Friday, April 7, 2017

How AI, IoT and blockchain will shake up procurement and supply chains

The next BriefingsDirect digital business thought leadership panel discussion focuses on how artificial intelligence (AI), the Internet of things (IoT), machine learning (ML), and blockchain will shake up procurement and supply chain optimization.

Stay with us now as we develop a new vision for how today's cutting-edge technologies will usher in tomorrow's most powerful business tools and processes. The panel was assembled and recorded at the recent 2017 SAP Ariba LIVE conference in Las Vegas. The discussion is moderated by Dana Gardner, principal analyst at Interarbor Solutions.

Listen to the podcast. Find it on iTunes. Get the mobile app. Read a full transcript or download a copy.
To learn more about the data-driven, predictive analytics, and augmented intelligence approach to supply chain management and procurement, please welcome the executives from SAP Ariba:
Here are some excerpts:

Gardner: It seems like only yesterday we were confident to have a single view of a customer, or clean data, or maybe a single business process end–to-end value. But now, we are poised to leapfrog the status quo by using words like predictive and proactive for many business functions.

Why are AI and ML such disrupters to how we've been doing business processes?

Shahane: If you look back, some of the technological impact  in our private lives, is impacting our public life. Think about the amount of data and signals that we are gathering; we call it big data.

We not only do transactions in our personal life, we also have a lot of content that gets pushed at us. Our phone records, our location as we move, so we are wired and we are hyper-connected.

Similar things are happening to businesses. Since we are so connected, a lot of data is created. Having all that big data – and it could be a problem from the privacy perspective -- gives you an opportunity to harness that data, to optimize it and make your processes much more efficient, much more engaged.

If you think about dealing with big data, you try and find patterns in that data, instead of looking at just the raw data. Finding those patterns collectively as a discipline is called machine learning. There are various techniques, and you can find a regression pattern, or you can find a recommendation pattern -- you can find all kinds of patterns that will optimize things, and make your experience a lot more engaging.

If you combine all these machine learning techniques with tools such as natural language processing (NLP), higher-level tools such as inference engines, and text-to-speech processing -- you get things like Siri and Alexa. It was created for the consumer space, but the same thing could be available for your businesses, and you can train that for your business processes. Overall, these improve efficiency, give delight, and provide a very engaging user experience.

Gardner: Sanjay, from the network perspective it seems like we are able to take advantage of really advanced cloud services, put that into a user experience that could be conversational, like we do with our personal consumer devices.

What is it about the cloud services in the network, however, that are game-changers when it comes to applying AI and ML to just good old business processes?

Multiple intelligence recommended

Almeida: Building on Dinesh’s comment, we have a lot of intelligent devices in our homes. When we watch Netflix, there are a lot of recommendations that happen. We control devices through voice. When we get home the lights are on. There is a lot of intelligence built into our personal lives. And when we go to work, especially in an enterprise, the experience is far different. How do we make sure that your experience at home carries forward to when you are at work?

From the enterprise and business networks perspective, we have a lot of data; a lot of business data about the purchases, the behaviors, the commodities. We can use that data to make the business processes a lot more efficient, using some of the models that Dinesh talked about.

How do we actually do a recommendation so that we move away from traditional search, and take action on rows and columns, and drive that through a voice interface? How do we bring that intelligence together, and recommend the next actions or the next business process? How do we use the data that we have and make it a more recommended-based interaction versus the traditional forms-based interaction?

Gardner: Sudhir, when we go out to the marketplace with these technologies, and people begin to use them for making better decisions, what will that bring to procurement and supply chain activities? Are we really talking about letting the machines make the decisions? Where does the best of what machines do and the best of what people do meet?

Bhojwani: Quite often I get this question, What will be the role of procurement in 2025? Are the machines going to be able to make all the decisions and we will have no role to play? You can say the same thing about all aspects of life, so why only procurement?

I think human intelligence is still here to stay. I believe, personally, it can be augmented. Let's take a concrete example to see what it means. At SAP Ariba, we are working on a product called product sourcing. Essentially this product takes a bill of material (BOM), and it tells you the impact. So what is so cool about it?

