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

Cognizant

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Sponsored by Cognizant

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

Cognizant

Cognizant (Nasdaq: CTSH) is dedicated to helping the world’s leading companies build stronger businesses — helping them go from doing digital to being digital.

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Philips is all in when it comes to the IoT

Lights may be a familiar sight whether you’re at home or anything to say about it, lighting will soon do a lot more than just illuminate.

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Sponsored by Cognizant

Stop reading for a moment and look up. Did you see a light on the ceiling, or maybe one over on the wall nearby? Of course you did. Lighting is everywhere we go — and not just indoors. It comes in all shapes, sizes, colours, and brightnesses, but if Philips Electronics has anything to say about it, lighting will soon do a lot more than just illuminate.

The Dutch electronics multinational — a leading global supplier of lighting technology — has announced that the IoT isn’t just an important technology, it’s going to be central to the company’s overall strategy in the future. That IoT-based strategy can be most clearly seen within Philips’ lighting division, where it even has its own name: Interact.

Related: Stepping into Digital with IOT – 14 Cases

“You can imagine all these devices — lamps, drivers, luminaries, sensors — being connected, sending information through software,” Philips Lighting CEO, Eric Rondolat, told attendees at the Light+Building exhibition in Frankfurt, earlier this year. “And all this software sending this information back to a cloud-based platform, an IoT platform that is called Interact,” he said.

The Power of Occupancy Sensing

While some of data gathered by Philips lighting will be related to energy consumption and other operational parameters for the lights themselves, there’s a lot more smart lighting can do.

One big area that Philips and the rest of the lighting industry is eyeing is occupancy sensing. The global occupancy sensor market was worth USD$1.7 billion in 2017, according to Market Prognosis, and is projected to reach USD$4.8 billion by 2023. Those numbers are being driven largely by a North American push to increase energy efficiency. Being able to know when someone is in a space that requires lighting, or HVAC, can lead to significant savings. But that same data has other value too, and Philips plans to leverage machine learning to unearth hidden insights trapped in that data.

Download] Stepping into Digital with IOT – 14 Cases

If these moves weren’t proof enough that Philips Lighting is betting big on the IoT, consider this: The company just hired former Cisco senior vice president, IoT sales, Chris White, to lead its Americas division. Then there’s the name. Philips Lighting announced in March that the company would change the company name to as Signify — a name it hopes will shine a light on the company’s beyond-lighting IoT focus.

DX Journal covers the impact of digital transformation (DX) initiatives worldwide across multiple industries.

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Navigating the AI Hype

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Welcome to Navigating the AI Hype. This will be a timely article that curates events in AI to tabulate AI’s journey as this unprecedented phenomenon makes its way into our lives: The Good, the Bad and the Ugly. We will acknowledge successes in AI as well as those that still require further progress. We will also highlight areas where human conscience will need to dictate policy and regulation as ethical standards will be built in lockstep with technology as it evolves. Finally, we will highlight references and resources for anyone wanting to dive in further into Artificial Intelligence. Enjoy!

The Good:

Uber applies for permission to test self-driving cars again

“ We have taken a measured, phased approach to returning to on-road testing, starting first with manual driving in Pittsburgh. We committed to deliver this safety report before returning to on-road testing in self-driving mode, and will go back on the road only when we’ve implemented improved processes.

Read more.

 

LinkedIn founder Reid Hoffman makes record-breaking gift to U of T’s Faculty of Information for chair in AI

“Artificial intelligence will revolutionize how we live, creating both incredible opportunity for benefits, as well as some disruption that will be important to manage,”

Read more.

MIT is investing $1 billion in an AI college

“Interdisciplinary learning should mean better, saner Artificial Intelligence”

Read more.

The Bad:

The future of border control agents might come in the form of an AI lie detector

A six-month trial will take place at four border crossing points in Hungary, Greece and Latvia.

 

Read more.

What to know about WhatsApp in Brazil ahead of Sunday’s election

“I don’t know where they found my phone number.”.

Read more.

Google wants to improve your smart home with iRobot’s room maps

The idea of Google using data about users’ home will be justifiably unsettling to some. Although Google doesn’t have as bad of a reputation for data leaks and breaches as Facebook, it’s still had a number of serious lapses.

Read more.

The Ugly:

Australia’s data breach numbers steady at 245 in three months

“Everyone who handles personal information in their work needs to understand how data breaches can occur so we can work together to prevent them”

Read more.

Radisson Hotel Group suffers data breach, customer info leaked

Radisson Hotel Group loyalty scheme members are affected and may have had their personal information stolen.

Read more.

China has been ‘hijacking the vital internet backbone of western countries’

“Using these numerous PoPs, [China Telecom] has already relatively seamlessly hijacked the domestic US and cross-US traffic and redirected it to China over days, weeks, and months”

Read more.

