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Tag Archives: Drives

e.l.f. Cosmetics Drives Growth, Accelerates B2B and B2C Sales with NetSuite OneWorld

August 1, 2018   NetSuite

Posted by Kelly Scott, Retail Industry Marketing Lead

As retailers boost omnichannel strategies, careful coordination of online and in-store efforts must address a somewhat surprising truth. While consumers research and vet their purchases online, more than 90 percent of actual purchasing decisions still occur in the store, according to RSR Research.

elf%202 e.l.f. Cosmetics Drives Growth, Accelerates B2B and B2C Sales with NetSuite OneWorldIt’s less surprising then that for companies born entirely online – like e.l.f. Cosmetics – growth has been fueled by its in-store channels – both its own brick-and-mortar presence and chiefly, on the shelves of the stores of the world’s largest big box retail chains.

In just a decade since its founding, e.l.f. Cosmetics has flipped its chief revenue stream from online B2C sales of its cosmetic and skin care products, to B2B sales with Walmart, Target and other retailers. It’s a shift that NetSuite helped to empower – and one which e.l.f. Cosmetics will continue to leverage its NetSuite partnership to fuel.

“In 2017, over 80% of our net sales was through national and international retailers,” said Ramesh Cooke, director of enterprise applications, e.l.f. Cosmetics. “NetSuite allows us to successfully attract and retain larger retail partners.”

Foundation for B2B growth

As e.l.f. Cosmetics’ online business surged and it sought to diversify its revenue stream, it needed to automate financial processes to enable efficiencies and prepare for growth. It began implementing NetSuite OneWorld in 2015 to replace a custom-built AS/400 system and automate financial and inventory management processes across a business network that spanned its Oakland, Calif.-based headquarters, its China-based manufacturing and its third-party distributors.

With best-in-class financial processes under its belt, e.l.f. Cosmetics shifted its focus in 2016 to optimizing the entire order-to-cash process. Orders coming in through a legacy system weren’t integrated with financial and inventory management data in NetSuite, meaning that the business had to manually rationalize numbers outside of the system to gain visibility into everything from order status to item availability. Employees spent needless time chasing down information on orders.

In 2016, e.l.f. Cosmetics brought on board Cooke, who has nearly two decades of experience in supply chain transformation, to optimize inventory and order management processes that spanned both B2B and B2C sales.

“The finance team had stable business processes in NetSuite, and operations and the rest of the organization were not getting as much benefit,” he said.

Cooke knew that one of the biggest barriers in speeding adoption and realizing the benefits from process automation is change management. Having worked heavily with SAP software prior to coming to e.l.f. Cosmetics, Cooke expected some resistance in trying to get staff that managed inventory and ordering processes to make the shift to NetSuite. But NetSuite’s flexibility made it easy to configure the software to users’ needs, and an intuitive user interface shortened training and adoption to accelerate that journey.

“NetSuite is simple to configure, flexible and easy to understand and provides a much shorter change management barrier,” he said.

End-to-end visibility lends efficiencies

NetSuite’s flexible development platform allowed the team to easily build an integration between the legacy order management system and NetSuite, exposing information through a dashboard that allows employees to consume unified financial, order and inventory data in real-time. This has streamlined transactions with its China-based manufacturing and third-party distributors to optimize its stock. Now, e.l.f. Cosmetics can meet fast turnaround times for invoicing partners. And with real-time visibility into inventory and orders, the business ensures accuracy in those processes, as well as on-time delivery across B2B and B2C channels.

e.l.f. Cosmetics has opened 22 brick and mortar stores across the United States which allow the company to test new products, solicit customer feedback and provide another avenue for the company to delight its consumers. Integrated, real-time inventory and order management has streamlined transactions with B2B partners.

With NetSuite’s multi-currency support, e.l.f. Cosmetics is positioned to expand internationally – and can leverage data for insight-driven decisions on where to launch new stores or partnerships. Additionally, NetSuite will easily scale to accommodate increases in the number and types of products e.l.f. Cosmetics sells as well as expansion into new markets and retailers.

“We have users comfortably using NetSuite, and we’ll continue to build out best practices and KPIs across the organization,” Cooke said.

Learn more about e.l.f. Cosmetics success with NetSuite OneWorld.

Posted on Mon, July 30, 2018
by NetSuite filed under

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People Are The Engine That Drives Finance Transformation: Part 1

May 18, 2018   BI News and Info

For nerds, the weeks right before finals are a Cinderella moment. Suddenly they’re stars. Pocket protectors are fashionable; people find their jokes a whole lot funnier; Dungeons & Dragons sounds cool.

Many CIOs are enjoying this kind of moment now, as companies everywhere face the business equivalent of a final exam for a vital class they have managed to mostly avoid so far: digital transformation.

But as always, there is a limit to nerdy magic. No matter how helpful CIOs try to be, their classmates still won’t pass if they don’t learn the material. With IT increasingly central to every business—from the customer experience to the offering to the business model itself—we all need to start thinking like CIOs.

Pass the digital transformation exam, and you probably have a bright future ahead. A recent SAP-Oxford Economics study of 3,100 organizations in a variety of industries across 17 countries found that the companies that have taken the lead in digital transformation earn higher profits and revenues and have more competitive differentiation than their peers. They also expect 23% more revenue growth from their digital initiatives over the next two years—an estimate 2.5 to 4 times larger than the average company’s.

But the market is grading on a steep curve: this same SAP-Oxford study found that only 3% have completed some degree of digital transformation across their organization. Other surveys also suggest that most companies won’t be graduating anytime soon: in one recent survey of 450 heads of digital transformation for enterprises in the United States, United Kingdom, France, and Germany by technology company Couchbase, 90% agreed that most digital projects fail to meet expectations and deliver only incremental improvements. Worse: over half (54%) believe that organizations that don’t succeed with their transformation project will fail or be absorbed by a savvier competitor within four years.

Companies that are making the grade understand that unlike earlier technical advances, digital transformation doesn’t just support the business, it’s the future of the business. That’s why 60% of digital leading companies have entrusted the leadership of their transformation to their CIO, and that’s why experts say businesspeople must do more than have a vague understanding of the technology. They must also master a way of thinking and looking at business challenges that is unfamiliar to most people outside the IT department.

In other words, if you don’t think like a CIO yet, now is a very good time to learn.

However, given that you probably don’t have a spare 15 years to learn what your CIO knows, we asked the experts what makes CIO thinking distinctive. Here are the top eight mind hacks.

1. Think in Systems

Q118 Feature3 img1 Jump People Are The Engine That Drives Finance Transformation: Part 1A lot of businesspeople are used to seeing their organization as a series of loosely joined silos. But in the world of digital business, everything is part of a larger system.

CIOs have known for a long time that smart processes win. Whether they were installing enterprise resource planning systems or working with the business to imagine the customer’s journey, they always had to think in holistic ways that crossed traditional departmental, functional, and operational boundaries.

Unlike other business leaders, CIOs spend their careers looking across systems. Why did our supply chain go down? How can we support this new business initiative beyond a single department or function? Now supported by end-to-end process methodologies such as design thinking, good CIOs have developed a way of looking at the company that can lead to radical simplifications that can reduce cost and improve performance at the same time.

They are also used to thinking beyond temporal boundaries. “This idea that the power of technology doubles every two years means that as you’re planning ahead you can’t think in terms of a linear process, you have to think in terms of huge jumps,” says Jay Ferro, CIO of TransPerfect, a New York–based global translation firm.

