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What A Chief Analytics Officer Really Does

May 17, 2017 /

As analytics continues to spread out across an organization, someone needs to orchestrate it all. The “best” person for the job is likely a chief analytics officer (CAO) who understands the business, understands analytics, and can help align the two.

The CAO role is a relatively new C-suite position, as is the chief data officer or CDO. Most organizations don’t have both and when they don’t, the titles tend to be used interchangeably. The general distinction is that the CAO focuses more on analytics and its business impact while the CDO is in charge of data management and data governance.

“The new roles are really designed to expand the use of data and expand the questions that data is used to answer,” said Jennifer Belissent, principal analyst at Forrester. “It’s changing the nature of data and analytics use in the organization, leveraging the new tools and techniques available, and creating a culture around the use of data in an organization.”

Someone in your organization may already have some or all of a CAO’s responsibilities and may be succeeding in the position without the title, which is fine. However, in some organizations a C-suite title and capability can help underscore the importance of the role and the organization’s shift toward more strategic data usage.

“The CAO needs to be able to evangelize the use of data, demonstrate the value of data, and deliver outcomes,” said Belissent. “It’s a role around cultural change, change management, and evangelism.”

If you’re planning to appoint a CAO, make sure that your organization is really ready for one because the role can fail if it is prevented from making the kinds of change the organization needs. A successful CAO needs the support of senior management, as well as the authority, responsibility, budget, and people skills necessary to affect change.

One mistake organizations make when hiring a CAO is placing too much emphasis on technology and not enough emphasis on business acumen and people skills.

The making of a CAO

When professional services company EY revisited its global strategy a few years ago, it was clear to its leadership that data and analytics were of growing importance to both its core business and the new services it would provide to clients.

Rather than hiring someone from the outside, EY chose its chief strategy officer, Chris Mazzei, for the role. His charter as CAO was to develop an analytics capability across EY’s four business units and the four global regions in which it operates.

[Want to learn more about CAOs and CDOs, read 12 Ways to Connect Data Analytics to Business Outcomes.]

Part of his responsibility was shaping the strategy and making sure each of the businesses had a plan they were executing against. He also helped expand the breadth and depth of EY’s analytical capabilities, which included acquiring 30 companies in four years.

The acquisitions coupled with EY’s matrixed organizational structure meant lots of analytics tools, lots of redundancies, and a patchwork of other technology capabilities that were eventually rationalized and made available as a service. Meanwhile, the Global Analytics Center of Excellence Mazzei leads was also building reusable software assets that could be used for analytics across the business and for client engagements.

Mazzei and his team also have been responsible for defining an analytics competency profile for practitioners and providing structured training that maps to it. Not surprisingly, his team also works in a consultative capacity with account teams to help enable clients’ analytical capabilities.

“The question is, ‘What is the strategy and how does analytics fit into it?’ It sounds obvious, but few organizations have a clear strategy where analytics is really connected into it across the enterprise and at a business level,” said Mazzei. “You really need a deep understanding of how the business creates value, how the market is evolving, what the sources of competitive differentiation are and how those could evolve. Where you point analytics is fundamentally predicated on having those views.”

Mazzei had the advantage of working for EY for more than a decade and leading the strategy function before becoming the CAO. Unlike a newly-hired CAO, he already had relationships with the people at EY with whom he’d be interfacing.

“Succeeding in this role takes building really trusted relationships in a lot of different parts of the organization, and often at very senior levels,” said Mazzei. “One reason we’ve seen CAOs fail is either because they didn’t have the skills to build those relationships or didn’t invest enough time on it during their tenure.”

Self-Service Analytics Are A Necessity

May 8, 2017 /

Lines of business are buying their own analytics solutions because IT is unable to deliver what they need fast enough. If the company has a data team, lines of business can ask for help, but like IT, the data team is faced with address more problems than there are people to solve them.

Smart IT organizations are building a foundation with governance built in. In that way, business users can get access to the data and analytics they need while the company’s assets are protected.

“IT has become more of a facilitator,” said Bob Laurent, VP of product marketing for self-service analytics platform provider Alteryx.  “If they’re able to give people access to data with the proper guardrails, then they’re out of the business of having to do mundane reports week in and week out.”

The shift to self-service analytics is happening across industries because organizations are under pressure to do more with their data and do it faster.

