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Predictive Maintenance-as-a-Service

Ness held a private Dinner Event on May 16th in Stuttgart, Germany, the heart of the “German Mittelstand” – the powerhouse of medium-sized, mainly family-run businesses that are extremely successful in their niche markets, and the engine behind the German exporting machine. The topic for discussion was “Predictive Maintenance-as-a-Service” which opened up discussions on common challenges and how to overcome them with the manufacturing companies in the audience. Ness works alongside clients leading this movement and we wanted to share our hands-on experience and learn from other companies within the ecosystem.

Predictive Maintenance is the hot topic at every recent conference on Industrial Internet of Things. The web is straining under the volume of articles published by consultancy companies and solution providers alike but, from my own experience of attending these conferences, one tends to come across a limited number of viewpoints from the heavy hitters like Bosch, Thyssen Krupp or GE.

What about the other 99% of the companies in Germany? Do they feel the need to adapt their business model to offer data-based services like Predictive Maintenance?  Do they have the competence and necessary skills to address such complex, data and software-intense initiatives?

As expected (predicted even), most of our event’s attendees have started exciting new initiatives in this direction.

Jean Paul de Vooght, Ness Solutioning Director DACH, presented a Point of View based on specific experience with our customer, a global leader in the manufacturing of industrial gas turbines. This is a journey from Schedulistan to Predictistan. [1]

We then introduced a hands-on compass for data-centric initiatives, the data ring canvas.

This canvas was pre-populated by Ness (from our experience) and used as a catalyst for discussion throughout the dinner to identify challenges and impediments the attendees faced while trying to move forward with their predictive maintenance strategies.

Even though they manufactured completely different products, some hotspots of common data-related challenges emerged:

  • Goal and value quadrant: The imperative of identifying a viable business case to communicate both to management (for investment approval) and respective clients (to improve their willingness and incentive to share machine data).
  • Tools quadrant: Challenges in preparing the relevant data. Aggregating data from different sources was tricky; cleansing that data and getting it ready in order to enable data science or experience engineering work to take place. This was cited as the biggest challenge.
  • Both tools and process quadrant: Ability to scale both in terms of systems architecture and technical resources to cope with scale-ups/growth in ambition.
  • Process quadrant: Benefits of the Minimum Viable Product approach executed in rapid iterations was emphasized as the most valuable execution model.

What conclusions can we draw from the Mittelstand audience feedback to the Ness Point of View on the current state of predictive maintenance?

  1. This is not just hype. It is real: All manufacturers are interested in offering or using Predictive Maintenance. They see the benefit of investing resources in this area very soon. This will no longer be “nice to offer” but a “must have” service to their clients (who want to self-serve in near real-time).
  2. It’s a journey: Predictive maintenance needs time and a common focused goal and commitment from the company leadership. This won’t have an ROI within 6 months, but it is a learning process for everybody involved. Here the family-run Mittelstand businesses have the advantage of their financial independence from capital markets; they can give themselves enough time to grow a profitable service-revenue business instead of having to satisfy stakeholder expectations each quarter.
  3. It’s an interdisciplinary effort: The journey might take companies from the island of “Schedulistan” to “Predictistan” but no man is an island and neither is a predictive maintenance team. Different skill sets are needed and team members from different backgrounds (technology, data, engineering, business, customer service….) must find a way to effectively collaborate. For many companies this means they must rely on finding external skills from a trusted partner, because there simply isn’t the relevant expertise (from a new discipline) within their organization. It is no secret that is hard to find the right data engineers, machine learning experts and data scientists. Finding those skills and folding them into a successful manufacturing culture is definitely going to be a significant challenge for everyone!
  4. It’s better to start simple than not to start at all: Some attendees started out developing simple smart maintenance apps with excellent user experience to overcome inertia and were able to deliver something tangible within a short period of time. This can serve as a foundational basis on which to develop more sophisticated solutions for a less cynical audience who have already seen value in the first effort.

Ness looks forward to being on the forefront of the evolution of Predictive Maintenance and continuing to help our customers innovate in order to stay competitive.

[1] As reference to Nassim Nicholas Taleb’s  concept of Mediocristan and Extremistan from his 2007 published book “The black Swan.

Ness Appoints Ed Galati to President and Chief Financial Officer

TEANECK, NJ – May 30, 2018 Ness Digital Engineering, a global provider of digital transformation and custom software engineering services, has appointed Ed Galati to President and Chief Financial Officer (CFO). To support Ness’s accelerated growth, Galati will have worldwide responsibility for the financial, operational, and information technology functions of the company and its various businesses around the globe. Galati will report directly to Ness Chief Executive Officer Paul Lombardo.

