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“Alexa, What Will Be Your Impact On E-Commerce?”

In a future teeming with robots and artificial intelligence, humans seem to be on the verge of being crowded out. But in reality the opposite is true.

To be successful, organizations need to become more human than ever.

Organizations that focus only on automation will automate away their competitive edge. The most successful will focus instead on skills that set them apart and that can’t be duplicated by AI or machine learning. Those skills can be summed up in one word: humanness.

You can see it in the numbers. According to David J. Deming of the Harvard Kennedy School, demand for jobs that require social skills has risen nearly 12 percentage points since 1980, while less-social jobs, such as computer coding, have declined by a little over 3 percentage points.

AI is in its infancy, which means that it cannot yet come close to duplicating our most human skills. Stefan van Duin and Naser Bakhshi, consultants at professional services company Deloitte, break down artificial intelligence into two types: narrow and general. Narrow AI is good at specific tasks, such as playing chess or identifying facial expressions. General AI, which can learn and solve complex, multifaceted problems the way a human being does, exists today only in the minds of futurists.

The only thing narrow artificial intelligence can do is automate. It can’t empathize. It can’t collaborate. It can’t innovate. Those abilities, if they ever come, are still a long way off. In the meantime, AI’s biggest value is in augmentation. When human beings work with AI tools, the process results in a sort of augmented intelligence. This augmented intelligence outperforms the work of either human beings or AI software tools on their own.

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AI-powered tools will be the partners that free employees and management to tackle higher-level challenges.

Those challenges will, by default, be more human and social in nature because many rote, repetitive tasks will be automated away. Companies will find that developing fundamental human skills, such as critical thinking and problem solving, within the organization will take on a new importance. These skills can’t be automated and they won’t become process steps for algorithms anytime soon.

In a world where technology change is constant and unpredictable, those organizations that make the fullest use of uniquely human skills will win. These skills will be used in collaboration with both other humans and AI-fueled software and hardware tools. The degree of humanness an organization possesses will become a competitive advantage.

This means that today’s companies must think about hiring, training, and leading differently. Most of today’s corporate training programs focus on imparting specific knowledge that will likely become obsolete over time.

Instead of hiring for portfolios of specific subject knowledge, organizations should instead hire—and train—for more foundational skills, whose value can’t erode away as easily.

Recently, educational consulting firm Hanover Research looked at high-growth occupations identified by the U.S. Bureau of Labor Statistics and determined the core skills required in each of them based on a database that it had developed. The most valuable skills were active listening, speaking, and critical thinking—giving lie to the dismissive term soft skills. They’re not soft; they’re human.

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This doesn’t mean that STEM skills won’t be important in the future. But organizations will find that their most valuable employees are those with both math and social skills.

That’s because technical skills will become more perishable as AI shifts the pace of technology change from linear to exponential. Employees will require constant retraining over time. For example, roughly half of the subject knowledge acquired during the first year of a four-year technical degree, such as computer science, is already outdated by the time students graduate, according to The Future of Jobs, a report from the World Economic Forum (WEF).

The WEF’s report further notes that “65% of children entering primary school today will ultimately end up working in jobs that don’t yet exist.” By contrast, human skills such as interpersonal communication and project management will remain consistent over the years.

For example, organizations already report that they are having difficulty finding people equipped for the Big Data era’s hot job: data scientist. That’s because data scientists need a combination of hard and soft skills. Data scientists can’t just be good programmers and statisticians; they also need to be intuitive and inquisitive and have good communication skills. We don’t expect all these qualities from our engineering graduates, nor from most of our employees.

But we need to start.

From Self-Help to Self-Skills

Even if most schools and employers have yet to see it, employees are starting to understand that their future viability depends on improving their innately human qualities. One of the most popular courses on Coursera, an online learning platform, is called Learning How to Learn. Created by the University of California, San Diego, the course is essentially a master class in human skills: students learn everything from memory techniques to dealing with procrastination and communicating complicated ideas, according to an article in The New York Times.

Although there is a longstanding assumption that social skills are innate, nothing is further from the truth. As the popularity of Learning How to Learn attests, human skills—everything from learning skills to communication skills to empathy—can, and indeed must, be taught.

These human skills are integral for training workers for a workplace where artificial intelligence and automation are part of the daily routine. According to the WEF’s New Vision for Education report, the skills that employees will need in the future fall into three primary categories:

  • Foundational literacies: These core skills needed for the coming age of robotics and AI include understanding the basics of math, science, computing, finance, civics, and culture. While mastery of every topic isn’t required, workers who have a basic comprehension of many different areas will be richly rewarded in the coming economy.
  • Competencies: Developing competencies requires mastering very human skills, such as active listening, critical thinking, problem solving, creativity, communication, and collaboration.
  • Character qualities: Over the next decade, employees will need to master the skills that will help them grasp changing job duties and responsibilities. This means learning the skills that help employees acquire curiosity, initiative, persistence, grit, adaptability, leadership, and social and cultural awareness.

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The good news is that learning human skills is not completely divorced from how work is structured today. Yonatan Zunger, a Google engineer with a background working with AI, argues that there is a considerable need for human skills in the workplace already—especially in the tech world. Many employees are simply unaware that when they are working on complicated software or hardware projects, they are using empathy, strategic problem solving, intuition, and interpersonal communication.

The unconscious deployment of human skills takes place even more frequently when employees climb the corporate ladder into management. “This is closely tied to the deeper difference between junior and senior roles: a junior person’s job is to find answers to questions; a senior person’s job is to find the right questions to ask,” says Zunger.

Human skills will be crucial to navigating the AI-infused workplace. There will be no shortage of need for the right questions to ask.

One of the biggest changes narrow AI tools will bring to the workplace is an evolution in how work is performed. AI-based tools will automate repetitive tasks across a wide swath of industries, which means that the day-to-day work for many white-collar workers will become far more focused on tasks requiring problem solving and critical thinking. These tasks will present challenges centered on interpersonal collaboration, clear communication, and autonomous decision-making—all human skills.

Being More Human Is Hard

However, the human skills that are essential for tomorrow’s AI-ified workplace, such as interpersonal communication, project planning, and conflict management, require a different approach from traditional learning. Often, these skills don’t just require people to learn new facts and techniques; they also call for basic changes in the ways individuals behave on—and off—the job.

Attempting to teach employees how to make behavioral changes has always seemed off-limits to organizations—the province of private therapists, not corporate trainers. But that outlook is changing. As science gains a better understanding of how the human brain works, many behaviors that affect employees on the job are understood to be universal and natural rather than individual (see “Human Skills 101”).

