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Top 50 #BigData Twitter Influencers

Jen Cohen Crompton

#BigData – Twitter Influencers

We are aimed at becoming an authority on business innovation and want to help you identify the top influencers so you can follow the latest trends, news and opinions of these influencers in the field of Big Data. We’ll be publishing more lists on cloud computing, analytics and enterprise mobility in the coming weeks. In the meantime, here is the list of Top 50 Big Data Influencers on Twitter.

Note: Big Data Twitter influencers were determined based on tweeted topics, influence as measured by Klout, number of followers, and number of tweets. Below are the “top” influencers at this time based on the combination of factors.

@bigdata – Ben Lorica
Big Data, Analytics, Cloud Computing resources from Ben Lorica, Chief Data Scientist @OReillyMedia – San Francisco, CA · http://www.bglorica.com
Klout – 49

@BigDataAnalysis – John Akred
Big Data R&D Lead at Accenture Technology Labs, Musician, Engineer, Technologist, Analog Audio and Vacuum Tube lover, Provocateur. These thoughts are my own. – Chicago / San Jose · http://www.linkedin.com/pub/john-akred/1/b0a/320
Klout – 44

@imbigdata – Manish Bhatt
News and Updates about BigData, NoSQL, Hadoop, BI and other Big Data related technologies from BigData enthusiast Manish Bhatt
Klout – 24

@ibmbigdata – IBM Big Data
Talking about the challenges and approaches to handling Big Data. Primarily managed by @TheSocialPitt – http://www.ibm.com/bigdata · http://www.smartercomputingblog.com/category/big-data/
Klout – 37

@bobgourley – Bob Gourley
A CTO. Also find me @CTOvision and @CTOlist. National Security, Cyber Security, Enterprise IT and tech fun are key topics of interest.
Washington, DC · http://ctovision.com
Klout – 50

@klintron – Klint Finley
I write for SiliconAngle. I also run Technoccult and build strange soundscapes.
Portland, OR · http://klintfinley.com
Klout – 45

@KristenNicole2 – Kristen Nicole
News editor at SiliconANGLE, writer at Appolicious, recovering social media addict –  http://kristennicole.com
Klout – 40

@dhinchcliffe – Dion Hinchcliffe
Business strategist, enterprise architect, keynote speaker, book author, blogger, & consultant on social business and next-gen enterprises. – Washington, D.C. · http://dachisgroup.com
Klout – 52

@dmkimball05 – Dan Kimball
CMO at Kontagent, helping companies interpret patterns in social & mobile data to optimize their customer economics – San Francisco, CA · http://www.linkedin.com/in/danielkimball
Klout – 18

@HadoopNews – John Ching
Latest news about Hadoop, NoSQL & BigData from John Ching, Big Data Guru, Consultant, and Evangelist for BI, Machine Learning, and Predictive Analytics
Klout – 44

@medriscoll – Michael E. Driscoll
CEO @Metamarkets. I ♥ Big Data, analytics, and visualization. – San Francisco, CA · http://medriscoll.com/
Klout – 48

@peteskomoroch – Pete Skomoroch
My mission is to create intelligent systems that help people make better decisions. Principal Data Scientist @LinkedIn. Machine Learning, Hadoop, Big Data.
Silicon Valley · http://datawrangling.com
Klout – 56

@hmason – Hilary Mason
chief scientist @bitly. Machine learning; I ♥ data and cheeseburgers.
NYC · http://www.hilarymason.com
Klout – 56

@TimGasper – Tim Gasper
@Infochimps product manager, @Keepstream co-founder, techie, app addict, music writer/lover, #BigData, #Cloud – Austin, TX · http://timgasper.com
Klout – 43

@flowingdata – Nathan Yau
Data, visualization, and statistics. Author of ‘Visualize This.’ Background in eating. – California · http://flowingdata.com
Klout – 58

@bradfordcross – Bradford Cross
design and data @prismatic – San Francisco · http://getprismatic.com/
Klout – 44

@CityAge – City Age
We amplify good ideas through unique dialogues and their associated campaigns. We’re now organizing The Data Effect, amid other projects. – Vancouver, BC · http://www.thedataeffect.org
Klout – 32