No wonder the SAP-Oxford transformation study found that one of the values transformational leaders shared was a tendency to look beyond silos and view the digital transformation as a company-wide initiative.

This will come in handy because in digital transformation, not only do business processes evolve but the company’s entire value proposition changes, says Jeanne Ross, principal research scientist at the Center for Information Systems Research at the Massachusetts Institute of Technology (MIT). “It either already has or it’s going to, because digital technologies make things possible that weren’t possible before,” she explains.

2. Work in Diverse Teams

When it comes to large projects, CIOs have always needed input from a diverse collection of businesspeople to be successful. The best have developed ways to convince and cajole reluctant participants to come to the table. They seek out technology enthusiasts in the business and those who are respected by their peers to help build passion and commitment among the halfhearted.

Digital transformation amps up the urgency for building diverse teams even further. “A small, focused group simply won’t have the same breadth of perspective as a team that includes a salesperson and a service person and a development person, as well as an IT person,” says Ross.

At Lenovo, the global technology giant, many of these cross-functional teams become so used to working together that it’s hard to tell where each member originally belonged: “You can’t tell who is business or IT; you can’t tell who is product, IT, or design,” says the company’s CIO, Arthur Hu.

One interesting corollary of this trend toward broader teamwork is that talent is a priority among digital leaders: they spend more on training their employees and partners than ordinary companies, as well as on hiring the people they need, according to the SAP-Oxford Economics survey. They’re also already being rewarded for their faith in their teams: 71% of leaders say that their successful digital transformation has made it easier for them to attract and retain talent, and 64% say that their employees are now more engaged than they were before the transformation.

3. Become a Consultant

Good CIOs have long needed to be internal consultants to the business. Ever since technology moved out of the glasshouse and onto employees’ desks, CIOs have not only needed a deep understanding of the goals of a given project but also to make sure that the project didn’t stray from those goals, even after the businesspeople who had ordered the project went back to their day jobs. “Businesspeople didn’t really need to get into the details of what IT was really doing,” recalls Ferro. “They just had a set of demands and said, ‘Hey, IT, go do that.’”

But that was then. Now software has become so integral to the business that nobody can afford to walk away. Businesspeople must join the ranks of the IT consultants. “If you’re building a house, you don’t just disappear for six months and come back and go, ‘Oh, it looks pretty good,’” says Ferro. “You’re on that work site constantly and all of a sudden you’re looking at something, going, ‘Well, that looked really good on the blueprint, not sure it makes sense in reality. Let’s move that over six feet.’ Or, ‘I don’t know if I like that anymore.’ It’s really not much different in application development or for IT or technical projects, where on paper it looked really good and three weeks in, in that second sprint, you’re going, ‘Oh, now that I look at it, that’s really stupid.’”

4. Learn Horizontal Leadership

CIOs have always needed the ability to educate and influence other leaders that they don’t directly control. For major IT projects to be successful, they need other leaders to contribute budget, time, and resources from multiple areas of the business.

It’s a kind of horizontal leadership that will become critical for businesspeople to acquire in digital transformation. “The leadership role becomes one much more of coaching others across the organization—encouraging people to be creative, making sure everybody knows how to use data well,” Ross says.

In this team-based environment, having all the answers becomes less important. “It used to be that the best business executives and leaders had the best answers. Today that is no longer the case,” observes Gary Cokins, a technology consultant who focuses on analytics-based performance management. “Increasingly, it’s the executives and leaders who ask the best questions. There is too much volatility and uncertainty for them to rely on their intuition or past experiences.”

Many experts expect this trend to continue as the confluence of automation and data keeps chipping away at the organizational pyramid. “Hierarchical, command-and-control leadership will become obsolete,” says Edward Hess, professor of business administration and Batten executive-in-residence at the Darden School of Business at the University of Virginia. “Flatter, distributive leadership via teams will become the dominant structure.”

Q118 Feature3 img3 rock People Are The Engine That Drives Finance Transformation: Part 15. Understand Process Design

When business processes were simpler, IT could analyze the process and improve it without input from the business. But today many processes are triggered on the fly by the customer, making a seamless customer experience more difficult to build without the benefit of a larger, multifunctional team. In a highly digitalized organization like Amazon, which releases thousands of new software programs each year, IT can no longer do it all.

While businesspeople aren’t expected to start coding, their involvement in process design is crucial. One of the techniques that many organizations have adopted to help IT and businesspeople visualize business processes together is design thinking (for more on design thinking techniques, see “A Cult of Creation“).

Customers aren’t the only ones who benefit from better processes. Among the 100 companies the SAP-Oxford Economics researchers have identified as digital leaders, two-thirds say that they are making their employees’ lives easier by eliminating process roadblocks that interfere with their ability to do their jobs. Ninety percent of leaders surveyed expect to see value from these projects in the next two years alone.

6. Learn to Keep Learning

The ability to learn and keep learning has been a part of IT from the start. Since the first mainframes in the 1950s, technologists have understood that they need to keep reinventing themselves and their skills to adapt to the changes around them.

Now that’s starting to become part of other job descriptions too. Many companies are investing in teaching their employees new digital skills. One South American auto products company, for example, has created a custom-education institute that trained 20,000 employees and partner-employees in 2016. In addition to training current staff, many leading digital companies are also hiring new employees and creating new roles, such as a chief robotics officer, to support their digital transformation efforts.

Nicolas van Zeebroeck, professor of information systems and digital business innovation at the Solvay Brussels School of Economics and Management at the Free University of Brussels, says that he expects the ability to learn quickly will remain crucial. “If I had to think of one critical skill,” he explains, “I would have to say it’s the ability to learn and keep learning—the ability to challenge the status quo and question what you take for granted.”

7. Fail Smarter

Traditionally, CIOs tended to be good at thinking through tests that would allow the company to experiment with new technology without risking the entire network.

This is another unfamiliar skill that smart managers are trying to pick up. “There’s a lot of trial and error in the best companies right now,” notes MIT’s Ross. But there’s a catch, she adds. “Most companies aren’t designed for trial and error—they’re trying to avoid an error,” she says.

Q118 Feature3 img4 fail People Are The Engine That Drives Finance Transformation: Part 1To learn how to do it better, take your lead from IT, where many people have already learned to work in small, innovative teams that use agile development principles, advises Ross.

For example, business managers must learn how to think in terms of a minimum viable product: build a simple version of what you have in mind, test it, and if it works start building. “You don’t build the whole thing at once anymore.… It’s really important to build things incrementally,” Ross says.

Flexibility and the ability to capitalize on accidental discoveries during experimentation are more important than having a concrete project plan, says Ross. At Spotify, the music service, and CarMax, the used-car retailer, change is driven not from the center but from small teams that have developed something new. “The thing you have to get comfortable with is not having the formalized plan that we would have traditionally relied on, because as soon as you insist on that, you limit your ability to keep learning,” Ross warns.

8. Understand the True Cost—and Speed—of Data

Gut instincts have never had much to do with being a CIO; now they should have less to do with being an ordinary manager as well, as data becomes more important.

As part of that calculation, businesspeople must have the ability to analyze the value of the data that they seek. “You’ll need to apply a pinch of knowledge salt to your data,” advises Solvay’s van Zeebroeck. “What really matters is the ability not just to tap into data but to see what is behind the data. Is it a fair representation? Is it impartial?”

