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Why AI is So Brilliant and So Stupid

September 18, 2018 /
AI

For all of the promise that artificial intelligence represents, a successful AI initiative still requires all of the right pieces to come together.

AI capabilities are advancing rapidly, but the results are mixed. While chatbots and digital assistants are improving generally, the results can be laughable, perplexing and perhaps even unsettling.

Google’s recent demonstration of Duplex, its natural language technology that completes tasks over the phone, is noteworthy. Whether you love it or hate it, two things are true: It doesn’t sound like your grandfather’s AI; the use case matters.

One of the striking characteristics of the demo, assuming it actually was a demo and not a fake, as some publications have suggested, is the use of filler language in the digital assistant’s speech such as “um” and uh” that make it sound human. Even more impressive, (again, assuming the demo is real), is the fact that Duplex reasons adeptly on-the-fly despite the ambiguous, if not confusing, responses provided by a restaurant hostess on the other end of the line.

Of course, the use case is narrow. In the demo, Duplex is simply making a hair appointment and attempting to make a restaurant reservation. In the May 8 Google Duplex blog introducing the technology, Yaniv Leviathan, principal engineer and Yossi Matias, VP of Engineering explain: “One of the key research insights was to constrain Duplex to closed domains, which are narrow enough to explore extensively. Duplex can only carry out natural conversations after being deeply trained in such domains. It cannot carry out general conversations.”

A common misconception is that there’s a general AI that works for everything. Just point it at raw data and magic happens.

“You can’t plug in an AI tool and it works [because it requires] so much manual tweaking and training. It’s very far away from being plug-and-play in terms of the human side of things,” said Jeremy Warren, CTO of Vivint Smart Home and former CTO of the U.S. Department of Justice. “The success of these systems is driven by dark arts, expertise and fundamentally on data, and these things do not travel well.”

Data availability and quality matter

AI needs training data to learn and improve. Warren said that if someone has mediocre models, processing performance, and machine learning experts, but the data is amazing, the end solution will be very good. Conversely, if they have the world’s best models, processing performance, and machine learning experts but poor data, the result will not be good.

“It’s all in the data, that’s the number one thing to understand, and the feedback loops on truth,” said Warren. “You need to know in a real way what’s working and not working to do this well.”

Daniel Morris, director of product management at real estate company Keller Williams agrees. He and his team have created Kelle, a virtual assistant designed for Keller Williams’ real estate agents that’s available as iPhone and Android apps. Like Alexa, Kelle has been built as a platform so skills can be added to it. For example, Kelle can check calendars and help facilitate referrals between agents.

“We’re using technology embedded in the devices, but we have to do modifications and manipulations to get things right,” said Morris. “Context and meaning are super important.”

One challenge Morris and his team run into as they add new skills and capabilities is handling longtail queries, such as for lead management, lead nurturing, real estate listings, and Keller Williams’ training events. Agents can also ask Kelle for the definitions of terms that are used in the real estate industry or terms that have specific meaning at Keller Williams.

Expectations are or are not managed well

Part of the problem with technology commercialization, including the commercialization of AI, is the age-old problem of over-promising and under-delivering. Vendors solving different types of problems claim that AI is radically improving everything from drug discovery to fraud prevention, which it can, but the implementations and their results can vary considerably, even among vendors focused on the same problem.

“A lot of the people who are really doing this well have access and control over a lot of first-party data,” said Skipper Seabold, director of decision sciences at decision science advisory firm Civis Analytics. “The second thing to note is it’s a really hard problem. What you need to do to deliver a successful AI product is to deliver a system, because you’re delivering software at the end. You need a cross-functional team that’s a mix of researchers and product people.”

Data scientists are often criticized for doing work that’s too academic in nature. Researchers are paid to test the validity of ideas. However, commercial forms of AI ultimately need to deliver value that either feeds bottom line directly, in terms of revenue, cost savings and ultimately profitability or indirectly, such as through data collection, usage and, potentially, the sale of that information to third parties. Either way, it’s important to set end user expectations appropriately.

“You can’t just train AI on raw data and it just works, that’s where things go wrong,” said Seabold. “In a lot of these projects you see the ability for human interaction. They give you an example of how it can work and say there’s more work to be done, including more field testing.”

Decision-making capabilities vary

Data quality affects AI decision-making. If the data is dirty, the results may be spurious. If it’s biased, that bias will likely be emphasized.

“Sometimes you get bad decisions because there are no ethics,” said Seabold. “Also, the decisions a machine makes may not be the same as a human would make. You may get biased outcomes or outcomes you can’t explain.”

Clearly, it’s important to understand what the cause of the bias is and correct for it.

Understanding machine rendered decisions can be difficult, if not impossible, when a black box is involved. Also, human brains and mechanical brains operate differently. An example of that was the Facebook AI Research Lab chatbots that created their own language, which the human researchers were not able to understand. Not surprisingly, the experiment was shut down.

“This idea of general AI is what captures people’s imaginations, but it’s not what’s going on,” said Seabold. “What’s going on in the industry is solving an engineering problem using calculus and algebra.”

Humans are also necessary. For example, when Vivint Smart Homes wants to train a doorbell camera to recognize humans or a person wearing a delivery uniform, it hires people to review video footage and assign labels to what they see. “Data labelling is sometimes an intensely manual effort, but if you don’t do it right, then whatever problems you have in your training data will show up in your algorithms,” said Vivint’s Warren.

Bottom line

AI outcomes vary greatly based on a number of factors which include their scope, the data upon which they’re built, the techniques used, the expertise of the practitioners and whether expectations of the AI implementation are set appropriately. While progress is coming fast and furiously, the progress does not always transfer well from one use case to another or from one company to another because all things, including the availability and cleanliness of data, are not equal.

9 Traits of Emerging Disruptors

August 25, 2018 /

Business leaders are understandably concerned about disruption. Business as usual is a dangerous proposition in an age when entire industries can be upended by a disruptor armed with cloud-based computing power, lots of data, and effective ways of leveraging that data.

