impacting culture, wherby the saviness in both directions needs to be embraced and developed.

Growth Mindset

We mentioned mindset a lot in the article. That because its hugely important. Having the right culture and mindset can make all the difference in team success. As Carol Dweck talks about in her book “Mindset”, you can broadly categorise them into two – growth and fixed. This can be applied in all walks of life, but for team building it can be critical.

In a fixed mindset students believe their basic abilities, their intelligence, their talents, are just fixed traits. They have a certain amount and that’s that, and then their goal becomes to look smart all the time and never look dumb. In a growth mindset students understand that their talents and abilities can be developed through effort, good teaching and persistence. They don’t necessarily think everyone’s the same or anyone can be Einstein, but they believe everyone can get smarter if they work at it.

Creating a team where being on a growth curve and failures are seen as learning can really enable a brilliant culture. As Michaelangelo said “I am still learning”. Especially as we evolve to six generations of developers. How do we ensure we are creating and mentoring the next set of leaders from interns through to experienced people?

Check a Ted talk from Carol here – link.

And most importantly … HAVE FUN!

Augmented and Virtual Reality: Now more about improving User Experience

We cannot eat popcorn wearing a virtual reality (VR) headset – Zaid Mahomedy : ImmersiveAuthority.com

IIn 1995, the cringe worthy Johnny Mnemonic was released where he used a VR headset and gesture monitoring gloves to control the “future internet”. Even though this movie was over 20 years ago, it is only in the past few years we are seeing commercially ready Virtual Reality (VR) and Augmented Reality (AR) technologies hit the market.

If you watch this clip, you will hopefully notice two aspects. The first is that the technology is clunky. The second is that the predicted user experience (UX) he has is rich (for the decade of movie production): information is available at speed, the gloves are accurate, and the path and usability is seamless. When he moves his hands, the VR3 responds instantaneous. It assists him at every turn. Yet twenty years later, we have not reached this quite yet. Why? Because the focus needs to shift from technology to other aspects to enable this industry to flourish.

1: Technology Moves Aside for User Experience.

A large amount of technology companies efforts in this space in the past two years has been mostly focused at determining can they squeeze enough compute power onto a pair of glasses. Other questions to be answered were around if the battery will last a decent amount of time and will the heat emissions be low enough not to inconvenience the user. Whilst there are still optimizations to be performed, the core of the technology has at least been proven, along with some clever innovations around leveraging smart phones to save on hardware investments.

In the coming years we will see a larger amount of these companies focusing on user experience we have with these technologies – ensuring the interfaces,gesture and motion recognition are close to perfect are high on companies to-do lists. The hardware road-map will ensure they are lighter, more robust and frankly – sexier to wear. Before we discuss other aspects of how improved UX will be the focus of the coming years, its not to stay that technology wont help on this. For example, the evolution of flexible compute paradigms specifically in the nano technology area will assist in building compute into glasses, instead of adding compute retrospectively.

2: Difference in Psychologies

Apart from the technology of VR and AR being quite different under the hood, the psychology of how they are used is also. With AR, we are injecting a digital layer between us and the physical world. With VR, we are immersing ourselves into a digital world. These are very different experiences and the user experience design must have this difference at its core. We must ensure that the layer we design for AR takes characteristics from both our physical environment and our own personas. With VR, its much more emphatic to ensure the person feels comfortable and safe in that world.

3: Interfaces to VR/AR UX

The UX Design of AR and VR technologies and applications will require careful management of multiple input styles. Using wearables, voice recognition, AR and AI, we will start to see seamless blending and integration with how technology interacts with us across various senses. Touch devices are still being used, but they will move aside for voice recognition, movement tracking and even brain waves to be used to control these smart devices. The communication will be much faster and intimate, and will force designers to completely rethink how we interact with these devices.

4: The Role of AI in UX

The UX of these devices will also require more human like interactions, to built trust between the devices and the users in an organic manner. We are seeing this with voice control technology like Siri and Google Home, but they are understanding our voice, with some sample responses. Soon they will learn to evolve their speech.

Artificial intelligence will take hold of the user experience to analyze the reaction to different experiences and then make changes in real time to those assessments. UX will become a much more intuitive and personalized experience in the coming years.

5: Convergence of VR and AR Standards

Already we are seeing a myriad of startups evolving in the space, some focusing on content development in software, some on the actual hardware itself. Some are brave enough to have both on offer. We also have the larger companies creating divisions to provide offerings in this space. Choice is great, but when it becomes painful trying on your fourteenth pair of glasses at your average conference, it is not. When one takes time to observe how companies are beginning to partner up to offer solutions ( a trend extremely common in the IoT industry) it is a small step towards some form of standardization. Excessive choice can be bad from a UX perspective, as with such segregation in initial design makes it harder for app designers to get it right on the hardware.