Meanwhile, average consumers have come to expect basic self-service analytics from their banks, insurance companies, brokerage firms, credit card companies, apps, and IoT devices. For an increasing number of businesses, self-service analytics is a necessity.

Higher Education Improves Performance

Colleges and universities are using self-service analytics to improve admission rates, enrollment rates, and more.

As an example, the Association of Schools and Programs of Public Health(ASPPH) built a system that allows its members to upload admissions data, graduate data, salary data, and financial data as well as information about their grants and research contracts. ASPPH verifies and validates the information and then makes the data available via dashboards that can be used for analysis.

“We needed to give them a place to enter their data so they weren’t burdened with reporting which they have to do every year,” said Emily Burke, manager, data analytics at ASPPH.

More than 100 schools and programs for public health are using the system to analyze their data, monitor trends and compare themselves to peers.  They’re also using the system for strategic planning purposes.

“A university will log in and see [their] university’s information and create a peer group that’s just above them in rankings. That way, they can see what marks they need to hit,” said Burke.  “A lot of them are doing that geographically, such as what the application numbers look like in Georgia.”

Drive Value from Self-Service Analytics

The value of self-service analytics is measured by two things: the number of active users, and the business value it provides an organization.  Knowing that, a number of vendors are now offering SaaS products that are easy to use, and don’t require a lot of training.

ASPPH built its own system in 2012. At the time, Burke and her team were primarily focused on the system’s functionality, but it soon became obvious that usability mattered greatly.

“We built this wonderful tool, we purchased the software we needed, we purchased a Tableau server, and then realized that our members really didn’t know how to use it,” said Burke.

Deriving the most value from the system has been a journey for ASPPH, which Burke will explain in detail at Interop during her Data-Driven Decision Making: Empowering Users and Building a Culture of Data session in Las Vegas on Thursday May 18.

If you’re implementing self-service analytics or thinking about it, you’ll be able to see a demonstration of the ASPPH system, hear Burke’s first-hand experiences, and walk away with practical ideas for empowering your users.

 

Data-Driven Effectiveness Is A Team Sport

May 2, 2017 /

Most companies are trying to understand how they can make the best use of their data. They’ve invested in tools and they’ve invested in people, but the results continue to fall short of expectations. Competitors are stealing customers, disruptors are upsetting the natural order or things, and business as usual is showing diminishing returns.

Why companies fall so behind or advance so fast isn’t always obvious, but there’s one thing that separates the leaders from the laggards:  The leaders have integrated data driven decision-making into their culture and business processes. In fact, their ability to use data effectively is part of their core competency.

“Legacy processes and procedures have led to really siloed organizations,” said Rich Wagner, CEO of business performance forecasting solutions provider Prevedere. “The analysts within each function all operate differently. They use different tools, different techniques, and different technologies to build their business plans to run the business so they’re not an integrated group.”

How to Make Teamwork Work

One sign of analytical maturity is the effectiveness of cross-functional problem solving. IT likely has the data, the data team needs to surface insights for the business, and the business has to be confident that the decisions they make advance their objectives. In today’s rapidly changing business environment, cross-functional teams are necessary because their collective knowledge and skills enables more effective problem solving, faster.

“You need to have a team that’s focused on a shared understanding of what the business problem is and what the objective is. Do not pass go until you do that because it’s a recipe for disaster,” said Chris Mazzei, chief analytics officer at professional services organization EY (Ernst & Young).

Unifying efforts doesn’t just happen. Business professionals need to understand what the data team and IT do and vice versa, which is best accomplished by working together to solve a business problem.

“[T]ask a team to solve a pretty big problem with a tight deadline and let each of them see the value that the other brings,” said Prevedere’s Wagner.

Success may also require some self-motivation. There’s significant value in spending time with members of the team that have different areas of expertise. By working together and being inquisitive, individuals can learn more about how other functions operate and why they operate that way, which is essential. Without that, important details may be overlooked.

For example, one of EY’s telecom clients wanted to improve its customer retention model. So the analytics team built a new model that could accurately identify customers who would leave within two weeks. That’s impressive, but marketing and sales needed four to six weeks to intervene.

“Nobody asked the marketing and sales team how far in advance they needed to know [a customer was leaving], said EY’s Mazzei. “We see that all the time.”

One of the cheapest ways to understand what works and what doesn’t is to hear what other companies have done right and wrong.

