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Living The Live Supply Chain: Why You Need Data Scientists

Hans Thalbauer

In Part 1 of this series we explored the essentials of deploying a live supply chain. In Part 2 we look at why data scientists will be increasingly key to supply chain success.

When it’s completed in 2030, the Square Kilometer Array will be the largest telescope ever built, and will capture 35,000 DVDs of data every second. When astronomers showed off an early iteration in July 2016, they pointed it at a moon-size section of sky. What did they find? Nearly 1,300 previously unknown galaxies.

Supply chain operators can be forgiven for feeling like those astronomers. The trove of new data they’re capturing — from business systems, IoT devices, social media, and so on — has the potential to transform their views of customers, suppliers, manufacturing, logistics, and more. But making sense of all that data can be more than challenging. For that, they’ll increasingly need data scientists.

From business as usual to business-critical

Actively managing supply chain performance has never been more business-critical. Globalization, regulatory requirements, technology complexity, volatility of supply and demand, and greater dependence on suppliers have all increased business risk. The only way to make sure the supply chain operates in a way that meets customer needs and drives business success is by leveraging data in as close to real time as possible.

Increasingly, that data will be both structured and unstructured. Structured information from business systems includes traditional transactional data such as purchasing, production orders, and sales.

But you can’t operate a truly real-time, or “live,” supply chain without unstructured data. And that will come from a variety of sources. The rapidly falling cost of IoT technology means you can embed sensors in everything from production equipment to low-cost consumer goods. Social media can contribute customer sentiment about companies and products to help you sense demand, risk, and opportunities. Crowdsourcing apps can let you track everything from weather to traffic to holiday spending.

Data scientists to the rescue

In the meantime, logistics operators are grappling with an aging, shrinking talent pool. Logistics employs 6 million people in the United States, but it will need another 270,000 new workers per year to keep up with growth. At the same time, 60 million baby boomers will exit the workforce over the next nine years, but only 40 million younger workers will replace them, according to U.S. Census data.

It’s no wonder 79% of participants in the 2016 Third-Party Logistics Study feel unprepared for the impact of the labor shortage on their supply chains. And only 38% of executives are “extremely or very confident” their supply chain has the competencies it needs.

In particular, a live supply chain requires the data scientists — and technology — that can wring the most value from your data. That starts with identifying relevant data sources, figuring out how to capture the data streams, and understanding how to harmonize it at the most granular level. It continues with the ability to parse useful information from data noise, and to analyze the useful information to extract new insights.

Those insights then need to be placed in the proper context for each function. The same information holds different value — and needs to be delivered in different ways — for R&D, production planners, logistics managers, executive decision makers, and so on.

Perhaps most important, data scientists must empower the supply chain with predictive analytics that let you quickly and accurately forecast demand. That needs to happen before competitors make the same predictions — and before your customers realize they have desires your business isn’t meeting.

Thanks to sophisticated scientists and technology, researchers just determined that the universe holds 10 times more galaxies than previously thought. With the right talent and tools, what vast new opportunities will your supply chain discover?

Learn more about how running a live supply chain can help you thrive today and innovate for tomorrow, visit us at SAP.com.

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Hans Thalbauer

About Hans Thalbauer

Hans Thalbauer is globally responsible for solution management and the go-to-market functions for SAP digital supply chain solutions and the SAP Leonardo portfolio of Internet of Things solutions. In this role, he is engaged in creative dialogues with businesses and operations worldwide, addressing customer needs and introducing innovative business processes, including the vision of creating a live business environment for everyone working in operations. Hans has more than 17 years with SAP and is based out of Palo Alto, CA, USA. He has held positions in development, product and solution management, and the go-to-market organization. Hans holds a degree in Business Information Systems from the University Vienna, Austria.

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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How Emotionally Aware Computing Can Bring Happiness to Your Organization

Christopher Koch


Do you feel me?

Just as once-novel voice recognition technology is now a ubiquitous part of human–machine relationships, so too could mood recognition technology (aka “affective computing”) soon pervade digital interactions.

Through the application of machine learning, Big Data inputs, image recognition, sensors, and in some cases robotics, artificially intelligent systems hunt for affective clues: widened eyes, quickened speech, and crossed arms, as well as heart rate or skin changes.




Emotions are big business

The global affective computing market is estimated to grow from just over US$9.3 billion a year in 2015 to more than $42.5 billion by 2020.

Source: “Affective Computing Market 2015 – Technology, Software, Hardware, Vertical, & Regional Forecasts to 2020 for the $42 Billion Industry” (Research and Markets, 2015)

Customer experience is the sweet spot

Forrester found that emotion was the number-one factor in determining customer loyalty in 17 out of the 18 industries it surveyed – far more important than the ease or effectiveness of customers’ interactions with a company.


Source: “You Can’t Afford to Overlook Your Customers’ Emotional Experience” (Forrester, 2015)


Humana gets an emotional clue

Source: “Artificial Intelligence Helps Humana Avoid Call Center Meltdowns” (The Wall Street Journal, October 27, 2016)

Insurer Humana uses artificial intelligence software that can detect conversational cues to guide call-center workers through difficult customer calls. The system recognizes that a steady rise in the pitch of a customer’s voice or instances of agent and customer talking over one another are causes for concern.