The typical response to the threat of disruption is digital transformation. However, digital transformation tends to be approached as an if/then statement. Specifically, if we embark on a digital transformation journey, then we’ll be able to compete effectively in the future.

“What they’re not recognizing is you have failed in your business,” said Jay Goldman, co-author of New York Times bestseller THE DECODED COMPANY: Know Your Talent Better Than You Know Your Customers and co-founder and managing director of digital workplace solution provider Sensei Labs, “You’re not being rewarded for doing something right,”

The quantum shifts that disruptions represent don’t happen overnight. A disruptor, like most startups, has an idea it hopes will change the world. It intends to challenge the status quo that has been created by an established order of market leaders with formidable market shares and deep pockets. However, the market leaders don’t serve everyone by design because not all business relationships are equally attractive or profitable, so they tend to focus on the most profitable segments and de-emphasize or ignore the less-profitable segments. Disruptors tend to take advantage of those opportunities, such as by serving niche markets or less-affluent customers.

The incumbents tend to ignore such startups because the new contender is relatively small, lacks resources and tends to have far less brand recognition. Moreover, the new contender has decided to address a market segment the market leaders have consciously decided not to serve. Then, when the new contender succeeds in those markets, it has to expand into other segments to continue growing and improving profitability. Ultimately, when the new contender starts to gain market share in the coveted market segments, the incumbents react, albeit later than they should have. As more market share is lost, the incumbents try to copy what the emerging leader is doing, which may not work well, if at all.

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“If you say in the next six months we’re going to execute this transformation project and at the end of that we’ll emerge from this cocoon a new butterfly with everything we need to remain competitive from that point forward, you missed the point,” said Goldman. “There isn’t a set transformation that will keep you forever in a competitive state, ready to respond to the business environment. The only way to do that is to transform the fundamental parts of the organization so you are in a constant state of evolution and disruption.”

Achieving that state requires changing the company’s culture, leadership structure and tools.

By comparison, disruptors don’t have to transform because they’re new and have the luxury of creating a culture, leadership structure and tool set. Following are a few other things that separate the disruptors from the disrupted.

#1:  They’re unified

Disruptors are on a mission to affect major economic, business, industry, or societal changes. They have a vision and purpose that are woven into everything they do and the mindsets of their employees.

Incumbents often form a separate innovation group or hire a mover-and-shaker with a C-title, such as a Chief Data Officer (CDO) to lead a separate group. This powerful and brilliant executive, who typically comes from a high-profile tech company or a company in another industry, is given a massive budget, an enviable working environment and the freedom to hire the people necessary for success. However, there is a fundamental flaw in the approach.

“They set the group up [as a separate entity] for a whole bunch of reasons: 1) We know our culture will kill it if we put it inside the business and, 2) The kind of people we need working in that division are never going to work for our company if we try to hire them outright,” said bestselling author and Sensei Labs co-founder and managing director Jay Goldman.

#2:  They have an authentic culture

Every company has a culture by design or by default. Since disruptors lack a decades-plus legacy, they don’t have to transform from something traditional to something modern.

They recognize the importance of culture and the need for everyone in the company to not only buy into the culture but to advocate, promote, and advance it. Having a unified culture enables the realization of a unified vision and the execution of a unified mission.

In contrast, incumbents try to counter the effects of disruptors by attempting to mimic them. In doing so, they miss a very important point, which is what works at Google works because Google is Google. Every company is unique in terms of its people, processes, tools and value proposition.

“The one value that you see coming out of [the tours given by innovative companies] is to come back terrified and convinced of the need to make change,” said Goldman. “[Usually, the CDO and C-suite executives are] going to come back with good notes of how they might do that, but they won’t recognize the depth of the threats they’re facing.”

#3:  They reflect modern values

Startups have the benefit of being born into whatever “modern” era exists at their founding date. Today’s startups reflect the values of the younger generations including Millennials and Generation Z (Gen Z), both of which are highly tech-savvy.

“It’s not just you have a different set of values and priorities,” said Goldman. “You have an intimate level of familiarity with technology that the leaders don’t have because they weren’t born into that age.”

Goldman once met with a group of C-suite executives who couldn’t understand why their successful life sciences company had trouble attracting and retaining younger employees. To better understand the issue, they asked employees for suggestions, many of which they considered ridiculous. For example, they didn’t understand why younger employees would want to wear jeans instead of suits. How would that improve work effectiveness?

“The reality is, that the executives who made a company successful are disconnected from what people want in the workforce today,” said Goldman. “People will take a pay cut to work at a business where they’re deeply aligned with the values of the company and they believe they’re doing good for the world. In my generation and the generation before me, you looked for a well-paying job, and company values were on a poster with a soaring Eagle on it in the break room.”

#4:  They have the latest tools

Cloud-based technologies enable startups to do what was cost-prohibitive in the past. Now, businesses of all sizes have affordable access to massive computer power, storage, data analytics, and AI. More importantly, they can experiment and iterate in low-risk, low-cost environments and scale as necessary to meet the growing requirements of their expanding customer bases.

In contrast, the life sciences company C-suite executives didn’t understand why employees didn’t want to use Lotus Notes!

#5:  Their leaders are enablers

Disruptors attract, hire, and cultivate highly-effective people. Changing the status quo of an industry or society at large not only requires bright, driven people, it requires leaders who are not threatened by other bright, driven people.

In a command-and-control hierarchical structure, power and great ideas may be reserved for the chosen few.

“Traditional roles are managers who are there to make sure things happen on time and on budget, and that you hire the right people to do the job,” said Goldman. “When it comes to topics like transformation, innovation, and disruption, you should be a gardener. Your job as a gardener is to make sure your plants get enough sunlight, water, and nutrients. You can weed out the weeds that would have prevented them from growing and you can protect the garden from being raided by animals.”

