Monthly Archives: January 2017

Google commits up to $4 million to help those impacted by Trump’s immigration order

google io 780x520 Google commits up to $4 million to help those impacted by Trump’s immigration order

As part of the largest crisis campaign of its company history, Google is expected to raise $ 4 million in support of people affected by President Trump’s immigration order, which was announced Friday.

News of Google’s campaign follows statements against the controversial ban by company CEO Sundar Pichai and the participation of its co-founder Sergey Brin in a protest at San Francisco International Airport, USA Today reports.

The $ 4 million — a composite of a $ 2 million fund put up by Google, and up to $ 2 million more in employee donations — will be donated to the American Civil Liberties Union, the Immigrant Legal Resource Center, the International Rescue Committee and the United Nation’s refugee agency (UNHCR.)

According to Pichai, Trump’s controversial order banning immigrants from seven Muslim-majority countries from entering the U.S. affects 187 members of Google’s staff alone.

“We’re concerned about the impact of this order and any proposals that could impose restrictions on Googlers and their families, or that could create barriers to bringing great talent to the U.S.,” he said in a statement. “We’ll continue to make our views on these issues known to leaders in Washington and elsewhere.”

Google is not the only tech company to speak out against Trump’s order.

Facebook, Apple, Lyft, and Uber have voiced varying degrees of alarm, Fortune’s Tory Newmyer reported Sunday.

Executives at Tesla Motors, Netflix and Airbnb (airbnb) have also denounced the policy. The latter announced this weekend it would offer free accommodation for refugees and others affected by the clampdown.

“Barring refugees and people who are not a threat from entering America simply because they are from a certain country is not right, and we must stand with those who are affected. The doors to America shall remain open, and any that are locked will not be for long,” Airbnb CEO Brian Chesky wrote on a note to employees posted on the company’s website Sunday.

This story originally appeared on Fortune.com. Copyright 2017

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Big Data – VentureBeat

10 Content Creation Mistakes That Make Me Crazy

blog title woman discovering content mistake 351x200 10 Content Creation Mistakes That Make Me Crazy

9. Making assertions without backing them up.

“Post-truth” era aside and all, as a business person, I want you to back up what you say. You don’t have to back up every single assertion, but the major ones should be supported by evidence, or at least other people’s opinion (evidence is better).

Here’s why this matters: Unless you’re an influencer (and a really major one, at that), nobody cares what you think. Sorry. Nobody cares what I think, either. That’s why we back up what we say.

When you do back up what you say, please: Go find the original research.

There’s a spooky game of telephone going on in a lot of content. It happens when someone cites a piece of research from another article. Let’s call this article in development “Article A,” and the article it’s citing “Article B.” Trouble is, article B took that information from Article C, which took it from Article D. Article D took it from the original research report.

Linking directly to the research doesn’t cost anything. So let your readers skip the breadcrumb trail. You do the work and find the original research.

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Act-On Blog

Top Ten Digitalist Magazine Posts Of The Week [January 30, 2017]

Blame House of Cards. The Netflix-produced hit was the result of an algorithm coupled with Netflix’s large collection of data on what viewers like to watch. Taking that as inspiration, ad agency McCann Erickson recently added the world’s first artificial intelligence (AI)-based creative director to its team in Japan. The memorably named AI-CD β will use data on award-winning commercials to produce ideas for new campaigns.

The company isn’t the first to let the math do the thinking: In 2014, Hong Kong–based venture capital firm Deep Knowledge Ventures announced a new addition to its board of directors, VITAL, which uses data to vote on potential investments. More recently, Finnish tech company Tieto welcomed Alicia T., an AI complete with a conversational interface, to the board (a win for board diversity?) of its new data-driven business services unit.

This doesn’t mean AI-based technology will become your overlords or replacements. Instead, they will very likely be colleagues. (Just don’t expect them to pay for lunch.)

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For all the fears of AI taking all our jobs, the reality will likely be less dramatic. Outside of repetitive work that requires no independent thought, AI-human collaboration, rather than outright replacement, is the future of work. Jobs will be shared, with some tasks delegated to AI and monitored, to some extent, by humans. According to McKinsey & Co., 60% of jobs could see 30% or higher automation of some tasks.

But there will need to be big technological and cultural shifts to make AI as common as your laptop. The corporate structure as we know it could disappear as conventional hierarchies are replaced by new models with different emphases and values. Meanwhile, the skills that executives and employees need to bring to an enterprise will change. Creativity and problem solving will become the highest-valued human abilities.

Better start preparing your organization now.

Meet Your New Colleague

When Oxford Martin School and Citi GPS released the 2015 report Technology at Work, one particular number garnered a lot of attention—and more than a bit of panic. According to the report’s authors, about 47% of U.S. jobs were at high risk from computerization (19% were at medium risk, and 33% at low risk).

McKinsey & Co. reports a slightly different take. Yes, automation is coming, but it might not be for your entire job, just part of it (hopefully, the part you don’t like to do). McKinsey thinks that looking at specific tasks is a much better predictor of automation. Highly repetitive work, such as data processing, is very likely to be automated, as are some management tasks, such as monitoring and measuring certain outputs and performance. But other, more relationship-centric levels of managing, decision making, and planning won’t be going to the machines anytime soon. In fact, the number of jobs that will be entirely replaced could be very low. (The bigger incidence of near-future automation is likely to happen in developing economies where there is still a large dependence on human labor.)

