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Ethical Tech: Myth or Reality?

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?

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?

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

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

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.

Why Businesses Must Start Thinking About Voice Interfaces, Now

Voice interfaces are going to have an immense impact on human-to-machine interaction, eventually replacing keyboards, mice and touch. For one thing, voice interfaces can be much more efficient than computers, laptops, tablets and smartphones. More importantly, they provide an opportunity to develop closer relationships with customers based on a deeper understanding of those customers.

Despite the popularity of Alexa among consumers, one might assume that voice interfaces are aspirational at best for businesses, although a recent Capgemini conversational commerce study tells a different story. The findings indicate that 40% of the 5,000 consumers interviewed would use a voice assistant instead of a mobile app or website. In three years, the active users expect 18% of their total expenditures will take place via a voice assistant, which is a six-fold increase from today. The study also concluded that voice assistants can improve Net Promoter Scores by 19%. Interestingly, this was the first such study by Capgemini.

“Businesses really need to come to grips with voice channels because they will change the customer experience in ways that we haven’t seen since the rise of ecommerce,” Mark Taylor, chief experience officer, DCX Practice, Capgemini. “I think it’s going to [have a bigger impact] than ecommerce because it’s broader. We call it ‘conversational commerce,’ but it’s really voice-activated transactions.”

Voice interfaces need to mimic humans

The obvious problem with voice interfaces is their limited understanding of human speech, which isn’t an easy problem to solve. Their accuracy depends on understanding of the words spoken in context, including the emotions of the speaker.

“We’re reacting in a human way to very robotic experience and as that experience evolves, it will only increase our openness and willingness to experience that kind of interaction,” said Taylor. “Businesses have recognized that they’re going to need a branded presence in voice channels, so some businesses have done a ton of work to learn what that will be.”

For example, brands including Campbell’s Soup, Sephora and Taco Bell are trying to understand how consumers want to interact with them, what kind of tone they have as a brand and what to do with the data they’re collecting.

“Brands have spent billions of dollars over the years representing how they look to their audience,” said Taylor. “Now they’re going to have to represent how they sound. What is the voice of your brand? Is it a female or male voice, a young voice or an older voice? Does it have a humorous or dynamic style? There are lots of great questions that will need to be addressed.”

Don’t approach voice like web or mobile

Web and mobile experiences guide users down a path that is meant to translate human thought into something meaningful, but the experience is artificial. In web and mobile experiences, it’s common to search using keywords or step through a pre-programmed hierarchy. Brands win and lose market share based on the customer experience they provide. The same will be true for voice, but the difference is that voice will enable deeper customer relationships.

Interestingly, in the digital world, voice has lost its appeal. Businesses are replacing expensive call centers with bots. Meanwhile, younger generations are using smartphones for everything but traditional voice phone calls. Voice interfaces will change all of that that, albeit not in the way older generations might expect. In Europe, for example, millennials prefer to use a voice assistant in stores rather than talking to a person, Taylor said.

“What’s interesting here is the new types of use cases [because you can] interact with customers where they are,” said Ken Dodelin, VP of Conversational AI Products at Capital One.

Instead of surfing the web or navigating through a website, users can simply ask a question or issue a command.

“[Amazon’s] dash button was the early version a friction-free thing where someone can extend their finger and press a button to go from thought to action,” said Dodelin. “Alexa is a natural evolution of that.”

In banking, there is considerable friction between wanting money or credit and getting it. Capital One has enabled financial account access via voice on Alexa and Cortana platforms. It is also combining visual and voice access on Echo Show. The reasoning for the latter is because humans communicate information faster by speaking and consume information faster visually.

“[I]t usually boils down to what’s the problem you’re solving and how do you take friction out of things,” said Dodelin. “When I think about what it means for a business, it’s more about how can we [get] good customer and business outcomes from these new experiences.”

When Capital One first started with voice interfaces, customers would ask about the balance on their credit cards, but when they asked about the balance due, the system couldn’t handle it.

“Dialogue management is really important,” said Dodelin. “The other piece is who or what is speaking?”

Brand image is reflected in the characteristics of the voice interface. Capital One didn’t have character development experts, so it hired one from Pixar that now leads the conversational AI design work.

“Natural language processing technology has progressed so much that we can expect it to become an increasingly common channel for customer experience,” said Dodelin. “If they’re not doing it directly through a company’s proprietary voice interface, they’re doing it by proxy through Alexa, Google Home or Siri and soon through our automobiles.”