One of our customers has a BOM, which is an eight-level deep tree with 10 million nodes in it. In this 10 million-node commodity tree, or BOM, a person is responsible for managing all the items. But how does he or she know what is the impact of a delay on the entire tree? How do you visualize that?

I think humans are very poor at visualizing a 10-million node tree; machines are really good at it. Well, where the human is still going to be required is that eventually you have to make a decision. Are we comfortable that the machine alone makes a decision? Only time will tell. I continue to think that this kind of augmented intelligence is what we are looking for, not some machine making complete decisions on our behalf.

Gardner: Dinesh, in order to make this more than what we get in our personal consumer space, which in some cases is nice to have, it doesn't really change the game. But we are looking for a higher productivity in business. The C-Suite is looking for increased margins; they are looking for big efficiencies. What is it from a business point of view that these technologies can bring? Is this going to be just a lipstick on a pig, so to speak, or do we really get to change how business productivity comes about?

Humans and machines working together

Shahane: I truly believe it will change the productivity. The whole intelligence advantage -- if you look at it from a highest perspective like enhanced user experience -- provides an ability to help you make your decisions.

When you make decisions having this augmented assistant helping you along the way -- and at the same time dealing with large amount of data combined in a business benefit -- I think it will make a huge impact.

Let me give you an example. Think about supplier risk. Today, at first you look at risk as the people on the network, and how you are directly doing business with them. You want to know everything about them, their profile, and you care about them being a good business partner to you.

But think about the second, third and fourth years, and some things become not so interesting for your business. All that information for those next years is not directly available on the network; that is distant. But if those signals can be captured and somehow surface in your decision-making, it can really reduce risk.
Reducing risk means more productivity, more benefits to your businesses. So that is one advantage I could see, but there will be a number of advantages. I think we'll run out of time if we start talking about all of those.

Gardner: Sanjay, help us better understand. When we take these technologies and apply them to procurement, what does that mean for the procurement people themselves?

Almeida: There are two inputs that you need to make strategic decisions, and one is the data. You look at that data and you try to make sense out of it. As Sudhir mentioned, there is a limit to human beings in terms of how much data processing that they can do -- and that's where some of these technologies will help quite a bit to make better decisions.

The other part is personal biases, and eliminating personal biases by using the data. It will improve the accuracy of your strategic decisions. A combination of those two will help make better decisions, faster decisions, and procurement groups can focus on the right stuff, versus being busy with the day-to-day tasks.

Using these technologies, the data, and the power of the data from computational excellence -- that's taking the personal biases out of making decisions. That combination will really help them make better strategic decisions.

Bhojwani: Let me add something to what Sanjay said. One of the biggest things we're seeing now in procurement, especially in enterprise software in general, is people's expectations have clearly gone up based on their personal experience outside. I mean, 10 years back I could not have imagined that I would never go to a store to buy shoes. I thought, who buys shoes online? Now, I never go to stores. I don't know when was the last time I bought shoes anywhere but online? It's been few years, in fact. Now, think about that expectation on procurement software.

Currently procurement has been looked upon as a gatekeeper; they ensure that nobody does anything wrong. The problem with that approach is it is a “stick” model, there is no “carrot” behind it. What users want is, “Hey, show me the benefit and I will follow the rules.” We can't punish the entire company because of a couple of bad apples.

By and large, most people want to follow the rules. They just don't know what the rules are; they don't have a platform that makes that decision-making easy, that enables them to get the job done sooner, faster, better. And that happens when the user experience is acceptable and where procurement is no longer looked down upon as a gatekeeper. That is the fundamental shift that has to happen, procurement has to start thinking about themselves as an enabler, not a gatekeeper. That's the fundamental shift.

Gardner: Here at SAP Ariba LIVE 2017, we're hearing about new products and services. Are there any of the new products and services that we could point to that say, aha, this is a harbinger of things to come

In blockchain we trust

Shahane: The conversational interfaces and bots, they are a fairly easy technology for anyone to adopt nowadays, especially because some of these algorithms are available so easily. But -- from my perspective -- I think one of the technologies that will have a huge impact on our life will be advent of IoT devices, 3D printing, and blockchain.