Harnessing the Disruptive Power of Analytics

April 17, 2017 /

There are two things that distinguish companies: what they say and what they do. Business theoreticians, marketers, and even research firms use buzzwords to sell books, products, and reports, respectively. Whenever a particular buzzword such as “disrupter” becomes popular, a lot of companies choose to use it whether it actually applies or not.

There are many ways to be disruptive, not all of which change the world like computers, smartphones, social networks, and self-driving cars do. Sometimes being disruptive isn’t just about being innovative, it’s a matter of opportunity and timing.

Disruptor or Disrupted?

Digital natives continue to impact the business models of companies in a growing number of industries as more things in the physical world are replicated or reimagined using ones and zeros. Who would have imagined that the largest bookseller would have no physical stores or that the largest taxi company would own no cars?

Today’s disruptions tend to be technology-enabled, and often a confluence of technologies is necessary for the business model to succeed. For example, connected cars and even fitness trackers require a combination of sensors, computing power, adequate bandwidth, and cloud agility. As history has demonstrated, a game-changing idea has a better chance of succeeding if there’s a practical way to implement it at scale.

For example, mobile advertising was expected to explode at the beginning of the millennium, but the cellular networks were comparatively slow, certainly not fast enough to support rich media. People carried cellphones back then, not smartphones, and there was a much smaller installed base of users. Sometimes, great ideas fail because the timing is wrong.

Meanwhile, Amazon has expanded from books to all sorts of product categories, threatening the viability of many types of storefronts. However, it has failed at several me-too initiatives that were similar to disruptive offerings from Apple, PayPal, Groupon, and others. Disrupters are not immune from disruption, nor are disrupted companies incapable of disruption.

For example, GE successfully reinvented itself and is innovating in the industrial IoT space. Big pharma companies are working closely with their competitors on R&D projects (outside of consortia work) out of necessity.

Reframing the Status Quo

More analog products are being disrupted by new-generation replacements that are digitized and connected, from smart buildings to connected inhalers. While some organizations are developing entirely new categories of technologies and products, others are finding new ways of using what already exists. Solar power is a good example of that. It isn’t new, but it’s a practical and affordable way to deliver electricity to consumers who lack access to a power grid. In rural Africa, consumers pay for their solar power using their cell phones because they don’t have bank accounts. And the solar power panel providers are using cellular networks to monitor and manage the units they’ve installed at residences and small businesses.

A number of large, established companies including Wells Fargo and Disney now have incubator or accelerator programs so they have direct insightinto start-up innovation. Acquisition is another means big companies use to become disruptive or sustain a disruptive track record.

Some businesses adapt a disruptive idea, such as Etsy’s ecommerce marketplace for handmade crafts. Other organizations including Travelocity, Hotels.com, and Zillow disrupted entire industries by eliminating the need for a middleman.

What Does Analytics Suggest?

What would happen if your company digitized a product or service in a way that provided more value to the customer and new sources of revenue for you? How could a disruptive trend in another industry apply to your industry? What insight might third-party data sources give you that would make a material difference to your customers and your company? How might your business model change if data and analytics were considered the core competency of your organization?

Why Recommendation Engines Still Aren’t Accurate

March 28, 2017 /

Recommendation engines are deeply embedded in American culture. Anyone who shops online, subscribes to a streaming media service, conducts on online search, or uses social media is encouraged to do something — buy this, click on that, listen to this song, watch that movie. Sometimes the recommendations are accurate. Sometimes they’re not.

For example, Google’s search engine thinks I’m male. Netflix thinks I might enjoy comedies aimed at college-age men. Amazon’s recommendations can be strange if not laughable.

“On some level, these algorithms are amazing, but the types of errors that can be made are foolish,” said Patrick Wolfe, professor of statistics and honorary professor of computer cience at University College London (UCL) and Chair of IEEE Signal Processing Society’s Big Data Special Interest Group. “It probably wouldn’t take a lot of data to teach Google you’re not male.”

Credit: Pixabay

Credit: Pixabay

Context is Everything

Google and Facebook continue to serve up ads for the fringed boots I bought at Macy’s two weeks ago. They’re also recommending the infant and toddler car seats I bought as a gift in the same time frame. Clearly, the recommendations lack appropriate context.

“One of the reasons I think recommendation engines aren’t as accurate as they could be is that much of machine learning is about making predictions — predicting the weather or whether a document is about politics or not,” said Thorsten Joachims, a professor in the Department of Computer Science and in the Department of Information Science at Cornell University. “A recommendation engine has to be smart about the actions it takes.”