@BigDataExpo – Big Data Expo
Join 30,000+ Delegates in 2012 at World’s Largest #Cloud Events! New York [June 11-14] Silicon Valley [Nov 5-8] Register & Save! ▸ http://bit.ly/tucY2B – New York/Silicon Valley · http://BigDataExpo.net
Klout – 34

@acmurthy – Arun C Murthy
Founder & Architect, Hortonworks. VP, Apache Hadoop, Apache Software Foundation i.e. Chair, Hadoop PMC. Moving Hadoop forward since day one, since 2006. – online · http://people.apache.org/
Klout – 43

@infoarbitrage – Roger Ehrenberg
Big Data VC at IA Ventures. Data junkie. Quant dude. Baseball coach. – ÜT: 40.76136,-73.980129 · http://www.iaventures.com
Klout – 56

@jeffreyfkelly – Jeff Kelly
I am an Industry Analyst covering Big Data and Business Analytics at The Wikibon Project and SiliconANGLE – Boston · http://wikibon.org
Klout – 45

@timoreilly – Tim O’Reilly
Founder and CEO, O’Reilly Media. Watching the alpha geeks, sharing their stories, helping the future unfold. – Sebastopol, CA · http://radar.oreilly.com
Klout – 69

@digiphile – Alex Howard
Gov 2.0 @Radar Correspondent, @OReillyMedia: alex@oreilly.com. Intrigued by technological change, taken with ideas, cooking, the outdoors, books, dogs and media – Washington, DC · http://radar.oreilly.com/alexh
Klout – 74

@band – William L. Anderson
Sociotechnical systems developer, open access advocate, and editor at CODATA Data Science Journal – austin texas
Klout – N/A

@HenryR – Henry Robinson
Engineer @ Cloudera, Zookeeper committer / PMC member, professional dilettante – San Francisco, CA · http://the-paper-trail.org/
Klout – 43

@furrier – John Furrier
Silicon Valley entrepreneur Founder SiliconANGLE Network. Inventing New Things, Blogging, Tweeting Social Media – Palo Alto, California · http://SiliconAngle.com
Klout – 49

@mikeolson – Mike Olson
Cloudera CEO – Berkeley, California · http://www.cloudera.com/
Klout – 48

@davenielsen – Dave Nielsen
Co-founder of CloudCamp & Silicon Valley Cloud Center – Mountain View, Ca · http://www.platformd.com
Klout – 44

@znmeb – M. Edward Borasky
Media Inactivist, Thought Follower, Sit-Down Comic, Former Boy Genius, Real-Time Data Journalism Researcher, Open Source Appliance Maker And Mathematician – Portland, OR · http://j.mp/compjournoserver
Klout – N/A

@rizzn – Mark ‘Rizzn’ Hopkins
I’m the editor in chief for SiliconANGLE and the purveyor of fine content at rizzn.com. · http://rizzn.com
Klout – N/A

@edd – Edd Dumbill
Telling the story of our future, where technology is headed, and what we need to know now. O’Reilly Strata and OSCON program chair. Incurably curious – California · http://eddology.com/
Klout – 57

@kellan – Kellan E
Technological solutions for social problems. CTO, Etsy. (if you follow me, consider introducing yourself with @kellan message) #47 – Brooklyn, NY · http://laughingmeme.org
Klout – 58

@mikeloukides – Mike Loukides
VP Content Strategy, O’Reilly Media, pianist, ham radio op usually in Connecticut
Klout – 54

@laurelatoreilly – Laurel Ruma
Director of Talent (speaker and author relations) at O’Reilly Media. Homebrewer, foodie, farmer in the city – Cambridge, MA · http://www.oreilly.com
Klout – 45

@neilraden – Neil Raden
VP/Principal Analyst,Constellation Research;Analytics, BigData, DecisionManagement. Author/Writer,Blogger,Speaker.Husband/(Grand)Father
Santa Fe, NM ·http://www.constellationrg.com/search/node/Neil%20Raden
Klout – 53