Leaders should be enablers instead of managers. Enablers want great people to do great work, so they create an environment that includes the freedom to do that. The traditional management mentality can be stifling by comparison when people can only rise to whatever level of competence or incompetence the manager himself or herself possesses.

#6:  Change drives them

Change is what drives innovation and disruption. It’s about affecting change and also having the agility to change when an experiment or even the entire business model fails.

Goldman said even though incumbents may be out interviewing customers and iterating products rapidly in response, they’re not doing the same internally. Heads of innovation tend to be brilliant at product innovation, but they’re not necessarily change agents,

“The actual MVP customers you should talk to are the P&L holders that will have to sponsor [the innovation lab],” said Goldman. “Don’t present something that’s so radical and transformative [the P&L holders] look at their products and realize they’ll probably lose their job.”

#7:  Their value proposition trumps products

Disruptive companies tend to view the world differently than their incumbent counterparts. The disruptive companies think in terms of value; incumbents tend to have product and solution portfolios that are presented and regarded as such. They articulate use cases, but they’re missing their company’s fundamental value proposition.

For example, when a fertilizer company was going through a transformation, it “did all the right things,” according to Goldman. It changed the business, empowered the leaders and trained all employees to think creatively using tools and modern problem-solving approaches. During the process, the company’s identity shifted from being a fertilizer company to one that improves crop yields. While the distinction may seem slight, the new definition enables the company to imagine and provide other products and services that improve crop yields. It’s now using satellite data to tell farmers about crop issues they’re not aware of so they can remediate the issues with unprecedented precision (arguably using the company’s fertilizer products). The satellite data is also the basis for a new subscription-based service that guarantees a certain level of crop yield improvement,

#8:  They create best practices

Disruptive companies tend to view the world differently than their incumbent counterparts. The disruptive companies think in terms of value; incumbents tend to have product and solution portfolios that are presented and regarded as such. They articulate use cases, but they’re missing their company’s fundamental value proposition.

For example, when a fertilizer company was going through a transformation, it “did all the right things,” according to Goldman. It changed the business, empowered the leaders and trained all employees to think creatively using tools and modern problem-solving approaches. During the process, the company’s identity shifted from being a fertilizer company to one that improves crop yields. While the distinction may seem slight, the new definition enables the company to imagine and provide other products and services that improve crop yields. It’s now using satellite data to tell farmers about crop issues they’re not aware of so they can remediate the issues with unprecedented precision (arguably using the company’s fertilizer products). The satellite data is also the basis for a new subscription-based service that guarantees a certain level of crop yield improvement,

#9:  They have the right talent

Disruptors couldn’t accomplish what they do if they didn’t have “the right” teams in place. Like any other organization, not everyone makes the cut as the company evolves or chooses to stay as circumstances change. However, they’re keenly aware of their goals and what must be done to achieve them, part of which is ensuring the right people are in the right jobs.

“Employee engagement is gaining momentum. How you keep people engaged has got to be front and center to your strategy,” said Randy Mysliviec, Managing Director of the Resource Management Institute. “Not only does talent management need to be more fluid, you can’t expect people to stay at your company for 20 years regardless of how you treat them.”

In the last two years, enterprise IT resource management has shifted from a simple supply and demand model to a more forward-looking strategic model that considers where the company wants to be in six months. So, when it comes to recruitment, hiring managers are now con

How to Prepare for the Machine-Aided Future

August 21, 2018 /

Intelligent automation is going to impact companies and individuals in profound ways, some of which are not yet foreseeable. Unlike traditional automation, which lacks an AI element, intelligent automation will automate more kinds of tasks in an organization, at all levels within an organization.

As history has shown, rote, repetitive tasks are ripe for automation. Machines can do them faster and more accurately than humans 24/7/365 without getting bored, distracted or fatigued.

When AI and automation are combined for intelligent automation, the picture changes dramatically. With AI, automated systems are not just capable of doing things; they’re also capable of making decisions. Unlike manufacturing automation which replaced factory-floor workers with robots, intelligent automation can impact highly-skilled, highly-educated specialists as well as their less-skilled, less-educated counterparts.

Intelligent automation will affect everyone

The non-linear impact of intelligent automation should serve as a wakeup call to everyone in an organization from the C-suite down. Here’s why: If the impact of intelligent automation were linear, then the tasks requiring the least skill and education would be automated first and tasks requiring the most skill and education would be automated last. Business leaders could easily understand the trajectory and plan for it accordingly.

However, intelligent automation is impacting industries in a non-linear fashion. For example, legal AI platform provider LawGeex conducted an experiment that was vetted by professors from Duke University School of Law, Stanford University and an independent attorney to determine which could review contracts more accurately: AI or lawyers. In the experiment, 20 lawyers took an average of 92 minutes to review five non-disclosure agreements (NDAs) in which there were 30 legal issues to spot. The average accuracy rating was 85%. The AI completed the same task in 26 seconds with a 94% accuracy level. Similar results were achieved in a study conducted by researchers at the University of California, San Francisco (UCSF). That experiment involved board-certified echocardiographers. In both cases, AI was better than trained experts at pattern recognition.

Interestingly, most jobs involve some rote, repetitive tasks and pattern recognition. CEOs may consider themselves exempt from intelligent automation but Jack Ma, billionaire founder and CEO of ecommerce platform Alibaba disagrees. “AI remembers better than you, it counts faster than you, and it won’t be angry with competitors.”

What the C-Suite Should Consider

Intelligent automation isn’t something that will only affect other people. It will affect you directly and indirectly. How you handle the intelligently automated future will matter to your career and the health of your organization.

You can approach the matter tactically if you choose. If you take this path, you’ll probably set a goal of using automation to reduce the workforce by XX%.

A strategic approach considers the bigger picture, including the potential competitive effects, the economic impact of a divided labor workforce, what “optimized” business processes might look like, and the ramifications for human capital (e.g., job reassignment, new roles, reimagined roles, upskilling).