6: Realistic Market Sensing

At some point, we have to get away from the “Toys” feel for these devices. We put them on for ten minutes in an airport or at an event to get a wow from it. Whilst the applications in the gaming industries are there to be seen, companies are beginning to focus on where else the market will be. Certain devices have flopped in the past two years, and you would wonder why with such strong brands. The first reason was awful UX. The second was the market just was not ready, with a distinct lack of content to make them anyway useful. Just because a few of these devices fail, doesn’t mean the movement stops. (Below info-graphic source is washington.edu)

Consumer and Industrial applications have very different requirements from a market perspective, with content choice and look and feel very important for consumer markets, system performance and governance sitting higher in industrial use cases. With the costs associated with adding these technologies to industrial environments under the microscope, companies must focus strongly on measuring and building the return on investment (ROI) models.

7: Protecting the User and the Experience

With these technologies predicted to get even closer than headsets (smart contact lenses for example -link here), its quite important the UX designers can intrinsically build in comfort and safety into any application. Too many times we have seen people fall through something whilst wearing a headset (more so with VR technologies). And that’s just the physical safety. When the threshold between physical and augmented worlds gets closer and closer (mixed reality), we want to avoid a scenario of interface overkill.

Whilst the past few years may indicate that these technologies are fads, the reality is far from it. They will become part of our social fabric as a new form of mobile technology. Ensuring the users experience with these technologies will be the critical enabler in their success and adoption rate.

Designing for AR and VR entails there be better understanding of a user’s need when it comes to context of use. It’s about building connections between the physical and digital world, requiring an interdisciplinary effort of service design, interaction design and industrial design.

Machine Learning 1.0 over Coffee

Article aimed at anyone (technical or non technical) who wants to understand the steps in Machine Learning at a high level. Readable in five minutes over coffee. I think.

What is Machine Learning?

Today we live in a world of seemingly infinitesimal connected devices, in both personal and commercial environments. The currency associated with these devices is data, which whizzes around in near real time, is stored locally and in cloud environments. The types of data vary greatly, with text, audio, video and numerical data just a sample of the data modalities generated.

As this data is a currency, there is value associated with it, but how do we extract this value? A high growth area is called data science which is used to extract value and insight from this data. It has numerous ingredients in the recipe, with data mining, data optimization, statistics and machine learning key to generating any successful flavor. And like an good recipe, you need a good chef. These chefs in data terms are called Data Scientists, who use a wide variety of tools to glean insight from the data to deliver impact for your business. The data-sets themselves can either be uni-variate (single variable or feature), or multivariate (multiple variables or features). A persons age would be an example of uni-variate, whereas multivariate would expand a person’s feature set to include age, weight and waist size for example.

Why do it?

Machine learning (ML) is born out of the perspective that instead of telling computers how to perform every task, perhaps we can teach them to learn themselves. Examples include predicting the sale price of your house based on a set of features (sq. feet, number of bedrooms, area), to try to determine if an image is of a dog rather than a cat to determining the sentiment of a set of restaurant reviews to be positive or negative. There are a host of applications across many industries, some of these are shown below (source Forbes)

Before the magic is induced from the algorithms, perhaps the most important step in any machine learning problem is the upfront data transformation and mining, towards optimization. Optimization is required as most of the algorithms that “learn” are sensitive to what they receive as an input, and can greatly impact the accuracy of the model that you build. It can also ensure you have a thorough understanding of your data-set and the challenge you are trying to solve. Some of the data transformation and mining techniques include record linkage, feature derivation, outlier detection, missing value management and vector representation. All this is sometimes called “Exploratory Data Analysis”.

Techniques once Optimized

Once data is presented in the right manner, there are a number of machine learning techniques one can apply. They are broken in supervised and unsupervised techniques, with supervised learning taking an input data set to train your model on, and with unsupervised no data-sets are provided. Unsupervised techniques include learning vector quantification and clustering. Supervised techniques include nearest neighbors and decision trees. Another techniques is Reinforcement learning, and this type of algorithm allows software agents and/or machines to automatically determine the ideal behavior within a specific context, to maximize its performance.