Augmentation isn’t a new philosophy. Engineer and inventor Douglas Engelbert—who created the first computer mouse in 1964—was one of the early tech pioneers who believed that technology would work alongside humans.

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His idea was that the true purpose of technology was to augment and amplify human capabilities, not completely replace human labor.

Indeed, most jobs have both repetitive and consistent tasks and intellectual and decision-making elements, says Sam Ransbotham, associate professor of information systems at the Carroll School of Management at Boston College. The question, he says, is how things will be divvied up between the humans and the machines. Just don’t expect it to be an all-or-nothing landscape.

“Much of the framing historically of automation has been around this dichotomy,” Ransbotham says. “That’s not really a productive way of thinking of this particular change. We’re not going to stop changes from happening, but it’s different when we’re talking about augmenting more knowledge-based work.”

AI as Decision Collaborator

Thomas H. Davenport, President’s Distinguished Professor of information technology and management at Babson College and co-author (with Julia Kirby) of Only Humans Need Apply: Winners and Losers in the Age of Smart Machines, thinks that delineation of automation in knowledge-based work will operate at the decision-making level. Highly repetitive decisions that are backed up with significant data will be relegated to AI. But even then a human role remains.

“Humans will need to check on the outcomes and see if the models and the algorithms and the rules are performing as intended, and then intervene if they don’t,” Davenport says. “I think that humans are more likely to be the integrators, the final decision makers, who sort of assemble the different opinions from different machines.”

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Even in a scenario such as Wall Street trading, where decisions are largely left to machines, it’s still important for checks and balances to be put in place, says Davenport. “It’s not easy to  intervene after a decision has been automated,” he says. “We have a tendency to let it ride, maybe often beyond the time when we should.”

Labor will be divided along the lines of what AI can and can’t do; creativity, abstract concepts, and many other human qualities are not AI capabilities, for example. It’s still a challenge to create AI that can truly replicate the kinds of human abilities we take for granted, and many of the decisions that managers and executives make on a daily basis don’t easily fit into the AI paradigm. The complexity of human relationships, minds, and cultures are currently beyond AI’s grasp, and this extends into the workplace. The algorithmically defined “right” decision might not necessarily be the best political and social decision. The role of AI in these instances could be to clarify and winnow options and to help identify opportunities.

Organizations will need to parse decision making not just in terms of its value but also according to its uniquely human elements. Much of a typical workday for many involves mechanical tasks that are often mistakenly thought of as creative and unique. They could potentially be farmed out to an AI bot (think of never needing to write a generic e-mail ever again, and rejoice). With those time-consuming tasks out of the way, there would be more time to focus on truly valuable pursuits, like determining on an empathetic level what customers really want and tapping into their aspirations.

How AI Will Change the Structure of Organizations

The changes that AI collaboration engenders could go beyond the task or employee level to challenge the entire way we think about how companies are organized. Most companies are currently organized like machines, with discrete elements, each focused on its own purview.

But AI could usher in a new style of corporate organization. Companies will become more connected and less siloed and hierarchical; they’ll operate in a more organic, flexible manner.

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These connected companies will be able to synthesize and distill inputs quickly and constantly. Instead of defining work by a fixed department or business unit, they will define it by projects and purpose.

This could change the manager–employee relationship as well. The dynamic nature of a connected organizational structure places less emphasis on seniority and more on ideas, so a junior employee’s good idea is more likely to be given weight. The power of the HiPPO—highest-paid person’s opinion—could be on the wane.

AI Designed for Humans

Good design creates a symbiotic relationship with technology. If the purpose of AI technology is to amplify human capabilities, then it must be crafted to have a giving personality to create an optimal experience for humans, with minimal frustration and maximum efficiency.

For example, AI programs could be designed to offer suggestions based on context, similar to those that Amazon provides to consumers as they shop on its site. AI could suggest apps based on what people in similar positions use, recommend collaborations and networks, research sources, and even intervene to help human colleagues when appropriate.

Will You Step Up?

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Roles will change as AI becomes a ubiquitous presence in the workplace. Where will your employees fit in?

In Only Humans Need Apply: Winners and Losers in the Age of Smart Machines, co-authors Thomas H. Davenport and Julia Kirby delineate five categories of work roles that will evolve as automation enters the organization:

Step up: Oversee automation within an organization and ensure it’s a good fit with the business and the larger world.

Step aside: Leverage creative, innovative human skills and emotions.

Step in: Keep AI on track by supervising its processes and results and making necessary adjustments.

Step narrowly: Perform highly specialized work that wouldn’t be economical to automate.

Step forward: Create the next-generation AI technology.

Stepping in, says Davenport, is the role with a high level of interaction with a machine, “almost as a colleague.” Many employees will likely transition to that role, but there will also be new roles that involve monitoring and improving AI performance. For example, stepping up is a managerial role that encompasses high-level decisions and resource management, akin to a portfolio manager. “As we have fewer people in organizations doing the day-to-day work, I think it will certainly be a more important part of the managerial role than it is today,” he says.

