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Looking to deliver IoT value? Don’t go it alone

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By: John Gonsalves

According to Gartner, more than $440 billion will be spent on Internet of Things initiatives by 2020. Yet networking company Cisco recently found that only 26% of organizations so far have completed an IoT initiative they consider a success. That percentage isn’t pretty. And companies don’t have much time to climb a steep learning curve and thus avoid costly failures.

So what is preventing companies from getting IoT right? Much of the challenge derives from focusing on implementing technologies without a comprehensive, holistic plan. Sensors? Smart devices? Data analytics? Cloud-based processing? All necessary and helpful. But how will they operate together? How will they collectively transform data into usable information – delivered in time to act in real time? That takes thought. And the thinking must be in the C-suite.

Related:  How manufacturers can unlock value with IOT analytics

Today’s IoT industry is maturing from stand-alone point applications to integrated systems that capture and process data to derive business insights and even quality of life outcomes. Smart parking solutions, for example, now leverage IoT to analyze parking inventory and guide drivers to available spaces, an outcome with great potential value projected to save hours, miles and even gallons of gas.

A more complex IoT “system of systems,” however, might integrate that parking platform with other IoT-enabled systems, exchanging data that leads to actionable insights. An owner/operator of parking structures could then optimize lighting use in different spaces, control climate for peak and off-peak times and temperatures, assess risks and security needs, respond to staffing shortfalls quickly, and even adjust prices for a particular facility based on volume, previous usage patterns, scheduled local events and consumers’ real-time needs.

An optimal pace to value

Companies have been slow to take their IoT solutions to this next level because they face genuine challenges. How to quantify the potential value to justify the investment? How to stay ahead of security risks? How to understand and leverage multiple complex technologies? Or identify physical installation and support services? Or aggregate, synthesize, store, analyze, act on and monetize their data?

To many, the feasibility of bringing partners and service providers together to create an end-to-end ecosystem is not yet clear. There is the pressure to anticipate integration points and ensure components and people work together. as well as understand and plan for the potential disruptive impact on the organization, customers and business models.

[Download]:  How manufacturers can unlock value with IOT analytics

Nevertheless, developing an integrated IoT constellation of devices, sensors, actuators, gateways and network and cloud service providers, a new breed of IoT platform providers, along with analytics, specialty visualization and integration with enterprise applications – all designed to work together – is the best way to optimize results.

The IoT Value Chain

Components of the IoT value chain should include:

  • Data capture. Data originates from sensors, actuators, controllers, devices and hardware in the field, but companies quickly learn that implementing a transformational IoT solution involves far more than adding sensors to their packaging, products and the machines that manufacture, deliver and service them. Sensors must collect data spatially (i.e., throughout a space) and temporally (i.e., across time), and be instrumented in such a way that vital signs can be signaled, collected and analyzed for downstream action. Such massive amounts of data must be cloud- and network-agnostic to ensure compatibility with various platforms.
  • Data communication and storage. To maximize their value, cloud and network services platforms need to deliver operational insights by working with devices at the edge – that is, where the data is gathered. They must be multilingual (able to talk to any sensors using any communications protocol) and hardware agnostic, so that data can be integrated, synthesized and stored, making it available for future context-aware analysis.
  • Data analysis and insights. Making sense of vast amounts of data and taking action is key to unleashing the power of IoT. Predictive analytics providers identify patterns, network effects or anomalies so undesirable outcomes can be anticipated and prevented. Such analytics prompt prescriptive action: Companies can course-correct on the fly – replacing a part or stopping an engine from overheating, for example – to optimize operational efficiency through improved asset performance and employee productivity. World-class algorithms must be domain-sensitive and industry-aware so they can be meaningfully applied to unique use cases; advanced visualization of data may be required in certain situations.
  • Infrastructure & security. Beyond cloud and network service providers, IoT solutions at scale need end-to-end security from device to edge to the cloud (and the apps), distributed device management and distributed data management.
  • Coordination between IoT components and systems of engagement. Systems of engagement may vary from mobile devices to advanced augmented/virtual reality (AR/VR), mixed-reality or other user interfaces. The focus of IoT solutions is now migrating beyond operations optimization to enable new business models (e.g., pay per use), imagine new products and services (e.g., software-based services), monetize data, etc. Indeed, the opportunities are massive as we develop and benefit from “system of systems.”
  • Partners. The variety and range of technologies for the IoT is enormous, yet still immature. Each participant would do well to understand its role in the IoT ecosystem and get to know adjacent players to partner with to deliver smart, connected IoT solutions at scale. Scale implementations need physical deployment of sensors (and device instrumentation), the operations technology and the digital value chain. And they require a skilled system integrator that also knows the associated IT and heritage systems, to develop and orchestrate the combined ecosystem and, thereby, realize business value.