“Ness’s business and footprint have been expanding rapidly, and Ed has a wealth of experience helping global organizations align resources with opportunities to achieve sustainable, corporate growth,” said Lombardo. “As we continue to extend our value to customers organically, through mergers and acquisitions, and via new business locations, Ed will help us generate further synergies and scale atop the strong foundation we have in place.”

Galati has held CFO positions for more than two decades at several organizations, including digital media and management company CMI and E2V Teledyne. Galati joins Ness from Computer Generated Solutions (CGS), where he was the CFO overseeing its worldwide business in ERP software, CRM, ecommerce, application development, SaaS and hosting, eLearning, and BPO services.

“Ness has proven and trusted capabilities in digital transformation services, and I am excited about the opportunity to leverage these resources to help the company expand and address emerging capabilities that will add further value to our clients’ business and to ours,” said Galati. “I look forward to working with Ness colleagues around the world to build on our momentum.”

About Ness Digital Engineering

Ness Digital Engineering designs, builds, and integrates digital platforms and enterprise software that help organizations engage customers, differentiate their brands, and drive profitable growth. Our customer experience designers, software engineers, data experts, and business consultants partner with clients to develop roadmaps that identify ongoing opportunities to increase the value of their digital solutions and enterprise systems. Through agile development of minimum viable products (MVPs), our clients can test new ideas in the market and continually adapt to changing business conditions—giving our clients the leverage to lead market disruption in their industries and compete more effectively to grow their business. For more information, visit www.ness.com. 

Media Contacts

Vivek Kangath
Senior Global Manager – Corporate Communications
Ness Digital Engineering
Mobile: +91 9742565583 | Tel: +91 80 41961000 | DID: +91 80 41961027

Amy Legere
Greenough
alegere@greenough.biz
617.275.6517

Systém pro vyřazování jaderných elektráren v Manchesteru

Zúčastnili jsme se odborné konference Nuclear Decommissioning Conference Europe v Manchesteru a představili zde systém pro vyřazování jaderných elektráren a management jaderného odpadu.

O našem řešení pro vyřazování jaderných elektráren (postaveném na SAP technologii), které jsme úspěšně zavedli na Slovensku v končící jaderné elektrárně Jaslovské Bohunice, už vědí i v Manchestru.

Tomáš Prejda, šéf našeho Marketingu, tam na evropské konferenci prezentoval naše řešení a zkušenosti. Naslouchali mu například zástupci vedení firmy EDF Cyclife, která vlastní všechny jaderné elektrárny ve Francii a Velké Británii, Westinghouse, globálního hráče v jaderné energetice či poradenské společnosti Deloitte.

Máme být na co hrdí, protože jsme při průzkumu trhu zatím nenarazili na podobně komplexní, a přitom funkční řešení v tomto relativně úzce zaměřeném, komplexním, regulovaném, ale přesto velmi zajímavém a perspektivním odvětví.

Ness Digital Engineering and S&P Global Open World-Class Innovation Center in Hyderabad

HYDERABAD, INDIA – May 16, 2018 Ness Digital Engineering, a provider of digital transformation and custom software engineering services, has opened the Orion Facility in Hyderabad in partnership with S&P Global to support its growing Center of Excellence for technology, data operations and core business process talent in the region. The new 100,000 sq. ft. center provides the region’s top talent with a world-class work environment for evolving S&P Global’s products and platforms across its varied business segments. S&P Global is a leading provider of transparent and independent ratings, benchmarks, analytics and data to the capital and commodity markets worldwide.

Designed to accommodate more than 850 associates, the Orion Facility is purpose-built to facilitate innovation by providing employees with access to the latest technologies and global communications infrastructure, as well as physical spaces that enable highly-collaborative teams. The associates at the facility are focused on software development, operations, quality assurance and data to enable S&P Global systems and products. This facility has state-of-the-art technology labs to exclusively focus on Artificial Intelligence, Internet of Things, Machine Learning and DevOps innovation and solutions.

“The opening of the Orion Facility is the culmination of our successful partnership with Ness, which used its deep regional knowledge and software engineering expertise to help us develop an exceptional innovation team,” said Nick Cafferillo, Chief Technology Officer at S&P Global. “Orion embodies our commitment to evolve our talent to deliver new insights, accelerate the speed at which we operate, fuel value for our customers, and establish our brand as the most innovative company powering the markets of the future.”

“In less than one year, we’ve been able to help S&P Global produce a strong network of innovators working together very effectively to deliver new solutions in the digital economy,” said Paul Lombardo, Ness Digital Engineering CEO. “We are proud to be S&P Global’s partner in this journey, and this new facility will help the organization fully leverage its growing base of talent to continue to lead the industry with next-generation solutions.”