What we’re looking at is a fundamental shift in mindset, says James Cham, partner at San Francisco–based venture capital firm Bloomberg Beta. Traditional software has been focused on precision and efficiency, he says, whereas AI is predictive. “Records of predictions, which capture what I’m thinking, calculate whether those predictions are right or wrong, and help to inform those predictions, will actually be of much higher value,” he says.

The amount of time we spend each day looking for answers will be minimized because AI will have the information. Asking the right questions, however, will become much more important. The executive of the near future will be trained to think creatively, a shift from the traditional emphasis on procedural knowledge to one of unstructured problem solving with AI as a patient, indefatigable research assistant.

In a bid to supply the next crop of executives with the tools to work with AI, some of the top business schools, including Harvard Business School and MIT’s Sloan School of Management, have recently begun offering courses on AI collaboration as part of their MBA programs. The goal is not only to educate students on AI in general but also to teach them how to use it as a decision-making tool. One class will include how AI can be used to create optimal teams.

The strategic advantage, says Cham, will come to those who best understand the AI models they create. They will be the companies that experience the fastest growth and increased effectiveness. “Executives should have an inventory of not just every app that they have but of every single model they deploy,” he says. “They should know what sort of return they get from that model, if they want to continue investing in it or want it to get better. When do they trust the model to make decisions by itself, or when do they say, ‘We need to have oversight on it?’”

Welcomed with Open Arms?

For all the promise of working with AI colleagues, there is, and will likely remain, some resistance to its implementation. Understanding how AI arrives at conclusions can help users feel more comfortable with their new collaborators. “Transparency of a decision’s logic is really critical,” says Babson College’s Davenport. “But with a lot of these relatively new technologies on deep learning and so on, there’s basically no transparency.”

Transparency should be part of an AI system’s design. A system can, for example, be programmed to offer a rationale for its thinking that allows a human to dig down through layers of information. This might be delivered through a report or even a voice system. So if an employee asks an AI system why it did what it did, the system will answer. Machine-learning models should not be black boxes; instead, they should be able to explain the confidence rate in the results, the error rates, and why the model is predicting or proposing certain things, so that people can follow up and double-check.

How AI-enabled tools are integrated into employees’ work is also important. It all comes down to trust. New AI colleagues should be introduced just as new human team members would be. Start by giving them small, easy tasks and then gradually give them bigger, more important jobs. That’s how trust is gained in the workplace, and it shouldn’t be any different for AI collaboration tools.

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Though some organizations may decide to allow AI to operate independently, building in the flexibility so that users can adjust or override their AI tools helps maintain a sense of purpose and control. It also helps if organizations explain employees’ future options and give them a sense of what new skills they may need. “It’s going to take some time, I think, to prepare, so you need to tell people about it,” says Davenport.

But generational changes might make AI acceptance easier in a more organic fashion. Millennials will soon be a significant force in the workplace, and they have grown up with technology. Their acceptance of it is stronger, and they take its presence in the workplace for granted. Using AI tools will be no different for them.

Determine the Best Use of AI

But before any of these big cultural shifts happen, enterprises must think about how they will deploy this technology. Many still think of AI only in terms of labor replacement or as a magical cure-all for business problems. “I think, in general, that kind of coarse-grained thinking is not helpful, and it is actually not accurate,” Cham says.

Feasibility is another important factor in implementing AI in the workplace. Just because automation is possible does not mean it is practical or cost effective. In some cases, human labor will remain less expensive and more effective for at least the foreseeable future.

Indeed, Cham thinks that a lot of money will be wasted on misguided AI investments. “Even after you solve the technical problems, we have a bigger problem, which is we don’t have good economic frameworks for determining when AI makes sense,” he says. “We need better intuitions around where can you get a better bang for the buck. What does AI complement, and what does it replace? I think that sort of thinking is what we really, really need now,” he says.

The right way to think about AI systems is to focus on their incredible value in reducing the cost of prediction, he says. “But also, critically, where can we expect the results to get better and faster over time?”

The impact of AI on the workplace is going to be enormous. We’re just starting to experience the current real-world applications of AI and understanding how they’ll develop in the future. Now is the time to begin formulating a plan for AI’s implementation in the workplace and to prepare and train employees for what’s ahead. Think of how to redesign processes to create the best combination of human and AI abilities, says Cham. “It’s a question of what you focus on and what kind of teams and infrastructure you build to actually make more effective decisions and run better processes.” D!

Read more thought provoking articles in the latest issue of the Digitalist Magazine, Executive Quarterly.


About the Authors:

Dinesh Sharma is entrepreneur-in-residence at SAP.io.

Sam Yen is Chief Design Officer and Managing Director at SAP.

Markus Noga is Vice President of Machine Learning Incubation at SAP.

Erik Marcade is Vice President of Advanced Analytics, Products and Innovation, at SAP.

Chandran Saravana is Senior Director, Advanced Analytics, at SAP.

Danielle Beurteaux writes about technology and business.