Human Skills 101

As neuroscience has improved our understanding of the brain, human skills have become increasingly quantifiable—and teachable.

Though the term soft skills has managed to hang on in the popular lexicon, our understanding of these human skills has increased to the point where they aren’t soft at all: they are a clearly definable set of skills that are crucial for organizations in the AI era.

Active listening: Paying close attention when receiving information and drawing out more information than received in normal discourse

Critical thinking: Gathering, analyzing, and evaluating issues and information to come to an unbiased conclusion

Problem solving: Finding solutions to problems and understanding the steps used to solve the problem

Decision-making: Weighing the evidence and options at hand to determine a specific course of action

Monitoring: Paying close attention to an issue, topic, or interaction in order to retain information for the future

Coordination: Working with individuals and other groups to achieve common goals

Social perceptiveness: Inferring what others are thinking by observing them

Time management: Budgeting and allocating time for projects and goals and structuring schedules to minimize conflicts and maximize productivity

Creativity: Generating ideas, concepts, or inferences that can be used to create new things

Curiosity: Desiring to learn and understand new or unfamiliar concepts

Imagination: Conceiving and thinking about new ideas, concepts, or images

Storytelling: Building narratives and concepts out of both new and existing ideas

Experimentation: Trying out new ideas, theories, and activities

Ethics: Practicing rules and standards that guide conduct and guarantee rights and fairness

Empathy: Identifying and understanding the emotional states of others

Collaboration: Working with others, coordinating efforts, and sharing resources to accomplish a common project

Resiliency: Withstanding setbacks, avoiding discouragement, and persisting toward a larger goal

Resistance to change, for example, is now known to result from an involuntary chemical reaction in the brain known as the fight-or-flight response, not from a weakness of character. Scientists and psychologists have developed objective ways of identifying these kinds of behaviors and have come up with universally applicable ways for employees to learn how to deal with them.

Organizations that emphasize such individual behavioral traits as active listening, social perceptiveness, and experimentation will have both an easier transition to a workplace that uses AI tools and more success operating in it.

Framing behavioral training in ways that emphasize its practical application at work and in advancing career goals helps employees feel more comfortable confronting behavioral roadblocks without feeling bad about themselves or stigmatized by others. It also helps organizations see the potential ROI of investing in what has traditionally been dismissed as touchy-feely stuff.

Q118 ft2 image3 automation DD “Alexa, What Will Be Your Impact On E Commerce?”In fact, offering objective means for examining inner behaviors and tools for modifying them is more beneficial than just leaving the job to employees. For example, according to research by psychologist Tasha Eurich, introspection, which is how most of us try to understand our behaviors, can actually be counterproductive.

Human beings are complex creatures. There is generally way too much going on inside our minds to be able to pinpoint the conscious and unconscious behaviors that drive us to act the way we do. We wind up inventing explanations—usually negative—for our behaviors, which can lead to anxiety and depression, according to Eurich’s research.

Structured, objective training can help employees improve their human skills without the negative side effects. At SAP, for example, we offer employees a course on conflict resolution that uses objective research techniques for determining what happens when people get into conflicts. Employees learn about the different conflict styles that researchers have identified and take an assessment to determine their own style of dealing with conflict. Then employees work in teams to discuss their different styles and work together to resolve a specific conflict that one of the group members is currently experiencing.

Q118 ft2 image5 talkingtoAI DD “Alexa, What Will Be Your Impact On E Commerce?”How Knowing One’s Self Helps the Organization

Courses like this are helpful not just for reducing conflicts between individuals and among teams (and improving organizational productivity); they also contribute to greater self-awareness, which is the basis for enabling people to take fullest advantage of their human skills.

Self-awareness is a powerful tool for improving performance at both the individual and organizational levels. Self-aware people are more confident and creative, make better decisions, build stronger relationships, and communicate more effectively. They are also less likely to lie, cheat, and steal, according to Eurich.

It naturally follows that such people make better employees and are more likely to be promoted. They also make more effective leaders with happier employees, which makes the organization more profitable, according to research by Atuma Okpara and Agwu M. Edwin.

There are two types of self-awareness, writes Eurich. One is having a clear view inside of one’s self: one’s own thoughts, feelings, behaviors, strengths, and weaknesses. The second type is understanding how others view us in terms of these same categories.

Interestingly, while we often assume that those who possess one type of awareness also possess the other, there is no direct correlation between the two. In fact, just 10% to 15% of people have both, according to a survey by Eurich. That means that the vast majority of us must learn one or the other—or both.

Gaining self-awareness is a process that can take many years. But training that gives employees the opportunity to examine their own behaviors against objective standards and gain feedback from expert instructors and peers can help speed up the journey. Just like the conflict management course, there are many ways to do this in a practical context that benefits employees and the organization alike.

For example, SAP also offers courses on building self-confidence, increasing trust with peers, creating connections with others, solving complex problems, and increasing resiliency in the face of difficult situations—all of which increase self-awareness in constructive ways. These human-skills courses are as popular with our employees as the hard-skill courses in new technologies or new programming techniques.

Depending on an organization’s size, budget, and goals, learning programs like these can include small group training, large lectures, online courses, licensing of third-party online content, reimbursement for students to attain certification, and many other models.
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Human Skills Are the Constant

Automation and artificial intelligence will change the workplace in unpredictable ways. One thing we can predict, however, is that human skills will be needed more than ever.

The connection between conflict resolution skills, critical thinking courses, and the rise of AI-aided technology might not be immediately obvious. But these new AI tools are leading us down the path to a much more human workplace.

Employees will interact with their computers through voice conversations and image recognition. Machine learning will find unexpected correlations in massive amounts of data but empathy and creativity will be required for data scientists to figure out the right questions to ask. Interpersonal communication will become even more important as teams coordinate between offices, remote workplaces, and AI aides.

While the future might be filled with artificial intelligence, deep learning, and untold amounts of data, uniquely human capabilities will be the ones that matter. Machines can’t write a symphony, design a building, teach a college course, or manage a department. The future belongs to humans working with machines, and for that, you need human skills. D!


About the Authors

Jenny Dearborn is Chief Learning Officer at SAP.

David Judge is Vice President, SAP Leonardo, at SAP.

Tom Raftery is Global Vice President and Internet of Things Evangelist at SAP.

Neal Ungerleider is a Los Angeles-based technology journalist and consultant.