About Ness Digital Engineering

Ness Digital Engineering designs, builds, and integrates digital platforms and enterprise software that help organizations engage customers, differentiate their brands, and drive profitable growth. Our customer experience designers, software engineers, data experts, and business consultants partner with clients to develop roadmaps that identify ongoing opportunities to increase the value of their digital solutions and enterprise systems. Through agile development of minimum viable products (MVPs), our clients can test new ideas in the market and continually adapt to changing business conditions—giving our clients the leverage to lead market disruption in their industries and compete more effectively to grow their business. For more information, visit www.ness.com.

About S&P Global
S&P Global is a leading provider of transparent and independent ratings, benchmarks, analytics and data to the capital and commodity markets worldwide. The Company’s divisions include S&P Global Ratings, S&P Global Market Intelligence, S&P Dow Jones Indices and S&P Global Platts. S&P Global has approximately 20,000 employees in 31 countries. For more information, visit www.spglobal.com.

Media Contacts

Vivek Kangath
Senior Global Manager – Corporate Communications
Ness Digital Engineering
Mobile: +91 9742565583 | Tel: +91 80 41961000 | DID: +91 80 41961027

Ness Panel Session: Maximizing Revenue by Unleashing the Latent Value of Your Data

I recently chaired two panel sessions on a hot topic that cuts across all industries and sectors: the core role that data plays in creating the seamless digital experience consumers demand. Whether it’s analytic insights, machine learning engines that can chat with users or a personalized online experience for consumers – it all starts with clean, usable data to maximize the value of your data.

Almost every company is trying to monetize their data, by using it to improve the efficiency of their internal processes, increasing revenue from their customers, or flat-out selling their data to third parties. No wonder we are seeing an “arms race” to acquire data, with splashy acquisitions like Microsoft buying LinkedIn and IBM acquiring weather.com’s climate information.

The New York session featured Bruce Kratz, CTO of Sparta Systems, which produces quality and compliance management systems for the life sciences industry. The Los Angeles session featured Mark Berner, VP of Engineering for TiVo, whose main product today is not Digital Video Recorder hardware but rather software and services that power entertainment experiences. At first glance, these seem like very different universes. But, when they talk about data, their plans, impediments and advice are strikingly similar.

Some examples: For both TiVo and Sparta, data (rather than some tangible object) is one of the main products they provide to their customers. Both companies leverage aggregate data from devices or system transactions to extract insights and to continually improve the overall product experience. Both companies are extremely sensitive to concerns around data security and privacy and exercise strict requirements to anonymize personal data.

The audience in both venues came from a diverse set of industries, including banking and financial services, media, government and retail. They chimed in with a number of fascinating use cases all centered around data, from Know Your Customer (KYC) regulatory requirements to General Data Protection Regulation (GDPR) to data-driven insurance offerings.

Another strong message from all participants: Machine Learning is real, not hype. It can bring benefit to all stages of the product lifecycle, from automating the cleansing of dirty data as it is ingested, to determining the optimal order in which to run regression tests so they will fail faster, to providing chatbot interfaces to reduce the cost of communicating with customers.

When the discussion turned to impediments, the level of commonality was even more striking:

  • Many companies have a poor understanding of corporate data assets, due to legacy systems and inorganic growth. Answering a simple question like which table is the source for a given data value that appears in multiple tables can require months of sleuthing by business analysts.
  • Enterprises across the board are having trouble integrating data from different sources into a common data lake from which insights can be extracted. The source data often suffers from major quality issues like missing or invalid values. When the data is of poor quality, so are the insights derived from that data. There are often multiple systems that can define the value of the same piece of data, with no capability to detect, reconcile or distribute a single consolidated value. There is often no way to share information about clean data that has already been harvested, so the same cleansing work is done over and over by different departments.
  • Many companies suffer from a skills gap. Good Big Data architects and engineers are hard to find and keep, especially in tech centers like Silicon Valley or New York City. When it comes to Machine Learning, there are even more companies pursuing even less talent.

If you are feeling discouraged by this bleak picture, cheer up: Our panelists and our audience had some excellent advice and recommendations that transcend specific industries:

  • Benchmark against your competition, to calibrate what is considered “table stakes” in your industry.
  • Make sure you have a solid use case that provides tangible value to the business, and a solid sponsor from the business side. Otherwise, your Big Data initiative will be perceived as technology in search of a problem.
  • Look for a quick win, by implementing a Minimum Viable Product (MVP). Otherwise the business side of the house will rapidly lose patience.
  • Decide your company’s core competency and focus on that. For all other “plumbing” issues like Big Data architecture, get help from a partner with experience in Big Data and Machine Learning across your industry and other industries.