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Walmart Rockstars

What one can do with a toy guitar…

The End Game of Digital Transformation

Watch the 3-minute video: 
  1. The Three Tsunamis of Digital Transformation – Be Prepared!
  2. Bots, AI and the Next 40 Months
  3. You Only Have 40 Months to Digitally Transform
  4. Digital Technologies and the Greater Good
  5. Video Report: 40 Months of Hyper-Digital Transformation
  6. Report: 40 Months of Hyper-Digital Transformation
  7. Virtual Moves to Real in with Sensors and Digital Transformation
  8. Technology Must Disappear in 2017
  9. Merging Humans with AI and Machine Learning Systems
  10. In Defense of the Human Experience in a Digital World
  11. Profits that Kill in the Age of Digital Transformation
  12. Competing in Future Time and Digital Transformation
  13. Digital Hope and Redemption in the Digital Age
  14. Digital Transformation and the Role of Faster
  15. Digital Transformation and the Law of Thermodynamics
  16. Jettison the Heavy Baggage and Digitally Transform
  17. Digital Transformation – The Dark Side
  18. Business is Not as Usual in Digital Transformation
  19. 15 Rules for Winning in Digital Transformation
  20. The End Goal of Digital Transformation
  21. Digital Transformation and the Ignorance Penalty
  22. Surviving the Three Ages of Digital Transformation
  23. The Advantages of an Advantage in Digital Transformation
  24. From Digital to Hyper-Transformation
  25. Believers, Non-Believers and Digital Transformation
  26. Forces Driving the Digital Transformation Era
  27. Digital Transformation Requires Agility and Energy Measurement
  28. A Doctrine for Digital Transformation is Required
  29. Digital Transformation and Its Role in Mobility and Competition
  30. Digital Transformation – A Revolution in Precision Through IoT, Analytics and Mobility
  31. Competing in Digital Transformation and Mobility
  32. Ambiguity and Digital Transformation
  33. Digital Transformation and Mobility – Macro-Forces and Timing
  34. Mobile and IoT Technologies are Inside the Curve of Human Time

************************************************************************

Kevin Benedict
Senior Analyst, Center for the Future of Work, Cognizant
View my profile on LinkedIn
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Join the Google+ Community Mobile Enterprise Strategies


***Full Disclosure: These are my personal opinions. No company is silly enough to claim them. I am a mobility and digital transformation analyst, consultant and writer. I work with and have worked with many of the companies mentioned in my articles.

2 Rockin’ Ways to Enable Editable Grids with Dynamics 365

grids 300x225 2 Rockin’ Ways to Enable Editable Grids with Dynamics 365

There are 2 ways to enable the new “Editable Grids” functionality delivered with Microsoft Dynamics 365. At the entity level, which will turn every view into an editable grid, or specifically on a sub-grid on any form. Today’s blog show you the steps for enabling either one!

Enable for the Entire Entity

Enabling Editable Grids at the entity level will then allow you to turn any view into an editable grid. The steps to do this are as follows:

  1. Navigate to Settings > Customizations > Customize the System then click on the entity that you wish to enable for editable grids

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2. Click on the Controls tab

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3. Click on Add Control and select “Editable Grid”

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4. Select where the editable grid should be available: Web, Phone, or Tablet

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5. You can configure a lookup field on a specific view which will allow more control over what happens when a user clicks on that field in the view. This is an optional step. If you wish to do so, click on Add Lookup and select a view and then available Lookup field.

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6. Choose the default view and set the additional parameters as desired. To finish, click OK and then Save and Publish

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7. Navigate to the entity and choose a view and you will see that it is now possible to edit any field in the view directly.

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Enable for single sub-grid view on a form

It is possible to enable a sub-grid on a form as an editable grid as needed. The steps to do this are as follows:

  1. Open up the form that has the sub-grid and then double click on the sub-grid to get to the “Set Properties” dialog box.

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2. Click on the Controls tab and select Editable Grid and click Add

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3. Select where the editable grid should be available: Web, Phone, or Tablet. Add the Lookup field for a given view if required and click OK to finish.

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4. Save your form and publish and your sub-grid will now be editable.

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Now that you know how to enable “Editable “Grids” you see why we are so excited about this powerful new feature available in Microsoft Dynamics 365. So what are you waiting for? Go try it out!

For more CRM for Dynamics 365 information, check out our Dynamics 365 landing page.

Happy CRM’ing!

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PowerObjects- Bringing Focus to Dynamics CRM

Expert Interview (Part 1): Co-Founder Neha Narkhede on Origins of Confluent and Kafka

At a recent Confluent partner event, Syncsort’s Paige Roberts sat down with Confluent Co-Founder CTO Neha Narkhede (@nehanarkhede) to discuss Apache KafkaTM, Confluent, the future of streaming data processing, and Women in Big Data.

In this first part, Neha explains the origins of Kafka and Confluent as a company, the trends that lead to its founding, and the advantages the platform brings to the streaming data world.

Paige:  So tell me who you are, and tell me about yourself.

Neha: My name is Neha Narkhede. I’m currently the Co-founder and CTO at Confluent. Prior to Confluent, I built Apache Kafka with two other people at LinkedIn. We open sourced Apache Kafka, and it became very popular, adopted across thousands of companies.

I thought that if there was going to be a company around Apache Kafka, then we should be the ones who created it. So I suggested it to my colleagues, and the three of us ended up creating Confluent two years ago. Two years down the line, I’m really enjoying building the company – every single aspect of it. I specifically run technology, engineering, customer operations and professional services.