Pandora serves up more music that I like than dislike, although my genre preferences, Native American flute music and smooth jazz, represent dramatically smaller universes of choices than classical music or rock and roll. Pandora doesn’t tell me anything about local musicians or the local music scene. Granted, that would be an extremely difficult undertaking given the number of cities that consider their local music scene an integral part of their culture — Chicago, Las Vegas, and Ithaca, New York, for example.

Ithaca College Associate Professor Doug Turnbull and Cornell’s Joachims are tackling that very problem. With the help of their students, they developed MegsRadio, a music app that recommends local musicians and local music events. The app is specific to Ithaca’s local music scene, although others could benefit from the work if they chose to do something similar in another city, Turnbull said.

I tried the app and created a smooth jazz station. The playlist included some of favorite smooth jazz artists and some very talented artists (presumably from Ithaca) with whom I was unfamiliar.

Solving the Problem Pragmatically

Turnbull and Joachims are using counterfactual machine learning to compensate for the fact that they lack a user base large enough to train the algorithms. Counterfactual learning considers the recommendations an algorithm made, users’ historical choices, the accuracy of the recommendations made by the algorithm, and what would happen if a different algorithm were applied in the same circumstance.

“Counterfactual learning is saying that if we don’t have new users but we do have what the old algorithm predicted and the probability of the thing it predicted and given what the user historically picked in the past, would this new algorithm be better?” said Turnbull. “It’s like an alternate parallel universe.”

By comparison, popular search engines, shopping sites, and streaming media companies use A/B testing on a small percentage of users to determine whether one algorithm performs better than another. If the new one performs better than the existing one, the new new algorithm is eventually rolled out to all users. Counterfactual learning enables offline research. Users can be brought in later for validation purposes.

“It’s really making these systems much more like an agent that thinks about what the effects of its actions will be so that it can learn from past mistakes,” said Cornell’s Joachims.

What’s Your Take on Recommendation Engines?

As more analytics become prescriptive, recommendation engines will find their way into more use cases. Are recommendation engines helping or hindering you? What improvements would you like to see?

Why Embedded Analytics Will Change Everything

March 15, 2017 /

Analytics are being embedded in all kinds of software. As a result, the ecosystem is changing, and with it so is our relationship to analytics. Historically, analytics and BI have been treated as something separate — we “do”analytics, we’re “doing” ad hoc reporting — but increasingly, analytics are becoming an integral part of software experiences, from online shopping to smart watches and to enterprise applications.

“We’re creating whole industries that are centered around data and analytics that are going to challenge the status quo of every industry,” said Goutham Belliappa, Big Data and Analytics practice leader, for Capgemini North America. “Analytics will become so ubiquitous, we won’t even notice it.  From a business perspective, it’s going to transform entire industries.”

Three drivers are collectively changing how we experience and think about analytics. The first, as previously mentioned, is embedding analytics into all kinds of software. The second is automation, and the third is a shift in the way software is built.

Automation is Fuel

Modern software generates and analyzes more data than ever, and the trend is going to accelerate. The resulting glut of data is outpacing humans’ ability to manage and analyze it, so some analytics necessarily have to be automated, as do some decisions. As a result, analytics has become invisible in some contexts, and it’s going to become invisible in still more contexts soon.

“Frictionless” is a good way to describe what people are striving for in effective user experiences.  Certainly, with more automation and more behind-the-scenes analytics, how we think of analytics will change,” said Gene Leganza, VP & research director at Forrester Research. “We’ll be thinking about the results — do we like the recommendations of this site’s or this app’s recommendation engine or is that one better?  We’ll gravitate towards the services that just work better for us without knowing how they do it.”

That’s not to say that automated analytics should be implemented as black boxes. While humans will apply less conscious thought to analytics because they are embedded, they will still want to understand how decisions were made, especially as those decisions increase in importance, Leganza said. Successful software will not just automate data management and analytics and chose the right combination of microservices to achieve a particular result, it will also be able to explain its path on demand.

Microservices Will Have an Impact

Software development practices are evolving and so is the software that’s being built. In the last decade, monolithic enterprise applications have been broken down into smaller pieces that are now offered as SaaS solutions. Functionality is continuing to become more modular with microservices, which are specific pieces of functionality designed to achieve a particular goal. Because microservices are essentially building blocks, they can be combined in different ways which impacts analytics and vice versa.