The latter approach is more constructive because work automation is not an end it itself. The reason business leaders need to think about intelligent automation now is underscored by a recent McKinsey study. It suggested that 30% of the tasks performed in 6 out of 10 jobs could be automated today.

Tomorrow, there will be even more opportunities for intelligent automation as the technology advances, so business leaders should consider its potential impacts on their organizations.

For argument’s sake, if 30% of every job in your organization could be automated today, what tasks do you consider ripe for automation? If those tasks were automated, how would it affect the organization’s structure, operations and value proposition? How would intelligent automation impact specific roles and departments? How might you lead the workforce differently and how might your expectations of the workforce change? What ongoing training are you prepared to provide so your workforce can adapt as more types of tasks are automated?

Granted, business leaders have little spare time to ponder what-if questions, but these aren’t what-if questions, they’re what-when questions. You can either anticipate the impact, observe and adjust or ignore the trend and react after the fact.

The latter strategy didn’t work so well for brick-and-mortar retailers when the ecommerce tidal wave hit…

What Managers Should Consider

The C-suite should set the tone for what the intelligently automated future looks like for the company and its people. Your job will be to manage the day-to-day aspects of the transition.

As a manager, you’re constantly dealing with people issues. In this case, some people will regard automation as a threat even if the C-suite is approaching it responsibly and with compassion. Others will naturally evolve as the people-machine partnership evolves.

The question for managers is how might automation impact their teams? How might the division of labor shift? What parts of which jobs do you think are ripe for automation? If those tasks were automated, how would peoples’ roles change? How would your group change? Likely, new roles would be created, but what would they be? What sort of training would your people need to succeed in their new positions?

You likely haven’t taken the time to ponder these and related questions, perhaps because they haven’t occurred to you yet. As a team leader, you owe it to yourself and your team to think about how the various scenarios might play out, as well as the recommendations you’d have for your people and the C-suite.

What Employees Should Consider

Everyone should consider how automation might affect their jobs, including managers and members of the C-suite, because everyone will be impacted by it somehow.

In this case, think about your current position and allow yourself to imagine what part of your job could be automated. Start with the boring routine stuff you do over and over, the kinds of things you wish you didn’t have to do. Likely, those things could be automated.

Next, consider the parts of your job that require pattern recognition. If your job entails contract review and contract review is automated, what would you do in addition to overseeing the automated system’s work? As the LawGeex experiment showed, AI is highly accurate, but it isn’t perfect.

Your choice is fight or flight. You can give into the fear that you may be automated out of existence and act accordingly, which will likely result in a self-fulling prophecy. Alternatively, consider what parts of your job could be automated and reimagine your future. If you no longer had to do X, what would Y be?  What might your job title be and what your scope responsibilities be?

If you consider how intelligent automation may impact your career, you’ll be in a better position to evolve as things change and you’ll be better prepared to discuss the matter with your superiors.

The Bottom Line

The intelligently automated future is already taking shape. While the future impacts aren’t entirely clear yet, business leaders, managers and professionals can help shape their own future and the future of their companies by understanding what’s possible and how that might affect the business, departments and individual careers. Everyone will have to work together to make intelligent automation work well for the company and its people.

The worst course of action is to ignore it, because it isn’t going away.

Workforce Analytics Move Beyond HR

August 14, 2018 /

Workforce analytics have traditionally focused on HR’s use of them when their value can actually have significant overall business impacts. Realizing this, more business leaders are demanding insights into workforce dynamics to unearth insights that weren’t apparent before.

Businesses often claim that talent is their greatest asset, but they’re not always able to track what’s working, what isn’t and why. For example, in Deloitte Consulting’s 2018 Global Human Capital Trends report, 71% of survey participants said their companies consider people analytics a high priority, but only 10% are “very ready” to deal with it. According to David Fineman, specialist leader at  Deloitte Consulting, who co-authored the report, business leaders want insights into six focus areas that include workforce planning and shaping, recruiting and staffing talent optimization, culture and engagement, performance and rewards, and HR service delivery.

“The important distinction between focus areas that are addressed today compared with the focus areas from prior years is the emphasis on issues that are important to business leaders, not limiting analytics recipients to an HR audience,” said Fineman.

In fact, the Deloitte report explicitly states that board members and CEOs want access to people analytics because they’re “impatient with HR teams that can’t deliver actionable information and insights…”

As businesses continue to digitize more tasks and functions, it’s essential for them to understand the current makeup of their workforces, what talent will be needed in the future, and what’s necessary to align the two.

Shebani Patel, People Analytics leader at professional services firm PricewaterhouseCoopers (PwC) said that companies now want to understand employee journeys from onboarding to daily work experiences to exit surveys.

“They’re trying to get more strategic about how all of that comes together to build and deliver an exceptional [employee] experience that ultimately has ROI attached to it,” she said.

What companies are getting right

The availability of more people analytics tools enables businesses to understand their workforces in greater detail than ever before. However, the insights sought are not just insights about people, but rather how those insights directly relate to business value such as achieving higher levels of customer satisfaction or improving product quality. Businesses are also placing more emphasis on organizational network analysis (ONA) which provides insight into the interactions and relationships among people.

While it’s technologically possible to track what individuals do, there are also privacy concerns that are best addressed using clustering techniques. For example, KPMG’s clients are looking at email patterns, chat correspondence and calendared meetings to understand how team behavior correlates with performance, productivity or sales.

“Organizations today are using [the data] to derive various hypotheses and then use analytics to prove them out,” said Paul Krasilnick, director, Advisory Services at KPMG. “They recognize that it needs to be done cautiously in terms of data privacy and access to information, but they also recognize the value of advancing their internal capabilities and maturity from descriptive reporting to more prescriptive [analytics].”

According to Deloitte’s Fineman, high performing people analytics teams are characterized by increasing the analytics acumen within the HR function and among stakeholders.