The move to voice interfaces is going to be a challenge for some brands and an opportunity for others. Now is the time for companies to experiment and if they’re successful, leap ahead of their competitors and perhaps even set a new standard for creating customer experiences.

Clearly, more works needs to be done on natural language processing, but already, some consumers have been tempted to thank Alexa, despite its early-stage capabilities, said Capgemini’s Taylor.

In short, voice interfaces are here and evolving rapidly. What will your brand do?

How SaaS Strategies Are Evolving

Enterprises are subscribing to more SaaS services than ever, with considerable procurement happening at the departmental level. Specialized SaaS providers target problems that those departments want solved quickly. Because SaaS software tends to be easy to set up and use, there appears to be no need for IT’s involvement, until something goes wrong.

According to the Harvey Nash /KPMG 2017 CIO Survey, 91% of the nearly 4,500 CIO and IT leaders who responded expect to make moderate or significant SaaS investments, up from 82% in 2016. The report also states that 40% of SaaS product procurement now happens outside IT.

“IT needs a new operating model,” said Gianna D’Angelo, principal of KPMG CIO Advisory. “CIOs must respond by continuing to focus on operational excellence while adopting a new operating model for IT to drive innovation and value in these changing times.”

Some IT shops are reacting to shadow IT like they reacted to “bring your own device” (BYOD), meaning if you can’t stop it, you have to enable it with governance in mind. However, issues remain.

“In the last three years, we’ve put policies and some governance in place, but it doesn’t matter. You pull out your credit card, you buy an open source application and I have a virus on my network,” said Todd Reynolds, CTO of WEX Health, which provides a platform for benefit management and healthcare-related financial management. “I don’t even know about it until there’s an issue.”

How SaaS pricing is changing

KPMG’s D’Angelo said most SaaS pricing is based on users or by revenue, and that the contract timeframe is three to five years. There has been some movement to shorter timeframes as low as two years.

Sanjay Srivastava, chief digital officer of Genpact, a global professional services company, said his firm sees a shift from user-based pricing to usage-based pricing, which in Genpact’s case takes the form of a per-item charge for a document or balance sheet, for example.

Regardless of what the SaaS pricing model is, SaaS providers are facing downward pricing pressure. According to Gartner, “Vendors are becoming more creative with their SaaS business models to reflect a need to stand out in the fast-growing subscription economy.”

For its part, WEX Health is responding with new services that drive additional revenue. It has also put some usage-based pricing in place for customers that require elastic compute capabilities. “Mobile is killing us,” said Wex Health’s Reynolds. “You’ve given somebody an application to use on their phone 24/7, so they’re starting to leverage that usage so much more. It’s good people are using [our software] more often, but it requires us to have more storage.”

Longer-term thinking is wise

When departments purchase SaaS software, they usually are seeking relief from some sort of business problem, such as multichannel marketing attribution – studying the set of actions that users take in various environments. What business people often miss is the longer-term requirement to share data across disparate systems.

“If you have half on-premises and half in different clouds, you might have a private cloud, some in Azure and some in Amazon because the technology stack is beneficial to the apps,” said WEX Health’s Reynolds. “Pulling all of that together and making it safe and accessible is the biggest challenge from an operational perspective on the IT side.”

While SaaS systems tend to have APIs that help with data exchange, most enterprises have hybrid environments that include legacy systems, some of which do not have APIs. In the older systems, the data dictionaries may not be up-to-date and Master Data Management (MDM) may not have been maintained. So enterprises often face substantial data quality issues that negatively impact the value they’re getting from their investments.

“If you really want to get value out of [SaaS] — if you want Salesforce to run CRM and you want it to run sales, integrated, and it still has to be connected to ERP — each thing has to be connected,” said Genpact’s  Srivastava. “There’s a lot of back and forth. Planning for that back and forth, and planning well, is really critical.”

Part of that back-and-forth is ensuring that the right governance, compliance and security controls are in place.

Bottom line

There’s more to SaaS investments than may be obvious to the people procuring them. At the same time, IT departments can no longer be the sole gatekeepers of all things tech.

“The challenge for CIOs is enormous, the stakes are large and change efforts of this magnitude take years, but transforming the IT operating model can be done,” said KPMG’s D’Angelo. “Complicating the effort is that IT must continue to support the existing portfolios, including retained infrastructure and legacy applications, during the transformation.”

This means that, for a period of time, IT will have to use a hybrid model comprising both the project-oriented, plan-build-run approach and the next-generation, broker-integrate-orchestrate approach, D’Angelo added.