Increasingly, businesspeople will need to do their analysis in real time, just as CIOs have always had to manage live systems and processes. Moving toward real-time reports and away from paper-based decisions increases accuracy and effectiveness—and leaves less time for long meetings and PowerPoint presentations (let us all rejoice).

Not Every CIO Is Ready

Of course, not all CIOs are ready for these changes. Just as high school has a lot of false positives—genius nerds who turn out to be merely nearsighted—so there are many CIOs who aren’t good role models for transformation.

Success as a CIO these days requires more than delivering near-perfect uptime, says Lenovo’s Hu. You need to be able to understand the business as well. Some CIOs simply don’t have all the business skills that are needed to succeed in the transformation. Others lack the internal clout: a 2016 KPMG study found that only 34% of CIOs report directly to the CEO.

This lack of a strategic perspective is holding back digital transformation at many organizations. They approach digital transformation as a cool, one-off project: we’re going to put this new mobile app in place and we’re done. But that’s not a systematic approach; it’s an island of innovation that doesn’t join up with the other islands of innovation. In the longer term, this kind of development creates more problems than it fixes.

Such organizations are not building in the capacity for change; they’re trying to get away with just doing it once rather than thinking about how they’re going to use digitalization as a means to constantly experiment and become a better company over the long term.

Q118 Feature3 img6 CIOready People Are The Engine That Drives Finance Transformation: Part 1As a result, in some companies, the most interesting tech developments are happening despite IT, not because of it. “There’s an alarming digital divide within many companies. Marketers are developing nimble software to give customers an engaging, personalized experience, while IT departments remain focused on the legacy infrastructure. The front and back ends aren’t working together, resulting in appealing web sites and apps that don’t quite deliver,” writes George Colony, founder, chairman, and CEO of Forrester Research, in the MIT Sloan Management Review.

Thanks to cloud computing and easier development tools, many departments are developing on their own, without IT’s support. These days, anybody with a credit card can do it.

Traditionally, IT departments looked askance at these kinds of do-it-yourself shadow IT programs, but that’s changing. Ferro, for one, says that it’s better to look at those teams not as rogue groups but as people who are trying to help. “It’s less about ‘Hey, something’s escaped,’ and more about ‘No, we just actually grew our capacity and grew our ability to innovate,’” he explains.

“I don’t like the term ‘shadow IT,’” agrees Lenovo’s Hu. “I think it’s an artifact of a very traditional CIO team. If you think of it as shadow IT, you’re out of step with reality,” he says.

The reality today is that a company needs both a strong IT department and strong digital capacities outside its IT department. If the relationship is good, the CIO and IT become valuable allies in helping businesspeople add digital capabilities without disrupting or duplicating existing IT infrastructure.

If a company already has strong digital capacities, it should be able to move forward quickly, according to Ross. But many companies are still playing catch-up and aren’t even ready to begin transforming, as the SAP-Oxford Economics survey shows.

For enterprises where business and IT are unable to get their collective act together, Ross predicts that the next few years will be rough. “I think these companies ought to panic,” she says. D!


About the Authors

Thomas Saueressig is Chief Information Officer at SAP.

Timo Elliott is an Innovation Evangelist at SAP.

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

Bennett Voyles is a Berlin-based business writer.

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Talent Drives Digital Success

August 29, 2017   BI News and Info

When it comes to buying things—even big-ticket items—the way we make decisions makes no sense. One person makes an impulsive offer on a house because of the way the light comes in through the kitchen windows. Another gleefully drives a high-end sports car off the lot even though it will probably never approach the limits it was designed to push.

We can (and usually do) rationalize these decisions after the fact by talking about needing more closet space or wanting to out-accelerate an 18-wheeler as we merge onto the highway, but years of study have arrived at a clear conclusion:

When it comes to the customer experience, human beings are fundamentally irrational.

In the brick-and-mortar past, companies could leverage that irrationality in time-tested ways. They relied heavily on physical context, such as an inviting retail space, to make products and services as psychologically appealing as possible. They used well-trained salespeople and employees to maximize positive interactions and rescue negative ones. They carefully sequenced customer experiences, such as having a captain’s dinner on the final night of a cruise, to play on our hard-wired craving to end experiences on a high note.

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Today, though, customer interactions are increasingly moving online. Fortune reports that on 2016’s Black Friday, the day after Thanksgiving that is so crucial to holiday retail results, 108.5 million Americans shopped online, while only 99.1 million visited brick-and-mortar stores. The 9.4% gap between the two was a dramatic change from just one year prior, when on- and offline Black Friday shopping were more or less equal.

When people browse in a store for a few minutes, an astute salesperson can read the telltale signs that they’re losing interest and heading for the exit. The salesperson can then intervene, answering questions and closing the sale.

Replicating that in a digital environment isn’t as easy, however. Despite all the investments companies have made to counteract e-shopping cart abandonment, they lack the data that would let them anticipate when a shopper is on the verge of opting out of a transaction, and the actions they take to lure someone back afterwards can easily come across as less helpful than intrusive.

In a digital environment, companies need to figure out how to use Big Data analysis and digital design to compensate for the absence of persuasive human communication and physical sights, sounds, and sensations. What’s more, a 2014 Gartner survey found that 89% of marketers expected customer experience to be their primary differentiator by 2016, and we’re already well into 2017.

As transactions continue to shift toward the digital and omnichannel, companies need to figure out new ways to gently push customers along the customer journey—and to do so without frustrating, offending, or otherwise alienating them.

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The quest to understand online customers better in order to influence them more effectively is built on a decades-old foundation: behavioral psychology, the study of the connections between what people believe and what they actually do. All of marketing and advertising is based on changing people’s thoughts in order to influence their actions. However, it wasn’t until 2001 that a now-famous article in the Harvard Business Review formally introduced the idea of applying behavioral psychology to customer service in particular.

The article’s authors, Richard B. Chase and Sriram Dasu, respectively a professor and assistant professor at the University of Southern California’s Marshall School of Business, describe how companies could apply fundamental tenets of behavioral psychology research to “optimize those extraordinarily important moments when the company touches its customers—for better and for worse.” Their five main points were simple but have proven effective across multiple industries:

  1. Finish strong. People evaluate experiences after the fact based on their high points and their endings, so the way a transaction ends is more important than how it begins.
  2. Front-load the negatives. To ensure a strong positive finish, get bad experiences out of the way early.
  3. Spread out the positives. Break up the pleasurable experiences into segments so they seem to last longer.
  4. Provide choices. People don’t like to be shoved toward an outcome; they prefer to feel in control. Giving them options within the boundaries of your ability to deliver builds their commitment.
  5. Be consistent. People like routine and predictability.

For example, McKinsey cites a major health insurance company that experimented with this framework in 2009 as part of its health management program. A test group of patients received regular coaching phone calls from nurses to help them meet health goals.

The front-loaded negative was inherent: the patients knew they had health problems that needed ongoing intervention, such as weight control or consistent use of medication. Nurses called each patient on a frequent, regular schedule to check their progress (consistency and spread-out positives), suggested next steps to keep them on track (choices), and cheered on their improvements (a strong finish).

McKinsey reports the patients in the test group were more satisfied with the health management program by seven percentage points, more satisfied with the insurance company by eight percentage points, and more likely to say the program motivated them to change their behavior by five percentage points.

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The nurses who worked with the test group also reported increased job satisfaction. And these improvements all appeared in the first two weeks of the pilot program, without significantly affecting the company’s costs or tweaking key metrics, like the number and length of the calls.