What needs to improve

Like any other analytics journey, what needs to be improved depends on an organization’s level of mastery.  While all organizations have people data, they don’t necessarily have clean data.  Further, the mere existence of the data does not mean it’s readily usable or necessarily valuable.

MIT Chaplain: Emerging Tech Leaders Care About Ethics

August 9, 2018 /

The tech industry’s approach to innovation will likely undergo a major shift as new generations of tech leaders come to power. Historically, innovation has been economically motivated for the benefit of individuals and shareholders, which will continue to be true, although the nature of innovation will likely evolve to consider its impacts on the world in greater depth than has been true historically.

“As an innovator, you may be able to make some term gain without having to worry or be concerned about ethics whatsoever,” said Greg Epstein, the newly-appointed first humanist chaplain at the Massachusetts Institute of Technology (MIT) and executive director of The Humanist Hub. “You may be able to achieve some things that we define as success in this world without caring about or even paying any attention to ethics, but in the long term, [that approach] will likely have some directly damaging consequences in your life. What I’m seeing students prepare for today is not just conventional success but to have an inner life that is meaningful.”

Values change from generation to generation, so it should be no surprise that what fueled the tech industry’s direction to date may change in the future. While it’s true that some of today’s tech leaders demonstrate a capacity for doing something good for society, the general trajectory is to innovate, grow, exit, maybe repeat the last three steps a few times and then focus on something like underprivileged individuals.

Doing something “good” later in life is consistent with mid-life realizations of mortality when one questions the legacy one is leaving behind. According to Epstein, the Millennials and Generation Z are more likely to ponder the societal value of their contributions earlier in their career than Baby Boomers or Generation X.

Tech Innovation for Good Versus Tech Innovation Is Good

Arguably, technology innovation has always focused on the positive, if “the positive” is defined as achieving the art of the possible. For example, cars are safer and more reliable than they once were, as the result of technology innovation.

However, the more technologically dependent people and things become, the more vulnerable they are to attacks. In other words, the negative potential consequences of new technology tend to be an afterthought, with the exception of products and services that are designed to protect people from negative consequences, such as cybersecurity products.

In previous generations, technology impacted society more slowly than it does today, so the mainstream positive and negative effects tended to take longer to realize. For example, adoption of the first mobile phones was relatively slow because they were large and heavy, and cellular service was spotty at best. Now, entire industries are being disrupted seemingly overnight by companies such as Uber and Airbnb.

Generational Differences Matter

Each generation is shaped, in part, by the world in which they mature. Over the past several decades, each subsequent generation has been exposed to not only more technology, but more sophisticated technology. The “new normal” is a connected world of devices, many of which are recording everything, and social media networks through which anything and everything can be shared.

“Increasingly, young people on campus want to create collaborative technology [so] that people can have a fair opportunity in life and human beings can help one another to achieve a better of life than we’ve ever had before,” said Epstein. “I think that people are hungry for conversations about what that could look like [and] what that could mean because human beings have never had this responsibility before to transform our collective lives for the better.”

Innovation for a Higher Purpose

Thus far, technology innovators have followed a pattern, which is to innovate, to capitalize, and to then deal with negative consequences later if and when they arise. In other words, the tech industry has been focused on the art of the possible, generally without regard for the entire spectrum of outcomes that results.

Ethical Tech: Myth or Reality?

July 4, 2018 /

New technologies continue to shape society, albeit at an accelerating rate. Decades ago, societal change lagged behind tech innovation by many years, a decade or more. Now, change is occurring much faster as evidenced by the impact of disrupters including Uber and Airbnb.

Central to much of the change is the data being collected, stored and analyzed for various reasons, not all of which are transparent. As the pace of technology innovation and tech-driven societal change accelerate, businesses are wise to think harder about the longer-term impacts of what they’re doing, both good and bad.

Why contemplate ethics?

Technology in all its forms is just a tool that can be used for good or evil. While businesses do not tend to think in those terms, there is some acknowledgement of what is “right” and “wrong.” Doing the right thing tends to be reflected in corporate responsibility programs designed to benefit people, animals, and the environment. Doing the wrong thing often involves irresponsible or inadvertent actions that are harmful to people, whether it’s invading their privacy or exposing their personal data.

While corporate responsibility programs in their current form are “good” on some level, ethics on a societal scale tends to be missing.

In the tech industry, for example, innovators are constantly doing things because they’re possible without considering whether they’re ethical. A blatant recent example is the human-sheep hybrid. Closer to home in high tech are fears about AI gone awry.

Why ethics is a difficult concept

The definition of ethics is simple. According to Merriam Webster, it is “the discipline dealing with what is good and bad and with moral duty and obligation.”

In practical application, particularly in relation to technology, “good” and “bad” coexist. Airbnb is just one example. On one hand, homeowners are able to take advantage of another income stream. However, hotels and motels now face new competition and the residents living next to or near Airbnb properties often face negative quality-of-life impacts.

According to Gartner research, organizations at the beginning stages of a digital strategy rank ethics a Number 7 priority. Organizations establishing a digital strategy rank it Number 5 and organizations that are executing a digital strategy rank it Number 3.

“The [CIOs] who tend to be more enlightened are the ones in regulated environments, such as financial services and public sector, where trust is important,” said Frank Buytendijk, a Gartner research vice president and Gartner fellow.

Today’s organizations tend to approach ethics from a risk avoidance perspective; specifically, for regulatory compliance purposes and to avoid the consequences of operating an unethical business. On the positive side, some view ethics as a competitive differentiator or better yet, the right thing to do.

“Unfortunately, it’s regulatory compliance pressure and risk because of all the scandals you see with AI, big data [and] social media, but hey, I’ll take it,” said Buytendijk. “With big data there was a discussion about privacy but too little, too late. We’re hopeful with robotics and the emergence of AI, as there is active discussion about the ethical use of those technologies, not onlyt by academics, but by the engineers themselves.”

