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Combining mixed data – unlocking the real value of IoT

Most companies are at a design and test phase in terms of Industrial Internet solutions. Integration into larger, complex systems remains somewhere on the horizon. The full potential of the gathered data will only be truly realized once comprehensive integration into these complex systems becomes a prominent trend. Niall O’Doherty, Director of Business Development Emerging Industries Team at Teradata Corporation, hopes that within five years, the technology necessary for such integration will be commonplace. The question then becomes — will corporate philosophies match the capabilities of these technologies?

Data environments are being inherently redefined due to developments across IoT and IIoT. To do away with detached data “pockets” – which is to say, with data that remains unintegrated into systems or with other data – an overall process of synthesis is necessary. Key to such a synthesis, and subsequent realization of the true value of IoT and IIoT, will be the integration of the already widespread use of sensor data.

“To get to the real transformational value, more of these systems must be put into place. In order for that to happen, sensor data needs to be integrated with product data, customer data, ERP (Enterprise Resource Planning) data and other traditional data. For many organizations, bringing sensor data together with traditional data – and making sense of it all – is still a major challenge,” states Niall O’Doherty.

“I hope that in the next five years we will be able to regard sensor data connected to communications infrastructure as a common feature of business,” he continues.

The increasing flow and current of data across organizations and systems naturally raises pertinent questions about data ownership. The fact that once data enters ecosystems, no single organization, agency or equipment manufacturer is going to have exclusive control of the data and its distribution, casts doubts over the approach of companies and – according to O’Doherty – over the attitudes of individuals.

“Are people going to be willing to share all this information? Are they going to be willing to take the output of their particular optimized process, and put it into the input of another, so that we can build a better understanding of what’s going on in a complex manufacturing environment? I think that a lot these commercial and cultural issues will need to be resolved, otherwise they can really trip up organizations.”

“I hope that in the next five years we will be able to regard sensor data connected to communications infrastructure as a common feature of business.”

Making sense of sensors

With the capacity to extract data from vast processes becoming more prominent, complex analytics must process data in ways that allow for more than simply deciphering averages and statistics. According to O’Doherty, this is particularly imperative for industrial and manufacturing companies.

“With the volumes that sensor data is generating, especially in the industrial world, coupled with the complexity of analytics, you really need to bring the analytics and algorithms to the data. In order to do that, you need a scalable IoT platform.”

In the material handling industry, such a platform could facilitate anything from predictive analytics to looking at how employees move on a factory floor, thus optimizing operations accordingly. O’Doherty uses the enhanced oil industry as an example. By putting highly instrumented equipment on rigs and sensors on the ocean-floor, the Industrial Internet has greatly aided in efficiency and optimizations of complex processes and systems. “What’s innovative for them is how they are now using vast amounts of data to understand the subsurface a lot better,” O’Doherty says.

Same products, new services

For O’Doherty, the creation of new business models via sensor data is not necessarily at the crux of Industrial Internet developments. Instead, he sees business models created for existing products as reaping the benefits of the Industrial Internet in the future.

“I see the power of sensor data and the Industrial Internet in allowing organizations to implement a scale for different business models. Those models may already exist, but as a result of this new data, they can be made more profitable and customer-oriented. It’s about understanding and mitigating risk so that you can potentially implement multiple models for the same products: to different markets, companies or customers.” This also increases the likelihood of new services emerging indirectly from existing products.

The notion of selling services, as opposed to products, is a concrete example of how the evolution of the Industrial Internet allows for the modification of, or experimentation with, existing business models. “For example, the notion of Power by the Hour – meaning a company won’t sell their customers an engine or a train, but instead the power needed to run them – was in fact coined in the 1960s by Bristol Siddeley. So, it’s not necessarily a new business model,” O’Doherty notes. Interestingly, later that decade Bristol Siddeley was bought out by Rolls Royce, currently one of the forerunners in embracing the Industrial Internet.

In order for the rest of the manufacturing world to keep up with the likes of Rolls Royce, O’Doherty reminds CIOs and CEOs of their roles as “enablers,” who first and foremost allow for businesses to change the way they approach products and services in general. “My advice – to a CIO in particular – would be to ensure you build the right infrastructure and environment to allow people in your company to access the data they need, and add the analysis they want,” O’Doherty concludes.