Indeed, an ongoing body of research shows that positive reinforcements and indirect suggestions influence our decisions better and more subtly than blatant demands. This concept hit popular culture in 2008 with the bestselling book Nudge.

Written by University of Chicago economics professor Richard H. Thaler and Harvard Law School professor Cass R. Sunstein, Nudge first explains this principle, then explores it as a way to help people make decisions in their best interests, such as encouraging people to eat healthier by displaying fruits and vegetables at eye level or combatting credit card debt by placing a prominent notice on every credit card statement informing cardholders how much more they’ll spend over a year if they make only the minimum payment.

Whether they’re altruistic or commercial, nudges work because our decision-making is irrational in a predictable way. The question is how to apply that awareness to the digital economy.

sap Q217 digital double feature1 images7 1024x572 Talent Drives Digital Success

In its early days, digital marketing assumed that online shopping would be purely rational, a tool that customers would use to help them zero in on the best product at the best price. The assumption was logical, but customer behavior remained irrational.

Our society is overloaded with information and short on time, says Brad Berens, Senior Fellow at the Center for the Digital Future at the University of Southern California, Annenberg, so it’s no surprise that the speed of the digital economy exacerbates our desire to make a fast decision rather than a perfect one, as well as increasing our tendency to make choices based on impulse rather than logic.

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Buyers want what they want, but they don’t necessarily understand or care why they want it. They just want to get it and move on, with minimal friction, to the next thing. “Most of our decisions aren’t very important, and we only have so much time to interrogate and analyze them,” Berens points out.

But limited time and mental capacity for decision-making is only half the issue. The other half is that while our brains are both logical and emotional, the emotional side—also known as the limbic system or, more casually, the primitive lizard brain—is far older and more developed. It’s strong enough to override logic and drive our decisions, leaving rational thought to, well, rationalize our choices after the fact.

This is as true in the B2B realm as it is for consumers. The business purchasing process, governed as it is by requests for proposals, structured procurement processes, and permission gating, is designed to ensure that the people with spending authority make the most sensible deals possible. However, research shows that even in this supposedly rational process, the relationship with the seller is still more influential than product quality in driving customer commitment and loyalty.

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Baba Shiv, a professor of marketing at Stanford University’s Graduate School of Business, studies how the emotional brain shapes decisions and experiences. In a popular TED Talk, he says that people in the process of making decisions fall into one of two mindsets: Type 1, which is stressed and wants to feel comforted and safe, and Type 2, which is bored or eager and wants to explore and take action.

People can move between these two mindsets, he says, but in both cases, the emotional brain is in control. Influencing it means first delivering a message that soothes or motivates, depending on the mindset the person happens to be in at the moment and only then presenting the logical argument to help rationalize the action.

In the digital economy, working with those tendencies means designing digital experiences with the full awareness that people will not evaluate them objectively, says Ravi Dhar, director of the Center for Customer Insights at the Yale School of Management. Since any experience’s greatest subjective impact in retrospect depends on what happens at the beginning, the end, and the peaks in between, companies need to design digital experiences to optimize those moments—to rationally design experiences for limited rationality.

This often involves making multiple small changes in the way options are presented well before the final nudge into making a purchase. A paper that Dhar co-authored for McKinsey offers the example of a media company that puts most of its content behind a paywall but offers free access to a limited number of articles a month as an incentive to drive subscriptions.

Many nonsubscribers reached their limit of free articles in the morning, but they were least likely to respond to a subscription offer generated by the paywall at that hour, because they were reading just before rushing out the door for the day. When the company delayed offers until later in the day, when readers were less distracted, successful subscription conversions increased.

Pre-selecting default options for necessary choices is another way companies can design digital experiences to follow customers’ preference for the path of least resistance. “We know from a decade of research that…defaults are a de facto nudge,” Dhar says.

For example, many online retailers set a default shipping option because customers have to choose a way to receive their packages and are more likely to passively allow the default option than actively choose another one. Similarly, he says, customers are more likely to enroll in a program when the default choice is set to accept it rather than to opt out.

Another intriguing possibility lies in the way customers react differently to on-screen information based on how that information is presented. Even minor tweaks can have a disproportionate impact on the choices people make, as explained in depth by University of California, Los Angeles, behavioral economist Shlomo Benartzi in his 2015 book, The Smarter Screen.

A few of the conclusions Benartzi reached: items at the center of a laptop screen draw more attention than those at the edges. Those on the upper left of a screen split into quadrants attract more attention than those on the lower left. And intriguingly, demographics are important variables.

Benartzi cites research showing that people over 40 prefer more visually complicated, text-heavy screens than younger people, who are drawn to saturated colors and large images. Women like screens that use a lot of different colors, including pastels, while men prefer primary colors on a grey or white background. People in Malaysia like lots of color; people in Germany don’t.

This suggests companies need to design their online experiences very differently for middle-aged women than they do for teenage boys. And, as Benartzi writes, “it’s easy to imagine a future in which each Internet user has his or her own ‘aesthetic algorithm,’ customizing the appearance of every site they see.”

Applying behavioral psychology to the digital experience in more sophisticated ways will require additional formal research into recommendation algorithms, predictions, and other applications of customer data science, says Jim Guszcza, PhD, chief U.S. data scientist for Deloitte Consulting.

In fact, given customers’ tendency to make the fastest decisions, Guszcza believes that in some cases, companies may want to consider making choice environments more difficult to navigate— a process he calls “disfluencing”—in high-stakes situations, like making an important medical decision or an irreversible big-ticket purchase. Choosing a harder-to-read font and a layout that requires more time to navigate forces customers to work harder to process the information, sending a subtle signal that it deserves their close attention.

That said, a company can’t apply behavioral psychology to deliver a digital experience if customers don’t engage with its site or mobile app in the first place. Addressing this often means making the process as convenient as possible, itself a behavioral nudge.

A digital solution that’s easy to use and search, offers a variety of choices pre-screened for relevance, and provides a friction-free transaction process is the equivalent of putting a product at eye level—and that applies far beyond retail. Consider the Global Entry program, which streamlines border crossings into the U.S. for pre-approved international travelers. Members can skip long passport control lines in favor of scanning their passports and answering a few questions at a touchscreen kiosk. To date, 1.8 million people have decided this convenience far outweighs the slow pace of approvals.

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The basics of influencing irrational customers are essentially the same whether they’re taking place in a store or on a screen. A business still needs to know who its customers are, understand their needs and motivations, and give them a reason to buy.

And despite the accelerating shift to digital commerce, we still live in a physical world. “There’s no divide between old-style analog retail and new-style digital retail,” Berens says. “Increasingly, the two are overlapping. One of the things we’ve seen for years is that people go into a store with their phones, shop for a better price, and buy online. Or vice versa: they shop online and then go to a store to negotiate for a better deal.”

Still, digital increases the number of touchpoints from which the business can gather, cluster, and filter more types of data to make great suggestions that delight and surprise customers. That’s why the hottest word in marketing today is omnichannel. Bringing behavioral psychology to bear on the right person in the right place in the right way at the right time requires companies to design customer experiences that bridge multiple channels, on- and offline.

Amazon, for example, is known for its friction-free online purchasing. The company’s pilot store in Seattle has no lines or checkout counters, extending the brand experience into the physical world in a way that aligns with what customers already expect of it, Dhar says.