To me, blockchain is the most exciting one. That will have huge impact on the way people look at the business network. Some people think about blockchain as a complementary idea to the network. Other people think that it is contradictory to the network. We believe it is complementary to the network.

Blockchain reaches out to the boundary of your network, to faraway places that we are not even connected to, and brings that into a governance model where all of your processes and all your transactions are captured in the central network.

I believe that a trusted transactional model combined with other innovations like IoT, where a machine could order by itself … My favorite example is when a washing machine starts working when the energy is cheaper … it’s a pretty exciting use-case.

This is a combination of open platforms and IoT combining with blockchain-based energy-rate brokering. These are the kind of use cases that will become possible in the future. I see a platform sitting in the center of all these innovations.

Gardner: Sanjay, let’s look at blockchain from your perspective. How do you see that ability of a distributed network authority fitting into business processes? Maybe people hadn't quite put those two together.

Almeida: The core concept of blockchain is distributed trust and transparency. When we look at business networks, we obviously have the largest network in the world. We have more than 2.5 million buyers and suppliers transacting on the SAP Ariba Network -- but there are hundreds of millions of others who are not on the network. Obviously we would like to get them.

If you use the blockchain technology to bring that trust together, it’s a federated trust model. Then our supply chain would be lot more efficient, a lot more trustworthy. It will improve the efficiency, and all the risk that’s associated with managing suppliers will be managed better by using that technology.

Gardner: So this isn’t a “maybe,” or an “if.” It’s “definitely,” blockchain will be a significant technology for advancing productivity in business processes and business platforms?

Almeida: Absolutely. And you have to have the scale of an SAP Ariba, have the scale from the number of suppliers, the amount of business that happens on the network. So you have to have a scale and technology together to make that happen. We want to be a center of a blockchain, we want to be a blockchain provider, and so that other third-party ecosystem partners can be part of this trusted network and make this process a lot more efficient.
Gardner: Sudhir, for those who are listening and reading this information and are interested in taking advantage of ML and better data, of what the IoT will bring, and AI where it makes sense -- what in your estimation should they be doing now in order to prepare themselves as an organization to best take advantage of these? What would you advise them to be doing now in order to better take advantage of these technologies and the services that folks like SAP Ariba can provide so that they can stand out in their industry?

Bhojwani: That’s a very good question, and that's one of our central themes. At the core of it, I fundamentally believe the tool cannot solve the problem completely on its own, you have to change as well. If the companies continue to want to stick to the old processes -- but try to apply the new technology -- it doesn’t solve the problem. We have seen that movie played before. People get our tool, they say, hey, we were sold very good visions, so we bought the SAP Ariba tool. We tried to implement it and it didn’t work for us.

When you question that, generally the answer is, we just tried to use the tool -- tried to change the tool to fit our model, to fit our process. We didn’t try to change the processes. As for blockchain, enterprises are not used to being for track and trace, they are not really exposing that kind of information in any shape or form – or they are very secretive about it.

So for them to suddenly participate in this requires a change on their side. It requires seeing what is the benefit for me, what is the value that it offers me? Slowly but surely that value is starting to become very, very clear. You hear more companies -- especially on the payment side -- starting to participate in blockchain. A general ledger will be available on blockchain some day. This is one of the big ideas for SAP.

If you think about SAP, they run more general ledgers in the world than any other company. They are probably the biggest general ledger company that connects all of that. Those things are possible, but it’s still a technology only until the companies want to say, “Hey, this is the value … but I have to change myself as well.”

This changing yourself part, even though it sounds so simple, is what we are seeing in the consumer world. There, change happens a little bit faster than in the enterprise world. But, even that is actually changing, because of the demands that the end-user, the Millennials, when they come into the workforce; the force that they have and the expectations that they have. Enterprises, if they continue to resist, won’t be sustainable.

They will be forced to change. So I personally believe in next three to five years when there are more-and-more Millennials in the workforce, you will see people adopting blockchain and new ledgers at a much faster pace.