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Video: Syncsort CTO on Trends in Data Science, Streaming & Cloud, and their Impact on Data Governance

During Strata Data Conference 2018 in San Jose, California, Syncsort CTO, Dr. Tendü Yoğurtçu sat down with theCUBE co-hosts George Gilbert and Lisa Martin at Big Data SV 2018. In the recorded interview, they discuss three key industry trends in Data Science, streaming and the Cloud, and how all of them create data governance challenges.

Watch the video to learn more about what organizations are doing as they work to make data their core strategy, and how Syncsort is working to help them.

blog banner landscape Video: Syncsort CTO on Trends in Data Science, Streaming & Cloud, and their Impact on Data Governance

Data Science Trends Complicate Data Governance

First, Tendü talks about how organizations are focused on preparing data for deep learning and artificial intelligence use. She also addresses how the data must be trusted to use with these technologies, heightening the importance of data integration and data quality to prepare, cleanse and match data.  Should we add supervised learning to this?  Tendü also addresses the advantage Syncsort has in having domain expertise to infuse machine learning algorithms and connect data profiling with data quality capabilities. That approach could help organizations recommend business rules and automate the mandated tasks.

Ensuring Data Governance Doesn’t Get “Cloudy”

Tendü explains that many organizations now have multiple workloads in hybrid clouds, creating governance challenges as well as necessitating more scoping and planning for the Cloud. She points out that Data Governance is the “umbrellas focus for everything we are doing at Syncsort,” because these other trends and developing next generation analytics environments require good data governance. The big driver is regulatory compliance, such as GDPR, which is “on the mind of every C-level exec” – not just for European companies – since most companies have European data sources in their environments. Security and availability of the data are key, and another critical aspect is delivering high quality data to data scientists.

Tendü talks about the importance of Syncsort’s design once, deploy anywhere strategy to enable organizations to run the same applications, without requiring any changes, across all their environments.

Data Governance Must Swim Up and Down Stream

Tendü also discussed another macro trend – streaming with connected devices. So much data is being generated, driving the need to process and stream data on the edge. In addition, the Kafka data bus is now a streaming data consumer, publishing data and making it available for applications and analytics in the data pipeline. Syncsort helps meet the resulting data governance challenges by providing CDC and real-time data replication capabilities.

For more on industry on trends in Data Science can be game-changing for IT organizations, be sure to check out our Strata Data Conference recap tomorrow!

Also, make sure to download our eBook, “The New Rules for Your Data Landscape“, and take a look at the rules that are transforming the relationship between business and IT.

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Philippine nonprofits amplify social impact with NetSuite

og image Philippine nonprofits amplify social impact with NetSuite

Zuellig Family Foundation, the Philippine Council for NGO Certification and Grupo Kalinangan, Inc. Improve Efficiency and Transparency with Oracle NetSuite Social Impact

SAN MATEO, Calif. and MAKATI CITY, Philippines—January 31, 2018—Oracle NetSuite, one of the world’s leading providers of cloud-based financials / ERP, HR, Professional Services Automation (PSA) and omnichannel commerce software suites, today announced three nonprofit customers in the Philippines that have streamlined mission-critical processes while saving significant time and costs by moving to NetSuite. The Zuellig Family Foundation (ZFF), the Philippine Council for NGO Certification (PCNC) and Grupo Kalinangan, Inc. are among a growing number of Philippine nonprofits and social enterprises that have improved operational and financial efficiency, transparency for donors, regulatory reporting and capacity for informed, data-driven decision making since graduating from entry-level business applications to NetSuite. By eliminating labor-intensive manual work, nonprofits are freeing up resources to better focus on their social missions across the Philippines in such areas as healthcare, education, business development, disaster relief and alleviation of poverty.

Zuellig Family Foundation, PCNC and Grupo Kalinangan are supported by the Oracle NetSuite Social Impact group, which makes available free and discounted software licensing to qualified nonprofits and social enterprises. The program also includes Suite Pro Bono, wherein NetSuite employees volunteer their time and expertise to help nonprofits with training and customizations to make the most of NetSuite. More than 1,000 nonprofits and social enterprises globally use Oracle NetSuite Social Impact offerings, including Philippine organizations like Coca-Cola Foundation Philippines, American Chamber Foundation Philippines, Cartwheel Foundation, Virlanie Foundation, Microventures Foundation, BagoSphere and Theo & Philo. To learn more about Oracle NetSuite Social Impact, please visit www.netsuite.com/socialimpact.

ZFF Improves Healthcare Delivery with Leadership Training

Zuellig Family Foundation (www.zuelligfoundation.org) has provided local training to improve healthcare in nearly 650 rural municipalities across 32 provinces since its founding in 2008. Established and funded by the Zuellig family, whose patriarch, Frederick Zuellig, emigrated from Switzerland in 1901 to establish trading companies in the Philippines, ZFF focuses on training local leaders to address healthcare issues. More than 2,000 leaders have participated in ZFF’s health leadership and governance training programs, applying their insights to improve healthcare at the local level. Based in Parañaque City with 80 full-time employees, ZFF partners with other organizations such as USAID, UNICEF and MSD Merck for Mothers, with funding rising 35 percent since 2013 to $ 196.8 million USD in 2016. NetSuite helps ZFF better manage its financials, gaining speed and visibility and eliminating time-consuming manual work the nonprofit experienced with its previous application. ZFF estimates PHP1.65 million in annual savings and cost avoidance compared to an SAP solution it evaluated before selecting NetSuite, going live in 2015. With those savings in licensing, IT infrastructure, IT personnel and bookkeeper, ZFF is better able to focus resources on improving rural healthcare.

“NetSuite has simplified and facilitated our compliance with reporting requirements by supporting internal controls and providing accurate, real-time reporting with a complete set of audit trails,” said Wesley Villanueva, Manager. “We have greater accountability and transparency and can generate real-time reports that aid in decision-making.”

PCNC Drives Effectiveness, Accountability with NGO Certifications

The Philippine Council for NGO Certification (www.pcnc.com.ph), a nonprofit that certifies NGOs as meeting organizational standards for financial management and transparency, helps improve accountability and effectiveness of nonprofits involved in education, healthcare, poverty alleviation and other missions. Created in 1997 by a consortium of NGO networks, PCNC currently certifies approximately 450 NGOs with a “Seal of Good Housekeeping,” based on evaluations conducted by more than a hundred volunteer evaluators on six criteria including mission, goals and financial management. That gives donors confidence that funding will be used for its intended purpose. Based in Manila with eight employees, PCNC also helps match NGOs with communities in need, improving local outcomes across the nation. Since going live in 2016, NetSuite has been playing a critical role by giving PCNC new capabilities to track and segment NGOs by mission, regions and other characteristics, helping bring together NGOs and beneficiaries. PCNC also uses NetSuite for automated communications, such as notification emails to NGOs six months before certification expires, and intends to use NetSuite LightCMS for a more modern website. NetSuite technical support and Suite Pro Bono volunteers have been instrumental in helping PCNC make the most of its system.

