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Live Product Innovation, Part 2: IoT, Big Data, and Smart Connected Products

John McNiff

In Part 1 of this series, we looked at how in-memory computing affects live product innovation. In Part 2, we explore the impact of the Internet of Things (IoT) and Big Data on smart connected products. In Part 3, we’ll approach the topic from the perspective of process industries.

Live engineering? Live product innovation? Live R&D?

To some people, these concepts sound implausible. When you talk about individualized product launches with lifecycles of days or weeks, people in industries like aerospace and defense (A&D) look askance.

But today, most industries—not just consumer-driven ones—need timely insights and the ability to respond quickly. Even A&D manufacturers want to understand the impact of changes before they continue with designs that could be difficult to make at the right quantity or prone to problems in the field.

The Internet of Everything?

Internet of Things (IoT) technologies promise to give manufacturers these insights. But there’s still a lot of confusion around IoT. Some people think it is about connected appliances; others think it’s just a rebranded “shop floor to top floor.”

The better way to think about IoT is from the perspective of data: We want to get data from the connected “thing.” If you’re the manufacturer, that thing is a product. If you buy the product and sell it to an end customer, that thing is an asset. If you’re the end customer, that thing is a fleet. Each stakeholder wants different data in different volumes for different reasons.

It’s also important to remember that the Internet has existed for far longer than IoT.

There’s a huge amount of non-IoT data that can offer useful insights. Point-of-sale data, news feeds, and market insights from social channels are all valuable. And think about how much infrastructure is now connected in “smart” cities. So in addition to products, assets, and fleets, there are also people, markets, and infrastructure. Big Data is everywhere, and it should influence what you release and when.

New data, new processes

It has been said that data in the 21st century is like oil in the 18th century: an immense, valuable, yet untapped asset. But if data is the new oil, then do we need a new refinery? The answer is yes.

On top of business data, we now have a plethora of information sources outside our company walls. Ownership of, and access to, this data is becoming complex. Manufacturers collecting data about equipment at customer sites, for example, may want to sell that data to customers as an add-on service. But those customers are likely using equipment from multiple manufacturers, and they likely have their own unique uses for the information.

So the new information refinery needs to capture information from everywhere and turn it into something that has meaning for the end user. It needs to leverage data science and machine learning to remove the noise and add insight and intelligence. It also needs to be an open platform to gather information from all six sources (products, assets, fleets, people, markets, infrastructure).

And wouldn’t it be great if the data refinery ran on the same platform as your business processes, so that you could sense, respond, and act to achieve your business goals?

Digital product innovation platform

If you start with the concept of a smart connected product, the data refinery — the digital product innovation platform — has five requirements:

  1. Systems design — Manufacturers need to design across disciplines in a systems approach. Mechanical, mechatronic, electrical, electronics, and software all need to be supported, with modeling capabilities that cover physical, functional, and logical structures.
  1. Requirements-linked platform design — Designers need to think about where and how to embed sensors and intelligence to match functional requirements. This will need to be forward-thinking to cover unforeseen methods of machine-to-machine interactions. In a world of performance-based contracts, it will be important to minimize the impact of design changes as innovation opportunities grow.
  1. Instant impact and insight environment — The platform must support fully traceable requirements throughout the lifecycle, from design concept to asset performance.
  1. Product-based enterprise processes — The platform needs to share model-based product data visually — through electrical CAD, electronic CAD, 3D, and software functions — to the people who need it. This isn’t new, but what’s different is that the platform can’t wait for complex integrations between systems. Think about software-enabled innovation or virtual inventory made possible by on-demand 3D printing. Production is almost real time, so design will have to be as well.
  1. Product and thing network — A complex, cross-domain design process involves a growing number of partners. That calls for a product network to allow for secure collaboration across functions and outside the company walls. Instead of every partner having its own portal for product data, the product network would store digital twins and allow instant sharing of asset intelligence.

If the network is connected to the digital product innovation platform, you can control the lifecycle both internally and externally — and take the product right into the service and maintenance domain. You can then provide field information directly from the assets back to design to inform what to update, and when. Add over-the-air software compatibility checks and updates, and discrete manufacturers can achieve a true live engineering environment.

Sound like a dream? It’s coming sooner than you think.