@greenplum – Greenplum
Greenplum, a division of EMC is driving the future of big data analytics.
San Mateo, California · http://www.greenplum.com/
Klout – 48

@squarecog – Dmitriy Ryaboy
Analytics Tech Lead at Twitter. Apache Pig committer.
San Francisco
Klout – 48

@BigData_paulz – Paul Zikopoulos
Director of Technical Professionals for IBM’s Information Management, BigData, and Competitive Database divisions. Published 15 books and over 350 articles.
Klout – 38

@moorejh – Jason H. Moore
Third century professor, Director of the Institute for Quantitative Biomedical Sciences at Dartmouth College, Editor-in-Chief of BioData Mining – Lebanon, NH, USA · http://www.epistasis.org
Klout – 47

@GilPress – Gil Press
I launched the #BigData conversation; Writing, research, marketing services; http://whatsthebigdata.com/ & http://infostory.wordpress.com/
Boston
Klout – 41

@ToddeNet – Todd E. Johnson PhD
Educational Access and Academic Sustainability • STEM •Data Informed Decisions (DID) • Always Dreaming and Learning(ADL)….Tweets here are my own!!
Olympia, WA · http://www.linkedin.com/in/toddenet
Klout – 27

@digimindci – Orlaith Finnegan
Provider of Competitive Intelligence & Market Intelligence Software. Online Reputation, Real-time Web Monitoring and Analysis, Social Media Monitoring.
Boston, Paris, Singapore · http://www.digimind.com
Klout – 36

@SmartDataCo – Smart Data Collective
Expert writers on analytics, BI and big data brought to you by the folks at Social Media Today.com · http://smartdatacollective.com
Klout – 42

@al3xandru – Alex Popescu
NOSQL Dreamer http://mynosql.tv, Software architect, Founder/CTO InfoQ.com, Web aficionado, Speaker, iPhone: 44.441881,26.139629 · http://mynosql.tv
Klout – 49

@marksmithvr – Mark Smith
CEO & Chief Research Officer at Ventana Research – http://www.ventanaresearch.com & follow

@ventanaresearch – San Ramon, CA · http://marksmith.ventanaresearch.com/
Klout – 51

@BernardMarr – Bernard Marr
Leading global authority and best-selling author on delivering, managing and measuring enterprise performance – London
Klout – 50

@johnlmyers44 – John L Myers
Senior Analyst for EMA Business Intelligence and Data Warehousing practice specializing in telecom analytics and business process management – Boulder, Colorado · http://www.enterprisemanagement.com/about/team/John_Myers.php
Klout – 54

@leenarao – Leena Rao
Tech Writer (tech Crunch), dog-lover, foodie, quirky – Chicago
Klout – 68

@HKotadia – Harish Kotadia Ph.D
Big Data, Predictive Analytics, Social CRM and CRM. Work for Infosys (NASDAQ: INFY). Views and opinion expressed are my own. – Dallas, Texas, USA · http://HKotadia.com/
Klout – 42

@chuckhollis – Chuck Hollis
technologist, marketeer, blogger and musician working for EMC.
Holliston, MA · http://chucksblog.emc.com
Klout – 49

Disclosure: I am being compensated by SAP to produce a series of posts on the innovation topics covered on this site. The opinions reflected here are my own.

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About Jen Cohen Crompton

Jen Cohen Crompton is a SAP Blogging Correspondent reporting on big data, cloud computing, enterprise mobility, analytics, sports and tech, and anything else innovation-related. When she's not blogging, she can be caught marketing, using social media and/or presenting at conferences around the world. Disclosure: Jen is being compensated by SAP to produce a series of articles on the innovation topics covered on this site. The opinions reflected here are her own.

How 3D Printing Could Transform The Chemical Industry

Stefan Guertzgen

The history of 3D printing started 30 years ago with Chuck Hull, the Thomas Edison of the 3D printing industry, who introduced the first 3D printer. Since then, 3D printing (also known as additive manufacturing) has been used to create everything from food and other consumer goods to automotive and airplane parts.