AI courses and resources

Machine Learning AI Certification by Stanford University (Coursera)

Artificial Intelligence Certification: Learn How To Build An AI (Udemy)

The week in breaches – Newsletter 

 

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Canadians up in arms: Privacy without consent and the dangerous precedent

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Canada data concept, DepositPhotos
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It’s the news that has taken Canada by storm of late, on Twitter, in the headlines, and in today’s parliamentary debate: Statistics Canada, Canada’s agency which issues statistical research on the state of Canada, its population, the economy and culture, unwittingly walked into the spotlight when Global News revealed the agency had asked TransUnion, a credit bureau that amasses credit information for many financial institutions to provide financial transactions and credit histories on approximately 500,000 Canadians, without their individual prior consent. The Liberal government has endorsed this move.

During the parliamentary debate, Conservative opposition Gérard Deltell declared,

If the state has no business in people’s bedrooms, the state has no business in their bank accounts either. There is no place for this kind of intrusion in Canada. Why are the Liberals defending the [Statistics Canada] indefensible? 

The data being demanded, according to Global News, consists of private information including name, address, date of birth, SIN, account balances, debit and credit transactions, mortgage payments, e-transfers, overdue amounts, and biggest debts on 15 years worth of data. Equifax, the other credit reporting agency that supports financial institutions in Canada has not been asked to provide data.

Francois-Philippe Champagne, Minister of Infrastructure and Communities was vague in his response. While he affirms StatsCanada’s upstanding practices in anonymizing and protecting personal data, he also admitted proper consent was not received,

StatsCan is going above the law and is asking banks to notify clients of this use. Stats Canada is on their side… We know data is a good place to start to make policy decisions in this country, and we will treat the information in accordance with the law. They can trust Statistics Canada to do the right thing.

Statistics Canada and the Liberal government failed to disclose the explicit use of this information, however,

By law, the agency can ask for any information it wants from any source.

I posed this question to former 3-term Privacy Commissioner, Ann Cavoukian, who currently leads the Privacy by Design Practice at Ryerson University, Toronto:

Ann Cavoukian Twitter

Ann Cavoukian Twitter

What’s troubling is that while the opposition cried foul, lashing out accusations of authoritarianism and surveillance, the latter outcome is not implausible.

According to Personal Information Protection and Electronic Documents Act (PIPEDA) Guidelines to Obtain Meaningful Consent, these are the main exceptions

  • if the collection and use are clearly in the interests of the individual and consent cannot be obtained in a timely manner;
  • if the collection and use with consent would compromise the availability or the accuracy of the information and the collection is reasonable for purposes related to investigating a breach of an agreement or a contravention of the laws of Canada or a province;
  • if disclosure is required to comply with a subpoena, warrant, court order, or rules of the court relating to the production of records;
  • if the disclosure is made to another organization and is reasonable for the purposes of investigating a breach of an agreement or a contravention of the laws of Canada or a province that has been, is being or is about to be committed and it is reasonable to expect that disclosure with the knowledge or consent of the individual would compromise the investigation;
  • if the disclosure is made to another organization and is reasonable for the purposes of detecting or suppressing fraud or of preventing fraud that is likely to be committed and it is reasonable to expect that the disclosure with the knowledge or consent of the individual would compromise the ability to prevent, detect or suppress the fraud;
  • if required by law.

For Statistics Canada, its broad legal reach is enough for the agency to circumvent explicit disclosure of data use and permission. This alone sets a dangerous precedent that wrestles with current European GDPR mandates, which will be referenced in the updated PIPEDA Act, at a time yet to be determined.

However, this privilege will not make StatsCanada immune to data breaches, but in fact, will make it a stronger target for data hackers. According to the Breach Level Index, since 2013 there have been 13+ billion records lost or stolen, with an average of 6.3+ million lost on a daily basis. The increasing centralization of data makes this more likely. For Statistics Canada, which has been collecting tax filings, census data, location, household, demographic, usage, health and economic data, it is increasingly amassing its data online. According to National Newswatch, the dwindling survey completions and costly census programs have necessitated a move to compile information from other organizations such as financial institutions, which come at more reasonable costs and better data quality.

If this is the catalyst to aggregate compiled information, with the goal of record linking, it will unearth significant privacy alarms in the process. For StatsCanada, which has received significant government support because of the critical information it lends to policy decisions, there are looming dangers of being the purveyor of every Canadian’s private information, beyond data breach vulnerabilities.