Tips for Ensuring Winning SaaS Strategies

SaaS software is not a one-size-fits-all proposition. Costs and benefits vary greatly, as do the short-term and long-term trade-offs. Following are a few things you can do along the way to ease the transition.

If you’re just starting out, chances are that most if not all of the software you procure will be SaaS because that’s the way things are going. In addition, SaaS allows for an economic shift to relatively low-cost subscriptions that include upgrades and maintenance (an operational expenditure). This is instead of substantial up-front, on-premises software investments that require subsequent maintenance investments and IT’s help (a capital expenditure). Regardless of what type of software you choose, though, it’s wise to think beyond today’s requirements so you have a better chance of avoiding unforeseen challenges and costs in the future.

If you’re piloting a new type of software, SaaS is probably the way to go because you can usually experiment without a long-term commitment. However, be mindful of the potential integration, security and governance challenges you may encounter as you attempt to connect different data sources.

If you’re in production, you’ll want to continuously assess your requirements in terms of software models, integration, compliance, governance and security. As you continue your move into the cloud, understand what’s holding you back. Finance and HR, for instance, may still hesitate to store their sensitive data anywhere but on-premises. For the foreseeable future, you’ll probably have a hybrid strategy that becomes more cloud-based with time.

At each stage, it’s wise to understand the potential risks and rewards beyond what’s obvious today.

Deloitte: 5 Trends That Will Drive Machine Learning Adoption

Companies across industries are experimenting with and using machine learning, but the actual adoption rates are lower than it might be seem. According to a 2017 SAP Digital Transformation Study, fewer than 10% of 3,100 executives from small, medium and large companies said their organizations were investing in machine learning. That will change dramatically in the coming years, according to a new Deloitte report, because researchers and vendors are making progress in five key areas that may make machine learning more practical for businesses of all sizes.

1. Automating data science

There is a lot of debate about whether data scientists will or won’t be automated out of a job. It turns out that machines are far better at doing rote tasks faster and more reliably than humans, such as data wrangling.

“The automation of data science will likely be widely adopted and speak to this issue of the shortage of data scientists, so I think in the near term this could have a lot of impact,” said David Schatsky, managing director at Deloitte and one of the authors of Deloitte’s new report.

Industry analysts are bullish about the prospect of automating data science tasks, since data scientists can spend an inordinate amount of time collecting data and preparing it ready for analysis. For example, Gartner estimates that 40% of a data scientist’s job will be automated by 2020.

Data scientists aren’t so sure about that, and to be fair, few people, regardless of their position, have considered which parts of their job are ripe for automation.

2. Reducing the need for training data

Machine learning tends to require a lot of data. According to the Deloitte report, training a machine learning model might require millions of data elements. While machine learning requirements vary based on the use case, “acquiring and labeling data can be time-consuming and costly.”

One way to address that challenge is to use synthetic data. Using synthetic data, Deloitte was able to reduce the actual amount of data required for training by 80%. In other words, 20% of the data was actual data and the remaining 80% was synthetic data.

“How far we can go in reducing the need for training data has two kinds of question marks: How far can you reduce the need for training data and what characteristics of data are most likely minimized and which require massive datasets?” said Schatsky.

3. Accelerating training

Massive amounts of data and heavy computation can take considerable time. Chip manufacturers are addressing this issue with various types of chips, including GPUs and application-specific integrated circuits (ASICs). The end result is faster training of machine learning models.

“I have no doubt that with the new processor architectures, execution is going to get faster,” said Schatsky. “[The chips] are important and necessary, but not sufficient to drive significant adoption on their own.”

4. Explaining results

Many machine learning models spit out a result, but they don’t provide the reasoning behind the result. As Deloitte points out, business leaders often hesitate to place blind faith in a result that can’t be explained, and some regulations require an explanation.

In the future, we’ll likely see machine learning models that are more accurate and transparent, which should open the door for greater use in regulated industries.

[Deloitte also recently discussed 9 AI Benefits Enterprises Are Experiencing Today.]

“No one knows how far you can go yet in terms of making an arbitrary neural network-based model interpretable,” said Schatsky. “We could end up hitting some limits identifying a fairly narrow set of cases where you can turn a black box model into an open book for certain kinds of models and situations, but there will be other scenarios where they work well but you can’t use them in certain situations.”

5. Deploying locally

Right now, machine learning typically requires a lot of data and training can be time-consuming. All of that requires a lot of memory and a lot of processing power, more than mobile and smart sensors can handle, at least for now.

In its report, Deloitte points out there is research in this area too, some of which has reduced the size of models by an order of magnitude or more using compression.