Key drivers of adoption

The tremendous growth of 3D printing has been driven by three key factors. First, the cost is rapidly decreasing due to lower raw material costs, stronger competitive pressures, and technological advancements. Second, printing speeds are increasing. For example, last year, startup company Carbon3D printed a palm-sized geodesic sphere in a little more than 6 minutes, which is 25 to 100 times faster than traditional 3D printing solutions. Third, new 3D printers are able to accommodate a wider variety of materials. Driven by innovations within the chemical industry, a broad range of polymers, resins, plasticizers, and other materials are being used to create new 3D products.

While it’s difficult to predict the long-term impact 3D printing will have on the overall economy, it is safe to say that the it could affect almost every industry and the way companies do business. In fact, the chemical industry has already implemented 3D applications in the areas of research and development (R&D) and manufacturing.

Innovative feedstocks and processes

3D printing provides a vast opportunity for the chemical industry to develop innovative feedstock and drive new revenue streams. While more than 3,000 materials are used in conventional component manufacturing, only about 30 are available for 3D printing. To put this into perspective, the market for chemical powder materials is predicted to be more than $630 million annually by 2020.

Plastics and resins, as well as metal powders and ceramic materials, are already in use or under evaluation for printing prototypes, parts of industry assets, or semi-finished goods—particularly those that are complex to produce and that require small batch sizes. Developing the right formulas to create these new materials offers an opportunity for constant innovation within the chemical field, which will likely produce even more materials in the future. For example, Covestro, a developer of polymer technology, is developing a range of filaments, powders, and liquid resins for all common 3D printing methods; 3M, working with its subsidiary Dyneon, recently filed a patent for using fluorinated polymers in 3D printing; and Wacker is testing 3D printing with silicones.

The chemical industry is also in the driver’s seat when it comes to process development. About 20 different processes now exist that share one common characteristic: layered deposition of printer feed. The final product could be generated from melting thermoplastic resins (for example, laser sinter technology or fused deposition modeling) or via (photo) chemical reaction such as stereo-lithography or multi-jet modeling. For both process types, the physical and chemical properties of feed materials are critical success factors for processing and for the quality of the finished product.

New tools and techniques in R&D and operations

Typically, the laboratory equipment used to do chemical synthesis is expensive and complex to use, and it often represents an obstacle in the research progress. With 3D printing, it is now possible to create reliable, robust miniaturized fluidic reactors as “micro-platforms” for organic chemical syntheses and materials processes, printed in few hours with inexpensive materials. Such micro-reactors allow building up target molecules via multi-step synthesis as well as breaking down molecular structures and detecting the building blocks through reagents which could be embedded during the 3D printing process.

Micro-reactors can also be used as small prototypes to simulate manufacturing processes.

In addition to printing equipment used in laboratories, some chemical manufacturers are using 3D printers for maintenance on process plant assets. For example, when an asset fails because of a damaged engine valve, the replacement part can be printed on site and installed in real time. Creating spare parts in-house can significantly reduce inventory costs and wait time for deliveries, hence contributing to increase overall asset uptime.

For companies that do not want to print the parts themselves, an on-demand manufacturing network is available that will print and deliver parts as needed. UPS has introduced a fully distributed manufacturing platform that connects many of its stores with 3D printers. When needed, UPS and its partners print and deliver requested parts to customers.

Commercial benefits

Across all industries, 3D printing promises to reduce costs across the supply chain. For example, the ability to print spare parts on demand can save money through improved asset uptime and more efficient workforce management. 3D printing also helps control costs with reduced waste and a smaller carbon footprint. In contrast to traditional “subtractive” manufacturing techniques in which raw material is removed, 3D printing is an additive process that uses only the amount of material that is needed. This can save significant amounts of raw materials. In the aerospace industry, for example, Airbus estimates 3D printing could reduce its raw material costs by up to 90 percent.

From a manufacturing perspective, 3D printing can streamline processes, accelerate design cycles, and add agility to operations. Printing prototypes on site speeds the R&D development cycle and shortens time to market. Researchers can make, test, and finalize prototypes in days instead of weeks. Also, the ability to print parts or equipment on demand will eliminate expensive inventory holding costs and restocking order requirements and free up floor space for other purposes. In the U.S. alone, manufacturers and trade inventories for all industries were estimated at $1.8 trillion in August 2016, according to the U.S. Census Bureau. Reducing inventory by just 2 percent would be a $36 billion savings.