IEEE ethics group emerges

In 2016, the IEEE launched the IEEE Global Initiative on Ethics of Autonomous and Intelligent Systems. Its goal is to ensure that those involved in the design and development of autonomous and intelligent systems are educated, trained, and empowered to prioritize ethical considerations so that technologies are advanced for the benefit of humanity.

From a business perspective, the idea is to align corporate values with the values of customers.

“Ethics is the new green,” said Raja Chatila, Executive Committee Member of the IEEE Global Initiative on Ethics of Autonomous and Intelligent Systems. “People value their health so they value products that do not endanger their health. People want to buy technology that respects the values they cherish.”

However, the overarching goal is to serve society in a positive way, not just individuals. Examples of that tend to include education, health, employment and safety.

“As an industry, we could do a better job of being responsible for the technology we’re developing,” said Chatila.

At the present time, 13 different committees involved in the initiative are contemplating ethics from different technological perspectives, including personal data and individual access control, ethical research and design, autonomous weapons, classical ethics in AI, and mixed reality. In December 2017, the group released “Ethically Aligned Design volume 2,” a 266-page document available for public comment. It includes the participation of all 13 committees.

In addition, the initiative has proposed 11 IEEE standards, all of which have been accepted. The standards address transparency, data privacy, algorithmic bias, and more. Approximately 250 individuals are now participating in the initiative.

Society must demand ethics for its own good

Groups within society tend to react to technology innovation differently due to generational differences, cultural differences, and other factors. Generally speaking, early adopters tend to be more interested in a new technology’s capabilities than its potential negative effects. Conversely, laggards are more risk averse. Nevertheless, people in general tend to use services, apps, and websites without bothering to read the associated privacy policies. Society is not protecting itself, in other words. Instead, one individual at a time is acquiescing to the collection, storage and use of data about them without understanding to what they are acquiescing.

“I think the practical aspect comes down to transparency and honesty,” said Bill Franks, chief analytics officer at the International Institute for Analytics(IIA). “However, individuals should be aware of what companies are doing with their data when they sign up, because a lot of the analytics –- both the data and analysis –- could be harmful to you if they got into the wrong hands and were misused.”

Right now, the societal impacts of technology tend to be recognized after the fact, rather than contemplated from the beginning. Arguably, not all impacts are necessarily foreseeable, but with the pace of technology innovation constantly accelerating, the innovators themselves need to put more thought into the positive and negative consequences of bringing their technology to market.

Meanwhile, individuals have a responsibility to themselves to become more informed than they are today.

“Until the public actually sees the need for ethics, and demands it, I just don’t know that it would ever necessarily go mainstream,” said Franks. “Why would you put a lot of time and money into following policies that add overhead to manage and maintain when your customers don’t seem to care? That’s the dilemma.”

Businesses, individuals, and groups need to put more thought into the ethics of technology for their own good and for the good of all. More disruptions are coming in the form of machine intelligence, automation, and digital transformation which will impact society somehow. “How” is the question.

Analytics Leaders and Laggards: Which Fits Your Company?

June 20, 2018 /

Different companies and industries are at different levels of analytical maturity. There are still businesses that don’t use analytics at all and businesses that are masters by today’s standards. Most organizations are somewhere in between.

So, who are the leaders and laggards anyway? The International Institute for Analytics (IIA) asked that question in 2016 and found that digital natives are the most mature and the insurance industry is the least mature.

How Industries and Sectors Stack Up

IIA’s research included 11 different industries and sectors, in addition to digital natives. The poster children included Google, Facebook, Amazon, and Netflix. From Day 1, data has been their business and analytics has been critical to their success.

The report shows the descending order of industries in terms of analytical maturity, with insurance falling behind because its IT and finance analytics are the weakest of all.

Another report, from business and technology consultants West Monroe Partners found that only 11% of the 122 insurance executives they surveyed think their companies are realizing the full benefits of advanced analytics. “Advanced analytics” in this report is defined as identifying new revenue opportunities, improving customer and agent experience, performing operational diagnostics, and improving control mechanisms.

Two of the reasons West Monroe cited for the immaturity of the insurance industry are the inability to quantify the ROI and poor data quality.

Maturity is a Journey

Different organizations and individuals have different opinions about what an analytics maturity model looks like. IIA defines five stages ranging from “analytically impaired” (organizations that make decisions by gut feel) to “analytical nirvana” (using enterprise analytics).

“Data-first companies haven’t had to invest in becoming data-driven since they are, but for the companies that aren’t data-first, understanding the multi-faceted nature of the journey is a good thing,” said Daniel Magestro, research director at IIA. “There’s no free lunch, no way to circumvent this. The C-suite can’t just say that we’re going to be data-driven in 2017.”

Others look at the types of analytics companies are doing: descriptive, predictive, and prescriptive. However, looking at the type of analytics doesn’t tell the entire story.

What’s interesting is that different companies at different stages of maturity are stumped by different questions: Do you think you need analytics? If the answer is no, then it’s going to be a long and winding road.

Why do you think you need analytics? What would you use analytics to improve? Those two related questions require serious thought. Scope and priorities are challenges here.

How would you define success? That can be a tough question because the answers have to be quantified and realistic to be effective. “Increase sales” doesn’t cut it. How much and when are missing.

One indicator of maturity is what companies are doing with their analytics. The first thing everyone says is, “make better business decisions,” which is always important. However, progressive companies are also using analytics to identify risks and opportunities that weren’t apparent before.

The degree to which analytics are siloed in an organization also impacts maturity as can the user experience. Dashboards can be so complicated they’re ineffective versus simple to prioritize and expedite decision-making.

Time is another element. IT-created reports have fallen out of favor. Self-service is where it’s at. At the same time, it makes no sense to pull the same information in the same format again and again, such as weekly sales reports. That should simply be automated and pushed to the user.

The other time element — timeliness whether real-time, near real-time, or batch — is not an indication of maturity in my mind because what’s timely depends on what’s actually necessary.

How Valuable Is Your Company’s Data?