The system has led to hard results: Humana says it has seen an 28% improvement in customer satisfaction, a 63% improvement in agent engagement, and a 6% improvement in first-contact resolution.


Spread happiness across the organization

Source: “Happiness and Productivity” (University of Warwick, February 10, 2014)

Employers could monitor employee moods to make organizational adjustments that increase productivity, effectiveness, and satisfaction. Happy employees are around 12% more productive.




Walking on emotional eggshells

Whether customers and employees will be comfortable having their emotions logged and broadcast by companies is an open question. Customers may find some uses of affective computing creepy or, worse, predatory. Be sure to get their permission.


Other limiting factors

The availability of the data required to infer a person’s emotional state is still limited. Further, it can be difficult to capture all the physical cues that may be relevant to an interaction, such as facial expression, tone of voice, or posture.



Get a head start


Discover the data

Companies should determine what inferences about mental states they want the system to make and how accurately those inferences can be made using the inputs available.


Work with IT

Involve IT and engineering groups to figure out the challenges of integrating with existing systems for collecting, assimilating, and analyzing large volumes of emotional data.


Consider the complexity

Some emotions may be more difficult to discern or respond to. Context is also key. An emotionally aware machine would need to respond differently to frustration in a user in an educational setting than to frustration in a user in a vehicle.

 


 

download arrowTo learn more about how affective computing can help your organization, read the feature story Empathy: The Killer App for Artificial Intelligence.


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Christopher Koch

About Christopher Koch

Christopher Koch is the Editorial Director of the SAP Center for Business Insight. He is an experienced publishing professional, researcher, editor, and writer in business, technology, and B2B marketing. Share your thoughts with Chris on Twitter @Ckochster.

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In An Agile Environment, Revenue Models Are Flexible Too

Todd Wasserman

In 2012, Dollar Shave Club burst on the scene with a cheeky viral video that won praise for its creativity and marketing acumen. Less heralded at the time was the startup’s pricing model, which swapped traditional retail for subscriptions.

For as low as $1 a month (for five two-bladed cartridges), consumers got a package in the mail that saved them a trip to the pharmacy or grocery store. Dollar Shave Club received the ultimate vindication for the idea in 2016 when Unilever purchased the company for $1 billion.

As that example shows, new technology creates the possibility for new pricing models that can disrupt existing industries. The same phenomenon has occurred in software, in which the cloud and Web-based interfaces have ushered in Software as a Service (SaaS), which charges users on a monthly basis, like a utility, instead of the typical purchase-and-later-upgrade model.

Pricing, in other words, is a variable that can be used to disrupt industries. Other options include usage-based pricing and freemium.

Products as services, services as products

There are basically two ways that businesses can use pricing to disrupt the status quo: Turn products into services and turn services into products. Dollar Shave Club and SaaS are two examples of turning products into services.

Others include Amazon’s Dash, a bare-bones Internet of Things device that lets consumers reorder items ranging from Campbell’s Soup to Play-Doh. Another example is Rent the Runway, which rents high-end fashion items for a weekend rather than selling the items. Trunk Club offers a twist on this by sending items picked out by a stylist to users every month. Users pay for what they want and send back the rest.

The other option is productizing a service. Restaurant franchising is based on this model. While the restaurant offers food service to consumers, for entrepreneurs the franchise offers guidance and brand equity that can be condensed into a product format. For instance, a global HR firm called Littler has productized its offerings with Littler CaseSmart-Charges, which is designed for in-house attorneys and features software, project management tools, and access to flextime attorneys.

As that example shows, technology offers opportunities to try new revenue models. Another example is APIs, which have become a large source of revenue for companies. The monetization of APIs is often viewed as a side business that encompasses a wholly different pricing model that’s often engineered to create huge user bases with volume discounts.

Not a new idea

Though technology has opened up new vistas for businesses seeking alternate pricing models, Rajkumar Venkatesan, a marketing professor at University of Virginia’s Darden School of Business, points out that this isn’t necessarily a new idea. For instance, King Gillette made his fortune in the early part of the 20th Century by realizing that a cheap shaving device would pave the way for a recurring revenue stream via replacement razor blades.

“The new variation was the Keurig,” said Venkatesan, referring to the coffee machine that relies on replaceable cartridges. “It has started becoming more prevalent in the last 10 years, but the fundamental model has been there.” For businesses, this can be an attractive model not only for the recurring revenue but also for the ability to cross-sell new goods to existing customers, Venkatesan said.

Another benefit to a subscription model is that it can also supply first-party data that companies can use to better understand and market to their customers. Some believe that Dollar Shave Club’s close relationship with its young male user base was one reason for Unilever’s purchase, for instance. In such a cut-throat market, such relationships can fetch a high price.

To learn more about how you can monetize disruption, watch this video overview of the new SAP Hybris Revenue Cloud.

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