A change on both sides

Shahane: I think Sudhir put it very nicely. I think enterprises need to be open to change. You can achieve transformation if the value is clearly articulated. One of the big changes for procurement is you need to transition yourself from being a spend controller into a value creator. There is a lot of technology that will benefit you, and some of the technology vendors like us, we cannot just throw a major change at our users. We have to do it gradually. For example, with AI it will start as augmented first, before it starts making algorithmic decisions.

So it is a change on both sides, and once that happens -- and once we trust each other on the system -- nice things will happen.

Almeida: One thing I would add to that is organizations need to think about what they want to achieve in the future and adopt the tool and technology and business processes for their future business goals. It’s not about living in the past because the past is going to be gone. So how do you differentiate yourself, your business with the rest of the competition that you have?

The past business processes and people and technology many not necessarily get you over there. So how do you leverage the technology that companies like SAP and Ariba provide? Think about what should be your future business processes. The people that you will have, as Sudhir mentioned, the Millennials, they have different expectations and they won’t accept the status quo.

Listen to the podcast. Find it on iTunes. Get the mobile app. Read a full transcript or download a copy. Sponsor: SAP Ariba.

Wednesday, April 5, 2017

Why effective IoT adoption is a team sport, and how to become a player

The next BriefingsDirect Voice of the Customer discussion highlights how Internet of things (IoT) adoption means more than just scaling-up networks. The complexity and novel architectural demands of IoT require a rethinking of the edge of nearly any enterprise.

We'll explore here how implementing IoT strategies is not a one-size-fits-all endeavor -- nor can it be bought off the shelf. What’s more, those new to the computational hive and analytical edge attributes of IoT are discovering that it takes a team approach.

Listen to the podcast. Find it on iTunes. Get the mobile app. Read a full transcript or  download a copy. 
To explain how many disparate threads of an effective IoT fabric come together, we're joined by Tushar Halgali, Senior Manager in the Technology Strategy and Architecture Practice at Deloitte Consulting in San Francisco, and Jeff Carlat, Senior Director of Technology Solutions at Hewlett Packard Enterprise (HPE) Strategic Alliances. The discussion is moderated by Dana Gardner, principal analyst at Interarbor Solutions.

Here are some excerpts:

Gardner: What the top trends making organizations recognize the importance of IoT?

Carlat: We're at the cusp of a very large movement of digitizing entire value chains. Organizations have more and more things connected to the Internet. Look at your Nest thermostats and the sensors that are on everything. The connectivity of that back to the data center to do analytics in real-time is critical for businesses to reach the next level of efficiencies -- and to maintain their competitiveness in the market.

Gardner: Tushar, this is a different type of network requirement set. We’re dealing with varied data types, speeds, and volumes in places that we haven't seen before. What are the obstacles that organizations face as they look at their current infrastructure and the need to adapt?

Halgali: One of the really interesting things we've seen is that traditionally organizations have been solving technology-related problems as all information technology (IT)-related problems. There was this whole concept of machine to machine (M2M) a while back. It connected machines to the Internet, but it was a very small segment.

Now, we're trying to get machines to connect to the Internet and have them communicate with each other. There are a lot of complexities involved. It's not just the IT pieces, but having the operational technology (OT) connect to the IT world, too. It creates a very complex ecosystem of components.

Gardner: Let’s parse out the differences between OT in the IT. How do you describe those? Why should we understand and appreciate how different they are?

Carlat: When we think of OT, you think of long-standing companies out there, Bosch, National Instruments (NI), and many other companies that have been instrumenting sensors for operations, shop floors, oil and gas, and with every pump being sensed. The problem is that humans would have to interact a lot around those sensors, to remediate or to understand when something like a bearing on a pump has failed. [Learn more on OT and IoT.]

What's key here is that IT, those core data-center technologies that HPE is leading the market in, has the ability of run analytics and to provide intelligence and insights from all of that sensor data. When you can connect the OT devices with the IT -- whether in the data center or delivering that IT to the edge, which we call the Intelligent Edge -- you can actually do your insights, create your feedback, and provide corrective actions even before things fail, rather than waiting.

Gardner: That failed ball bearing on some device isn't just alerting the shop floor of a failure, it's additionally automating a process where the inventory is checked. If it’s not there, the supply chain is checked, the order is put in place, it’s delivered and ready to install before any kind of breakdown -- or did I oversimplify that?