Omnichannel helps counter some people’s tendency to believe their purchasing decision isn’t truly well informed unless they can see, touch, hear, and in some cases taste and smell a product. Until we have ubiquitous access to virtual reality systems with full haptic feedback, the best way to address these concerns is by providing personalized, timely, relevant information and feedback in the moment through whatever channel is appropriate. That could be an automated call center that answers frequently asked questions, a video that shows a product from every angle, or a demonstration wizard built into the product. Any of these channels could also suggest the customer visit the nearest store to receive help from a human.

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The omnichannel approach gives businesses plenty of opportunities to apply subtle nudges across physical and digital channels. For example, a supermarket chain could use store-club card data to push personalized offers to customers’ smartphones while they shop. “If the data tells them that your goal is to feed a family while balancing nutrition and cost, they could send you an e-coupon offering a discount on a brand of breakfast cereal that tastes like what you usually buy but contains half the sugar,” Guszcza says.

Similarly, a car insurance company could provide periodic feedback to policyholders through an app or even the digital screens in their cars, he suggests. “Getting a warning that you’re more aggressive than 90% of comparable drivers and three tips to avoid risk and lower your rates would not only incentivize the driver to be more careful for financial reasons but reduce claims and make the road safer for everyone.”

Digital channels can also show shoppers what similar people or organizations are buying, let them solicit feedback from colleagues or friends, and read reviews from other people who have made the same purchases. This leverages one of the most familiar forms of behavioral psychology—reinforcement from peers—and reassures buyers with Shiv’s Type 1 mindset that they’re making a choice that meets their needs or encourages those with the Type 2 mindset to move forward with the purchase. The rational mind only has to ask at the end of the process “Am I getting the best deal?” And as Guszcza points out, “If you can create solutions that use behavioral design and digital technology to turn my personal data into insight to reach my goals, you’ve increased the value of your engagement with me so much that I might even be willing to pay you more.”

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Many transactions take place through corporate procurement systems that allow a company to leverage not just its own purchasing patterns but all the data in a marketplace specifically designed to facilitate enterprise purchasing. Machine learning can leverage this vast database of information to provide the necessary nudge to optimize purchasing patterns, when to buy, how best to negotiate, and more. To some extent, this is an attempt to eliminate psychology and make choices more rational.

B2B spending is tied into financial systems and processes, logistics systems, transportation systems, and other operational requirements in a way no consumer spending can be. A B2B decision is less about making a purchase that satisfies a desire than it is about making a purchase that keeps the company functioning.

That said, the decision still isn’t entirely rational, Berens says. When organizations have to choose among vendors offering relatively similar products and services, they generally opt for the vendor whose salespeople they like the best.

This means B2B companies have to make sure they meet or exceed parity with competitors on product quality, pricing, and time to delivery to satisfy all the rational requirements of the decision process. Only then can they bring behavioral psychology to bear by delivering consistently superior customer service, starting as soon as the customer hits their app or website and spreading out positive interactions all the way through post-purchase support. Finishing strong with a satisfied customer reinforces the relationship with a business customer just as much as it does with a consumer.

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The best nudges make the customer relationship easy and enjoyable by providing experiences that are effortless and fun to choose, on- or offline, Dhar says. What sets the digital nudge apart in accommodating irrational customers is its ability to turn data about them and their journey into more effective, personalized persuasion even in the absence of the human touch.

Yet the subtle art of influencing customers isn’t just about making a sale, and it certainly shouldn’t be about persuading people to act against their own best interests, as Nudge co-author Thaler reminds audiences by exhorting them to “nudge for good.”

Guszcza, who talks about influencing people to make the choices they would make if only they had unlimited rationality, says companies that leverage behavioral psychology in their digital experiences should do so with an eye to creating positive impact for the customer, the company, and, where appropriate, the society.

In keeping with that ethos, any customer experience designed along behavioral lines has to include the option of letting the customer make a different choice, such as presenting a confirmation screen at the end of the purchase process with the cold, hard numbers and letting them opt out of the transaction altogether.

“A nudge is directing people in a certain direction,” Dhar says. “But for an ethical vendor, the only right direction to nudge is the right direction as judged by the customers themselves.” D!

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


About the Authors:

Volker Hildebrand is Global Vice President for SAP Hybris solutions.

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

Fawn Fitter is a freelance writer specializing in business and technology.

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Purpose Drives Productivity: 5 Steps To Optimize Communication For Your Workforce

July 3, 2017   BI News and Info

Dan McCaffrey has an ambitious goal: solving the world’s looming food shortage.

As vice president of data and analytics at The Climate Corporation (Climate), which is a subsidiary of Monsanto, McCaffrey leads a team of data scientists and engineers who are building an information platform that collects massive amounts of agricultural data and applies machine-learning techniques to discover new patterns. These analyses are then used to help farmers optimize their planting.

“By 2050, the world is going to have too many people at the current rate of growth. And with shrinking amounts of farmland, we must find more efficient ways to feed them. So science is needed to help solve these things,” McCaffrey explains. “That’s what excites me.”

“The deeper we can go into providing recommendations on farming practices, the more value we can offer the farmer,” McCaffrey adds.

But to deliver that insight, Climate needs data—and lots of it. That means using remote sensing and other techniques to map every field in the United States and then combining that information with climate data, soil observations, and weather data. Climate’s analysts can then produce a massive data store that they can query for insights.

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Meanwhile, precision tractors stream data into Climate’s digital agriculture platform, which farmers can then access from iPads through easy data flow and visualizations. They gain insights that help them optimize their seeding rates, soil health, and fertility applications. The overall goal is to increase crop yields, which in turn boosts a farmer’s margins.

Climate is at the forefront of a push toward deriving valuable business insight from Big Data that isn’t just big, but vast. Companies of all types—from agriculture through transportation and financial services to retail—are tapping into massive repositories of data known as data lakes. They hope to discover correlations that they can exploit to expand product offerings, enhance efficiency, drive profitability, and discover new business models they never knew existed.

The internet democratized access to data and information for billions of people around the world. Ironically, however, access to data within businesses has traditionally been limited to a chosen few—until now. Today’s advances in memory, storage, and data tools make it possible for companies both large and small to cost effectively gather and retain a huge amount of data, both structured (such as data in fields in a spreadsheet or database) and unstructured (such as e-mails or social media posts). They can then allow anyone in the business to access this massive data lake and rapidly gather insights.

It’s not that companies couldn’t do this before; they just couldn’t do it cost effectively and without a lengthy development effort by the IT department. With today’s massive data stores, line-of-business executives can generate queries themselves and quickly churn out results—and they are increasingly doing so in real time. Data lakes have democratized both the access to data and its role in business strategy.

Indeed, data lakes move data from being a tactical tool for implementing a business strategy to being a foundation for developing that strategy through a scientific-style model of experimental thinking, queries, and correlations. In the past, companies’ curiosity was limited by the expense of storing data for the long term. Now companies can keep data for as long as it’s needed. And that means companies can continue to ask important questions as they arise, enabling them to future-proof their strategies.

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Prescriptive Farming

Climate’s McCaffrey has many questions to answer on behalf of farmers. Climate provides several types of analytics to farmers including descriptive services, which are metrics about the farm and its operations, and predictive services related to weather and soil fertility. But eventually the company hopes to provide prescriptive services, helping farmers address all the many decisions they make each year to achieve the best outcome at the end of the season. Data lakes will provide the answers that enable Climate to follow through on its strategy.