Tableau has embraced microservices so its customers can combine B2B tools in a seamless way.  For example, Tableau is now embedded in Salesforce.com, so a sales rep can get insights about a customer as well as the customer details that were already stored in Salesforce.com.

“The more embedded you get, APIs and developer extensions become more relevant because you need more programmability to make [analytics] more invisible, to be seamless, to be part of a core application even though it comes from somewhere else,” said Francois Ajenstat, chief product officer at Tableau.

Software continues to become more modular because modularity provides flexibility. As the pace of business accelerates, software has to be able adapt to changing circumstances quickly and without unnecessary overhead.

“In order to automate more and more actions and to enable adapting to a myriad of conditions, we’ll be having software dynamically cobble together microservices as needed.  The granularity of the services will have to be synced to the patterns in the data.  For the near future, the task will be to make the software flexible enough to adapt to the major patterns we’re seeing,” said Forrester’s Leganza.

What Healthcare Analytics Can Teach The Rest of Us

February 17, 2017 /

Healthcare analytics is evolving rapidly. In addition to using traditional business intelligence solutions, there is data flowing from hospital equipment, medical-grade wearables, and FitBits.

The business-related data and patient-related data, sometimes combined with outside data, enable hospitals to triage emergency care patients and treat patients more effectively, which is important. For example, in the U.S., Medicare and Medicaid are embracing “value-based care” which means hospitals are now being compensated for positive outcomes rather than on the number of services they provide, and they’re docked for “excessive” readmissions. Similarly, doctors are increasingly being compensated for positive outcomes rather than the number of patients they see. In other words, analytics is more necessary than ever.

There are a couple of things the rest of us can learn from what’s happening in the healthcare space, and there are some surprises that may interest you. The main message is to learn how to adapt to change, because change is inevitable. So is the rise of machine intelligence.

The Effect of the IoT

Medical devices are becoming connected devices in operating rooms and hospital rooms. Meanwhile, pharmaceutical companies are beginning to connect products such as inhalers to get better insight into a drug’s actual use and effects, and they’re experimenting with medical-grade (and typically application-specific) devices in clinical trials to reduce costs and hassles while getting better insights into a patient’s physical status. Surgeons are combining analytics sometimes with telemedicine to improve the results of a surgical procedures. Slowly but surely, analytics are seeping into just about every aspect of healthcare to lower costs, increase efficiencies, and reduce patient risks in a more systematic way.

One might think devices such as FitBits are an important part of the ecosystem, and from a consumer perspective they are. Doctors are less interested in that data because it’s unreliable, however. After all, it’s one thing for a smartwatch to err in monitoring a person’s heart rate. For a medical-grade device, faulty monitoring could lead to a heart attack and litigation. At this point, doctors are more interested in the fact that someone wears a FitBit because it indicates health consciousness.

Not surprisingly, predictive analytics is important because mitigating or avoiding healthcare-related episodes is preferable to dealing with an episode after the fact. From an IoT perspective, there is a parallel here with equipment and capital asset management. One way to reduce the risk of equipment failure is to compare the performance of a piece of equipment operating in real-world conditions against a virtual representation that is operating normally under the same conditions. Similarly, patient “signatures” will make it easier to spot complications earlier, such as weight gain which is an indicator of congestive heart disease or fluid retention which may indicate the likelihood of a heart attack. Imagine if the same predictive concept were applied in your industry or business.

Machine Intelligence Reveals Insights

“Insights” is a oft-misused term. It is related to analytics but not synonymous with analytics. Insights means new knowledge, and that is precisely the reason why machine learning is gaining traction in the healthcare space. Traditional medical research has been hypothesis-driven. Machine learning doesn’t necessarily have a theory or the same veil of human biases.

Take diabetes research, for example, a machine learning-based research project found that a person who has been hospitalized is at greater risk for subsequent hospitalization, which comes as no surprise to doctors. However, several more interesting factors were unearthed, the biggest surprise of which was flu shots. It turns out the diabetics who did not get flu shots were more likely to be hospitalized and following hospitalization their health became unstable or unmanageable.

The lesson here is one of adaptation: as machine learning becomes mainstream, more of us will have to get comfortable with insights we hadn’t anticipated. In a business context, machine learning may reveal opportunities, risks, or competitive forces you hadn’t considered. In short, more of us will have to embrace an ethos of exploration and learning.