End of Downtime

Carlat: That’s a fair representation. We're working closely with a company called Flowserve. We’re building the telemetry within the pumps so that when a cavitation happens or a bearing is starting to wear out, it will predict the mean time for failure and alert them immediately. It's all truly connected. It will tell you when it’s going to fail. It provides the access to fix it ahead of time, or as part of a scheduled maintenance plan, rather than during downtime, because downtime in an oil production facility or any business can cost millions of dollars.

Gardner: Tushar, are there any other examples you can think of to illustrate the power and importance of OT and IT together?
How to Gain Business Insights
From the Intelligent IoT Edge
Halgali: If our readers ever get a chance to check out one of the keynote speakers [at HPEDiscover London 2016] on the Intelligent Edge, there's a good presentation by PTC ThingWorx software, which is an IoT platform and the HPE Edgeline servers in a manufacturing facility. You have conveyor belts that need certain improvements, they're constantly producing things, and they're part of the production facility. It’s all tied to the revenue of the organization, and the minute it shuts down, there are problems.

Maintenance needs to be done on those machines, but you don’t want to do it too soon because you're just spending money unnecessarily and it’s not efficient. You don’t want it too late, because then there's downtime. So, you want to find the equilibrium between the two.

IoT determines the right window for when that maintenance needs to be done. If there's a valve problem, and something goes down quickly, sensors track the data and we analyze the information. The minute that data goes off a certain baseline, it will tell you about this problem -- and then it will say that there’s the potential in the future for a major problem.

It will actually generate a work order, which then feeds from the OT systems into the IT systems, and it’s all automatic. Then, when mechanics come in to try to solve these problems, they can use augmented reality or virtual reality to look at the machine and then fix the problem.

It’s actually a closed-loop ecosystem that would not have happened in the M2M base. It’s the next layer of maturity or advancement that IoT brings up.

Gardner: We can measure, we can analyze, and we can communicate. That gives us a lot of power. We can move toward minimum viable operations, where we're not putting parts in place when they're not needed, but we’re not going down either.

It reminds me of what happened on the financial side of businesses a decade or two ago, where you wanted to have spend management. You couldn't do it until you knew where all your money was, where all the bills had to be paid, but then doing so, you could really manage things precisely. Those were back office apps, digital ledgers.

So, it’s a maturity coming to devices -- analog, digital, what have you, and it’s pretty exciting. What's the impact here financially, Jeff?

Carlat: Well, huge. Right now, IDC predicts IoT to represent about a $1.3 trillion opportunity by2020. It's a huge opportunity, not only for incremental revenue for businesses, but increased efficiencies, reducing cost, reducing downtime, reducing risk; so, a tremendous benefit. Companies need to strongly consider a movement for digitizing the value chains to remain competitive in the future.

Bigger and Better Data at the Edge

Gardner: Okay. We understand why it's important and we have a pretty good idea of what you need to do. Now, how do you get there? Is this big data at the edge? I heard a comment just the other day that there's no bigger data than edge data and IoT data. We're going to have to manage scales here that we haven’t seen before.

Carlat: It’s an excellent point. Jet engines that are being used today are generating 5 TB of data every time they land or take off. Imagine that for every plane, every engine that’s flying in the sky, every day, every year. The amount of data is huge. This brings me to the unique way that HPE is approaching this, and we truly believe we are leaders in the data center now and are leaders within IT.

We're taking that design, that implementation, that knowledge, and we're designing infrastructure, data center quality infrastructure, that’s put on the edge, ruggedized compute or analytics, and providing the ability to do that analysis, the machine learning, and doing it all locally, rather than sending all that data to the cloud for analytics. Imagine how expensive that would be.

That's one approach we're taking on within HPE. But, it’s not just about HPE tackling this. Customers are asking where to start. "This is overwhelming, this is complex. How do we do this?" We're coming together to do advisory services, talking our customers through this, hand-holding, building a journey for them to do that digitization according to their plans and without disrupting their current environment.