Niall O’Doherty works as Director of Business Development Emerging Industries Team at Teradata Corporation

Image credit: alphaspirit / Shutterstock.com

Interview w/ Niall O’Doherty

2 Comments

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  • Marko Yli-Pietila 06.07.2017 12:57

    It seems that companies are willing to share their data if they see the sharing valuable to them. One good example of successful data exchange is Braincube, https://braincube.com/.

  • Mohamed Sharaf 02.06.2017 17:15

    Are people going to be willing to share all this information? ???

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An IoT platform for material handling

The IoT is all about using sensor data to make better decisions. For decades, warehouse management systems have relied on scans from barcode scanners to confirm floor level activities have occurred correctly. In the last ten years, other automatic identification technologies have achieved the same types of process reliability.

According to an article in Forbes, material handling systems are a natural generator of sensor data. Contributor Steve Banker cites SensorThink, a digital platform for the connected warehouse. Making its debut at ProMat, the leading material handling conference in North America, this platform includes a warehouse control system, a digital platform for capturing IoT data, and Cloud analytics for analyzing the data.

“The digital platform collects the IoT data, cleanses it and harmonizes it. The data can come from material handling systems, lift truck sensors, building automation systems – which control the temperature and humidity of buildings, and security systems,” Banker writes. SensorThink compresses massive amounts of data by only collecting change of state data.

Read more about new potential for analytics and optimization in warehousing at: https://www.forbes.com/sites/stevebanker/2017/04/05/an-iot-platform-for-material-handling/#77581e9b1182

Image credit: zhu difeng/ Shutterstock.com

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Learning by doing and the value of pivoting in an IIoT environment

Data-driven technologies and business models are becoming increasingly linked. For many companies, this means that the skills and assets they are today investing in bear little relation to those they sought five years ago, and neither do the services they offer. Mika Parikka, Managing Consultant at LINK Design and Development Oy, holds that the Industrial Internet of Things plays a central role in this redefinition process, as it is fundamentally affecting how businesses are being run and what kind of talent is deemed necessary.

According to Mika Parikka, the Industrial Internet has introduced a distinct shift in business models from times past, when often what companies sold was technology or machinery itself. Parikka is the former CEO of TreLab, an electrical manufacturing company whose activities include retrofitting wireless smart sensor solutions for industrial equipment.

“The question of IIoT must be understood as not being primarily about technology, but instead about positioning and differentiation. After you understand how the machinery is being used, you can begin to see how the business is, or should be, run,” Parikka says.

He doesn’t see the Industrial Internet as being completely defined by technology, although it determines the types of data companies will be able to offer their customers. Herein lies the crux to his argument; customers will essentially come to define the businesses which serve them. Parikka maintains that for companies to reap the benefits of IIoT and IoT, mentalities need to shift form ‘we are only selling hardware,’ to ‘we are selling services that benefit our customers.’

“This affects who the products and services are being marketed to, as well as how contracts between parties are being drawn up,” Parikka says. “At TreLab we provided our customers with technology which they could utilize in IoT and IIoT scenarios. We began seeing the kinds of problems that customers were having in selling and conceptualizing their services as they began telling us that their businesses were fundamentally changing, and that they are no longer billing their customers for what they used to.”

This is not a surprise when considering the types of customer relations that the Industrial Internet is spawning. “Previous business models were largely about selling machinery and technology to customers, whereas in the future, they will largely be defined by customers wanting total solutions. Thus, the most significant change that will occur is that sales, customer services and most notably contracts, will need to be done from the perspective of customer value, as opposed to selling things in the traditional sense.”

“The question of IIoT must be understood as not being primarily about technology, but instead about positioning and differentiation. After you understand how the machinery is being used, you can begin to see how the business is, or should be, run.”

Trial and error yields the best results

Parikka cites Kati Hagros, the Chief Digital Officer at Aalto University and former CIO of KONE, as someone who often speaks about the need for companies to learn from their customers.

“She often urges companies to go with the customer and try things out. Then after these tryouts, once the business model begins to take shape, to fund it lavishly so that is has the ability to overcome corporate inertia, which tends to stifle ideas that appear radical at first,” Parikka relates.