Common Biases That Skew Analytics

January 28, 2017 /

How do you know if you can trust analytical outcomes? Do you know where the data came from? Is the quality appropriate for the use case? Was the right data used? Have you considered the potential sources and effects of bias?

All of these issues matter, and one of the most insidious of them is bias because the source and effects of the bias aren’t always obvious. Sadly, there are more types of bias than I can cover in this blog, but following are a few common ones.

Selection bias

Vendor research studies are a good example of selection bias because several types of bias may be involved.

Think about it: Whom do they survey? Their customers. What are the questions? The questions are crafted and selected based on their ability to prove a point. If the survey reveals a data point or trend that does not advance the company agenda, that data point or trend will likely be removed.

Data can similarly be cherry-picked for an analysis. Different algorithms and different models can be applied to data, so selection bias can happen there. Finally, when the results are presented to business leaders, some information may be supplemented or withheld, depending on the objective.

This type of bias, when intentional, is commonly used to persuade or deceive. Not surprisingly, it can also undermine trust. What’s less obvious is that selection bias sometimes occurs unintentionally.

Confirmation bias

A sound analysis starts with a hypothesis, but never mind that. I want the data to prove I’m right.

Let’s say I’m convinced that bots are going to replace doctors in the next 10 years. I’ve gathered lots of research that demonstrates the inefficiencies of doctors and the healthcare system. I have testimonials from several futurists and technology leaders. Not enough? Fine. I’ll torture as much data as necessary until I can prove my point.

As you can see, selection bias and confirmation bias go hand-in-hand.

Outliers

Outliers are values that deviate significantly from the norm. When they’re included in an analysis, the analysis tends to be skewed.

People who don’t understand statistics are probably more likely to include outliers in their analysis because they don’t understand their effect. For example, to get an average value, just add up all the values and divide by the sum of the individuals being analyzed (whether that’s people, products sold, or whatever). And voila! End of story. Except it isn’t…

What if 9 people spent $100 at your store in a year, and the10th spent $10,000? You could say that your average customer spend per year is $1,090. According to simple math, the calculation is correct. However, it would likely be unwise to use that number for financial forecasting purposes.

Outliers aren’t “bad” per se, since they are critical for such use cases as cybersecurity and fraud prevention, for example. You just have to be careful about the effect outliers may have on your analysis. If you blindly remove outliers from a dataset without understanding them, you may miss an important indicator or the beginning of an important trend such as an equipment failure or a disease outbreak.

Simpson’s Paradox

Simpson’s Paradox drives another important point home: validate your analysis. When Simpson’s Paradox occurs, trends at one level of aggregation may reverse themselves at different levels of aggregation. Stated another way, datasets may tell one story, but when you combine them, they may tell the opposite story.

A famous example is a lawsuit that was filed against the University of California at Berkeley. At the aggregate level, one could “prove” more men were accepted than women. The reverse proved true in some cases at the departmental level.

Emotional Analytics is Next. Are You Ready?

January 26, 2017 /

In the near future, more organizations will use emotional analytics to fine-tune their offerings, whether they’re designing games or building CRM systems. Already, there are platforms and software development tools that allow software developers to build emotional analytics into desktop, mobile, and web apps. In a business context, that can translate to mood indicators built into dashboards that show whether the customer on the phone or in a chat discussion is happy, whether the customer service rep is effective, or both — in real time.

Such information could be used to improve the efficiency of escalation procedures or to adapt call scripts in the moment. It could also be used to refine customer service training programs after the fact. In many cases, emotional analytics will be used in real time to determine how a bot, app, IoT device, or human should react.

Although the design approaches to emotional analytics differ, each involves some combination of AI, machine learning, deep learning, neural nets, natural language processing, and specialized algorithms to better understand the temperament and motivations of humans. The real-time analytical capabilities will likely affect the presentation of content, the design of products and services, and how companies interact with their customers. Not surprisingly, emotional analytics requires massive amounts of data to be effective.

Emotion isn’t an entirely new data point, and at the same time, it is. In a customer service or sales scenario, a customer’s emotion may have been captured “for training purposes” in a call or in a rep’s notes. In the modern sense, emotions will be detected and analyzed in real time by software that is able to distinguish the nuances of particular emotions better than humans. Because the information is digital, it can be used for analytical purposes like any other kind of data, without transformation.