“What used to take a day of gathering and aggregating data now takes minutes,” said Luis Morales, PCNC Executive Director. “We’re saving a lot of time with the ability to generate data very easily and that means less cost and less manual work. We didn’t realize what we were missing with NetSuite. NetSuite really knows how to work with nonprofits.”

Grupo Kalinangan Advocates Preservation of Filipino Heritage

Grupo Kalinangan, Inc. (www.grupokalinangan.org), an organization founded in 2015 in Makati City, develops and deploys IT tools, services and support systems for local government and grassroots communities for improved cultural heritage management in the Philippines. Bringing together advocates and practitioners from allied fields including social science, architecture and IT, GKI is building a data platform and map of Philippine history and culture, while implementing projects and activities aligned with understanding how to better preserve and promote Philippine culture and heritage through big data and analytics. A key focus area for GKI, with five board members and about 50 volunteers, is the use of data exchange, analytics and software such as Arches, an open-source GIS tool to collect and share data, heightening awareness of the importance of Filipino heritage. GKI, being an early adopter of technology, has always embraced modern applications to streamline its own operations. Replacing a free Wave Accounting application with NetSuite, which went live in 2016, NetSuite helps GKI save time and money in managing financials, preparing annual reports, and improving transparency for donors and in government filings. Despite Grupo Kalinangan’s initial concern that NetSuite would be too complicated, the system has proven to be easy to use, especially with the training provided by Suite Pro Bono volunteers over a six-week period. As a more cost-efficient organization, GKI can put full focus on heritage without needless overhead.

“We’ve definitely improved our processes with NetSuite and its capabilities,” said Karl Aguilar, Corporate Treasurer. “We can focus on the most important things for our organization without getting bogged down by problems and complicated financial processes. NetSuite does the work for us.”

Addressing Modern Challenges with Modern Cloud Technology

Nonprofits and social enterprises in the Philippines and elsewhere see transformational improvements by using NetSuite for functions such as accounting, donor and project management, online commerce, fundraising and marketing. At the same time, NetSuite helps nonprofits address key pain points such as tight budgets, limited IT resources and increasing competition for funding. Benefits include:

Efficient financials and operations. Nonprofits eliminate error-prone, paper-based processes, dramatically reducing labor and cost and channeling savings into social impact programs.

Real-time visibility. Management can easily track revenue vs. expenses, fundraising effectiveness and project status, enabling informed decisions on priority focus areas and complete tracking of results.

Greater transparency. Grant accounting at a line-item level helps nonprofits track how, when and where a grant is spent on any given project, helping to meet exacting donor requirements.

Regulatory reporting. Nonprofits speed up and simplify external audits as well as reporting to regulatory bodies, publicly documenting key financial metrics used by prospective donors to assess a nonprofit’s worthiness of funding.

Increased fundraising effectiveness. Organizations gain a 360-degree view of their constituents and can utilize email marketing tools for personalized outreach and building long-term relationships.

Founded in 2006, the Oracle NetSuite Social Impact group is empowering nonprofits to use NetSuite to further their mission, regardless of their ability to pay. More than 1,000 nonprofits and social enterprises around the world are supported by NetSuite Social Impact, which makes available free and discounted software licensing to qualified organizations. The program also includes Suite Pro Bono, under which NetSuite employees provide their expertise to help nonprofits with training and customizations to make the most of the platform. To learn more about NetSuite Social Impact, please visit www.netsuite.com/socialimpact.

About Oracle NetSuite
Oracle NetSuite pioneered the Cloud Computing revolution in 1998, establishing the world’s first company dedicated to delivering business applications over the internet. Today, it provides a suite of cloud-based financials / Enterprise Resource Planning (ERP), HR and omnichannel commerce software that runs the business of companies in more than 100 countries.

For more information, please visit http://www.netsuite.com.

Follow NetSuite’s Cloud blog, Facebook page and @NetSuite Twitter handle for real-time updates.

About Oracle
The Oracle Cloud offers complete SaaS application suites for ERP, HCM and CX, plus best-in-class database Platform as a Service (PaaS) and Infrastructure as a Service (IaaS) from data centers throughout the Americas, Europe and Asia. For more information about Oracle (NYSE:ORCL), please visit us at oracle.com.

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Expert Interview (Part 3): Katharine Jarmul on Women in Tech and the Impact of Biased Data in Both Human & Machine Learning Models

At the recent Cloudera Sessions event in Munich, Germany, Paige Roberts, Syncsort’s Big Data Product Marketing Manager, had a chat with Katharine Jarmul, founder of KJamistan data science consultancy, and author of Data Wrangling with Python from O’Reilly. She had just given an excellent presentation on the implications of GDPR for the European data science community. Part 3 dives into the position of being one of the women in tech, the challenges of creating an inclusive company culture, and how bias doesn’t only exist in machine learning data sets.

In the first part of the interview, we talked about the importance of being able to explain your machine learning models – not just to comply with regulations like GDPR, but also to make the models more useful.

In part 2, Katharine Jarmul went beyond the basic requirements of GDPR, to discuss some of the important ethical drivers behind studying the data fed to machine learning models. A biased data set can make a huge impact in a world increasingly driven by machine learning.

Paige Roberts:I know, I’m probably a little obsessive about it, but one of the things I do is look around at every event, and calculate the percentage of women to men. And I must say, the percentage at this event is a little low on women.

Katharine Jarmul: Yeah.

So, do you find yourself in that situation a lot? Do you get that, “I’m the only woman in the room” feeling?

I would say that one of the biggest problems that I see in terms of women in technology is not that there’s not a lot of amazing women interested in tech, and it’s difficult for a lot of really talented women in tech to get recognized and promoted.

blog banner 2018 Big Data Trends eBook Expert Interview (Part 3): Katharine Jarmul on Women in Tech and the Impact of Biased Data in Both Human & Machine Learning Models

It feels like women have to be twice as good, to be recognized as half as good.

Yeah. And I think we’re finding out now, there’s a lot of other minority groups as well, who find it difficult, such as women of color. Maybe you have to work four times as hard. We see this exponential thing, and when you’re at an event where it’s mainly executives, or people that have worked their way up for a while, then you just tend to see fewer women, and that’s really sad. I don’t see it as a pipeline problem. I know a lot of people talk about it as a pipeline problem, and yeah, okay, we could have a better pipeline.