Verifying your model is also an important step, and we often use confusion matrices to do that. This involves building a table of four results – true positives, true negatives, false positive and false negatives. A set of test data is applied to the classifier and the result are analysed to assess performance. Sometimes, the result of the model is still questionable. When this happens, machine learning has an answer in the form of ensemble methods, which essentially you build a series of models that you build your final prediction from. Examples here include boosting and bagging on the training data. Bagging splits the training data into multiple input sets, boosting works by building a series of increasingly complex models.

There are complimentary techniques used in any successful machine learning problem – these include data management and visualization, and software languages such as python and java have a variety of libraries that can be used for your projects.

Going further

Taking a step further from machine learning, you are into a complimentary area called artificial intelligence (AI), which leans more on methods such as neural networks and natural language processing which look to mimic the operation of the human brain. This is showing how human centric design in technology is evolving, and how much excitement there is for how humans and technology will work together in the future. It can be said this excitement is born from revealing that as we evolve our understanding of what it means to be human, it outweighs anything that technology alone can deliver. People have always been at the core of innovation, and this has led to an evolution in how improved our lives are.

Man vs Machine: Why the competition?

With the continued evolution of industries such as Data Science and the Internet of Things, there is a mix of excitement and fear amongst the populous. Excitement for what they can do for our lives or businesses, but fear of what it will mean for humanity.

The fears are normally sourced from the media or some childhood movies we watched where pretty large robots take over planet earth. Quotes of “Will the robots take our jobs?” “There will be nothing left for us to do with the evolution of the computer”.

In reality a synergy between humans and technology can lead to better all round solutions, rather than in isolation. This is something that is rarely considered in current engineering circles. With so much technology choice, why would we need to stupid humans?

A brief story to set the tone

As far back as 2007, I hosted questions like this as part of the day job. Increased automation in manufacturing is a natural spore for questions of this nature. An example of this was an computer vision application that I built for a Masters dissertation whilst working for Alps Electric (one of the coolest companies in the world). It was inspecting graphics on buttons for correctness, both in finish and symbol.  Naturally this was a task done by humans historically. We were using classification techniques to perform the task on the images, and the receiver operator characteristic curves (ROC Curves) showed that the classifier was right 93% of the time, which was a pretty good first pass result. Please note that this was a time that data science was called “doing your job”.

We wanted to achieve 100%, so in order to improve the algorithm, we decided to use the main source of intelligence in the room, the humans! By presenting the failures to the operator on the production line, and asking them a particularly simple binary yes/no “Is this a genuine failure?”, saving their response and the original image, we were able to get the classifier to close to 99% accuracy.

This proved something that I felt was always the case. Humans and machines can work in tandem as opposed to viewing it as a competition. With the rapid advancement of technology, along with the obsession with using technology to optimize our lives, I pose a question: Have we forgotten how these can compliment each-other? If any data science/machine learning application can get an accuracy level of 70% for example, we try to squeeze extra accuracy out of it through “fine tuning” the algorithm. Perhaps we could present the results in some way to a human for final classification?

Bring it all together

Last April, I tried to draw out how I saw Data Science, IoT and Intelligence (both computing and human) fit together, which is shown below. It is an evolutionary map of sort, where we have always had the verticals and data modalities (data type), and we began on our data journey by building some simple data processing/mining applications (either by us manually or by using algorithms). And lots of the current challenges in data science can be solved by this tier. However, we are seeing an increase in the requirement to bring in machine learning applications to solve more advanced challenges. This is a natural evolution towards artificial intelligence,or deep learning. If we look right down the map in a holistic sense, this is where the top class really comes to the fore.

Humans are by our very nature, true intelligent, which evolves are we do, and also NOT very good at mass processing. Computing on the other hand are not so intelligent to begin with, but are incredibly good at mass processing. A natural hybrid would be true intelligence and mass processing, and that should be the aim for modern Artificial Intelligence companies/ enthusiasts.

Now I am not saying that all applications in IoT and Data Science can be solved like this. Of course there will be exceptions. But there are some real tangible benefits to this approach. Consider the area of street crime. Imagine every camera in a city feeding video into a central location, and asking a human to monitor it. In reality, this is actually happening individually per building, park, mall, where security guards are monitoring areas in real time. With advancements in video analytics, it was feared that technology would replace humans. But it is not the case. What has happened as more devices/cameras hit our streets, it becomes impossible to monitor everything. By using advanced video analytics/machine learning capability to flag the anomalies to security, it means they can monitor a bigger space.