Behind the scenes at Climate is a deep-science data lake that provides insights, such as predicting the fertility of a plot of land by combining many data sets to create accurate models. These models allow Climate to give farmers customized recommendations based on how their farm is performing.

“Machine learning really starts to work when you have the breadth of data sets from tillage to soil to weather, planting, harvest, and pesticide spray,” McCaffrey says. “The more data sets we can bring in, the better machine learning works.”

The deep-science infrastructure already has terabytes of data but is poised for significant growth as it handles a flood of measurements from field-based sensors.

“That’s really scaling up now, and that’s what’s also giving us an advantage in our ability to really personalize our advice to farmers at a deeper level because of the information we’re getting from sensor data,” McCaffrey says. “As we roll that out, our scale is going to increase by several magnitudes.”

Also on the horizon is more real-time data analytics. Currently, Climate receives real-time data from its application that streams data from the tractor’s cab, but most of its analytics applications are run nightly or even seasonally.

In August 2016, Climate expanded its platform to third-party developers so other innovators can also contribute data, such as drone-captured data or imagery, to the deep-science lake.

“That helps us in a lot of ways, in that we can get more data to help the grower,” McCaffrey says. “It’s the machine learning that allows us to find the insights in all of the data. Machine learning allows us to take mathematical shortcuts as long as you’ve got enough data and enough breadth of data.”

Predictive Maintenance

Growth is essential for U.S. railroads, which reinvest a significant portion of their revenues in maintenance and improvements to their track systems, locomotives, rail cars, terminals, and technology. With an eye on growing its business while also keeping its costs down, CSX, a transportation company based in Jacksonville, Florida, is adopting a strategy to make its freight trains more reliable.

In the past, CSX maintained its fleet of locomotives through regularly scheduled maintenance activities, which prevent failures in most locomotives as they transport freight from shipper to receiver. To achieve even higher reliability, CSX is tapping into a data lake to power predictive analytics applications that will improve maintenance activities and prevent more failures from occurring.

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Beyond improving customer satisfaction and raising revenue, CSX’s new strategy also has major cost implications. Trains are expensive assets, and it’s critical for railroads to drive up utilization, limit unplanned downtime, and prevent catastrophic failures to keep the costs of those assets down.

That’s why CSX is putting all the data related to the performance and maintenance of its locomotives into a massive data store.

“We are then applying predictive analytics—or, more specifically, machine-learning algorithms—on top of that information that we are collecting to look for failure signatures that can be used to predict failures and prescribe maintenance activities,” says Michael Hendrix, technical director for analytics at CSX. “We’re really looking to better manage our fleet and the maintenance activities that go into that so we can run a more efficient network and utilize our assets more effectively.”

“In the past we would have to buy a special storage device to store large quantities of data, and we’d have to determine cost benefits to see if it was worth it,” says Donna Crutchfield, assistant vice president of information architecture and strategy at CSX. “So we were either letting the data die naturally, or we were only storing the data that was determined to be the most important at the time. But today, with the new technologies like data lakes, we’re able to store and utilize more of this data.”

CSX can now combine many different data types, such as sensor data from across the rail network and other systems that measure movement of its cars, and it can look for correlations across information that wasn’t previously analyzed together.

One of the larger data sets that CSX is capturing comprises the findings of its “wheel health detectors” across the network. These devices capture different signals about the bearings in the wheels, as well as the health of the wheels in terms of impact, sound, and heat.

“That volume of data is pretty significant, and what we would typically do is just look for signals that told us whether the wheel was bad and if we needed to set the car aside for repair. We would only keep the raw data for 10 days because of the volume and then purge everything but the alerts,” Hendrix says.

With its data lake, CSX can keep the wheel data for as long as it likes. “Now we’re starting to capture that data on a daily basis so we can start applying more machine-learning algorithms and predictive models across a larger history,” Hendrix says. “By having the full data set, we can better look for trends and patterns that will tell us if something is going to fail.”

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Another key ingredient in CSX’s data set is locomotive oil. By analyzing oil samples, CSX is developing better predictions of locomotive failure. “We’ve been able to determine when a locomotive would fail and predict it far enough in advance so we could send it down for maintenance and prevent it from failing while in use,” Crutchfield says.

“Between the locomotives, the tracks, and the freight cars, we will be looking at various ways to predict those failures and prevent them so we can improve our asset allocation. Then we won’t need as many assets,” she explains. “It’s like an airport. If a plane has a failure and it’s due to connect at another airport, all the passengers have to be reassigned. A failure affects the system like dominoes. It’s a similar case with a railroad. Any failure along the road affects our operations. Fewer failures mean more asset utilization. The more optimized the network is, the better we can service the customer.”

Detecting Fraud Through Correlations

Traditionally, business strategy has been a very conscious practice, presumed to emanate mainly from the minds of experienced executives, daring entrepreneurs, or high-priced consultants. But data lakes take strategy out of that rarefied realm and put it in the environment where just about everything in business seems to be going these days: math—specifically, the correlations that emerge from applying a mathematical algorithm to huge masses of data.

The Financial Industry Regulatory Authority (FINRA), a nonprofit group that regulates broker behavior in the United States, used to rely on the experience of its employees to come up with strategies for combating fraud and insider trading. It still does that, but now FINRA has added a data lake to find patterns that a human might never see.

Overall, FINRA processes over five petabytes of transaction data from multiple sources every day. By switching from traditional database and storage technology to a data lake, FINRA was able to set up a self-service process that allows analysts to query data themselves without involving the IT department; search times dropped from several hours to 90 seconds.

While traditional databases were good at defining relationships with data, such as tracking all the transactions from a particular customer, the new data lake configurations help users identify relationships that they didn’t know existed.

Leveraging its data lake, FINRA creates an environment for curiosity, empowering its data experts to search for suspicious patterns of fraud, marketing manipulation, and compliance. As a result, FINRA was able to hand out 373 fines totaling US$ 134.4 million in 2016, a new record for the agency, according to Law360.

Data Lakes Don’t End Complexity for IT

Though data lakes make access to data and analysis easier for the business, they don’t necessarily make the CIO’s life a bed of roses. Implementations can be complex, and companies rarely want to walk away from investments they’ve already made in data analysis technologies, such as data warehouses.

“There have been so many millions of dollars going to data warehousing over the last two decades. The idea that you’re just going to move it all into a data lake isn’t going to happen,” says Mike Ferguson, managing director of Intelligent Business Strategies, a UK analyst firm. “It’s just not compelling enough of a business case.” But Ferguson does see data lake efficiencies freeing up the capacity of data warehouses to enable more query, reporting, and analysis.

sap Q217 digital double feature3 images6 Purpose Drives Productivity: 5 Steps To Optimize Communication For Your WorkforceData lakes also don’t free companies from the need to clean up and manage data as part of the process required to gain these useful insights. “The data comes in very raw, and it needs to be treated,” says James Curtis, senior analyst for data platforms and analytics at 451 Research. “It has to be prepped and cleaned and ready.”

Companies must have strong data governance processes, as well. Customers are increasingly concerned about privacy, and rules for data usage and compliance have become stricter in some areas of the globe, such as the European Union.

Companies must create data usage policies, then, that clearly define who can access, distribute, change, delete, or otherwise manipulate all that data. Companies must also make sure that the data they collect comes from a legitimate source.