That’s great. Very cool. Who are the other two people who founded Confluent with you?

Jun Rao and Jay Kreps. They created Apache Kafka with me and are also co-founders of Confluent.

I’ve been seeing a large uptake of Kafka. It seems like Kafka is becoming the central nervous system for almost every kind of company.

There you go. You picked up the term!

I did. I wrote a post on it for your blog recently using that analogy. Why do you think it took off so much? What is it about Kafka that appeals to everybody?

You know, there are several reasons. We saw a trend about six years ago that was growing in the industry that companies are becoming a lot more digital. That means, data is not just created by human actions, it’s also created by machines. As a result of that, data is orders of magnitude larger than ever before, and legacy systems are falling apart.

Also, the typical way for businesses to know what’s going on was through batch processing once a day at midnight. That’s too slow, now. At LinkedIn, we were responsible for LinkedIn’s data systems and we said, “Wow. We should actually know how LinkedIn is doing – how users are accessing the website in real time.” So we looked at everything in the space that was available, and there wasn’t really a good solution for it.

blog banner HadoopPerspectives2017 1 Expert Interview (Part 1): Co Founder Neha Narkhede on Origins of Confluent and Kafka

It just turned out that this is an industry-wide trend. People want visibility into the health of their business. They want to know new market opportunities, new cost savings or opportunities – and they want to do that in real time. So we open-sourced Apache Kafka.

Really, I think the rise of Apache Kafka has been driven by the fact that it solves an industry-wide problem, and it does that as an open source technology. So, it’s very easy to adopt and get started.

I think the third reason for Kafka’s popularity is that it was put to very large-scale use at LinkedIn. Before we released it, it went through a sufficient amount of testing and operationalization, which resulted in the user experience being very good.

Related: The Ins & the Outs: Using Kafka for Data Streaming

It was already solid.

It was already solid, yes. I think even when we created the company two years ago, the Kafka experience was already pretty solid. All of those factors – that it’s open source, that it just works and that it solves a real problem companies are facing – contribute to Kafka’s success today.

There’s also a last one which is sort of surprising to me. Since Confluent was created, there’s been a marked increase in terms of Kafka adoption. I think people are betting on it because there’s an enterprise supporting it. I find that interesting.

The technology enabled the corporation and the corporation is enabling the technology.

Yeah, it’s like a good network effect, right?

Yeah, yeah.

For us, or at least for me, creating the company is sort of the vehicle to the end mission. And the end mission is to be able to put this Kafka-based streaming platform in the heart of every company.

Ah! That’s ambitious.

And really, the way to do that is to create a company around it. It’s very much like the mission required creating a company, and that is why we’re doing that.

A great part of my job is to learn about new tech. I read the book authored by Confluent Co-founder Jay Kreps. He came to Data Day Texas, and I attended his lecture. I’ve spent time with customers using Kafka. I think I’ve got a pretty good handle on Kafka.

Tell me about the Confluent platform as far as what it brings to the table in addition to Kafka.

There are two things. First, there’s Confluent Open Source which is a 100% open source distribution of Apache Kafka. And then there’s Confluent Enterprise, which is Confluent Open Source with proprietary value-add features.

So, if you think of Apache Kafka as a powerful engine, you realize what you really need is a car. The Confluent platform is the car.

Right.

Confluent Open Source is the set of tools and software that you need to use alongside Kafka to really succeed with it in a company. Confluent Enterprise provides users the out-of-the-box capability to connect it with existing systems, use different kinds of clients and connectors, and to secure and monitor Kafka.

One of our recent big announcements is Confluent Enterprise. We just took the feature set, and made it a lot more operational, more rock solid, especially for companies who are serious about using Kafka in production. They need things like multi-data center capability, much more significant monitoring capability, and a lot of operational smarts. That goes in the enterprise version.

In part 2 of this conversation with Neha Narkhede, we’ll look at the Schema Registry – what it is, why it’s needed, and where it fits in the Kafka ecosystem. Ms. Narkhede will also talk about the big announcement Confluent made earlier this year.

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3 Simple Strategies for Successful Customer Engagement

3 Customer Engagement Strategies CRM Software Blog Image 3 Simple Strategies for Successful Customer Engagement

Volumes have been written about customer engagement, which is an increasingly vital part of boosting sales, increasing membership, and satisfying both customers and prospects. While successful customer engagement requires a multi-level strategy, it doesn’t have to be complex… just thoughtful and complete.

At its essence, customer engagement can be described as sharing a pertinent message with an interested audience at the right time and frequency. And it’s not just from “you” to “them.” The very definition of engagement calls for shared interests and activities, which means giving customers and prospects the opportunity to engage with you.

Once your approach to engagement has been established, Dynamics 365 empowers you to execute your strategy efficiently and accurately measure your results for continuous improvement.

Why Engage?

Before we talk about technical and strategic customer engagement strategies, let’s look at the human side of the equation. Building relationships are at the heart of engagement: organization-to-organization, and person-to-person. Whether you’re buying, selling, or helping others deliver a product or service, it all comes down to collaborating with others to achieve mutual goals.