How to get started

A good systems integrator will start by asking the question, “What business problem do you want to solve?” From there, the company can help to create the business case for an IoT solution by articulating its value, be it cost reduction, revenue enhancement, asset optimization, customer experience transformation or increased safety.

Then it’s on to the heart of the engagement: defining new operational processes, recommending IoT-enabling information and operational technologies, identifying and classifying the right ecosystem players in the IoT value chain, and assembling and integrating them based on who plays best where.

[Download]:  How manufacturers can unlock value with IOT analytics

Challenges certainly remain, but the opportunities for new revenue streams, increased operational efficiency and improved customer engagement have never been greater for companies that leverage end-to-end IoT solutions in consumer, commercial or industrial settings.

This article originally appeared on the Digitally Cognizant Blog

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Retail

IoT + Data = Retail Intelligence

In the equation IoT + X = Intelligence, what role can consumer and supply chain data play as the X factor?

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Valued at USD $10 billion in 2017, the retail segment of the Internet of Things (IoT) market is expected to grow at a phenomenal 19% compounded annual rate and hit $35 billion in 2024. New ways of collecting data at the source are enabling this growth. IoT-embedded sensors on shelves and in refrigerators; store beacons that can sense and measure foot traffic; RFID tags on clothes and smartphones with Bluetooth technology are all collaborators in this dance to log and analyze data. Artificial intelligence can then analyze the sheer volumes of numbers generated and give retailers intelligence to increase efficiencies and sales.

The promise of IoT is that it can enable retailers to improve backend supply chain operations and the customer service experience. The following examples illustrate use cases of both.

Frictionless shopping

Amazon Go is a test case for effective use of RFID tags and store beacons to bypass the checkout process altogether. Every item on the shelves has an RFID tag and when the customer walks out of the store, the products he or she leaves with are scanned and billed to the corresponding Amazon account. The IoT at play here delivers more than a seamless customer experience: it also gives the retailer live status updates about inventory, intelligence that can be relayed up and down the supply chain.

An eye on perishables

IoT-embedded sensors in refrigerators can predict when the machine might be about to malfunction based on current temperature and humidity profiles. A similar IoT-driven system used in warehouses alerts vendors about potential spoilage and can prevent waste. While the edge use case of IoT in driving alerts in real-time is an important one, retailers can also extract long-term intelligence about inventory, store traffic and more simply by reading the data and looking for the corresponding patterns.

Interactive shopping experience

At a time when the drumbeats about the demise of brick-and-mortar stores are growing louder, IoT is injecting some much needed theatre into the customer service experience. Digital mirrors in fitting rooms read RFID tags on the garments customers bring in, pull up those items on the mirror and suggest complementary accessories. Customers can also push a button to request the outfits in a different size or colour. 

If a customer has signed on for notifications from a store, in-store beacons through the customer’s Bluetooth can deliver custom product recommendations through push notifications. Such live interactions increase the value of in-person shopping while also delivering intelligence about shopper behaviour.

While IoT dramatically improves backend efficiencies, the customer-retailer interaction can be much more complicated because of data privacy laws. Customers need to willingly opt in to receive notifications and trade data for the value that retailers deliver. 