Thankfully, one of the high growth areas in technology is in Human Machine Interfaces (HMI’s), and there are some really good examples on how humans and computers can work together. Daqri‘s smart helmet is one such product, which is the worlds first wearable HMI. Their mandate is to use technology to improve and optimize how we work. by integrating compute, sensors and computer vision technology into a well designed helmet. Work, in the Future.

As we enter the next phase of digital transformation, ask yourself: How can humans improve/compliment the work of technology in your application?

it@Cork European Technology Summit 2015 – a WOW event!

I wanted to change direction slightly and give an update on an event I had the privilege of being involved with this week, the it@Cork European Technology Summit. The event was held at Cork City Hall, on Wednesday May 5th, with a full day technology summit, followed by a black tie dinner with 3d wearables fashion show.

An epic journey over the past few months, with way more ups than downs resulted in…

1 Day – 4 Sections – 20 speakers – 4 Chair Speakers – 400+ Day attendees – #1 Trending on Twitter – 9 Amazing artisan food stalls – Lots of Sponsors – 200+ Night Attendees – 2 Fashion Designers – 1 Model Agency – 10 Models – 2 Fire Dancers – 4 3D printed bow ties !

So how did I arrive there? Last year, Gillian Bergin from EMC asked me to get involved with it@Cork, as part of the Tech Talk committee. I’m delighted she did, as over the past few months, I got to partake in and help organise some excellent tech talks from a variety of people, including my fellow technologist, Mr Steve Todd of EMC. The tech talk series is just one of many successful strands of it@Cork, holding six high end, rockstars speakers/panels per year. The series is full up until 2016, but if you are a “rockstar” speaker interested in speaking, please contact us directly. From this, James O’Connell of VMWare who passed over the tech talk committee chair to Barry O’Connell, took on chair of the Summit Organising committee. James, coupled with myself and Paddy O’Connell of Berkley Group, (known collectively now as the Macroom or Muskerry Mafia 🙂 ) assisted Sarah Walsh of it@Cork in organising the day summit. The night summit was excellently organised by Marnie O’Leary Daly of VMWare.

The event was kicked off by James, and then Ronan Murphy, chairman of the board it@Cork, CEO Smarttech gave an address that spoke about how Cork needs a cluster manager to help drive more employment in the region. More from Ronan here by the Examiner.  Donal Cahalane, from Teamwork.com, gave an insightful talk on how he saw the industry progressing, with some excellent advice for everyone from startups through to multinationals .

The four sections throughout the day offered a mix balance between raw technology (Cloud- challenge the fear, Internet of Everything) along with Digital Marketing and a Tech Talent/ Diversity panel. I found this to work quite well, as it ensured the audience got a variety of speakers.

The cloud session on “challenging the fear” was an excellent one to start with, as it had a mix of SME’s from companies such as Kingspan (John Shaw), Trend Micro (Simon Walsh) and Barricade (David Coallier), but also had representation from the legal profession, in the form of Michael Valley, Barrister and Noel Doherty – Solicitor who spoke at length on cloud governance. This session was chaired by Anton Savage of The Communications Clinic, who hosted a panel discussion with all five presenters at the end.

All of the sections were split by networking opportunities in the exhibition halls, where companies from the region presented their organisations, and some even demonstrated their wares. The athmosphere was great to see with lots of chatter, tweeting and drinking of coffee! 😀

The second section was a panel session on Tech Talent, the chair being Paddy O’Connell from Berkely, and the facilitators were Meghan M Biro, founder and CEO of TalentCulture, and Kevin Grossman, who co founded and co hosts the the TalentCulture #TChat show with Meghan. They later presented their TChat show live from the Clarion hotel Cork. It was awesome!

Such variety (no pun intended!) in the panel, with David Parry Jones, VP UKI VMWare and Noelle Burke Head of HR Microsoft Ireland representing industry, Michael Loftus – Head of Faculty of Engineering and Science CIT representing academia, and the hugely impressive student Ciara Judge, one of the Kinsale winners of the 2013 Google Science Award. Everyone inspired in their own way, and the dynamic at lunchtime was one of motivation, hope and leadership.

Having started my own personal digital marketing brand last year, and learning by making mistakes, I was exceptionally excited by our third section – Digital Marketing. Again, Anton did an incredible job of asking the right questions, and effortless listenership followed. To listen to experts such as Meghan, Antonio Santos, Niall Harbison and Raluca Saceanu was a privilege, and I also got the opportunity to speak with the directly (as did many others). This was true of all the speakers throughout the day. I believe a huge number of people got lots of what I call “advice snippets” that they can take away and grow their own brand.