Consider balancing emotional and business considerations as you develop your messages. Your goal is to build valued relationships that:

  • Encourage open, two-way communication
  • Provide value to both parties
  • Are built on trust and confidence
  • Promote satisfaction and success
  • Make everyone feel important

The more you align your customer engagement strategy with these principles, the more productive your relationships and results will be.

1) Your Message

By message we mean discrete information that you believe will benefit your audience. Your message should educate, illuminate, raise awareness and most importantly, contain something of value. For example, you could emphasize what readers will gain from using your product or service, or share insights that can be readily applied to improve individual and organizational performance.

The bottom line is your message should be persuasive enough to improve sales and upsell opportunities, satisfy customer wants and needs, and attract and retain different customer groups.

Dynamics 365 holds the insights you need to craft a compelling message. Stored within its data is a single view of every customer’s story: products they’ve purchased, problems they’ve experienced, prior responses to your communications and outreach, and insights into how your organization can fulfill their needs.

2) Your Audience

Once you’ve identified and developed your message, identify which groups are most interested in what you have to say. Dynamics 365 has a multitude of ways to segment your customer, prospect and partner data and create a targeted distribution list. This allows you to communicate in more personalized, purposeful, and powerful ways.

Before moving forward, make sure your message is framed around the language and priorities of your audience. For example, CEOs focus on organizational growth, profitability, reputation, and compliance, while marketing executives solve for brand awareness, lead generation and lead nurturing, and seek tools to monitor and improve their effectiveness.

Improve the results of your engagement activities by understanding and speaking directly to the needs of your audience.

3) Your Timing

Consider timing and frequency when developing a customer engagement campaign. Timing is simple: don’t talk about taxes in July or ignore buying or budgeting seasons when promoting products. For existing customers, Dynamics 365 can alert you to recent issues or tell you when it’s time to recommend an upgrade or the use of a complementary product or service. Engaging with customers proactively and anticipating their needs can have a positive impact on loyalty and satisfaction.

Frequency is another matter. If you’re executing an email drip campaign, you’ll want to communicate often enough to stay top of mind, but not so frequently that you increase opt-outs. And what’s the right rhythm for social media? Daily? Weekly? Although more is better when it comes to posting, it’s equally important that your posts are pertinent and have value. Be guided by your ability to create rich, compelling content on a consistent basis, and post at a frequency that matches that ability.

Engaging with Dynamics 365

All successful customer engagement combines business technology with the art of relationship, and Microsoft Dynamics 365 is the engine behind customer engagement.

Contact us and let’s discuss how InfoGrow can help you develop a robust customer engagement strategy boosts sales and builds profitable relationships with your best customers. Check out our No Results Missed pledge and take advantage of our expertise and commitment to your success.

Bob Sullivan – President, InfoGrow, Microsoft Dynamics CRM Partner

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How Klöckner’s Finance Team Is Redefining The Steel Industry

Blame House of Cards. The Netflix-produced hit was the result of an algorithm coupled with Netflix’s large collection of data on what viewers like to watch. Taking that as inspiration, ad agency McCann Erickson recently added the world’s first artificial intelligence (AI)-based creative director to its team in Japan. The memorably named AI-CD β will use data on award-winning commercials to produce ideas for new campaigns.

The company isn’t the first to let the math do the thinking: In 2014, Hong Kong–based venture capital firm Deep Knowledge Ventures announced a new addition to its board of directors, VITAL, which uses data to vote on potential investments. More recently, Finnish tech company Tieto welcomed Alicia T., an AI complete with a conversational interface, to the board (a win for board diversity?) of its new data-driven business services unit.

This doesn’t mean AI-based technology will become your overlords or replacements. Instead, they will very likely be colleagues. (Just don’t expect them to pay for lunch.)

Feature 2 image 2 How Klöckner’s Finance Team Is Redefining The Steel Industry

For all the fears of AI taking all our jobs, the reality will likely be less dramatic. Outside of repetitive work that requires no independent thought, AI-human collaboration, rather than outright replacement, is the future of work. Jobs will be shared, with some tasks delegated to AI and monitored, to some extent, by humans. According to McKinsey & Co., 60% of jobs could see 30% or higher automation of some tasks.

But there will need to be big technological and cultural shifts to make AI as common as your laptop. The corporate structure as we know it could disappear as conventional hierarchies are replaced by new models with different emphases and values. Meanwhile, the skills that executives and employees need to bring to an enterprise will change. Creativity and problem solving will become the highest-valued human abilities.

Better start preparing your organization now.

Meet Your New Colleague

When Oxford Martin School and Citi GPS released the 2015 report Technology at Work, one particular number garnered a lot of attention—and more than a bit of panic. According to the report’s authors, about 47% of U.S. jobs were at high risk from computerization (19% were at medium risk, and 33% at low risk).

McKinsey & Co. reports a slightly different take. Yes, automation is coming, but it might not be for your entire job, just part of it (hopefully, the part you don’t like to do). McKinsey thinks that looking at specific tasks is a much better predictor of automation. Highly repetitive work, such as data processing, is very likely to be automated, as are some management tasks, such as monitoring and measuring certain outputs and performance. But other, more relationship-centric levels of managing, decision making, and planning won’t be going to the machines anytime soon. In fact, the number of jobs that will be entirely replaced could be very low. (The bigger incidence of near-future automation is likely to happen in developing economies where there is still a large dependence on human labor.)