Yeah, we need a few more women graduating, but that’s not the problem. The problem is they don’t get as far as they should once they graduate.

Exactly, and maybe eventually they leave because they are tired of not being promoted, having somebody else promoted over them, not getting the cool projects so they can shine.

And some of it is just cultural in tech companies. You get that exclusionary feeling. I had a conversation recently, somebody I was talking to… Oh, I was talking to Tobi Bosede. She’s a woman of color, and she’s a machine learning engineer who did a presentation at Strata. She said something along the lines of, the guys I work with say, “Let’s go play basketball after work.” And everybody on the team does. She’s thinking, “I don’t even like basketball. I don’t really want to go play basketball with the guys after work, but I still feel left out.”

Yeah, I get that. It’s difficult to make a good team culture that’s inclusive. I think you must really work for it. I know some great team leads who are doing things that help, but I think especially if say, you’re a white guy that didn’t grow up with a lot of diversity in your family or your neighborhood, it might be more difficult for you to learn how to create that culture. You must work for it. It’s not just going to happen.

It’s almost like a biased data set in your life. You don’t recognize bias in yourself, until you stop and think about it. It doesn’t just jump out and make itself known.

Of course.

Jarmul pt3 quote women in tech Expert Interview (Part 3): Katharine Jarmul on Women in Tech and the Impact of Biased Data in Both Human & Machine Learning Models

I did an interview with Neha Narkhede, she’s the CTO at Confluent, and she was talking about hiring bias. Even as a woman of color herself, when hiring, she catches herself doing it, and must stop and think, and deliberately avoid bias. It’s in your own head. You think, I should know better.

Yeah, yeah. And I think these unconscious biases are things that we have, as humans. We all have some affinity bias, right? So, if somebody is like me, I’m going to automatically think that they’re clearer. They think like me, so I can more easily see their point. That’s fine but, one of the things that helps teams grow is having arguments, …

Having different points of view, and accepting that, “Okay, this guy thinks completely different from me, but maybe he’s got a point.”

I find myself doing the thing where I think, “Why did they disagree with me? How could they?”

They’re wrong, obviously. [laughing]

[laughing] Especially when I notice that I’m doing it like that, I say, “Okay, I need to sit down and think through this. Is there perhaps a cardinal truth here? Or something that bothers me because it doesn’t necessarily fit into my world view? And should I, perhaps, poke at that a little bit, and figure it out?”

Stop and think, introspect.

Yeah [laughs].

That’s a good word. I like that.

We have our own mental models, and we need to question the bias in them, too.

Be sure to check out part 4 of this interview where we’ll discuss some of the work Ms. Jarmul is doing in the areas of anonymization so that data can be repurposed without violating privacy, and creating artificial data sets that have the kind of random noise that makes real data sets so problematic.

For a look at 5 key Big Data trends in the coming year, check out our report, 2018 Big Data Trends: Liberate, Integrate & Trust

Related Posts:

Neha Narkhede, CTO of Confluent, Shares Her Insights on Women in Big Data

Yolanda Davis, Sr Software Engineer at Hortonworks, on Women in Technology

Katharine Jarmul on If Ethics is Not None

Katharine Jarmul on PyData Amsterdam Keynote on Ethical Machine Learning

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Expert Interview (Part 1): Wikibon’s James Kobielus Discusses the Explosive Impact of Machine Learning

It’s hard to mention the topics of automation, artificial intelligence or machine learning without various parties speculating that technology will soon throw everybody out of their jobs. But James Kobielus (@jameskobielus) sees the whole mass unemployment scenario as overblown.

The Future of AI: Kobielus Sees Progress Over Fear

Sure, AI is automating a lot of knowledge-based and not-so-knowledge-based functions right now. It is causing dislocations in our work and in our world. But the way Kobielus looks at it, AI is not only automating human processes, it’s augmenting human capabilities.

“We make better decisions, we can be more productive … We’re empowering human beings to do far more with less time,” he says. “If fewer people are needed for things we took for granted, that trend is going to continue.”

It’s anybody’s guess how the world will look in the future, Kobielus says. But he doesn’t believe in the nightmare scenarios in which AI puts everyone out of a job. Why? Basic economics.

The industries that are deploying AI won’t have the ability to get customers if everyone is out of a job.

“There needs to be buying power in order to power any economy, otherwise the AI gravy train will stop,” he says.

blog kobielus quote2 Expert Interview (Part 1): Wikibon’s James Kobielus Discusses the Explosive Impact of Machine Learning

Kobielus is the lead analyst with Wikibon, which offers market research, webinars and consulting to clients looking for guidance on technology. His career in IT spans more than three decades and three-quarters of it has been in analyst roles for different firms. Before going to Wikibon, he spent five years at IBM as a data science evangelist in a thought leadership marketing position espousing all things Big Data and data science.

He talks regularly on issues surrounding Big Data, artificial intelligence, machine learning and deep learning.

How Machine Learning is Impacting Industry Today

Machine learning is a term that’s been around for a while now, Kobielus says. At its core, it’s simply using algorithms and analytics to find patterns in data that you wouldn’t have been able to find otherwise. Regression models and vector machines are examples of more established forms of machine learning. Today, newer crops of algorithms are lumped under what are called neural networks or recurrent neural networks.

“That’s what people think of as machine learning – it’s at the heart of industry now,” Kobielus says.

Brands are using these neural network tools for face and voice recognition, natural language processing and speech recognition.

Applied to text-based datasets, machine learning is often used to identify concepts and entities so that they can be distilled algorithmically to determine people’s intentions or sentiments.

blog banner 2018 Big Data Trends eBook Expert Interview (Part 1): Wikibon’s James Kobielus Discusses the Explosive Impact of Machine Learning

“More and more of what we see in the machine learning space is neural networks that are deeper,” Kobielus says. “[They’re] not just identifying a face, but identifying a specific face and identifying the mood and context of situation.”

They’re operating at much higher levels of sophistication.

And rather than just being used in a mainframe, more often these algorithms are embedded in chips that are being put into phones, smart cars and other “smart” technologies.

Consumers are using these technologies daily when they unlock their phones using facial recognition, ask questions to tools like Alexa or automatically tag their friends on Facebook photos.

More and more industries are embracing deep learning – machine learning that is able to process media objects like audio and video in real time, offering automated transcription, speech to text, facial recognition, for instance. Or, the ability to infer the intent of a user from their gesture or their words.