June 14, 2018 /

Companies are amassing tremendous volumes of data, which they consider their greatest asset, or at least one of their greatest assets. Yet, few business leaders can articulate what their company’s data is worth.

Successful data-driven digital natives understand the value of their data and their valuations depend on sound applications of that data. Increasingly venture capitalists, financial analysts and board members will expect startup, public company and other organizational leaders to explain the value of their data in terms of opportunities, top-line growth, bottom line improvement and risks.

For example, venture capital firm Mercury Fund recently analyzed SaaS startup valuations based on market data that its team has observed. According to Managing Director Aziz Gilani, the team confirmed that SaaS company valuations, which range from 5x to 11x revenue, depend on the underlying metrics of the company. The variable that determines whether those companies land in the top or bottom half of the spectrum is the company’s annual recurring revenue (ARR) growth rate, which reflects how well a company understands its customers.

Mercury Fund’s most successful companies scrutinize their unit economics “under a microscope” to optimize customer interactions in a capital-efficient manner and maximize their revenue growth rates.

For other companies, the calculus is not so straightforward and, in fact, it’s very complicated.

Direct value

When business leaders and managers ponder the value of data, their first thought is direct monetization which means selling data they have.

“[I]t’s a question of the holy grail because we know we have a lot of data,” said David Schatsky, managing director at Deloitte. “[The first thought is] let’s go off and monetize it, but they have to ask themselves the fundamental questions right now of how they’re going to use it: How much data do they have? Can they get at it? And, can they use it in the way they have in mind?”

Data-driven digital natives have a better handle on the value of their data than the typical enterprise because their business models depend on collecting data, analyzing that data and then monetizing it. Usually, considerable testing is involved to understand the market’s perception of value, although a shortcut is to observe how similar companies are pricing their data.

“As best as I can tell, there’s no manual on how to value data but there are indirect methods. For example, if you’re doing deep learning and you need labeled training data, you might go to a company like CrowdFlower and they’d create the labeled dataset and then you’d get some idea of how much that type of data is worth,” said Ben Lorica, chief data officer at O’Reilly Media. “The other thing to look at is the valuation of startups that are valued highly because of their data.”

Observation can be especially misleading for those who fail to consider the differences between their organization and the organizations they’re observing. The business models may differ, the audiences may differ, and the amount of data the organization has and the usefulness of that data may differ. Yet, a common mistake is to assume that because Facebook or Amazon did something, what they did is a generally-applicable template for success.

However, there’s no one magic formula for valuing data because not all data is equally valuable, usable or available.

“The first thing I look at is the data [a client has] that could be turned into data-as-a-service and if they did that, what is the opportunity the value [offers] for that business,” said Sanjay Srivastava, chief digital officer at global professional services firm Genpact.

Automation value

More rote and repeatable tasks are being automated using chatbots, robotic process automation (RPA) and AI. The question is, what is the value of the work employees do in the absence of automation and what would the value of their work be if parts of their jobs were automated and they had more time to do higher-value tasks?

“That’s another that’s a shortcut to valuing that data that you already have,” said O’Reilly’s Lorica.

Recombinant value

Genpact also advances the concept of “derivative opportunity value” which means creating an opportunity or an entirely new business model by combining a company’s data with external data.

For example, weather data by zip code can be combined with data about prevalent weeds by zip code and the available core seed attributes by zip codes. Agri-food companies use such data to determine which pesticides to use and to optimize crops in a specific region.

“The idea is it’s not just selling weather data as a service, that’s a direct opportunity,” said Srivastava. “The derivative opportunity value is about enhancing the value of agriculture and what value we can drive.”

It is also possible to do an A/B test with and without a new dataset to determine the value before and after the new data was added to the mix.

Algorithmic value

Netflix and Amazon use recommendation engines to drive value. For example, Netflix increases its revenue and stickiness by matching content with a customer’s tastes and viewing habits. Similarly, Amazon recommends products, including those that others have also viewed or purchased. In doing so, Amazon successfully increases average order values through cross-selling and upselling.

“Algorithmic value modeling is the most exciting,” said Srivastava. “For example, the more labeled data I can provide on rooftops that have been damaged by Florida hurricanes, the more pictures I have of the damage caused by the hurricanes and the more information I have about claim settlements, the better my data engine will be.”

For that use case, the trained AI system can automatically provide an insurance claim value based on a photograph associated with a particular claim.

Risk-of-Loss value

If a company using an external data source were to lose access to that data source, what economic impact would it have? Further, given the very real possibility of cyberattacks and cyberterrorism, what would the value of lost or corrupted data be? Points to consider would be the financial impact which may include actual loss, opportunity cost, regulatory fines and litigation settlement values. If the company has cybersecurity insurance, there’s a coverage limit on the policy which may differ from the actual claim settlement value and the overall cost to the company.

A bigger risk than data loss is the failure to use data to drive value, according to Genpact’s Srivastava.

There’s no silver bullet

No single equation can accurately assess the value of a company’s data. The value of data depends on several factors, including the usability, accessibility and cleanliness of the data. Other considerations are how the data is applied to business problems and what the value of the data would be if it were directly monetized, combined with other data, or used in machine learning to improve outcomes.

Further, business leaders should consider not only what the value of their company’s data is today, but the potential value of new services, business models or businesses that could be created by aggregating data, using internal data or, more likely, using a combination of internal and external data. In addition, business leaders should contemplate the risk of data loss, corruption or misuse.

While there’s no standard playbook for valuing data, expect data valuation and the inability to value data to have a direct impact on startup, public company, and merger and acquisition target valuations.

Why Operationalizing Analytics is So Difficult

June 2, 2018 /

Today’s businesses are applying analytics to a growing number of use cases, but analytics for analytics’ sake has little, if any, value. The most analytically astute companies have operationalized analytics, but many of them, particularly the non-digital natives, have faced several challenges along the way getting the people, processes and technology aligned in a way that drives value for the business.

Here are some of the hurdles that an analytics initiative might encounter.

