Blockchain And Machine Learning

Digitalist Magazine

A business network is far more than the business itself; it’s a collection of many connected things—customers, suppliers, playmates, and more. The transferring of assets to build value for business networks is happening continuously with participants, transactions, and contracts.

There is always a need for a collective ledger across the business network to operate efficiently with broader participation to reduce cost, reduce risk and fraud, and increase trust. Blockchain technology provides a distributed database of records that have been executed and collective among participating parties.

They have these attributes:

  1. Business network participation
  2. Consensus for transaction validation
  3. Provenance for audit trials
  4. Immutability regardless of space and time
  5. Finality to the absolute

Blockchain can operate in two modes—private and public. Blockchain is essentially an append-only distributed system of records collective across a business network where no one possesses, anyone can add, and no one can delete it. And every transaction is secure, authenticated, and verifiable with adequate visibility.

Machine learning use cases for blockchain technology

Blockchain technology has good potential beyond the financial industry. There are many profound applications of this technology across many industries, most importantly in the artificial intelligence and machine-learning aspects.

In a collective ledger system, there are two patterns of machine learning use cases:

  1. Silo machine learning and predictive models addressing a particular segment of the chain
  2. Model chains addressing a segment or the entire chain

The silo machine learning or predictive model is no different from what we do today with data at forearm. Model chains are more complicated, since they must learn and adjust on the fly given chain dependence.

Want to hear more about machine learning and predictive analytics subjects? Join us at BI2017 at Orlando. And read the other blogs in our Predictive Thursdays series.

Article published by Chandran Saravana. It originally appeared on SAP BusinessObjects Analytics and has been republished with permission.


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MMA #CEOCMO Summit 2017: five Takeaways

Recently I had the chance to represent SAP and our business unit SAP Digital Interconnect at the MMA Global CEO CMO Summit in Napa, California. The two-day event was designed to join business leaders and marketers from around the globe in this picturesque, family-friendly environment.

More significant, there were some interesting sessions and presentations. For me, this newfound skill boiled down to five critical lessons:

1. Everything is mobile, and mobile is everything

While the organizer of the summit was the Mobile Marketers Association (MMA), almost everyone attending, including the organizers themselves, recognize that mobile is no longer a channel or a separate consideration in the marketer’s toolbox. Mobile is deeply embedded in all facets of modern life for consumers and businesses, and it must be part of the mainstream strategy for a CEO or a CMO. This means challenging and switching the constructs of ideas and approaches that may have worked in the past but now need to be adapted.

For example, Sanjay Gupta of Allstate collective his company’s practice of creating 15-second movies for TV. When the company edited the same content down to six seconds and less for mobile movies, it found engagement to be equal and at times greater. The key lesson: We need to realize the omnipresence of mobile as a channel in our lives and consumer’s lives, and act accordingly. Gone are the days of spending money on creative and formats that are aligned with older medium of engagement.

The SMOX probe from MMA clearly demonstrates the influence mobile has on sales and provides evidence that correlations inbetween click-through and sales may not actually be that effective. Hmmm…consider this before throwing down a bunch of your marketing dollars for enhancing click-throughs.

Two. Contextual—but not creepy—marketing

As with any industry conference, there was a healthy representation of service providers and organizations pitching their offerings. I noticed an breathtaking percentage of startups focused on location and location-based marketing. This contextual treatment to marketing and understanding consumer behavior will be increasingly relevant in the digital economy.

However, I proceed to be biased toward contextual information based on mobile ambient data like SAP Consumer Insight 365, which helps businesses better understand their consumers at an aggregated and anonymized level, thus respecting the privacy and rights of an individual consumer. I am skeptical about any service that provides information or insight at an individual level where the end consumer is not aware of how her data is being captured, collective or used.

I would caution CEOs and CMOs to use contextual information to make their services and offers relevant to their customers—but to stand against the urge to be “creepy” in their tactics. Nothing irritates consumers more than discovering that their movements are being tracked, or receiving offers based on latest online behaviors or deeds.

Three. Conversations with customers matter more than ever

Machine learning and artificial intelligence (AI) are becoming a reality swifter than we may have imagined. Consumer interactions that used to take one or two clicks now require zero clicks—case in point, voice assistants like Amazon Echo.

One interesting case probe that brought this conversational aspect of customer interaction to the forefront was a presentation by Yin Rani, of the Campbell Soup Company, and DJ Reali of The Weather Company (an IBM Business): Campbell brought its recipes and ingredients to IBM’s Watson and layered these with weather data, which enabled customers to have conversations around “What’s for dinner?” In this way, Watson is helping generate fresh recipes and enabling brands like Campbell to have rich conversations with its customers.

On that note, the presentation by Andrew Kauffman of Marriott International Inc. displayed how Marriott brings together old, fresh, and emerging technologies to provide customer care and engage in conversations with their customers at all points of their purchase journey. What fascinated me was how Marriott has blended channels like SMS, mobile, talk, and social influencers in its customer interactions and customer care. We at @SAPInterconnect are sultry about ensuring meaningful multichannel customer engagement, as I highlighted in my session: Connecting Everyone, Everything, Everywhere in the Digital Economy.