IoT is already delivering valuable intelligence to retailers. A major grocery store, for example, saved millions by outfitting in-store refrigeration systems with IoT sensors. As the cost-value ratio of IoT devices decreases, expect retailers to leverage the power of IoT even more to deliver crucial intelligence about customer shopping behaviour and increase transparency in the supply chain.

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What challenges face IT leaders in 2020?

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With 2020 underway, digital transformation is still very much a focus for business leaders — but what about the processes being used to hit those targets? According to Stephanie Overby at The Enterprise Project, DX preparation is ongoing, but full culture change is on the horizon for 2020. 

Digital is certainly still a priority with respect to funding, but a recent Gartner report shows that two-thirds of companies not only fail to deliver on their promises but also reveal “enterprise weaknesses, causing organizations to see a gap between expectations and results.”

So what’s holding companies back? For CIO.com, journalist Paul Heltzel outlined the top nine challenges that IT leaders will face in 2020:

The gig economy

It’s hardly a secret that both the gig economy and telecommuting are exploding. With that comes the issue of data and IP security. While the advantages of distributed teams include flexibility and quick-pivoting, the aforementioned Gartner report warns that “A growing remote workforce, in both a work-from-home and co-workspace model will unintentionally expose the organization to vulnerabilities in data privacy and the security of confidential information.” Another gig economy concern? Finding the right talent.

Data privacy

The specific requirements of the GDPR and the California Consumer Privacy Act (CCPA) need to be addressed by various segments within organizations, at the risk of stiff penalties. Innovative vendors will continue working on unique solutions and features to meet these needs. 

The ROI of new technology

Advanced technologies like AI and automation need to “weigh the benefits of innovation with provable benefits to the business rather than simply adopting trending technology,” explains Mahi Inampudi, CTO and CPO at Envoy Global. “It’s about finding the right business case.”

Security

According to Jake Olcott, vice president of security ratings for BitSight, “Zero-day vulnerabilities receive the most attention from the media, but in 2020, hackers won’t bother with these highly publicized attacks.” Instead, simple strategies will be at work, such as gaining access to a network through a vendor. 

Another concern is the rise on ransomware, with some experts suggesting organizations will need to create a new role entirely, dedicated to combating this new cybersecurity threat.    

Risk management (and expectations)

“Businesses and customers now expect software and solutions to have rapid releases that adapt over time, similar to consumer technology,” explains Matt Mead, CTO of SPR. “CIOs need to manage all IT projects in a way that mitigates risk. Start by making sure projects are using a modern agile approach and place all high-risk activities early in a project’s life cycle.”

Skills gap

According to John Ferron, CEO at Resolve Systems, the skills gap in IT will cause organizations to look to automation for solutions. “As we look to 2020, IT teams should expect to see increasing focus on intelligent automation and AIOps to help them truly do more with less by automating repetitive tasks and processes and enabling each IT pro to manage increasingly more infrastructure on a per-person basis.” 

Upskilling

Technologies evolve quickly, and as a result, developing new skills can be a challenge. A culture of learning and development can help improve retention.  

“Cloud whiplash”

“As more and more organizations begin to adopt the hybrid cloud, we’ll eventually see a trend of cloud repatriation,” Adrian Moir, lead technology evangelist at Quest Software says, “which is what happens when companies don’t take the time to invest properly in migrating to the cloud. The best solution? Companies should analyze the data and workloads before moving to the cloud, to determine costs and potential service impacts involved, explains Moir. 

Culture change

More important than a reliance on technology, with respect to digital transformation? A change of mindset within the organization. “In the coming year, business leaders will need to understand that the digital transformation doesn’t end but instead becomes part of how business leaders solve challenges,” says Geoff Webb, vice president of strategy at software company PROS

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The 2020 outlook for artificial intelligence

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While 9 out of 10 respondents to the 2019 MIT Sloan Management Review and Boston Consulting Group (BCG) Artificial Intelligence Global Executive Study and Research Report agree that AI represents a business opportunity for their company, fewer than 2 out of 5 report business gains from AI in the past three years.