Voice Inflection

What people say is one thing. How they say it provides context. Voice inflection is important because in the not-too-distant future, more IoT devices, computing devices, and apps will use voice interfaces instead of keyboards, keypads, or gestures designed for mobile devices.

Because humans and their communication styles are so diverse, contextual information is extremely important. Demographics, personas, account histories, geolocation, and what a person is doing in the moment are just a few things that need to be considered. Analyzing all that information, making a decision about it, and acting upon it requires considerable automation for real time relevance. The automation occurs inside an app, an enterprise application, or a service that acts autonomously, notifies humans, or both.

Body Language

Body language adds even more context. Facial expressions, micro expressions, posture, gait, and gestures all provide clues to a person’s state of mind.

Media agency MediaCom is using emotional analytics to more accurately gauge reactions to advertisements or campaigns so the creative can be tested with greater accuracy and adjusted.

Behavioral health is another interesting application. Using emotional analytics, healthcare providers can gain insight into conditions such as depression, anxiety, and schizophenia.

The potential applications go on, including law enforcement interrogations, retail, and business negotiations, to name a few.

A Tough Problem

Natural language processing, which is necessary for speech and text analysis, is hard enough to get right. Apple Siri, Microsoft Cortana, and even spellcheckers are proof that there’s a lot of room for improvement. Aside from getting the nuances of individual languages and their dialects right, there are also cultural nuances that need to be understood – not only in the context of words but the way in which words are spoken.

The same thing goes for gestures. Large gestures are fine in Italy, but inappropriate in Japan, for example. The meaning of gestures can change with culture, which intelligent systems must understand.

As a result, emotional analytics will crawl before it walks or runs, like most technologies.

Misunderstood? Try Data Storytelling

December 29, 2016 /

Data visualizations help explain complex data, although individuals can and do come to different conclusions nevertheless. It’s a significant problem for data scientists and data analysts, especially when they’re trying to explain something important to business people.

Part of the problem is one’s ability to communicate. Another problem is expecting too much from data visualizations — specifically, the clear communication of an analytical result.

Data storytelling can help, because it goes beyond data visualizations. It also helps individuals think a bit harder about what the data is saying and why.

Are data visualizations dead?

Clearly not. They remain an extremely important part of turning data into insights, but they do have their limitations. The first limitation is that data visualizations don’t always explain the details of what the data is saying and why. Another limitation, as I mentioned earlier, is the possibility of diverse interpretations and therefore diverse conclusions, which, in a business context, can lead to some rather heated and unpleasant debates.

A simple form of data storytelling is adding text to data visualizations to promote a common understanding. Like PowerPoint, however, it’s entirely possible to add so much text or so many bullets to a data visualization that the outcome is even more confusing than it was without the “improvement.”

The same observation goes for infographics. Bright colors, geometric shapes, and “bleeds” (the absence of a border) do little to aid communication when used ineffectively. It’s important to avoid clutter if you want others to understand an important point quickly.

One complaint I hear about using data visualizations alone is that they lack context. Data storytelling helps provide that context.

How to tell a good data story

Humans tend to be storytellers naturally, whether they’re explaining how a car accident happened or why they weren’t home at 7:00, again. However, when it comes to telling data stories, it’s easy to forget what an effective story entails.

An effective story has a beginning, a middle, and an end like a book or a movie. A data story should have those elements, but beware of telling linear stories that are passively consumed. Interactive stories tend to be more effective in this day and age because people have become accustomed to interacting with data at work and at home. In addition, work styles have become more collaborative over time. Allowing audience members to do some of their own exploration enables them ask more informed, if not challenging, questions. In addition, unlike storytelling generally, data story endings tend not to be definite (e.g., “… triumphant at last, they rode off into the sunset”) but rather possibilities.

Data stories are also vulnerable to the same kinds of flaws that detract from blogs, articles, presentations, and books: typos. Make sure to proof your work. Otherwise, you may lose credibility. Also avoid jargon, not only because it’s considered a bad practice, but because it may confuse at least part of the audience, which brings me to another important point: consider the audience,

Data scientists often are criticized for failing to understand their audiences — namely, business people. It’s fine to talk about linear regressions and sample variances among people who understand what they are, how they work, and why they’re important. A business person’s concern is business impact, not particular forms of statistics.

While you’re at it, be careful about language use generally. The word, “product” can mean different things to different people who work at the same company. Bottom line, it’s all about making it easier for other people to understand what you intend to communicate.

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