Four. CMO and marketing roles are ever-evolving

A session by Kimberly Whitler, assistant professor of marketing, Darden School of Business, focused on why the CMO has the highest failure rate in the C-suite. Sharing insight on what makes the CMO role difficult to “get right” and why so many firms set the CMO up to fail, Whitler touched upon a key issue: Marketing as a function, and the CMO as its leader, spans numerous groups within the organization and is often held to results without the capability to influence switch or influence various areas. I noticed many goes nodding in agreement, and my one-on-one conversations with attendees confirmed this idea. One thing is for sure: As we stir into the digital economy, these boundaries will get more and more blurred. For example, one attendee asked, “What is the role of a CDO vs. a CMO in a company?”

Five. Family, joy, and work coexist

The most interesting discussions, as often happens, occurred outside the formal sessions and were not included on the agenda, including a beautiful, engaging spectacle by Daya.

Conferences often get the “fun” part wrong. But whether due to the laid-back Napa effect, the overall California vibe, or perhaps MMA finding a ideal balance inbetween formal and informal agenda items, the greatest achievement of this conference, in my view, was having conversations in a casual setting with attendees and their families. This was identically as significant as forging meaningful dialogue and discussions. Perhaps it is something we should hold dear in light of all the advancement in AR and VR.


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About Rohit Tripathi

Rohit Tripathi General Manager, Head of Industry/LoB Products, SAP Digital Interconnect, and brings with him over twenty years of practice in software and business operations. In his current role, Rohit concentrates on bringing to market value-added products and solutions that help SAP Digital Interconnect customers get more engaged, secure, and gather actionable insights in the Digital World. Previously, Rohit held various leadership positions at SAP in the areas of technology and products. Prior to joining SAP, Rohit was with The Boston Consulting Group where he advised senior executives of Fortune five hundred companies on business strategy and operations. Rohit also serves on the North American Board of Directors for Mobile Marketers Association.

Why Artificial Intelligence Is Not Indeed Artificial – It Is Very Tangible

The topic of artificial intelligence (AI) is zizzing through academic conferences, predominant business strategy sessions, and making swings in the public discussion. Every presentation I see includes it, even if it’s only used as a buzzword – its frequency is rivaling the use of “Uber for X” that’s been so popular in latest years.

While AI is a trending topic, it’s not mere hum. It is already deeply ingrained into the strategy and design of our products – well beyond a mere shout-out in presentations. As we strive to optimize our products to better serve our customers and fucking partners, it is worth taking AI gravely because of its unique role in product innovation.

AI will be inherently disruptive. Now that it has left the area of academic projects and theoretical discussion – now that it is directly driving speed and hyper-automation in the business world – it is significant to embark with a review that de-mystifies the serious decisions facing business leaders and clarifies the value for users, customers, and playmates. I’ll also share some practices on how AI is contributing to solutions that run business today.

Let’s very first embark with the basics: the difference inbetween AI, machine learning, and deep learning.

  • Artificial intelligence (AI) is broadly defined to include any simulation of human intelligence exhibited by machines. This is a growth area that is branching into numerous areas of research, development, and investment. Examples of AI include autonomous robotics, rule-based reasoning, natural language processing (NLP), skill representation mechanisms (skill graphs), and more.
  • Machine learning (ML) is a subfield of AI that aims to instruct computers how to accomplish tasks using data inputs, but without explicit rule-based programming. In enterprise software, ML is presently the best method to treatment the goals of AI.
  • Deep learning (DL) is a subfield of ML describing the application of (typically multilayer) artificial neural networks. Neural networks take inspiration from the human brain, with processors consisting of petite neuron-like computing units connected in ways that resemble biological structures. These networks can learn sophisticated, non-linear problems from input data. The layering of the networks permits cascaded learning and abstraction levels. This can accomplish tasks like: kicking off with line recognition, progressing to identifications of shapes, then objects, then total scene. In latest years, DL has led to breakthroughs in a series of AI tasks including speech, vision, and language processing.

AI applications for cloud ERP solutions

Industry Four.0 describes the trend of automation and data exchange in manufacturing. This comprises cyber-physical systems, the Internet of Things (IoT), cloud computing, and cognitive computing – everything that adds up to create a “smart factory.” There is a parallel in the world beyond manufacturing, where data- and service-based sectors need to capture and analyze more data quickly and act on that information for competitive advantage.

By serving as the digital core of the organization, enterprise resource planning (ERP) solutions play a key role in business transformation for companies adapting to the emerging reality of Industry Four.0. AI solutions powered by ML will be a broad, high-impact class of technologies that serve as a key pole of more responsive business capabilities – both in manufacturing and all the sectors beyond. As such, ERP must embrace AI to supply the vision for the future: smarter, more efficient, more supple, more automated operations.