Gardner: Tushar, when you have a small data center at the edge, you're going to eke out some obvious efficiencies, but this portends unforeseen circumstances that could be very positive. What can you do when you have this level of analytics, and you take it to a macro level? You can begin to analyze things on an industry-level, and then have the opportunity to drill down and find new patterns, new associations, perhaps even new ways to design processes, factory floors, retail environments? What are we talking about in terms of the potential for the analytics when we capture and manage this larger amount of data?

Halgali: We've noted there are a lot of IoT use cases, and the value that generates so far has been around cost optimization, efficiencies, risk management, and those kinds of things. But by doing things on the edge, not only can you do all of those, you can start getting into the higher-value areas, such as revenue growth and innovation.

A classic example is remote monitoring. Think of yourself as a healthcare provider who would not be able to get into the business of managing people's health if they're all located remotely. If we have certain devices in homes through sensors and everything, you can start tracking their behaviors and their patterns. When they're taking medicine and those kinds of things, and have all the information created through profiles of those people. You have now distributed the power of taking care of all the constituents in your base, without having to have them physically be in a hospital.

Gardner: Those feedback loops are not just one way where you can analyze, but you can start to apply the results, the benefits of the analysis, right back into the edge.

Carlat: Health and life sciences are great examples of using IoT as a way of more efficiently managing the hospital beds. It costs a lot of money to have people sit in a hospital when they don't need to be there. To be able provide patient access remotely, to be able monitor them, to be able to intervene on an as-needed basis, drives much greater efficiencies.

We’ve talked a little bit about industrial IoT, we’ve talked a little bit about health and life sciences, but this extends into retail and smart stores, too. We're doing a lot with Home Depot to deliver the store of the future, bridging the digital with the brick-and-mortar across 2,200 stores in North America.

It also has to do with the experience around campus and branch networks. At Levi’s Stadium in Santa Clara, California, HPE built that out with indoor Global Positioning System (GPS) and built out a mobile app that allows indoor wayfinding. It allows the patrons visiting the game to have a totally new, immersive experience.

They found uploads and downloads of photos, and they found hotspots by mapping out in the stadium. The hotspots had a great unobstructed view of the field, so there were a lot of people there taking pictures. They installed a food stand nearby and they have increased revenues because of strategic placement based on this IoT data. Levi’s Stadium recognized $1 million in additional revenue in the first season and 10 times the growth in the number of contacts that they have in their repository now.

Gardner: So, it's safe to say that edge computing and intelligence is a gift that will keep giving, at levels organizations haven’t even conceived of yet.

Carlat: I believe it’s a necessity to stay competitive in the world of tomorrow.
How to Gain Business Insights
From the Intelligent IoT Edge
Gardner: If your competitor does this, and you don't, that’s going to be a big question mark for your customers to mull over.

While we are still on the subject of the edge technical capabilities, by being able to analyze and not just pass along data, it seems to me it's also a big help when it comes to compliance and security, which are big concerns.

Not only does security get mitigated by hardening or putting up a wall, probably the safest bet is to be able to analyze when something is breached or something is going wrong, and then to control or contain that. Tell me why the HPE Edgeline approach of analyzing data fast and on the edge can also be a big boost to security risk containment and other compliance issues.

Carlat: We do a lot around asset tracking. Typically, you need to send someone out there to remediate. By using Edgeline, using our sensor data, and using asset tagging, you can ensure that the right person can be identified as the service person physically at the pump to replace it, rather than just saying that they did it, writing on paper, and actually being off doing something else. You have security, you have the appropriate compliance levels with the right people fixing the right things in the right manner, and it's all traceable and trackable.

Halgali: When you begin using edge devices, as well as geolocation services, you have this ability to do fine-grained policy management and access control for not just the people, but also devices. The surface area for IoT is so huge there are many ad-hoc points into the network. By having a security layer, you can control that and edge devices certainly help with that.

A classic example would be if you have a camera in a certain place. The camera is taking constant feeds of things that are going on that are wrong or right; it’s constantly recording the data. But the algorithms that have been fed into the edge device allow it to capture things that are normal, so it can not only alert authorities at the right time, but also store feed only for that. Why store days and day’s worth of images, when you can pick only the ones that truly matter?