Come to SAPPHIRE NOW 2017 in Orlando, Florida from May 16 – 18th, 2017, and check out my session “Boost Visibility into Operations for Connected Products with SAP Leonardo” on Tuesday, May 16th, 2017 from 1-1:40 p.m. in Business Application BA324, or check out our R&D sessions.

Follow the conversation on @SCMatSAP and #SAPPHIRENOW.

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John McNiff

About John McNiff

John McNiff is the Vice President of Solution Management for the R&D/Engineering line-of-business business unit at SAP. John has held a number of sales and business development roles at SAP, focused on the manufacturing and engineering topics.

Data Analysts And Scientists More Important Than Ever For The Enterprise

Daniel Newman

The business world is now firmly in the age of data. Not that data wasn’t relevant before; it was just nowhere close to the speed and volume that’s available to us today. Businesses are buckling under the deluge of petabytes, exabytes, and zettabytes. Within these bytes lie valuable information on customer behavior, key business insights, and revenue generation. However, all that data is practically useless for businesses without the ability to identify the right data. Plus, if they don’t have the talent and resources to capture the right data, organize it, dissect it, draw actionable insights from it and, finally, deliver those insights in a meaningful way, their data initiatives will fail.

Rise of the CDO

Companies of all sizes can easily find themselves drowning in data generated from websites, landing pages, social streams, emails, text messages, and many other sources. Additionally, there is data in their own repositories. With so much data at their disposal, companies are under mounting pressure to utilize it to generate insights. These insights are critical because they can (and should) drive the overall business strategy and help companies make better business decisions. To leverage the power of data analytics, businesses need more “top-management muscle” specialized in the field of data science. This specialized field has lead to the creation of roles like Chief Data Officer (CDO).

In addition, with more companies undertaking digital transformations, there’s greater impetus for the C-suite to make data-driven decisions. The CDO helps make data-driven decisions and also develops a digital business strategy around those decisions. As data grows at an unstoppable rate, becoming an inseparable part of key business functions, we will see the CDO act as a bridge between other C-suite execs.

Data skills an emerging business necessity

So far, only large enterprises with bigger data mining and management needs maintain in-house solutions. These in-house teams and technologies handle the growing sets of diverse and dispersed data. Others work with third-party service providers to develop and execute their big data strategies.

As the amount of data grows, the need to mine it for insights becomes a key business requirement. For both large and small businesses, data-centric roles will experience endless upward mobility. These roles include data anlysts and scientists. There is going to be a huge opportunity for critical thinkers to turn their analytical skills into rapidly growing roles in the field of data science. In fact, data skills are now a prized qualification for titles like IT project managers and computer systems analysts.

Forbes cited the McKinsey Global Institute’s prediction that by 2018 there could be a massive shortage of data-skilled professionals. This indicates a disruption at the demand-supply level with the needs for data skills at an all-time high. With an increasing number of companies adopting big data strategies, salaries for data jobs are going through the roof. This is turning the position into a highly coveted one.

According to Harvard Professor Gary King, “There is a big data revolution. The big data revolution is that now we can do something with the data.” The big problem is that most enterprises don’t know what to do with data. Data professionals are helping businesses figure that out. So if you’re casting about for where to apply your skills and want to take advantage of one of the best career paths in the job market today, focus on data science.

I’m compensated by University of Phoenix for this blog. As always, all thoughts and opinions are my own.

For more insight on our increasingly connected future, see The $19 Trillion Question: Are You Undervaluing The Internet Of Things?

The post Data Analysts and Scientists More Important Than Ever For the Enterprise appeared first on Millennial CEO.

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Daniel Newman

About Daniel Newman

Daniel Newman serves as the Co-Founder and CEO of EC3, a quickly growing hosted IT and Communication service provider. Prior to this role Daniel has held several prominent leadership roles including serving as CEO of United Visual. Parent company to United Visual Systems, United Visual Productions, and United GlobalComm; a family of companies focused on Visual Communications and Audio Visual Technologies. Daniel is also widely published and active in the Social Media Community. He is the Author of Amazon Best Selling Business Book "The Millennial CEO." Daniel also Co-Founded the Global online Community 12 Most and was recognized by the Huffington Post as one of the 100 Business and Leadership Accounts to Follow on Twitter. Newman is an Adjunct Professor of Management at North Central College. He attained his undergraduate degree in Marketing at Northern Illinois University and an Executive MBA from North Central College in Naperville, IL. Newman currently resides in Aurora, Illinois with his wife (Lisa) and his two daughters (Hailey 9, Avery 5). A Chicago native all of his life, Newman is an avid golfer, a fitness fan, and a classically trained pianist

When Good Is Good Enough: Guiding Business Users On BI Practices

Ina Felsheim

Image_part2-300x200In Part One of this blog series, I talked about changing your IT culture to better support self-service BI and data discovery. Absolutely essential. However, your work is not done!