Anonymized Data Doesn’t Mean Anonymous Forever

I spoke to Alejandro Saucedo, the Chief Scientist at The Institute for Ethical AI & Machine Learning, a UK-based research center that develops industry standards and frameworks for responsible machine learning development and asked him to weigh in on this issue:

Canadians are rightly worried. It concerns me that StatsCanada is suggesting that just discarding names and addresses would be enough to anonymize the data. Not to point out the obvious, but data re-identification is actually a big problem. There have been countless cases where anonymized datasets have been reverse engineered, let alone datasets as rich as this one. 

Re-identification is used to reverse-engineer the anonymity data state and uses alternative data sources to link information to identity. Using publicly available data, easily found in today’s BigData environment, coupled with the speed of advanced algorithms, Saucedo points to successful attempts of re-identification: reverse engineering credit card data, or when this engineer was able to create a complete NYC taxis data dump of 173 million trips and fare logs by decoding the cryptographically secure hashing function that anonymized the medallion and taxi number.

Ethical hacks are not new to banking or any company that collects and manages significant data volumes. These are intentional hacks propagated internally and intentionally by corporations against their existing infrastructure to ensure mitigation of vulnerabilities on-premise and online. This practice ensures the organization is up to par with the latest methods for encryption and security as well as current breach mechanisms. As Saucedo points out:

Even if StatsCanada didn’t get access to people’s names (e.g. requested the data previously aggregated), it concerns me that there is no mention of more advanced methods for anonymization. Differential Privacy, for example, is a technique that adds statistical noise to the entire dataset, protecting users whilst still allowing for high-level analysis. Some tech companies have been exploring different techniques to improve privacy – governments should have a much more active role in this space.

Both Apple and Uber are incorporating Differential Privacy. The goal is to mine and analyze usage patterns without compromising individual privacy. Since the behavioral patterns are more meaningful to the analysis, a “mathematical noise” is added to conceal identity. This is important as more data is collected to establish these patterns. This is not a perfect methodology but for Apple and Uber, they are making momentous strides in ensuring individual privacy is the backbone of their data collection practices

Legislation Needs to be Synchronous with Technology

GDPR is nascent. Its laws will evolve as technology surfaces other invasive harms. Government is lagging behind technology. Any legislation that does not enforce fines for significant breaches in the case of Google Plus, Facebook or Equifax will certainly ensure business and government maintain the status quo.

Challenges of communicating the new order of data ownership will continue to be an uphill battle in the foreseeable future. Systems, standards and significant investment into transforming policy and structure will take time. For Statistics Canada and the Canadian government, creating frameworks that give individuals unequivocal control of their data require education, training, and widespread awareness. Saucedo concedes,

 A lot of great thinkers are pushing for this, but for this to work we need the legal and technological infrastructure to support it. Given the conflict of interest that the private sector often may face in this area, this is something that the public sector will have to push. I do have to give huge credit to the European Union for taking the first step with GDPR – although far from perfect, it is still a step in the right direction for privacy protection.

 (Update) As of Friday, November 1, 2018, this Petition E-192 (Privacy and Data Protection) was put forward to the House of Commons calling for the revocation of this initiative. 21,000 signatures have been collected to date. Canadians interested in adding their names to this petition can do so.
Petition to the House of Commons
Whereas:
  • The government plans to allow Statistics Canada to gather transactional level personal banking information of 500,000 Canadians without their knowledge or consent;
  • Canadians’ personal financial and banking information belongs to them, not to the government;
  • Canadians have a right to privacy and to know and consent to when their financial and banking information is being accessed and for what purpose;
  • Media reports highlight that this banking information is being collected for the purposes of developing “a new institutional personal information bank”; and
  • This is a gross intrusion into Canadians’ personal and private lives.
We, the undersigned, Citizens and Residents of Canada, call upon the Government of Canada to immediately cancel this initiative which amounts of a gross invasion of privacy and ensure such requests for personal data never happen again.

This post first appeared on Forbes.

Hessie Jones is the Founder of ArCompany advocating AI readiness, education and the ethical distribution of AI. She is also Cofounder of Salsa AI, distributing AI to the masses. As a seasoned digital strategist, author, tech geek and data junkie, she has spent the last 18 years on the internet at Yahoo!, Aegis Media, CIBC, and Citi, as well as tech startups including Cerebri, OverlayTV and Jugnoo. Hessie saw things change rapidly when search and social started to change the game for advertising and decided to figure out the way new market dynamics would change corporate environments forever: in process, in culture and in mindset. She launched her own business, ArCompany in social intelligence, and now, AI readiness. Through the weekly think tank discussions her team curated, she surfaced the generational divide in this changing technology landscape across a multitude of topics. Hessie is also a regular contributor to Towards Data Science on Medium and Cognitive World publications.

This article solely represents my views and in no way reflects those of DXJournal. Please feel free to contact me h.jones@arcompany.co

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