Many companies are responding by hiring chief data officers (CDOs) to ensure that as more employees gain access to data, they use it effectively and responsibly. Indeed, research company Gartner predicts that 90% of large companies will have a CDO by 2019.

Data lakes can be configured in a variety of ways: centralized or distributed, with storage on premise or in the cloud or both. Some companies have more than one data lake implementation.

“A lot of my clients try their best to go centralized for obvious reasons. It’s much simpler to manage and to gather your data in one place,” says Ferguson. “But they’re often plagued somewhere down the line with much more added complexity and realize that in many cases the data lake has to be distributed to manage data across multiple data stores.”

Meanwhile, the massive capacities of data lakes mean that data that once flowed through a manageable spigot is now blasting at companies through a fire hose.

“We’re now dealing with data coming out at extreme velocity or in very large volumes,” Ferguson says. “The idea that people can manually keep pace with the number of data sources that are coming into the enterprise—it’s just not realistic any more. We have to find ways to take complexity away, and that tends to mean that we should automate. The expectation is that the information management software, like an information catalog for example, can help a company accelerate the onboarding of data and automatically classify it, profile it, organize it, and make it easy to find.”

Beyond the technical issues, IT and the business must also make important decisions about how data lakes will be managed and who will own the data, among other things (see How to Avoid Drowning in the Lake).

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How to Avoid Drowning in the Lake

The benefits of data lakes can be squandered if you don’t manage the implementation and data ownership carefully.

Deploying and managing a massive data store is a big challenge. Here’s how to address some of the most common issues that companies face:

Determine the ROI. Developing a data lake is not a trivial undertaking. You need a good business case, and you need a measurable ROI. Most importantly, you need initial questions that can be answered by the data, which will prove its value.

Find data owners. As devices with sensors proliferate across the organization, the issue of data ownership becomes more important.

Have a plan for data retention. Companies used to have to cull data because it was too expensive to store. Now companies can become data hoarders. How long do you store it? Do you keep it forever?

Manage descriptive data. Software that allows you to tag all the data in one or multiple data lakes and keep it up-to-date is not mature yet. We still need tools to bring the metadata together to support self-service and to automate metadata to speed up the preparation, integration, and analysis of data.

Develop data curation skills. There is a huge skills gap for data repository development. But many people will jump at the chance to learn these new skills if companies are willing to pay for training and certification.

Be agile enough to take advantage of the findings. It used to be that you put in a request to the IT department for data and had to wait six months for an answer. Now, you get the answer immediately. Companies must be agile to take advantage of the insights.

Secure the data. Besides the perennial issues of hacking and breaches, a lot of data lakes software is open source and less secure than typical enterprise-class software.

Measure the quality of data. Different users can work with varying levels of quality in their data. For example, data scientists working with a huge number of data points might not need completely accurate data, because they can use machine learning to cluster data or discard outlying data as needed. However, a financial analyst might need the data to be completely correct.

Avoid creating new silos. Data lakes should work with existing data architectures, such as data warehouses and data marts.

From Data Queries to New Business Models

The ability of data lakes to uncover previously hidden data correlations can massively impact any part of the business. For example, in the past, a large soft drink maker used to stock its vending machines based on local bottlers’ and delivery people’s experience and gut instincts. Today, using vast amounts of data collected from sensors in the vending machines, the company can essentially treat each machine like a retail store, optimizing the drink selection by time of day, location, and other factors. Doing this kind of predictive analysis was possible before data lakes came along, but it wasn’t practical or economical at the individual machine level because the amount of data required for accurate predictions was simply too large.

The next step is for companies to use the insights gathered from their massive data stores not just to become more efficient and profitable in their existing lines of business but also to actually change their business models.

For example, product companies could shield themselves from the harsh light of comparison shopping by offering the use of their products as a service, with sensors on those products sending the company a constant stream of data about when they need to be repaired or replaced. Customers are spared the hassle of dealing with worn-out products, and companies are protected from competition as long as customers receive the features, price, and the level of service they expect. Further, companies can continuously gather and analyze data about customers’ usage patterns and equipment performance to find ways to lower costs and develop new services.

Data for All

Given the tremendous amount of hype that has surrounded Big Data for years now, it’s tempting to dismiss data lakes as a small step forward in an already familiar technology realm. But it’s not the technology that matters as much as what it enables organizations to do. By making data available to anyone who needs it, for as long as they need it, data lakes are a powerful lever for innovation and disruption across industries.

“Companies that do not actively invest in data lakes will truly be left behind,” says Anita Raj, principal growth hacker at DataRPM, which sells predictive maintenance applications to manufacturers that want to take advantage of these massive data stores. “So it’s just the option of disrupt or be disrupted.” D!

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


About the Authors:

Timo Elliott is Vice President, Global Innovation Evangelist, at SAP.

John Schitka is Senior Director, Solution Marketing, Big Data Analytics, at SAP.

Michael Eacrett is Vice President, Product Management, Big Data, Enterprise Information Management, and SAP Vora, at SAP.

Carolyn Marsan is a freelance writer who focuses on business and technology topics.

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Better Data Drives Better Decisions: Five Lessons from Domopalooza 2017

April 22, 2017   NetSuite
websitelogo Better Data Drives Better Decisions: Five Lessons from Domopalooza 2017

Posted by Evan Heby, Advertising, Media and Publishing Marketing Lead

For fast-growing businesses seeking to capitalize on the power of the cloud and the data stashed across their business, the recent Domopalooza 2017, the annual conference from Domo, a cloud-based analytics software provider, offered some key insights.

Held in downtown Salt Lake City, Utah, just a short drive north of the Domo headquarters in American Fork, Utah, over 3,000 people from more than 1,250 different companies—both start-up and well established—came to the event to discuss the past, present and future of data and business intelligence.

Over 100 businesses that run NetSuite were in attendance, many of whom spoke at the event. As a sponsor of the event, NetSuite shared its perspective on the lessons we’ve learned from being a modern cloud company.

Here are a few of the key takeaways from Domopalooza 2017:

  • Every company is a cloud company: As technology continues to develop, modern cloud business solutions are becoming an absolute requirement, instead of a ‘nice to have.’ It is crucial to have the right business tools in place to get to the next level—whether that means securing a round of funding, acquiring another company, or making an exit—companies need the proper infrastructure and the right data to support their plans.
  • Embrace hybrid business models: Many companies start with one line of business like a product or a service. Hybrid business models are blurring the lines and companies that blend these models are no longer an exception to the rule, but rather they are becoming the norm.
  • Businesses must deliver omnichannel experiences: From the back-end platform to the customer facing website, and everything in between, businesses requires a smooth, harmonious experience to keep customers happy and the company’s data healthy.
  • Clean, accessible data a must: Tools that capture and feed data into digestible metrics and reports are a MUST have as the company expands; good data depends on the power of business tools to deliver that data – don’t be left behind.
  • Change is the only constant: Customization is not a dirty word when it comes to business software. Every business is unique in one way or another, so flexibility is key for all businesses.

NetSuite allows businesses to be flexible through major growth stages, and gives the entire business a sightline into what’s working vs. what’s not. NetSuite’s user conference, SuiteWorld, is coming up from April 24th-27th and we plan to share more of what we’ve learned about being a modern cloud company. We hope you can join us – click here to register.

Posted on Thu, April 20, 2017
by NetSuite filed under

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Lenovo’s “Disciplined” Supply Chain Drives Global Leadership

February 15, 2017   SAP
Untitled 13 Lenovo’s “Disciplined” Supply Chain Drives Global Leadership

“Lenovo’s been the number one PC company for over three years now,” says Gerry Smith, an executive vice president at Lenovo and president of its Data Center Group, in a recent video.