The willingness to engage in these tryouts necessitates a significant internal philosophical shift in companies, most of all from the people at top management level. However, Parikka feels that a mentality shift shouldn’t be seen as totally restructuring what a company has been doing or selling up until that point. This is particularly true for larger organizations who have the resources for testing and experimentation, as well as for setting up smaller units that have more autonomy in terms of serving the customer.

“I believe that those at top management level must start acting more like venture capitalists. Potential solutions to customer needs exist, particularly among those at the frontlines of organizations who understand and see both problems and solutions. The role of top management is to then recognize and understand which of the possible solutions to fund to put the company in the best possible position, and to allow the frontline people lead the change,” Parikka says

He asserts that this is particularly true for industrial organizations who manufacture and produce material products. “Especially for organizations that are now moving into providing anything as a service, the leadership need to clearly lead the way, saying: ‘this is important in our business, we need to do this and above all else try that.’ It is important to prioritize what seems to be the best option, going for it, and changing direction ­– or pivoting as they say in the start-up world – if it doesn’t work. Learning by doing is definitely the way to go.”

The birth of the knowledge worker

After successful learning and experimentation has taken place, the emerging business models will set new demands upon the workforce, breathing new life into them. Drawing on the topic of companies requiring new expertise and new talent, Parikka highlights how knowledge workers will play an essential role from an operational standpoint.

“Most of the work will be about defining the procedures and algorithms, when there are a lot of sensors and subsequent data coming in from different sources. A knowledge worker will need to be able to analyze vast quantities of data, and really understand where all the data comes from. On top of this, the worker will need to create ways to deduce action-oriented alerts from the data. In most cases this is not what we would call programming, but instead, the type of work we would be doing every day in Excel – creating rules and procedures to act on.”

Parikka, who is an engineer by trade, holds that this workforce will be increasingly made up of people with a technological prowess, who will then take the lead when it comes to business initiatives. He concurs with Bill Gates’ statement that ‘it’s very difficult to teach technology to business people, and thus much easier to teach business to technology people.’ Approaching business from a technological mindset will mean that issues such as IIoT data ownership will be prioritized and dealt with early on in contracts signed between service or data providers and their customers. For tech people to spearhead business model realization processes, they ultimately need the license, capacity and funding to experiment to get the best results.

 

Image credit: Rawpixel.com / Shutterstock.com

Interview w/ Mika Parikka

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How to use plant floor data to make smart strategic business decisions

In the case of HK Metalcraft, a manufacturer specializing in precision metal stampings, IoT has made possible the harnessing of plant floor data. “Connecting the plant floor to HK’s business operations through cloud ERP turned that data into actionable information,” according to an article published by Industry Week. The piece is based on a White Paper published by US software company Plex.

When coupled with what happens on a plant floor, a cloud ERP solution enables “the kind of insight and control manufacturers need to make critical business decisions.” Cloud ERP has allowed HK Metalcraft to manage the downtime of operators and see everything from the direct overhead down to the specific amount of time that each operator has spent doing a specific job. “Now not only does HK Metalcraft know exactly what caused the downtime but they also have actionable data to improve processes and overall equipment effectiveness.”

Read more about how HK Metalcraft turned data into actionable information at: http://www.industryweek.com/cloud-computing/how-use-plant-floor-data-make-smart-strategic-business-decisions

Image credit: Pavel L Photo and Video/ Shutterstock.com

Via Industry Week

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The future of IoT and machine learning – what role will humans play?

Despite having been around for over two decades, machine learning and its integration into business models is yet to become commonplace. Jari Salminen, Managing Director of Cumulocity, has witnessed the unfolding of machine learning and noted the progress made in its adoption into a wide range of industries over recent years. He says that rather than spend time in building grand strategies then assume that value can immediately be realized, companies should take pragmatic steps to connect their assets and start collecting data.

“What we are seeing today is that there typically exists a bit of a delay when companies start connecting assets and collecting information to be able to rely on machine learning algorithms and their accuracy,” says Salminen. “The training of these algorithms requires large amounts of data and thus time. It takes time for any individual company to move through the cycle of starting with very basic use cases and moving onto more complex algorithms and dependencies, and eventually introducing machine learning.”