Barriers to adoption

As with most new technology, barriers must be overcome for this potential to fully be realized. One much-discussed but unresolved issue is intellectual property protection. Similar to the way digital music is shared, 3D printable digital blueprints could be shared illegally and/or unknowingly either within a company or by outside hackers.

In addition to digital files, users can print molds from scanned objects and use them to mass-produce exact replicas that are protected under copyright, trademark, and patent laws. This problem will continue to grow as companies move to an on-demand manufacturing network, requiring digital blueprints to be shared with independent fabricators. This poses a huge threat on companies losing billions of dollars every year in intellectual property globally.

Regulatory issues are slowing the adoption of 3D printer applications. This is especially applicable in the medical and pharmaceutical industries but has potential impact in many markets. For example, globally regulating what individuals will create with access to the Internet and a 3D chemical printer will be difficult. Also, as 3D printing drives small and customer-specific lot sizes, it will likely spur an explosion of proprietary bills of material and recipes, which will be hard to track and control under REACH or REACH-like regulations. Because this is a new frontier, many regulatory issues must be addressed.

In addition to legal and regulatory challenges, the industry has a long way to go in reliably reproducing high-quality products. Until 3D printing can match the speed and quality output requirements of conventional manufacturing processes, it will likely be reserved for prototypes or small-sized lots.

3D printing: a new frontier

While 3D printing has not reached the point of use for large-scale production or to consistently make custom products, ongoing innovations drive high demand. 3D printer market forecasts estimate that shipments of industrial 3D printers will grow by ~400% through 2021 to a value of about $26 billion. Global inventory value is estimated to be over $10 trillion. Reducing global inventory by just 5% would free up $500 billion in capital. Manufacturing overall is estimated to contribute ~16% to the global economy. If 3D printing just would capture 5% of this $12.8 trillion market, it would create a $640 billion+ opportunity.

3D printing will initially help chemical companies increase profitability by lowering costs and improving operational efficiency. However, the industry-changing opportunity is the chance to develop new feeds and formulations. The most successful chemical companies of the future will be the ones with the vision to begin developing and implementing 3D printing solutions today.

Learn more about SAPPHIRE NOW and secure your spot today!

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About Stefan Guertzgen

Dr. Stefan Guertzgen is the Global Director of Industry Solution Marketing for Chemicals at SAP. He is responsible for driving Industry Thought Leadership, Positioning & Messaging and strategic Portfolio Decisions for Chemicals.

The Digital Future Of Healthcare

Andy David

The global population is projected to reach 10 billion people by 2050. Chronic diseases are rising: 73% of all deaths are expected to result from chronic diseases by 2020. Populations are aging: the number of people aged 60 years and older will rise from 900 million to 2 billion between 2015 and 2050; that’s a rise from 12% to 22% of the total global population. In this new era of digital connection, patients are transitioning from passive healthcare recipients to active value-seeking consumers.

Clearly, the world is changing. And so must the face of healthcare.

The digital era

Today, we are witnessing an unmatched era of digitally driven innovation in healthcare that will help healthcare providers overcome some of their major challenges. Breakthrough technologies such as cloud computing, supercomputing, Big Data, and the Internet of Things (IoT) have matured and hit scale together – opening a whole world of adjacent possibilities that will revolutionize how we approach and provide healthcare.

Research suggests that the digital health market is expected to reach US$206 billion by 2020, driven in particular by the mobile and wireless health market. That’s huge. And Asia-Pacific is expected to be a key region contributing to its growth.

So, what will the digital future of healthcare look like? We paint three scenarios.

Hyperconnected healthcare: Patient, professional, provider, and machine

Healthcare will be hyperconnected. Every patient, professional, provider, and machine will be connected, changing established rules for healthcare channels and driving collaboration.

Every patient will act as a human sensor – providing real-time healthcare data that translates into actionable insights not only for each person, but also feeding into larger medical research platforms that integrate and analyze data from many sources to uncover patterns that improve the wider community’s care.