The bottom line

Machine learning is having profound effects in different industries ranging from TV pilots to medical diagnoses. It seems somewhat magical and somewhat scary to the uninitiated, though the barriers to adoption are falling. As machine learning becomes more practical for mainstream use, more businesses will use it whether they realize it or not.

“[The five] things [we identified in the report] are converging to put machine learning on a path toward mainstream adoption,” said Schatsky.  “If companies have been sitting it out waiting for this to get easier and more relevant, they should sit up instead and start getting involved.”

What Data Analysts Want to See in 2018

The demand for data analysts is at an all-time high, but organizations don’t always get the value they expect, mainly because the organization, or parts of it, are getting in the way.

Being an analyst can be a frustrating job if your position isn’t getting what it needs in terms of data, tools and organizational support. Are you getting what you need? Here are some of the things your contemporaries are saying.

More Data

Despite the glut of data companies have, analysts don’t always get the data they need, often because the data owners are concerned about privacy, security, losing control of their data or some combination of those things.

“The problem of data ownership and data sharing is universal,” said Sam Ruchlewicz, director of Digital Strategy & Data Analytics at advertising, digital, PR and brand agency Warschawski. “For analytics professionals, these artificial barriers hinder the creation of comprehensive, whole-organization analyses that can provide real, tangible value and serve as a catalyst for the creation (and funding) of additional analytics programs.”

Jesse Tutt, program lead of the IT Analytics Center of Excellence at Alberta Health Services said getting access to the data he needs takes a lot of time because he has to work with the data repository owners to get their approval and then work with the technologists to get access to the systems. He also has to work with the vendors and the data repository subject matter experts.

“We’ve worked really hard getting access to the data sets, correlating the different datasets using correlation tables and cleaning up the data within the source systems,” he said. “If you ask a specific set or data repository what something is, it can tell you, but if you can snapshot it on a monthly basis you can see a trend. If you correlate that across other systems, you can find more value. In our case, the highest value is connecting the system and creating the capability in a data warehouse, reporting you can correlate across the systems.

Four years ago, people at Alberta Health Services wanted to see trend data instead of just snapshots, so one system was connected to another. Now, 60 connected data sources are connected with 60 more planned by the end of 2017. The company has a total of about 1,600 data sources, many of which will be connected in the next couple of years.

More Respect

The most effective data analytics align with business objectives, but what happens when your data analysts aren’t informed? Warschawski’s Ruchlewicz recently had dinner with the CEO of a large, international agency who spent millions of dollars on a marketing campaign that failed simply because the executive didn’t want to listen to “the analytics kids.” Never mind the fact that the analytics team had identified a major issue the target audience had with the client’s brand.

“[The CEO] dismissed them as analytics kids who didn’t know what they were talking about and proceeded to launch the campaign,” said Ruchlewicz. “Only later, after millions of dollars in spending (with no results to show for it), did the CEO allow them to make their case and implement their recommendations.”

Ultimately, their recommendations turned the campaign around. Ruchlewicz said.

“I wish this as a one-off story. It’s not. I wish this was confined to ‘old school’ companies. It’s not,” said Ruchlewicz. “Until analytics teams are given a seat at the table where decisions are made, analytics will continue to be undervalued and underappreciated across the entire organization.”

Analysts have to earn respect like anyone else, however. That requires communicating to business professionals in business terms.

“Executives and investors today are hyper-focused on the bottom line, and most that I’ve interacted with perceive analytics as a line item expenditure,” said Ruchlewicz. “[A]nalytics professionals need to take the first step toward resolution. There are several methods that allow the creation of a rigorous, defensible first approximation, which is sufficient to get the conversation started (and usually, some data shared).”

To help turn the tide, analytics practitioners are well-advised present information and construct business cases around their activities.

More Consistency

If everyone in the organization used the same terminology for everything, always had the right database fields accessible, and always entered data correctly and in the same manner, some enterprise data would be much cleaner than it is today. However, the problem doesn’t stop there

“If a person says, ‘I want an analytical tool,’ how do you group that and do trending on it when a person may call it one of the 100 different analytical tool names or they’ll say I need to do analysis on data? The words the submit are often different from what they actually want,” said Alberta Health Services’ Tutt

Tutt and his team are endeavoring to better understand what people are requesting in service desk tickets so the company can manage its software investments more effectively. Now that his team has access to the different systems, they know who’s using a product and when they used it. They’re looking at the problem from a Robotics Process Automation (RPA) perspective so software can be automatically removed if it hasn’t been used in a certain time period.