Augmentation isn’t a new philosophy. Engineer and inventor Douglas Engelbert—who created the first computer mouse in 1964—was one of the early tech pioneers who believed that technology would work alongside humans.

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His idea was that the true purpose of technology was to augment and amplify human capabilities, not completely replace human labor.

Indeed, most jobs have both repetitive and consistent tasks and intellectual and decision-making elements, says Sam Ransbotham, associate professor of information systems at the Carroll School of Management at Boston College. The question, he says, is how things will be divvied up between the humans and the machines. Just don’t expect it to be an all-or-nothing landscape.

“Much of the framing historically of automation has been around this dichotomy,” Ransbotham says. “That’s not really a productive way of thinking of this particular change. We’re not going to stop changes from happening, but it’s different when we’re talking about augmenting more knowledge-based work.”

AI as Decision Collaborator

Thomas H. Davenport, President’s Distinguished Professor of information technology and management at Babson College and co-author (with Julia Kirby) of Only Humans Need Apply: Winners and Losers in the Age of Smart Machines, thinks that delineation of automation in knowledge-based work will operate at the decision-making level. Highly repetitive decisions that are backed up with significant data will be relegated to AI. But even then a human role remains.

“Humans will need to check on the outcomes and see if the models and the algorithms and the rules are performing as intended, and then intervene if they don’t,” Davenport says. “I think that humans are more likely to be the integrators, the final decision makers, who sort of assemble the different opinions from different machines.”

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Even in a scenario such as Wall Street trading, where decisions are largely left to machines, it’s still important for checks and balances to be put in place, says Davenport. “It’s not easy to  intervene after a decision has been automated,” he says. “We have a tendency to let it ride, maybe often beyond the time when we should.”

Labor will be divided along the lines of what AI can and can’t do; creativity, abstract concepts, and many other human qualities are not AI capabilities, for example. It’s still a challenge to create AI that can truly replicate the kinds of human abilities we take for granted, and many of the decisions that managers and executives make on a daily basis don’t easily fit into the AI paradigm. The complexity of human relationships, minds, and cultures are currently beyond AI’s grasp, and this extends into the workplace. The algorithmically defined “right” decision might not necessarily be the best political and social decision. The role of AI in these instances could be to clarify and winnow options and to help identify opportunities.

Organizations will need to parse decision making not just in terms of its value but also according to its uniquely human elements. Much of a typical workday for many involves mechanical tasks that are often mistakenly thought of as creative and unique. They could potentially be farmed out to an AI bot (think of never needing to write a generic e-mail ever again, and rejoice). With those time-consuming tasks out of the way, there would be more time to focus on truly valuable pursuits, like determining on an empathetic level what customers really want and tapping into their aspirations.

How AI Will Change the Structure of Organizations

The changes that AI collaboration engenders could go beyond the task or employee level to challenge the entire way we think about how companies are organized. Most companies are currently organized like machines, with discrete elements, each focused on its own purview.

But AI could usher in a new style of corporate organization. Companies will become more connected and less siloed and hierarchical; they’ll operate in a more organic, flexible manner.

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These connected companies will be able to synthesize and distill inputs quickly and constantly. Instead of defining work by a fixed department or business unit, they will define it by projects and purpose.

This could change the manager–employee relationship as well. The dynamic nature of a connected organizational structure places less emphasis on seniority and more on ideas, so a junior employee’s good idea is more likely to be given weight. The power of the HiPPO—highest-paid person’s opinion—could be on the wane.

AI Designed for Humans

Good design creates a symbiotic relationship with technology. If the purpose of AI technology is to amplify human capabilities, then it must be crafted to have a giving personality to create an optimal experience for humans, with minimal frustration and maximum efficiency.

For example, AI programs could be designed to offer suggestions based on context, similar to those that Amazon provides to consumers as they shop on its site. AI could suggest apps based on what people in similar positions use, recommend collaborations and networks, research sources, and even intervene to help human colleagues when appropriate.

Will You Step Up?

sap Q117 digital double feature2 images 4 half How Klöckner’s Finance Team Is Redefining The Steel Industry

Roles will change as AI becomes a ubiquitous presence in the workplace. Where will your employees fit in?

In Only Humans Need Apply: Winners and Losers in the Age of Smart Machines, co-authors Thomas H. Davenport and Julia Kirby delineate five categories of work roles that will evolve as automation enters the organization:

Step up: Oversee automation within an organization and ensure it’s a good fit with the business and the larger world.

Step aside: Leverage creative, innovative human skills and emotions.

Step in: Keep AI on track by supervising its processes and results and making necessary adjustments.

Step narrowly: Perform highly specialized work that wouldn’t be economical to automate.

Step forward: Create the next-generation AI technology.

Stepping in, says Davenport, is the role with a high level of interaction with a machine, “almost as a colleague.” Many employees will likely transition to that role, but there will also be new roles that involve monitoring and improving AI performance. For example, stepping up is a managerial role that encompasses high-level decisions and resource management, akin to a portfolio manager. “As we have fewer people in organizations doing the day-to-day work, I think it will certainly be a more important part of the managerial role than it is today,” he says.