Every healthcare professional will collaborate, functioning as human sensors – with treatment decisions and outcomes captured and analyzed in real-time, translating into medical insights that support future care.

By becoming connected to patients and professionals, the healthcare provider will be able to balance demand and supply with real-time insight and predictive analytics to optimize service offerings and eliminate waste.

Personalized patient-centered healthcare

We will witness the transformation to “healthcare made for me,” where empowered patients receive anticipatory services personalized to the segment-of-one – each individual patient.

Visualize a personalized health app for patient Samuel. He gets a reminder for a recommended routine checkup due to his personal health plan and age. His physician offers online appointment scheduling that Samuel initiates right through the app.

During Samuel’s checkup, his physician finds that a biopsy is needed and instantly orders the procedure, which is positive for cancer. Through the app, Samuel researches various sources and finds out that personalized offerings exist for his cancer type. He also finds out that his employer pays for such programs. The diagnostic service provider recommends specific treatment options and clinical studies based on Samuel’s genetic profile and the latest findings in clinical research. Samuel then starts treatment at a hospital that specializes in his type of cancer.

All his relevant data is made available anonymously through the digital health network for secondary scientific research and clinical trials, enabling continuous learning from each individual case.

Today, we already see examples of this, such as CancerLinQ, a groundbreaking Big Data solution that enables clinical data sharing and analysis on a massive scale. This allows clinicians and researchers to move beyond the merely three percent of patients represented in oncology clinical trials today, and also tap insights from the 97% of cancer patient data that’s been locked away in unconnected files and servers.

Digital hospital

Across the healthcare ecosystem, processes will be transformed completely. Hospitals will become digital by default and truly paperless from the back office, to patient flow management, to electronic medical records.

Imagine nurse Amy administering a critical medicine to a patient in Ward 7. The pharmaceutical inventory gets updated immediately and, because 50 other nurses also issued the same critical drug this week, it recognizes that the stock is running low and prompts procurement to purchase the product. The system automatically places an order, as it falls within preset parameters. All this time, finance can see the entire process.

Meanwhile, patient Nancy requires this critical medicine before she can be discharged from the hospital after a lengthy stay. Thanks to these digital processes, it will arrive tomorrow. Since this medication will allow Nancy to be discharged, the hospital knows it can admit a new patient who needs the hospital bed.

Nancy’s doctor updates her and shares details on her post-hospitalization care plan. It includes a health app that will connect to her weight scale, fitness tracker, and glucose meter and automatically capture measurements. The system will automatically trigger alerts so Nancy’s doctor can respond quickly to changing conditions and adapt the treatment plan until Nancy’s next appointment, even inviting her to video consultations before that appointment if the need arises.

All these digital technologies boost efficiency, slash costs, and improve clinical outcomes.

With these advances, healthcare organizations can create the digital future today. Are you ready to start your journey? Download more resources on digital transformation for healthcare.

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Andy David

About Andy David

Andy David is Director of Healthcare, Life Sciences, and Postal Industry Markets for the Asia-Pacific Japan region at SAP. He has more than 14 years of professional experience in IT applications for government, health, and manufacturing industries. He has been working with Public Sector organisation for over 12 years. As a member of the Public Sector team for Asia Pacific and Japan, Andy plays a pivotal role in determining the strategy across the region, which covers market analysis, business development, and customer reference, and building the SAP brand.

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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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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Enterprise Information Management: The Foundational Core Of Digital Transformation Success

Paul Lewis

The definition and implementation of digital transformation has become so muddled that no two organizations are focusing on the same strategies and initiatives. Many companies choose to engage in e-commerce and social media to extend their customer base with engaging, personalized, and round-the-clock shopping experiences. Some eye operational efficiencies through the Internet of Things (IoT) and artificial intelligence. And a growing segment is enticed by game-changing insights from analytics and social sentiments.

No matter the digital strategy, data is the foundation of all of these efforts. The customer experience is about understanding clients and offering services that answer their needs. Decision making requires stored knowledge that can be easily shared, secured, and applied. Operational excellence runs on meaningful insight that drives performance and keeps workers safe.