More Power to Affect Change

Industry analysts are pushing back on “data-driven” mantras because they think companies should be “insight-driven.” While they have a valid point, insights without action have little value.

For example, a large U.S. health provider has a massive analytics team that’s generating highly-actionable insights, but those insights are not being acted upon by the business. They can meet with a functional unit such as risk or compliance and show them insights. The operating unit will say, “That’s interesting,” but there’s no way to connect insights and action.

“The data teams are frustrated because they’re not getting the operational support they need,” said Adam Nathan, CEO and Founder of analytics strategy firm The Bartlett System. “The data teams don’t know how to drive that, except to get frustrated and quiet and get more value elsewhere. I think the tipping point will come when the company realizes it’s falling behind competitors. They’ll realize the company isn’t getting the value it could from analytics and that will put pressure on them to do something with those insights.”

How Today’s Analytics Change Recruiting

HR is late to the analytics game by modern standards, and yet, HR metrics is not a new concept. The difference is that modern analytics enable HR professionals and recruiters to measure more things in less time and derive more insight than ever before.

Rosemary Haefner

Rosemary Haefner

“If you’re looking at recruiting, there have always been metrics such as time to hire and cost per hire, but you’re seeing other channels and avenues opening up,” said Rosemary Haefner, chief human resources officer at online employment website, CareerBuilder.com.

The “time to hire” or “time to fill” metric measures how many days it takes from the time a requisition is posted until the time an offer is accepted. The longer a position remains open, the higher the cost of talent acquisition. In addition, if a position remains open, an intervention may be necessary to ensure the work at hand is getting done.

If time to fill were the only measure of success, then, in theory, the faster a position is filled, the better. However, as most working professionals have experienced, the person who can be hired the fastest isn’t necessarily (and probably isn’t), the best candidate.

On the other hand, moving too slowly can cost organizations sought-after talent.

“There’s the time to fill, the cost of the person you hire, whether that person is high-potential and what their expected tenure in the organization is. That’s an example of four interrelated metrics,” said Muir Macpherson, Americas analytics leader, People Advisory Services at EY. “HR needs to stop thinking about individual metrics and consider the problem they’re trying to solve and how to optimize across a set of metrics simultaneously.”

Beyond keywords

Talent marketplaces and talent acquisition software made it easier to navigate a sea of resumes using keywords and filters. In response, some candidates stuffed their resumes full of keywords so their resumes would rank higher in searches. If one’s resume ranked higher in searches, then more people would see it, potentially increasing the candidate’s chance of getting interviews and landing a job.

Masterful keyword use demonstrated an awareness that the recruiting process was changing from a paper-based process to a computer or web-based process. However, other candidates who might have been better fits for positions risked getting lost in the noise.

The whole keyword trend was a noble effort, but keywords, like anything else, are not a silver bullet.

With today’s analytics tools, HR departments and search firms can understand much more about candidates and the effectiveness of their operations.

“You can use a variety of big data and machine learning techniques that go way beyond the keyword analysis people have been doing for a while that integrates all of the data available about a candidate into one, unified prediction score that can then be used as one additional piece of information that recruiters and hiring managers can look at when making their decisions,” said Macpherson.

Data impacts recruiters too

Recruiters now have access to data analytics tools that enable them to better match candidates with potential employers and improve the quality of their services. Meanwhile, HR departments want insight into what recruiters are doing and how well they’re doing it. The Scout Exchange marketplace provides transparency between the two.

“We can look at every candidate [a recruiter] submits to see how far they got in the process and whether they got hired. We use that for ratings so [companies and the recruiters they use] can see the other side’s rating,” said Scout Exchange CEO Ken Lazarus.

The site enables organizations to quickly find appropriate recruiters who can identify the best candidates for a position. HR departments also allows HR departments to see data and trends specific to their company.

Bottom line

Analytics is providing HR departments, recruiters and business leaders with quantitative information they can use to improve their processes and outcomes.

“Knowledge is power and having that data is helpful. For me, the first step is knowing what you’re solving for,” said CareerBuilder’s Haefner.

Right now, HR analytics tend to emphasize recruitment. However, attracting talent is sometimes easier than retaining it so it’s important to have insight throughout the lifecycle of employee relationships. EY’s Macpherson said HR departments should think in terms of “employee lifetime value” similar to the way marketers think about customer lifetime value.

“[HR analytics represents] a huge opportunity because for most companies, people and compensation are their biggest costs and yet there has been very little effort put into analyzing those costs or getting the most out of those investments that companies are making,” said EY’s Macpherson.

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