Analytics is considered a technology problem

Some organizations consider analytics a technology problem, and then they wonder why the ROI of their efforts is so poor. While having the right technology in place matters, successful initiatives require more.

“The first key challenge is designing how and in what way an analytics solution would affect the outcome of the business,” said Bill Waid, general manager of Decision Management at FICO. “We start by modeling the business problem and then filling in the analytic pieces that address that business problem. More often than not, there’s a business process or business decision that needs to be incorporated into the model as we build the solution.”

Framing the business problem is essential, because if the analytics don’t provide any business value, they won’t get used.

“Better than 80% of analytics never end up being used. A lot of that stems from the fact that an analysis gets built and it might make sense given the dataset but it’s not used to make something happen,” said Waid. “That’s probably the hardest element.”

Placing analytics in the hands of the business requires access to the right data, but governance must also be in place.

“[T]he technical aspects are becoming easier to solve and there are many more options for solving them, so the people and the process challenges that you’ll face obviously have to come along,” said Bill Franks, chief analytics officer at the International Institute for Analytics (IIA). “In a non-digital-native company, the people and process progress does not match the technology progress.”

Operationalizing analytics lacks buy in

Many analytics initiatives have struggled to get the executive and organizational support they need to be successful. Operationalizing analytics requires the same thing.

“When you operationalize analytics, you’re automating a lot of decisions, so the buy-in you require from all of the various stakeholders has to be high,” said IIA’s Franks. “If you’re a digital native, this is what you do for a living so people are used to it. When you’re a large, legacy company dipping your toe into this, the first couple of attempts will be painful.”

For example, if an organization is automating what used to be batch processes, there need to be more safety checks, data checks, and accuracy checks. Chances are high that everything won’t be done right the first time, so people have to get comfortable with the concept of iteration, which is just part of the learning process.

Analytical results are not transparent

If your company operates in a regulated environment, you need to be able to explain an analytical result. Even if you’re not in a regulated industry, business leaders, investors and potential M&A partners may ask for an explanation.

“We refer to it as ‘reasoning code’ or ‘the outcomes,’ but in AI it’s a form of explainable AI where you can explain to a business owner or a business user why the analytics came to the conclusion it came to,” said FICO’s Waid. “The second thing that you need to provide the business person with is some kind of dashboard for them to be able to change, adjust or accommodate different directions.”

4 Ways Companies Impede Their Analytics Efforts

May 28, 2018 /

Businesses in the race to become “data-driven” or “insights-driven” often face several disconnects between their vision of an initiative and their execution of it. Of course, everyone wants to be competitive, but there are several things that differentiate the leaders from the laggards. Part of it is weathering the growing pains that companies tend to experience, some of which are easier to change than others. These are some of the stumbling blocks.

Business objectives and analytics are not aligned

Analytics still takes place in pockets within the majority of organizations. The good news is that various functions are now able to operate more effectively and efficiently as a result of applying analytics. However, there is greater power in aligning efforts with the strategic goals of the business.

In a recent research note, Gartner stated, “Internally, the integrative, connected, real-time nature of digital business requires collaboration between historically independent organizational units. To make this collaboration happen, business and IT must work together on vision, strategy, roles and metrics. Everyone is going to have to change, and everyone is going to have to learn.”

All of that requires cultural adjustment, which can be the most difficult challenge of all.

There’s insight but no action

It’s one thing to get an insight and quite another to put that insight into action. To be effective, analytics need to be operationalized, which means weaving analytics into business processes so that insights can be turned into meaningful actions. Prescriptive analytics is part of it, but fundamentally, business processes need to be updated to include analytics. A point often missed is that decisions and actions are not ends in themselves. They, too, need to be analyzed to determine their effectiveness.

An EY presentation stresses the need to operationalize analytics. Specifically, it says, ” The key to operationalizing analytics is to appreciate the analytics value chain.”

Interestingly, when most of us think about “the analytics value chain” we think of data, analytics, insights, decisions and optimizing outcomes. While that’s the way work flows, EY says our thought process should be the reverse. Similarly, to optimize a process, one must understand what that process is supposed to achieve (e.g., thwart fraud, improve customer experience, reduce churn).

They’re not looking ahead

Less analytically mature companies haven’t moved beyond descriptive analytics yet. They’re still generating reports, albeit faster than they used to because IT and lines of business tend to agree that self-service reporting is better for everyone. Gartner says “the BI and analytics market is in the final stages of a multiyear shift from IT-lead, system-of-record reporting to business-led, self-service analytics. As a result, the modern business intelligence and analytics platform has emerged to meet new organizational requirements for accessibility, agility and deeper analytical insight.”

Still, organizations can only get so far with descriptive analytics. If they want to up their competitive game, they need to move to predictive and prescriptive analytics.

Poor data quality prevents accurate analytics

If you don’t have good data or a critical mass of the right data, your analytical outcomes are going to fall short. Just about any multichannel (and sometimes even single-channel) communication experience with a bank, a telephone company, a credit card company, or a vendor support organization will prove data quality is still a huge issue. Never mind the fact some of these companies are big brand companies who invest staggering amounts of money in technology, including data and analytics technologies.

In a typical telephone scenario, a bot asks the customer to enter an account number or a customer number. If the customer needs to be transferred to a live customer service representative (CSR), chances are the CSR will ask the customer to repeat the number because it doesn’t come up on their screen automatically. If the CSR can’t resolve the issue, then the call is usually transferred to a supervisor or different department. What was your name and number again? It’s a frustrating problem that’s all too common.

The underlying problem is that customer’s information is stored in different systems for different reasons such as sales, CRM and finance.

I spoke with someone recently who said a company he worked with had gone through nearly 20 acquisitions. Not surprisingly, data quality was a huge issue. The most difficult part was dealing with the limited fields in a legacy system. Because the system did not contain enough of the appropriate fields in which to enter data, users made up their own workarounds.

These are just a few of the challenges organizations face on their journey.

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