Tolstoy once wrote, “Happy families are all alike; every unhappy family is unhappy in its own way.” Companies that are struggling to monetizing their data will be surprised to discover that their unhappiness is shared by many other companies in many other industries, and they are alike for many of the same reasons. Fortunately, “unhappy” companies can take advantage of many common strategies used by more successful companies to achieve success in unleashing the latent value of their data.

Controlling the Power of Social Media Data

Nearly 10 years ago, I ran research and development for Data Persona, a startup that provided personalization as a service for media web sites, using customer data obtained from several sources including social media. To test the product, we built a dummy Facebook app and asked our company’s employees to install it. When we checked our database, we were amazed to discover that Facebook had given us good personalization information (e.g., groups and likes) not only about me, but also about all my Facebook friends. At the time I assumed it was just a bug in the Facebook API. I did not complain because I was happy to get more user data to train our personalization algorithm.

It seems Data Persona was not the only beneficiary of Facebook’s “generosity.” In 2014 a psychologist created a personality quiz Facebook app and convinced 250,000 people to install it. From those 250,000 installations he was able to harvest data about more than 50 million Facebook users. This data eventually ended up in the hands of Cambridge Analytica, which used it to create psychometric profiles of American voters, and then deployed that information to enable targeted campaign ads for the U.S. Presidential election. This has created a stir in the media, as the world now discovers the potential capability of social media to influence human behavior.

Full disclosure: I am an active Facebook user. I post photos of my travels, and I read my news feed every day. As news of Cambridge Analytica’s influence on U.S. election advertising broke, I expected it to have some impact on the conversation among my Facebook friends, up to and including decisions to leave Facebook entirely. But, much to my surprise, there was no reaction at all – just the usual stream of links, rants and photos. So, I posted my own observation: “I am amazed by the lack of discussion (on my feed at least) about Facebook’s inability to preserve users’ privacy.” The responses I got were mainly bored indifference: “I always assumed Facebook was using data about me, but I consider it a fair deal in return for getting a free service.” As one friend put it, “Once you install freeware, you are the product.”

I believe this argument misses the point. The 250,000 Facebook users who actually installed the personality quiz app certainly should not be surprised that their social data was sold. That’s the deal they implicitly made when they installed the app. But, what about their friends who comprise the rest of the 50 million people whose social data was sold? They did not knowingly give up their social data to Cambridge Analytica, and certainly had no idea that this could happen as a result of accepting a friend invitation from one of the app installers.

Facebook has admitted that this kind of data usage should never have been allowed to happen and has promised to take measures to ensure that it cannot happen again. However, I believe this self-regulation is not enough. In this case, government regulation is required for the following reasons:

  1. Governments regulate areas where public safety is at risk, e.g., emission controls for cars and safety standards for nuclear reactors. Social media data fits into this category, because:
    1. It provides such a complete picture of who you are, based on perfect and permanent tracking of all your likes, dislikes, conversations and affiliations.
    2. Analytical tools have progressed to the stage where they can use this data to not only predict your actions (predictive analytics), but also to persuade you to take a specified action by controlling the information to which you are exposed (persuasive analytics).
    3. The persuasive power of social media data puts it in same category as other aspects of public welfare and safety that the government regulates.
  2. Governments regulate monopolies to ensure fair practices. Social media (like much of high tech) is effectively a monopoly, due to its winner-take-all nature. Potential competitors who manage to overcome this network effect are typically purchased by the 500-pound gorilla before they can develop into full-fledged competition.
  3. The alternative to government regulation would be self-regulation. I do not believe this can work because social media companies have an inherent conflict of interest that clouds their judgement. Facebook makes its money by harvesting your social information and selling it to advertisers in the form of highly targeted ads. The more data Facebook can harvest, the better the ad targeting, and one of the best ways to determine your interests is to examine the interests of your friends, because birds of a feather flock together. This kind of logic led Facebook to make information about app users’ friends available via their application programming interface (API). Even if they have fixed this security hole, they are still subject to the same conflict of interest that makes such holes possible. For example, Facebook’s troubles have not stopped Snapchat from announcing the launch of third party apps on their social media platform.

Given the power of social media data and the inability of social media companies to self-regulate, government regulation is needed. To be clear, I do not mean that the government should collect and analyze all personal data from social networks and then decide who can access it. I mean that the government should ensure that such powerful data is not aggregated at all, via regulation and inspection. I am aware of how inefficient and confining such regulation can be, yet I see no better alternative, short of ditching social media entirely, and that isn’t happening, to judge from my Facebook friends’ response to Cambridge Analytica.

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