Beyond just translating or offering automated transcriptions, machine learning provides a real-time map of all the people and places being mentioned and shares how they relate to each other.

Looking at the internet of things market, anybody in the consumer space that wants to build a smart product is embedding deep learning capabilities right now.

Top Examples of Machine Learning: Self-Driving Cars and Translations

Kobielus points to self-driving vehicles as a prime example of how machine learning is being used.

“They would be nothing if it weren’t for machine learning – that’s their brains.”

Self-driving vehicles process a huge variety of input including images, sonar, proximity, and speed as well as the behavior of the people inside– inferring their intent, where they want to go, what alternative routes might be acceptable based on voice, gestures, their history of past travel and more.

Kobielus is also excited about advances in translation services made possible by machine learning.

“Amazon Translate, human translation between human languages in real-time, is becoming scary accurate, almost more accurate than human translation,” Kobielus says.

In the not-too-distant future, he predicts that people will be able to just wear an earpiece that will translate a foreign language in real-time so they will be able to understand what people are saying around them enough to at least get by, if not more.

“The perfect storm of technical advances are coming together to make it available to everybody at a low cost,” he says.

Learn more about the top Big Data trends for 2018 in Syncsort’s eBook based on their annual Big Data survey.

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How the Holidays Can Impact Your FICO® Score

It’s no secret that the holidays mean more spending. But that can become a problem when spending leads to significantly higher credit card balances, missed payments, and a lower credit score.

If this happens to you during the holidays, you aren’t alone. Average revolving debt – the type of debt incurred by using credit cards – was 4.5% higher in January 2017 than in October 2016. Young consumers (age 18-34) exhibited the largest percent increase (5.0%) of any age group, driven by the fact that their average revolving debt levels are the lowest of all age groups.

Even more notable, 33% of Americans increased their total credit card debt by 10% or more between October 2016 and January 2017.

Not only can increased credit card debt feel stressful, but credit card debt can have an impact on FICO® Scores. Credit card debt increases your credit utilization ratio, which impacts your credit score. Credit utilization is a key component of the “amounts owed” category of the FICO® Score, which determines roughly 30% of a consumer’s FICO® Score. “Amounts owed” is the second most important category in the FICO score, behind only whether you make your payments on time, which makes up some 35% of the FICO® Score calculation.

So the amount of debt on revolving accounts is an important driver of your FICO® Score. Racking up large revolving balances may mean that you are overextended, and more likely to miss payments.  Similarly, people with lower debt and credit utilization levels tend to be less likely to miss payments, and as such, are rewarded by the score for their more careful use of credit.

To quantify the impact that a holiday ramp up in revolving debt can have on the FICO score, we analyzed FICO score shifts of those consumers with an increase of 10% or more in revolving debt amount between October and January of the following year.  To analyze the stability of this trend over time, we examined the FICO score impacts of holiday shopping over the last 3 winters (see Figure 1).

  • Of this population, almost twice as many consumers see their FICO® Score decrease vs. increase between October and January
  • 57% of these consumers experience a FICO® Score decrease between October and January, while only 33% see their FICO® Score increase
  • 1 in 5 of these consumers experience FICO® Score decreases of 20 points or more
    • This is higher than the total population, where only 1 in 9 consumers have FICO® Score decreases of 20 points or more

Figure 1

Because of the additional spending during this time, we also saw a small increase (0.35%) in the percent of consumers with one or more missed payments in the last three months. In October 2016, 9.27% of consumers had a recent delinquency, compared to 9.62% in January 2017. Young consumers had the largest increase (0.53%) in recent payment delinquencies of any age group (from 12.45% in October 2016 to 12.99% in January 2017), often because they do not have as much experience with how their credit score works.

For those consumers with recent delinquencies as of January, almost half experience FICO® Score decreases of 20 points or more between October and January (Figure 2). Roughly two out of three of these consumers see their FICO® Score decrease. This is in contrast to the total population, where only 36% of consumers have a FICO® Score decrease.

Figure 2

The score impact of a recent missed payment is greater for high-scoring consumers.  This is intuitive, as higher-scoring consumers have further to fall should payment blemishes suddenly begin appearing on their previously spotless credit record. Roughly 9 in 10 of consumers scoring 750 or higher in October 2016 have a score decrease of 20 or more points when they had a recent delinquency as of January 2017, while this is true for only 3 of 10 consumers for those scoring less than 600. So it is especially important for high-scoring individuals to ensure that they make their payments on time during the holidays.

While you may feel the need to spend more for the sake of holiday cheer, it’s critical to keep your credit score healthy this holiday season. Resist opening store credit cards or consider leaving your credit card at home – opt to shop with cash to keep from overspending. To avoid the post-holiday blues for your credit score, monitor your holiday spending and make sure to pay your bills on time. These two factors are the most important components of the FICO® Score calculation. If you successfully rein in your spending during the holidays, you may not need to make any resolutions around getting your FICO score back in shape in the New Year!

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The impact of self-learning software now and in the foreseeable future

 The impact of self learning software now and in the foreseeable future

We’ve spent so long wringing our hands and worrying about artificial and virtual intelligence that we forgot to roll out the welcome mat when they finally arrived.

Now, when major tech companies give their annual keynotes, they can’t help but pepper the narrative with phrases like “machine learning.” What does it all mean, though? Should we crank up the worry now that it looks like every tent-pole feature of self-learning software could also be a critical flaw?

The future is here — and it’s equal parts excitingand terrifying. Now that our world is populated with computer programs that can teach themselves new tricks, how will things change? What’s still worth worrying about?

Self-learning software for business and personal use

With 2018 upon us, the worlds of both business and personal software are ramping up to make the next few years something of an artificial intelligence arms race. On the consumer side of things, machine learning and AI make our lives easier in small ways. Case in point: many of us now have a smart speaker like an Amazon Echo or Google Home sitting on our countertops.

While these kinds of AI applications are helpful and entertaining, their self-learning capabilities are limited, to say the least.

In the world of business, there’s more immediate potential for self-learning software.

“We are drowning in information,” says Vita Vasylyeva of Artsyl Technologies. “The biggest bottlenecks in any business process involve the handling of documents and manual input of data from those documents. At the heart of those bottlenecks is the transformation of unstructured content into structured data.”

Nevertheless, both the business and consumer worlds have distinct needs and roles to play, and I fully expect machine learning in both realms to grow more sophisticated and capable.