The last session was on an area close to my heart, the Internet of everything (IoE), and I had the privilege of chairing the session. We had speakers from Climote (Derek Roddy), my future employer Tyco (Craig Trivelpiece), Salesforce (Carl Dempsey), Dell (Marc Flanagan) and Xanadu (David Mills). All these companies are in different stages on their IoE journey, but the message was consistent: IoE is going to make a huge impact on our smart futures. I really like how Craig spoke of “if you want to improve something, measure it”  and how Tyco are looking at predictive maintenance and pushing intelligence/insight back out to the devices. Derek showed how Climote is changing how we live, David did the same in relation to sport. Marc gave an excellent account of Dells practical approach to IOT, showing the capabilities needed for IoE projects. Carl got me really excited about Salesforce’ plans in the IoE space. The session really closed out the event well, and the numbers in attendance stayed consistent.

Having attended a huge number of tech events over the years, it was great to see again, year on year growth of Munsters premier Technology Summit. The athmosphere was electric all day, both locally and on Twitter. The tweet wall was a big success, and we expect that next years event will be bigger and better again.

The black tie dinner was also a huge success, with the Millenium Hall in City Hall packed to capacity. Marnie O’Leary Daly, along with Emer from Lockdown model agency, put on an amazing dinner (superb catering by Brooks) and fashion show, with 3D wearables fashion provided by Aoibheann Daly from LoveandRobots and Rachael Garrett from Limerick School of Art and Design (@LSAD). Special mention to FabLab also for helping Rachael get her garments ready. It really was a spectacular evening. The Clarion hotel was also hugely supportive of the night element. (Photos to follow!) Emer will also blog on the night event fashion soon and do a much better job than me!

It@Cork European Technology Summit 2016. Watch this space. 

If you are interested in getting involved in 2016, please contact Sarah Walsh at it@Cork.

Platform Architecture Pre Considerations for IoT

Apart from the sheer volume of data generated by IoT devices, there are also a huge number of different data customers requirements, both known and unknown that will need to be considered. In this regard, the platform technology will need to be agile enough to meet this variation. How will this scale both horizontally and vertically to ensure sustainability? I started to think of profiling requirements, and looking to give personality to the IoT customer type, so that the platform can morph and adjust itself based on not only the inputs (data type, frequency, format, lifetime), but also what outputs it needs to provide.

Data latency will also be a requirement that any platform will need to firstly understand, and then address, depending on the application and customer requirements. In an interesting discussion today in Silicon Valley with Jeff Davis (my original hiring manager in EMC, and now senior director of the xGMO group looking at operations cloud, analytics and infrastructure services ), he mentioned having worked in a previous company in the sensor business, latency represented a huge challenge, especially when the amount of data grew exponentially. We chatted more and more about how the consumer of now wants their devices/ technology interactions to be instant. How long will people be willing to wait for smart light bulbs/ switches? What if my devices are distributed? More importantly, Jeff outlined a key question. “How much are the consumer willing to pay for the added services provided by adding “smarts” to standard everyday sensors”? This is a “understand the market” question, and should be a consideration for anyone looking at building an IoT platform.

When one starts to consider that most applications in the IoT space might require more than one industry working together, cross collaboration is key to making it work. Consider some of the taxi apps in use currently, whereby the taxi company provides the car locations, the application needs to offer information on locations, then the banking is used to pay for it from your account, and perhaps there is advertisement shown on your receipt, if a suitable arrangement is not formed between the various It companies, it becomes too easy for the “blame game” to ruin the user’s experience of the application when something goes wrong.

Central to the satisfying both the varying requirements of the customers and latency management will be the concept of a customer or business data lake, powered by Hadoop or Spark technology, will form the primary storage and processing in the data center. There is also an option to look at tiering to help address the variation in requirements for the platform, with the possibility to send the “big hitting data”, which brings the most value in close to real time, to an in memory database, to provide fast cache insightful analytics. In a later blog post, I will elaborate greatly on this paragraph, so stay tuned. If the same dataset can be used by multiple applications, in a multi-tenant schema, then there will be clear orchestration challenges in ensuring that this data can be processed in real time.  Other features of any data architecture for IoT could also include:

• Multiple Data Format Support
• Real Time Processing
• High Volume Data Transfer
• Geographically Agnostic
• Data Lake Archival and Snipping

As with all technology, IoT will evolve, which means that we will build on top of previous technologies, and new technologies will add to the ecosystem. The enterprise data warehouse will continue to play an important role, but a series of technology platforms will be necessary. While numerous platforms have and will be created, one such platform, ThingWorx is the subject of case study in my next blog.