According to the report, “early AI winners are focused on organization-wide alignment, investment, and integration.”

Forrester’s AI predictions for 2020 focus on this being the year when “companies become laser-focused on AI value, leap out of experimentation mode, and ground themselves in reality to accelerate adoption,” explains VP, Research Director Srividya Sridharan.

As we look to the year ahead, “CIOs will need to better assess the value of their AI bets and prove that ROI to the business,” explains TetraVX Director of Product Management Kara Longo Korte, to business and tech reporter Stephanie Overby in The Enterprisers Project.

And while this promises to be an active year for AI investment, Overby outlines the 10 biggest AI trends to watch for 2020:

Measuring AI impact

As mentioned above, fewer than two in five companies report business gains from AI in the last three years. But as AI investment increases, this needs to change — and it can be done by altering how we measure results. “Think reporting against things like ease of use, improved processes, and customer satisfaction,” writes Overby.

Think Operationalization

“This year will be a tipping point for the infrastructure needed to support effective deployments, providing integrated learning environments and data ecosystems that support adaptive decision making by AI,” says Jean-François Gagné, CEO and co-founder of software provider Element AI.

Data pipelines

“Next year, the luster of AI and ML will wear off as companies realize it’s not magic, but math,” explains Pat Ryan, executive VP of enterprise architecture at SPR. With high-quality data as a foundation for AI/ML, 2020 will see a “heightened sense of appreciation and need” for everything-data — governance, analysts, engineers, and ML engineers — with a goal of creating a pipeline for continuous data that’ll drive more successful AI projects.

AI innovators in high demand

At 74% annual growth, AI Specialist is #1 on LinkedIn’s top 15 emerging jobs for the US in 2020. “[AI and ML] have both become synonymous with innovation, and our data shows that’s more than just buzz,” says the report.

Data modeling moves to the edge

As Overby explains, “expect a shift from cloud-only to cloud-edge hybrid strategies to enable machine learning (ML) in the next year.” Forrester is predicting that edge cloud service market will grow by at least 50 percent in 2020. “By implementing edge-first solutions, organizations can synthesize data locally, identify machine learning inferences on core raw data sets, and deliver enhanced predictive capabilities,” says Senthil Kumar, VP, Software Engineering for FogHorn.

The B2B benefits of AI

“Machine and deep learning are making it possible for users of complex B2B services to define and match complex requirements to ideal trading partners through an intuitive, needs-identification process and a vast understanding of potential trading partner strengths and capabilities,” says Keith Hausmann, chief revenue officer at Globality.

Human and machine work together

AI can work as a complement to contact service centre agents and teams, providing better/more timely informed responses. The challenge? “It’s important that organizations keep their customer service experiences human,” minimizing a potentially ‘too automated’ look. (The question can then be asked: When will standalone conversational AI emerge?)

Hyperautomation

One of Gartner’s top 10 strategic technology trends for 2020? Hyperautomation — i.e. “The application of advanced technologies like AI and ML to automate processes and augment humans across a range of tools and at a higher level of sophistication.” The goal? “More AI-driven decision-making,” explains Gartner.

Heterogenous architectures will emerge

“Today, AI-enabled applications and networks rely on different processing architectures,” writes Overby. But according to ABI Research’s 54 Technology Trends to Watch, that’ll change in 2020. “AI and ML frameworks will be multimodal by their nature and may require heterogeneous computing resources for their operations.”

Mistakes happen

AI is, of course, not perfect. As a final prediction, it isn’t hard to imagine that high-profile mistakes can be anticipated in 2020, but overall trust in AI will not erode. From deep fakes to the misuse of facial recognition, AI has the potential to perpetuate discrimination and cause harm, offence, and general uneasiness. Ultimately? It comes down to the importance of responsible use.

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