Briefly, here are three very different applications for self-learning software:

1. Smartphones: Machine learning is turning smartphones into veritable supercomputers. From learning what your face looks like by poring through your photos to delivering more timely and relevant app and location suggestions, our devices are learning who we are and what we want.

More critically, machine learning is also training modern smartphones to become better at identifying and quarantining known threat vectors such as malware and viruses. It’s not all about fun and games.

2. Medicine: Diagnostic medicine is a difficult branch of science. Some types of cancer scans currently require as many as four specialists to study and come to a consensus on treatment.

With machine learning, physicians can practice this type of diagnostic medicine much faster, more accurately, and with fewer people-hours required.

3. Marketing and business management: The marketing applications of self-learning software perfectly marry the promises and the privacy worries of machine learning.

Some industry experts predict that within 10 years, even the humblest small businesses will engage in machine learning to improve their reach.

Another critical application is the promise of easier bookkeeping and organization. Newer document- and data-capture software suites take cues from the user to automatically identify and categorize types of documents and transactions, and in the process, significantly cut down on the labor and expense of staying organized and profitable.

Naturally, this is an abridged version of the emerging opportunities machine learning represents. Nearly every industry will likely come to rely on self-learning software in the future to make modern life more efficient.

The opportunities

So why the controversy? Why are folks like Elon Musk and Stephen Hawking doing their best Chicken Little impressions about AI and machine learning? Whether or not you subscribe to their possible doomsday scenarios, it’s fairly clear by now that the vast opportunity SLS offers is counterbalanced by some legitimate concerns.

For example, a major opportunity available now is the use of smarter machines to allocate resources more efficiently. For a smaller-scale look at what this means, consider the benefits of using self-learning software to make micro-variation adjustments to the way server farms consume electricity.

The result, according to researchers, is something almost eerily alive: a kind of silicon brain switching parts of itself on and off as needed to conserve basic resources. It’s the sort of thing that could help us come to terms with global warming and the sixth mass extinction in progress.

Removing the error-prone human element from the operation of automobiles is another huge opportunity made possible by machine learning. According to firsthand reports, the uncanniness of flying down a highway at 65 mph while an algorithm does the piloting wears off after a short while. Self-driving cars, in other words, are the future.

Alongside improved battery technology, we stand to benefit by dramatically slashing or eliminating our use of fossil fuels by making our commutes and traffic jams more efficient, and nonexistent, respectively. Cars of the future will be able to communicate with each other and pool data on things like road construction, obstructions, weather, and emerging incidents that could affect the drive.

The risks

Every one of the features above represents some type of privacy concern. Siri, Bixby, Cortana, and Google can’t perform their magic tricks without gathering data about their users.

Every tech giant that oversees these virtually intelligent personal assistants seems to take a different tack on user privacy. Your smartphone will send various types of personal data to distant server farms for processing each time you make an inquiry. What that company does with the information from there — and who they sell it to — is the stuff of terms of service fine print.

Beyond privacy, the other very real concerns about self-learning software are all about the consequences of removing human judgment — and in some cases emotion — from critically human experiences and interactions.

Wells Fargo and other major financial institutions wish to use artificial intelligence to dispassionately come to conclusions about their customers’ creditworthiness, for example — an idea that will either eliminate or greatly worsen preexisting cultural biases.

As far as self-driving cars go, a major learning curve is making ourselves comfortable with a world where our cars can solve the grisly “trolley problem” to our satisfaction. Are we comfortable writing software for a car that instructs the vehicle to end a human life to save five others?

Humans have historically had to bear the weight of that moral calculus — or didn’t have time to perform it at all in the vital split-seconds before a car crash. For better and worse, it seems machines can now do some of our ethical moralizings for us.

As you can see, determining the direction of where AI innovation will take us is a complex issue — but one that’s chock-full of potential.

The trick is getting scientists, philosophers, business leaders, citizens, and politicians on the same page.

Kayla Matthews is Senior Writer for MakeUseOf. Her work has also appeared on VICE, The Next Web, The Week, and TechnoBuffalo.

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Oracle + NetSuite Social Impact Launches Buildathon 4Good in Manila

Posted by Debra Askanase, Project Manager, Oracle NetSuite Social Impact

For Hazel May Pajotagana, CFO of HiGi Energy, a social enterprise focused on clean energy and environmentally friendly products, balancing the demands of managing finances for a busy nonprofit organization leaves little time to experiment and optimize HiGi Energy’s ERP system. That’s where the Oracle + NetSuite Social Impact Buildathon 4Good comes in.

The one-day event in Manila, Philippines provided real-time NetSuite customizations for seven local Non-Governmental Organizations (NGOs) and one social enterprise, all Social Impact donation recipients. They included the Philippine Foundation for Science and Technology, UP System Information Technology Foundation, HiGi Consultancy Corporation (HiGi Energy), Bayan Academy for Social Entrepreneurship and Human Resource Development, Ramon Magsaysay Award Foundation, Generation Hope, ChildHope Asia Philippines, and Save the Children Philippines.

The Buildathon is a new effort for the Social Impact group. Unlike the Hackathon 4Good held at SuiteWorld, the Buildathon 4Good focuses on helping multiple nonprofits solve the same challenge, and are deployed the same day. Organizations were invited to participate in either of two challenges: track the donor life cycle, or develop customized dashboards.

Suite Pro Bono 

Through the Suite Pro Bono program, 32 local NetSuite employees worked collaboratively with seven nonprofit organizations and one social enterprise to craft customized solutions to the challenges. Employees represented the Product, Support, and Professional Services departments. Four highly skilled employees served as team advisors who checked solution feasibility. Prizes were awarded to the teams that were the first to deploy, and to the overall challenge winners.

Judges included Hazel del Rosario-Lee (Managing Director of Oracle NetSuite Philippines), Anton Ancheta (Oracle NetSuite Consulting Senior Practice Director), Chester Que (CEO, Achieve Without Borders) and VJ Africa (CEO, Tech4Good).

Buildathon Oracle + NetSuite Social Impact Launches Buildathon 4Good in Manila

Hazel del Rosario Lee, Managing Director of Oracle NetSuite Philippines addressing the Buildathon 4 Good

For the Social Impact organizations, this was a rare chance to receive pro bono consulting. Every nonprofit works with leads, whether they are potential members, donors, or participants. Developing a customization to track the lead-to-donor lifecycle is a big step to increasing efficiencies.

Results 

The organizations left with at least one unique customization enabling them to better utilize NetSuite, and work more effectively. Many teams deployed solutions above and beyond the challenge requirements, adding additional customizations.