In digital transformation, every change relies on converting data into actionable decisions. According to Capgemini, companies that act on an enterprise information management (EIM) strategy outperform their rivals by as much as 26%.

The EIM difference in digital transformation

A data point by itself may seem unrelated and inconsequential. But when enterprise data is united and managed as one asset, decision makers finally have trusted, complete, and relevant information they need to seize opportunities and avoid risks that were previously hidden in the background.

One of my clients, Pravine Balkaran, global head of IT at Spin Master, one of the world’s largest toy and media entertainment companies, said it best: “It’s about being able to apply standardization and automation to the entire ecosystem to bring value and move the business forward.”

EIM derives new value by incorporating the traditional functions of data, including business intelligence, data science, analytics, data storage and archiving, data stewardship, and data mobility technology. The more data added, the more valuable the ecosystem becomes – without the complexity commonly experienced when searching for potentially valuable data across a diverse set of existing applications.

By applying EIM to the core of its digital strategy, companies like Spin Master are capturing and coalescing data from a variety of sources and turning it into actionable information to drive better decision making, innovate new products, enter new markets, and encourage a more responsive customer experience.

The EIM road map towards rapid creation of new value

Now for the hard part: Putting EIM into action and at the center of your digital transformation business strategy. There are five things you should do now before moving to a more digitalized and data-driven way of doing business.

1. Inventory available information

Most companies believe that their data resides in core databases and a data model of known entities such as claims, transactions, vendors, and suppliers. Although this is a widely used approach to determining the class of your information, it is only a small part of what you actually own. Structured, unstructured, and semi-structured data; log files; conversations; customer sentiment; and real-time information from suppliers and vendors, for example, should be integrated as part of the overall EIM philosophy.

2. Classify your inventory

Data typically can be classified with one or more of these six attributes:

  • Real-time, streaming data, which potentially comes from machines
  • Static data from production databases
  • Valuable data in real time once stored
  • Realizes value over time and as it changes
  • Relevant to a particular government mandate or legislative concern
  • Objective and relative importance to divisions of the overall enterprise, including customers and the business network

With this exercise, you can begin to understand the function that each data point serves and its usefulness in the future.

3. Encourage the business culture to appreciate the value of discovery

Data-driven decision making is not based on blind faith that data always tells the right story. Rather, it is asking the right questions, and knowing how to dig deep into the data helps us make the connections we need to get an accurate picture of the current situation. Once you discover those nuggets of insight gold, data science and advanced analytics can be applied to pinpoint the appropriate solution. Later, you can leverage data visualization tools to communicate findings and proposed action in a format that is quick and easy for all levels of the enterprise to consume.

4. Shift your focus from yesterday to today and beyond

Traditionally, data analysis is an exercise of looking backward to determine the how, what, when, and why an event happened. However, the pace of change in every aspect of the business has accelerated so much, that it’s rendered this retrospective approach to analytics nearly useless. Real-time access to data allows decision makers to know what’s happening in the moment and how it will impact the future to seize opportunities and mitigate risks.

The path to digital transformation is paved with data

The volume of data generated by people across the entire business network – from employee to consumer and everyone in between – represents a veritable trove of information, insights, and inspiration for innovation. But first, companies need to know where to find this data and how to best apply it to everyday decision making. With EIM, data can be broken down and reassembled into a manageable form that is meaningful, outcome-driven, and transformational.

Learn more about how to uncover Data – The Hidden Treasure Inside Your Business.

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Paul Lewis

About Paul Lewis

Paul Lewis is the Chief Technology Officer in Hitachi for the Americas, responsible for the leading technology trend mastery and evangelism, client executive advocacy, and external delivery of the Hitachi vision and strategy especially related to digital transformation and social innovation. Additionally, Paul contributes to field enablement of data intelligence and analytics; interprets and translates complex technology trends including cloud, mobility, governance, and information management; and represents the Americas region in the Global Technology Office, the Hitachi LTD R&D division. In his role of trusted advisor to the CIO community, Paul’s explicit goal is to ensure clients’ problems are solved and opportunities realized. Paul can be found at his blog, on Twitter, and on LinkedIn.