Enterprise applications powered by AI and ML will drive massive productivity gains via automation. This is not automation in the sense of repetitive, preprogrammed processes, but rather capabilities for software to treat administrative tasks and learn from user behavior to anticipate what every individual in the company might need next.

Cloud-based ERP is ideal for companies looking to accelerate transformation with AI and ML because it produces innovation quicker and more reliably than any onsite deployment. Users can take advantage of rapid iterations and optimize their processes around outcomes rather than upkeep.

Case in point: intelligent ERP applications need to include a digital assistant. This should be context-aware, designed to make business processes more efficient and automated. By providing information or suggestions based on the business context of the user and the situation, the digital assistant will permit every user to spend more time to concentrate on higher-value thinking instead of on repetitive tasks. Combined with built-in collaboration implements, this upgrade will speed reaction to switching conditions and create more time for innovation.

Imagine a system that, like a very capable assistant, can greet you in the morning with a helpful insight: “Hello Sven, I have assessed your situation and the most latest data – here are the areas you should concentrate on very first.” This treatment to contextualized analysis of real-time data is far more effective than a hard-programmed workflow or dump of information that leaves you to sort through outdated information.

Private assistants have been around in the consumer space for some time now, but it takes an ML-based treatment to bring that practice, and all its benefits, to the enterprise. Based on the tempo of switch in ML, a cloud-based ERP can best supply the latest innovations to users in a form that has instantaneous business applications.

An early application of ML in the enterprise will be intelligence derived from past patterns. The system will capture much richer detail of customer- and use-case-specific behavior, without the costs of by hand defining hard rule sets. ML can apply predictive detection methods, which are trained to support specific business use cases. And unlike pre-programmed rules, ML updates regularly as strategies – not monthly or weekly – but by the day, hour, and minute.

How ML and AI are making cloud ERP increasingly more intelligent

Digital has disrupted the world and switched the way businesses operate, creating a fresh level of complexity and speed. To stay competitive, businesses must convert to achieve a fresh level of agility. At the same time, advances in consumer technology (Siri, Alexa, and Google Now in the private assistant space, and uncountable mobile apps beyond that) have created a desire and need for intuitive user interfaces that anticipate the user’s needs. Building powerful devices that are effortless to interact with will rely on ML and predictive analytics solutions – all of which are uniquely suited to cloud deployment.

The next wave of innovation in enterprise solutions will integrate IoT, ML, and AI into daily operations. The devices will operate on every type of device and will apply native-device capabilities, especially around natural language processing and natural language interfaces. Augment this interface with machine learning, and you’ll see a system that deeply understands users and supports them with incredible speed.

What are some use cases for this intelligent ERP?

Digital assistants already help users keep better notes and take intelligent screenshots. They also link notes to the apps users were working on when they were created. Intelligent screenshots permit users to navigate to the app where the screenshot was taken and apply the same filter parameters. They recognize business objects within the application context and permit you to add them to your collection of notes and screenshots. Users can talk right from the business application without coming in a separate collaboration room. Because the digital assistants are powered by ML, they help you budge quicker the more you use them.

In the future, intelligent cloud ERP with ML will supply value in many ways. To name just a few examples (just scraping the surface):

  1. Finance accruals. Finance teams use a very manual and speculative process to determine bonus accruals. Applying ML to these calculations could instead generate a set of unbiased accrual figures, so finance teams have more time during closing periods for activities that require review and judgment.
  1. Project bidding. Companies rely powerfully on private practice when determining to bid for commercial projects. ML would give sales and project teams access to decades-worth of projects from around the world at the touch of a button. This capability would help firms determine whether to bid, how much to bid, and how to plan projects for greatest profitability.
  1. Procurement negotiation. Procurement involves a broad range of information and continuous supplier communication. Because costs go directly to the bottom line, anything that improves efficiencies and reduces inventory will make a real difference. ML can mine historical data to predict contract lifecycles and forecast when a purchasing contract is expected so that you can renegotiate to suit actual needs, rather than basing decisions on a hunch.

What does the near future hold?

An intelligent ERP puts the customer at the center of the solution. It produces lithe automation using AI, ML, IoT, and predictive analytics to drive digital transformation of the business. It produces a better practice for end users by providing live information in context and learning what the user needs in every screenplay. It eliminates decisions made on incomplete or outdated reports.

Digitization resumes to disrupt the world and switch the way businesses operate, creating a fresh level of complexity and speed that companies must navigate to stay competitive. Powering business innovation in the digital age will be possible by building and deploying the latest in AI-powered capabilities. We intend to stay deeply engaged with our most innovative playmates, our trusted customers, and end users to achieve the promises of the digital age – and we will judge our success by the extent to which everyone who uses our system can drive innovation.

Learn how SAP is helping customers deploy fresh capabilities based on AI, ML, and IoT to produce the latest technology seamlessly within their systems

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