Hazel May Pajotagana, of HiGi Energy, learned a lot from the dashboard challenge.

“With the help of the team, I learned a lot about NetSuite…and NetSuite is helping us master the software.”

Bayan Academy for Social Entrepreneurship and Human Resource Development was awarded the overall dashboard winner, and ChildHope Asia Philippines won the lead-to-donor life cycle challenge. Philippines Director, Hazel del Rosario-Lee, in her concluding remarks, praised the team efforts and the Buildathon 4Good: “Thank you for maximizing NetSuite functionality.” The Social Impact group looks forward to many more successful Buildathon 4 Good events in the future.

Buildathon2 Oracle + NetSuite Social Impact Launches Buildathon 4Good in Manila

Learn more about what the Hackathon 4Good did for Found Animals Foundation and the winners of one recent Hackathon 4Good.

For more information on Oracle + NetSuite Social Impact, read our data sheet here and check out our website. For questions about the social donation program, contact socialimpact@netsuite.com.

Posted on Thu, December 14, 2017
by NetSuite filed under

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NCAP Medical Collection Removals are Rare and Have No Material Impact to FICO® Scores

Medical Collections Banner Image NCAP Medical Collection Removals are Rare and Have No Material Impact to FICO® Scores

The National Consumer Assistance Plan (NCAP) is a comprehensive series of initiatives intended to evaluate the accuracy of credit reports, the process of dealing with credit information, and consumer transparency. In a previous post, we showed that July 2017 NCAP public record removals (civil judgments and some tax liens) had no material impact to FICO® Scores. In mid-September 2017, the three consumer reporting agencies (CRAs) are also scheduled to remove the following from credit reports:

  • Medical collections less than 180 days old
  • Medical collections that are ‘paid by insurance’

FICO recently conducted research on a representative sample of millions of US consumers to assess the impact of the NCAP-driven removal of these 3rd party medical collection agency accounts on the FICO® Score.  Our results showed that NCAP-related medical collection removals have no material impact on the aggregate population to the FICO® Score’s predictive performance, odds-to-score relationship, or score distribution.

The removal of medical collections less than 180 days old is based on the date of first delinquency. Since most medical collections aren’t reported to the CRAs until more than 180 days after the first delinquency, we found that only 0.1% of the total FICO scorable population (roughly 200,000 consumers out of ~200 million) has a medical collection less than 180 days old. Medical collections that are identified in the credit file as being ‘paid by insurance’ are even less common.

Of the impacted population, roughly 3 in 4 saw score changes of less than 20 points. Most consumers with these medical collections have other derogatory information on their credit files, resulting in minimal impact to their FICO® Score once these collections are removed.

In 2014, FICO® Score 9 introduced a more sophisticated way of assessing collections, by ignoring all paid collections and differentiating unpaid medical collections from unpaid non-medical collections. Therefore, paid medical collections removed because of NCAP would already have been bypassed from FICO® Score 9.

FICO® Score 9’s enhancements led to improved predictive performance, while ensuring that those with different types of collections would receive a score commensurate with their credit risk. So these enhancements benefited lenders with a more predictive FICO® Score, while benefiting consumers who took a positive step toward credit responsibility and paid off a collection.

In conclusion, since NCAP medical collection removals are so rare, we observed virtually no perceptible impact to the ability of FICO® Scores to rank-order risk, volumes above or below score cut-offs, or bad rates at any given FICO® Score. Lenders can continue to rely on the stability and predictive performance of the industry standard FICO® Score.

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Introducing per-user usage metrics: know your audience and amplify your impact

This June, we released usage metrics for reports and dashboards in the Power BI service. The response has been tremendous. Usage metrics has already been used hundreds of thousands of times by report authors to measure their impact and prioritize their next investments. Since release, by far the biggest request has been for a way understand which users were taking advantage of your content.

You asked, and we delivered.

Today, we are excited to announce that we’re supercharging usage metrics bysurfacingthe names of your end users. And of course, you’re free to copy and customize the pre-built usage metrics reports to drill into the data. The change is currently rolling out worldwide, and should be completed by the end of the week.

This simple change has the potential to magnify your impact like never before. Now, you can understand exactly who your audience is, and reach out to your top users directly to gather asks and feedback.

Excited? Read on for a walkthrough for what’s new in this update of usage metrics.

Feature Overview

Going forward, when you go to dashboard or report usage metrics, you’ll also see a breakdown of number of views by user. The visual includes the display names and login names of your end users.

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Above:new visual in the pre-built usage metrics showing views by user

Note: if you have an existing personalized copy of the usage metrics report, you can continue using it as usual. However, you’ll need to re-personalize a copy to get the new per-user data.

With the “save as” feature, you can copy and customize the pre-built usage metrics report to further drill into how your end users are interacting with your reports. In this release, we’ve augmented the users dimension with display name, login name and the user’s GUID.

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Above: the updated users dimension when customizing the usage metrics report

Once you copy the report, you can remove the pre-set dashboard/report filter to see the usage data – including usernames and UPNs – for the entire workspace.

Tip: the UserGuid (aka Object ID) and UserPrincipalName are both unique identifiers for the user in AAD. That means if you export the usage metrics data, you could join the usage metrics data against more data from your directory, like organizational structure, job title, etc.

For a full overview of usage metrics and its capabilities, read through our documentation.

Administering Usage Metrics in Your Organization

As an IT admin, we understand that you may be tasked with ensuring that Power BI remain compliant with a variety of compliance regulations and standards. With this release, we are giving IT admins further control over which users in their organizations can take advantage of usage metrics.

The usage metrics admin control is now granular, allowing you to enable usage metrics for a subset of your organization. In addition, a new option in the admin portal will allow you to disable all existing usage metrics reports in your organization.

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Above: granular admin controls governing who has access to usage metrics

Together, these features give you full control over who in your organization can use usage metrics, regardless of how it’s currently being used. For example, if you have users taking advantage of usage metrics, yet would like to control its rollout, you could delete all usage metrics content for users to start with a blank slate. From there, you could enable the feature for an increasing set of security groups as the rollout progresses.

Next steps

  • Try out the feature! To get started, head to the Power BI service and go to any pre-built usage metrics report. Note that we’re still rolling out the feature worldwide, so you may have to wait until the end of the week to see it in action
  • Learn more about the feature through our documentation
  • Have comments? Want to share a cool use case for the feature? We’d love to hear it! Please leave comments below or in the community forums
  • Have ideas for where we should take the feature? Create new ideas or vote on existing ones in UserVoice

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