Self-service BI and data discovery will drive the number of users using the BI solutions to rapidly expand. Yet all of these more casual users will not be well versed in BI and visualization best practices.

When your user base rapidly expands to more casual users, you need to help educate them on what is important. For example, one IT manager told me that his casual BI users were making visualizations with very difficult-to-read charts and customizing color palettes to incredible degrees.

I had a similar experience when I was a technical writer. One of our lead writers was so concerned with readability of every sentence that he was going through the 300+ page manuals (yes, they were printed then) and manually adjusting all of the line breaks and page breaks. (!) Yes, readability was incrementally improved. But now any number of changes–technical capabilities, edits, inserting larger graphics—required re-adjusting all of those manual “optimizations.” The time it took just to do the additional optimization was incredible, much less the maintenance of these optimizations! Meanwhile, the technical writing team was falling behind on new deliverables.

The same scenario applies to your new casual BI users. This new group needs guidance to help them focus on the highest value practices:

  • Customization of color and appearance of visualizations: When is this customization necessary for a management deliverable, versus indulging an OCD tendency? I too have to stop myself from obsessing about the font, line spacing, and that a certain blue is just a bit different than another shade of blue. Yes, these options do matter. But help these casual users determine when that time is well spent.
  • Proper visualizations: When is a spinning 3D pie chart necessary to grab someone’s attention? BI professionals would firmly say “NEVER!” But these casual users do not have a lot of depth on BI best practices. Give them a few simple guidelines as to when “flash” needs to subsume understanding. Consider offering a monthly one-hour Lunch and Learn that shows them how to create impactful, polished visuals. Understanding if their visualizations are going to be viewed casually on the way to a meeting, or dissected at a laptop, also helps determine how much time to spend optimizing a visualization. No, you can’t just mandate that they all read Tufte.
  • Predictive: Provide advanced analytics capabilities like forecasting and regression directly in their casual BI tools. Using these capabilities will really help them wow their audience with substance instead of flash.
  • Feature requests: Make sure you understand the motivation and business value behind some of the casual users’ requests. These casual users are less likely to understand the implications of supporting specific requests across an enterprise, so make sure you are collaborating on use cases and priorities for substantive requests.

By working with your casual BI users on the above points, you will be able to collectively understand when the absolute exact request is critical (and supports good visualization practices), and when it is an “optimization” that may impact productivity. In many cases, “good” is good enough for the fast turnaround of data discovery.

Next week, I’ll wrap this series up with hints on getting your casual users to embrace the “we” not “me” mentality.

Read Part One of this series: Changing The IT Culture For Self-Service BI Success.

Follow me on Twitter: @InaSAP

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The Future of Cybersecurity: Trust as Competitive Advantage

Justin Somaini and Dan Wellers

 

The cost of data breaches will reach US$2.1 trillion globally by 2019—nearly four times the cost in 2015.

Cyberattacks could cost up to $90 trillion in net global economic benefits by 2030 if cybersecurity doesn’t keep pace with growing threat levels.

Cyber insurance premiums could increase tenfold to $20 billion annually by 2025.

Cyberattacks are one of the top 10 global risks of highest concern for the next decade.


Companies are collaborating with a wider network of partners, embracing distributed systems, and meeting new demands for 24/7 operations.

But the bad guys are sharing intelligence, harnessing emerging technologies, and working round the clock as well—and companies are giving them plenty of weaknesses to exploit.

  • 33% of companies today are prepared to prevent a worst-case attack.
  • 25% treat cyber risk as a significant corporate risk.
  • 80% fail to assess their customers and suppliers for cyber risk.

The ROI of Zero Trust

Perimeter security will not be enough. As interconnectivity increases so will the adoption of zero-trust networks, which place controls around data assets and increases visibility into how they are used across the digital ecosystem.