As Jeff said, it allows workplace restrictions and compliance, but also in an open area, it allows you to track events that are specific.

In other cases, let’s say the mining industry or the oil and gas industry, where you have workers that are going to be in very remote locations and it’s very hard to track each one of them. When you have the ability to track the assets over time, if things go wrong, then it’s easier to intervene and help out.

Carlat: Here is a great personal example. I went to my auto dealership and I pulled into the garage. Immediately, I was greeted at my door by name, “Hello Mr. Carlat. Are you in for your service?"

I thought, “How do you know I came in? Are you tracking me? How are you doing that?” It turns out, they have radio-frequency identification (RFID) tags. When you come in for service, they apply these tags. As soon as you pull in, they provide a personalized experience.

Also, it yields a net benefit of location tracking. They know exactly where my car is at all stages. If I moved to a rental car that they have there, my profile is automatically transferred over there. It starts their cycle time metrics, too, the traceability of how they're doing on remediating whatever my service level may be. It's a whole new experience. I'm now a lifetime-loyal customer of this auto dealer because of that personalization; it’s all coming from implementation of IoT.

Gardner: The implications are vast; whether it’s user experience, operational efficiency, risk reduction, or insights and analysis at different levels of an industry and even a company.

It's very impressive stuff, when you can measure everything and you can gather as much data as you want and then you can triage, and analyze that data and put the metadata out to the cloud; so much is possible.

We've established why this is of interest. Now, let’s think a little bit about how you get there for organizations that are thinking more about re-architecting their edge in order to avail themselves of some of these benefits. What is it about the HPE and Deloitte alliance that allows for a pathway to get on board and start doing things in a proper order to make this happen in the best fashion?

Transformation Journey, One Step at a Time

Halgali: Dana, anytime you do an IoT initiative, the key thing to realize that it should be part of a major digital transformation initiative. Like any other transformation story, there are the people, process, and the technology components of it. Jeff and I can talk about these three at a very high level when you begin talking about the process and the business model.
Deloitte has a huge practice in the strategy and the process space. What we're looking at is digital industrial value-chain transformation. Let’s look at something like a smart factory.
What’s the value chain for an organization that's making heavy machinery, end-to-end, all the way from R and D and planning, to procurement and development and shipment, and after-sale repairs, the entire value chain? What does that look like in the new IoT era? Then, decompose that into processes and use cases, and then identify which are the most high-value use cases, quantifying them, because that’s important.

Identifying the use cases that will deliver immediate tangible value in the near term provides the map of where to begin the IoT journey. If you can’t quantify concrete ROI, then what’s the point of investing? That addresses the reason of what IoT can do for the organization and why to leverage this capability. And then, it's about helping our clients build out the business cases, so that they can justify the investments needed from the shareholders and the board — and can start implementing.
At a very high level, what’s the transformation story? What's the impact on the business model for the organization? Once you have those strategy questions answered, then you get into the tactical aspects, which is how we execute on it.
From an execution standpoint, let’s look at enablement via technology. Once you have identified which use-cases to implement, you can utilize the pre-integrated, pre-configured IoT offerings that Deloitte and HPE have co-developed. These offerings address use cases such as asset monitoring and maintenance (in oil and gas, manufacturing, and smart cities), and intelligent spaces (in public venues such as malls, retail stores, and stadiums), and digital workplaces (in office buildings). One must also factor in organization, change and communication management as addressing cultural shifts as one of the most challenging aspects of an IoT-enabled digital transformation. Such a holistic approach helps our clients to think big, start small, and scale fast.

Gardner: Tushar just outlined a very nice on-ramp process. What about some places to go for information or calls for action? Where should people get started as they learn how to implement on the process that Tushar just described?
How to Gain Business Insights
From the Intelligent IoT Edge
Carlat: We're working as one with Deloitte to deliver these transformations. Customers with interest can come to either Deloitte or HPE. We at HPE have a strong group of technology services consultants who can step in and help in partnership with Deloitte as well.

So, come to either company. Any of our partner representatives can get all of this and our websites are loaded with information. We're here to help. We're here to hold the hand and lead our customers to digitize and achieve these promised efficiencies that can be garnered from digital value chains.