At Cumulocity, Salminen deals with several manufacturing and industrial companies. He recognizes companies that require warehouses – or those whose supply chains do – currently expect sophisticated IoT solutions from a production and manufacturing point of view.

“Things are changing at such a pace that it is now very cost efficient even for smaller companies to deploy off-the-shelf IoT solutions for their supply chains.”

Salminen encourages companies who have examined the cost of IoT solutions for manufacturing or supply chain management over recent years to do so again. “Things are changing at such a pace that it is now very cost efficient even for smaller companies to deploy off-the-shelf IoT solutions for their supply chains as the price of hardware, connectivity and software has dramatically reduced over the last 5 years,” he reasons.

Holding algorithms accountable

The accuracy and efficiency at which these solutions can be implemented will greatly depend on the algorithms triggering them. According to Salminen, the more automated these algorithms become, the greater influence they will have not only on supply chains, but beyond them as well.

“Today, most actions are still done by human users and there are several reasons why that is. For example, accountability – in the long-term, we will need to ask how this will change. Will decisions be made based solely on the algorithms that machine learning will make possible? Maybe. However, such questions and their answers go beyond technology itself, as they are concerned with making sure that somebody other than a computer takes responsibility for decisions. It’s a complex domain,” Salminen says.

So, how to legally and ethically approach decision-making when no human is involved in the process? Very similar questions are currently being asked in the automotive industry.

Closely tied to the accountability of machine-to-machine decision-making within IoT projects is the matter that is security. In general, issues surrounding security are never too far away when connectivity, machines and material networks are concerned.

Multilevel security management

“The issue with security is that it exists on so many levels in IoT projects: from hardware – meaning devices, machines and assets – and connectivity, whether that entails using mobile connections or new narrowband IoT solutions, to the backend, meaning the cloud or servers. On top of all that you might have special applications for company users, partners or even consumers. Security needs to be controlled and monitored at all these levels.”

“Often, security breaches take place when more than one area has been overlooked. A most common area attracting hackers and attacks is wherever any connected device exposes ports reachable from the internet. These are being scanned by hackers,” Salminen continues. He reminds companies that if they are dealing with hardware, that they should keep their wide area network ports closed; from a connectivity perspective, they must make sure that everything is fully encrypted in transit.

“If you are providing cloud services on top of hardware and connectivity, make sure that your whole system is robust and secure. Ultimately, there is no single thing companies must consider when it comes to security, but rather, it is an area that must be owned end-to-end by someone in the project.”

Setting future standards

When asked how he sees the future of machine learning and IoT taking shape, Salminen is simultaneously restrained and excited. He sees industries as being past the initial hype. The implementation of more advanced and sophisticated use cases is now becoming a reality.

“The connectivity costs per device are decreasing, and thus enabling many new developments when it comes to industrial solutions. This is happening across widespread assets, meaning that for example, narrowband technologies are becoming more popular. However, what is still lacking from IoT and will need to be addressed in the next few years is standardization.”

While Salminen is adamant that a comprehensive philosophy regarding standardization must be adopted, he does not foresee the use of one single underlying standard that will apply across industries.

“My guess is that there won’t be an overarching standard that will include everything from devices to data structures. There will be so many different IoT use cases that it will be impossible to create something that would cover all of them,” he says. “We see lot of traction with MQTT (Message Queuing Telemetry Transport) as a messaging protocol due to the fact it doesn’t even try to standardize all parts of the IoT stack. For example, it does not deal with message payload format which is left to the developer to decide.”

On the other hand, Salminen believes that standards like Lightweight M2M by Open Mobile Alliance are not being picked up by market players because probably it doesn’t fit many use cases, among other reasons.

Nonetheless, he reminds those looking to initiate IoT projects to certainly consider available standards, but not to be limited by them. He also states that the key thing to ensure is that you are not locked into any standard, but to have flexibility in case your needs change in the future.

“If I were starting an IoT project, I would be looking at the most recent connectivity options, the role of standards, if such exist, and whether or not they are relevant to my business. However, I wouldn’t force any standards at the moment, as there are in fact very few that are relevant,” Salminen concludes.

 

Image credit: ImageFlow/ Shutterstock.com

Interview w/ Jari Salminen

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