A Layered Approach

Companies that embrace trust as a competitive advantage will build robust security on three core tenets:

  • Prevention: Evolving defensive strategies from security policies and educational approaches to access controls
  • Detection: Deploying effective systems for the timely detection and notification of intrusions
  • Reaction: Implementing incident response plans similar to those for other disaster recovery scenarios

They’ll build security into their digital ecosystems at three levels:

  1. Secure products. Security in all applications to protect data and transactions
  2. Secure operations. Hardened systems, patch management, security monitoring, end-to-end incident handling, and a comprehensive cloud-operations security framework
  3. Secure companies. A security-aware workforce, end-to-end physical security, and a thorough business continuity framework

Against Digital Armageddon

Experts warn that the worst-case scenario is a state of perpetual cybercrime and cyber warfare, vulnerable critical infrastructure, and trillions of dollars in losses. A collaborative approach will be critical to combatting this persistent global threat with implications not just for corporate and personal data but also strategy, supply chains, products, and physical operations.


Download the executive brief The Future of Cybersecurity: Trust as Competitive Advantage.


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To Get Past Blockchain Hype, We Must Think Differently

Susan Galer

Blockchain hype is reaching fever pitch, making it the perfect time to separate market noise from valid signals. As part of my ongoing conversations about blockchain, I reached out to several experts to find out where companies should consider going from here. Raimund Gross, Solution Architect and Futurist at SAP, acknowledged the challenges of understanding and applying such a complex leading-edge technology as blockchain.

“The people who really get it today are those able to put the hype in perspective with what’s realistically doable in the near future, and what’s unlikely to become a reality any time soon, if ever,” Gross said. “You need to commit the resources and find the right partners to lay the groundwork for success.”

Gross told me one of the biggest problems with blockchain – besides the unproven technology itself – was the mindset shift it demands. “Many people aren’t thinking about decentralized architectures with peer-to-peer networks and mash-ups, which is what blockchain is all about. People struggle because often discussions end up with a centralized approach based on past constructs. It will take training and experience to think decentrally.”

Here are several more perspectives on blockchain beyond the screaming headlines.

How blockchain disrupts insurance, banking

Blockchain has the potential to dramatically disrupt industries because the distributed ledger embeds automatic trust across processes. This changes the role of longstanding intermediaries like insurance companies and banks, essentially restructuring business models for entire industries.

“With the distributed ledger, all of the trusted intelligence related to insuring the risk resides in the cloud, providing everyone with access to the same information,” said Nadine Hoffmann, global solution manager for Innovation at SAP Financial Services. “Payment is automatically triggered when the agreed-upon risk scenario occurs. There are limitations given regulations, but blockchain can open up new services opportunities for established insurers, fintech startups, and even consumer-to-consumer offerings.”

Banks face a similar digitalized transformation. Long built on layers of steps to mitigate risk, blockchain offers the banking industry a network of built-in trust to improve efficiencies along with the customer experience in areas such as cross-border payments, trade settlements for assets, and other contractual and payment processes. What used to take days or even months could be completed in hours.

Finance departments evolve

Another group keenly watching blockchain developments are CFOs. Just as Uber and Airbnb have disrupted transportation and hospitality, blockchain has the potential to change not only the finance department — everything from audits and customs documentation to letters of credit and trade finance – but also the entire company.

“The distributed ledger’s capabilities can automate processes in shared service centers, allowing accountants and other employees in finance to speed up record keeping including proof of payment supporting investigations,” said Georg Koester, senior developer, LoB Finance at the Innovation Center Potsdam. “This lowers costs for the company and improves the customer experience.”

Koester said that embedding blockchain capabilities in software company-wide will also have a tremendous impact on product development, lean supply chain management, and other critical areas of the company.

While financial services dominate blockchain conversations right now, Gross named utilities, healthcare, public sector, real estate, and pretty much any industry as prime candidates for blockchain disruption. “Blockchain is specific to certain business scenarios in any industry,” said Gross. “Every organization can benefit from trust and transparency that mitigates risk and optimizes processes.”

Get started today! Run Live with SAP for Banking. Blast past the hype by attending the SAP Next-Gen Boot Camp on Blockchain in Financial Services and Public Sector event being held April 26-27 in Regensdorf, Switzerland.

Follow me on Twitter, SCN Business Trends, or Facebook. Read all of my Forbes articles here.

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