What we’re looking at is a fundamental shift in mindset, says James Cham, partner at San Francisco–based venture capital firm Bloomberg Beta. Traditional software has been focused on precision and efficiency, he says, whereas AI is predictive. “Records of predictions, which capture what I’m thinking, calculate whether those predictions are right or wrong, and help to inform those predictions, will actually be of much higher value,” he says.

The amount of time we spend each day looking for answers will be minimized because AI will have the information. Asking the right questions, however, will become much more important. The executive of the near future will be trained to think creatively, a shift from the traditional emphasis on procedural knowledge to one of unstructured problem solving with AI as a patient, indefatigable research assistant.

In a bid to supply the next crop of executives with the tools to work with AI, some of the top business schools, including Harvard Business School and MIT’s Sloan School of Management, have recently begun offering courses on AI collaboration as part of their MBA programs. The goal is not only to educate students on AI in general but also to teach them how to use it as a decision-making tool. One class will include how AI can be used to create optimal teams.

The strategic advantage, says Cham, will come to those who best understand the AI models they create. They will be the companies that experience the fastest growth and increased effectiveness. “Executives should have an inventory of not just every app that they have but of every single model they deploy,” he says. “They should know what sort of return they get from that model, if they want to continue investing in it or want it to get better. When do they trust the model to make decisions by itself, or when do they say, ‘We need to have oversight on it?’”

Welcomed with Open Arms?

For all the promise of working with AI colleagues, there is, and will likely remain, some resistance to its implementation. Understanding how AI arrives at conclusions can help users feel more comfortable with their new collaborators. “Transparency of a decision’s logic is really critical,” says Babson College’s Davenport. “But with a lot of these relatively new technologies on deep learning and so on, there’s basically no transparency.”

Transparency should be part of an AI system’s design. A system can, for example, be programmed to offer a rationale for its thinking that allows a human to dig down through layers of information. This might be delivered through a report or even a voice system. So if an employee asks an AI system why it did what it did, the system will answer. Machine-learning models should not be black boxes; instead, they should be able to explain the confidence rate in the results, the error rates, and why the model is predicting or proposing certain things, so that people can follow up and double-check.

How AI-enabled tools are integrated into employees’ work is also important. It all comes down to trust. New AI colleagues should be introduced just as new human team members would be. Start by giving them small, easy tasks and then gradually give them bigger, more important jobs. That’s how trust is gained in the workplace, and it shouldn’t be any different for AI collaboration tools.

Feature 2 image 6 1024x579 How Klöckner’s Finance Team Is Redefining The Steel Industry

Though some organizations may decide to allow AI to operate independently, building in the flexibility so that users can adjust or override their AI tools helps maintain a sense of purpose and control. It also helps if organizations explain employees’ future options and give them a sense of what new skills they may need. “It’s going to take some time, I think, to prepare, so you need to tell people about it,” says Davenport.

But generational changes might make AI acceptance easier in a more organic fashion. Millennials will soon be a significant force in the workplace, and they have grown up with technology. Their acceptance of it is stronger, and they take its presence in the workplace for granted. Using AI tools will be no different for them.

Determine the Best Use of AI

But before any of these big cultural shifts happen, enterprises must think about how they will deploy this technology. Many still think of AI only in terms of labor replacement or as a magical cure-all for business problems. “I think, in general, that kind of coarse-grained thinking is not helpful, and it is actually not accurate,” Cham says.

Feasibility is another important factor in implementing AI in the workplace. Just because automation is possible does not mean it is practical or cost effective. In some cases, human labor will remain less expensive and more effective for at least the foreseeable future.

Indeed, Cham thinks that a lot of money will be wasted on misguided AI investments. “Even after you solve the technical problems, we have a bigger problem, which is we don’t have good economic frameworks for determining when AI makes sense,” he says. “We need better intuitions around where can you get a better bang for the buck. What does AI complement, and what does it replace? I think that sort of thinking is what we really, really need now,” he says.

The right way to think about AI systems is to focus on their incredible value in reducing the cost of prediction, he says. “But also, critically, where can we expect the results to get better and faster over time?”

The impact of AI on the workplace is going to be enormous. We’re just starting to experience the current real-world applications of AI and understanding how they’ll develop in the future. Now is the time to begin formulating a plan for AI’s implementation in the workplace and to prepare and train employees for what’s ahead. Think of how to redesign processes to create the best combination of human and AI abilities, says Cham. “It’s a question of what you focus on and what kind of teams and infrastructure you build to actually make more effective decisions and run better processes.” D!

Read more thought provoking articles in the latest issue of the Digitalist Magazine, Executive Quarterly.


About the Authors:

Dinesh Sharma is entrepreneur-in-residence at SAP.io.

Sam Yen is Chief Design Officer and Managing Director at SAP.

Markus Noga is Vice President of Machine Learning Incubation at SAP.

Erik Marcade is Vice President of Advanced Analytics, Products and Innovation, at SAP.

Chandran Saravana is Senior Director, Advanced Analytics, at SAP.

Danielle Beurteaux writes about technology and business.

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