In fact, Lenovo’s 2015 PC sales represented about 20% of total market share according to Gartner.

That’s a pretty remarkable accomplishment for a company that The Wall Street Journal, described, “as recently as 2005… was a little-known computer maker that sold only in China.”

But a lot has changed in the past 10 years or so – especially in Lenovo’s now worldwide supply chain.

These days, Lenovo has operations in 60 countries, customers around the planet, and it relies on a revamped supply chain driven by such thoroughly modern imperatives as sustainability, product security, real-time planning, and customer-centricity.

Major acquisitions deliver global growth

Lenovo’s dramatic growth has made headlines. In 2005, the company bought IBM’s personal computer business. Then, in 2014 came the acquisitions of both IBM’s Intel-based server business and Motorola Mobility.

And as a result of such bold moves, Lenovo has had to adapt to an increasingly global marketplace and a growing list of international standards and regulations.

As part of an integrated response to its full-scale globalization, Lenovo has established comprehensive sustainability programs across its broad supply chain. These initiatives address operations from internal manufacturing to packaging and logistics. Still other programs help Lenovo evaluate external suppliers on criteria such as employee working conditions, environmental footprint, and the use of environmentally preferred materials. In total, Lenovo reports that it uses more than 25 key indicators to measure vendor transparency, commitment, and performance.

“Working with trusted suppliers – as well as owning and running our own factories – promotes end-to-end security in Lenovo’s supply chain,” says Smith. “These controls help us ensure that products are built with components from known suppliers, guard against hijacking, and protect against compromised firmware updates once our products are deployed.”

Process efficiency is part of the plan

Lenovo’s global supply chain strategy also employs solutions designed to optimize process efficiency. For example, Lenovo implemented an advanced planning and optimization component on an in-memory computing platform to help plan and execute supply chain processes for the newly acquired server business. Additionally, the company partnered  co-developed new applications that support supplier collaboration and help to perform cost forecasting calculations in near real-time.

“We’ve dramatically improved our supply chain performance,” says Smith, “reducing our planning time from 10 hours to 10 minutes.”

As Smith sees it, Lenovo has all the tools in place “to make our supply chain, not only the best PC and server supply chain in the world, but one of the best supply chains across all industries.”

Others obviously agree with Smith’s assessment.

Gartner named Lenovo among its top 25 supply chain companies for 2016. In particular, Gartner cites the high-tech company for the “disciplined approach” it took to integrate its supply chain in the wake of recent acquisitions.

Gartner also notes that Lenovo’s supply chain team ran specific programs to enhance customer experience and operational excellence – like the creation of a customer social/digital platform for key global accounts that presents content tailored to each customer’s preference in terms of order status, new product information, and technical support information. Further, Gartner highlights the fact that Lenovo assigns a supply chain staff member as an executive sponsor for each major account.

A lot has changed since Lenovo was a little-known computer maker that sold only in China.

Please follow me on Twitter at @JohnGWard3.

  • Hear more from Lenovo’s Gerry Smith in this SAP video.
  • Read more about how Lenovo is optimizing supply chain efficiency in this SAP Business Transformation Study.

This story originally appeared on Business Trends on the SAP Community.

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Bazaarvoice Drives Brand Engagement, Awareness, Sales with API Traffic

October 13, 2016   TIBCO Spotfire
Bazaarvoice logo Bazaarvoice Drives Brand Engagement, Awareness, Sales with API Traffic

Bazaarvoice is the world’s largest network of active shoppers, connecting more than one-half billion consumers monthly to thousands of retailers and brands that represent millions of products and services. Its solutions help brands and retailers capture, display, and analyze consumer generated ratings, reviews, photos, videos, and other social content about their brands, products, and services.

When Bazaarvoice embarked on a re-architecture of its consumer facing display, it used best practices of client side Javascript and mobile apps calling a published API. As this API grew richer, it became more popular. Leading the adoption were large retailers with huge volumes of products and reviews. These retailers had heavy API usage patterns that demanded a more sophisticated architecture including caching.

The company chose to focus its engineering resources on growing its consumer content network rather than building an internal API management capability. There would be significant volumes of API traffic to manage, and leveraging the expertise of a partner would be a good choice. TIBCO Mashery was chosen for its maturity. It had the necessary infrastructure, and the dependability, to handle the volume of traffic coming from some of the biggest brands in the world. And, in fact, over the course of 18 months, traffic increased from 790 million to 7.9 billion calls per month, a 900% increase.

Read about the strategy Bazaarvoice developed for its API program, what it achieved, and its goals for the future.

Join the TIBCO customer reference program to have your business transformation story shared globally with the technology industry, and trade and business press. Your story in print, web, and video format can boost your status as a thought leader and increase awareness with technology leaders, helping you raise your company visibility and attract and retain top talent. Email customermarketing@tibco.com today!

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Minds&More drives growth and efficiency with NetSuite Service Resource Planning

October 12, 2016   NetSuite
og image Minds&More drives growth and efficiency with NetSuite Service Resource Planning

NetSuite SRP Replaces Multiple Systems at Belgian Consultancy

London—11 October 2016—NetSuite Inc. (NYSE: N), the industry’s leading provider of cloud financials / ERP and omnichannel commerce software suites, today announced that Minds&More, a preferred partner for business growth through sales, marketing & transformation excellence in Belgium, has implemented NetSuite Services Resource Planning (SRP) to run its end-to-end business from financials, project and resource management, customer relationship management (CRM), time and expense reporting, to client billing all within one unified solution. With NetSuite SRP, Minds&More has a solid foundation for its ambitious growth plans that can manage processes from lead to customer to project to delivery, while improving operational efficiency and providing critical real-time business insights.

Minds&More provides businesses with transformative sales and marketing services and solutions through a network of more than 100 freelance associates. As the company grew and leadership outlined an ambitious growth plan, they knew the company needed to replace its existing tangle of applications, including Salesforce.com for CRM, a local accounting system and Excel spreadsheets for project management, with a single system. NetSuite SRP was the only unified cloud-based solution that met the company’s needs.

“We knew that to properly scale and deliver on our vision of delivering top rate consulting services through our network of associates we would need a system that could scale with us,” said Pieterjan Kempynck, Managing Partner at Minds&More. “NetSuite provides it all with comprehensive, real-time visibility into all aspects of our business on a single system.”

“Minds&More is a great example of the many Belgian companies that are turning to NetSuite’s cloud-based platform to innovate and grow their businesses,” said Mark Woodhams, SVP and Managing Director of NetSuite EMEA. “NetSuite SRP is a perfect fit for this business and we look forward to their continued success.”

Implemented by Global System Integrator partner Deloitte within a rapid five-month time frame, NetSuite SRP is providing Minds&More with the following benefits:

  • A broad range of robust ERP and global financial functionality designed for modern businesses, enabling the company to streamline its mission-critical business processes.
  • Profitability tracking at the project and client level, enabling Minds&More to generate data-driven insights to optimise processes.
  • Resource optimisation to put the right associates on the right projects by tracking such key metrics as resources skillset, time to completion, on-time delivery, client satisfaction and more.
  • Built-in business intelligence that provides real-time insights into business performance, including personalised dashboards for executives that provide visibility into project profitability and opportunities by month.
  • Anywhere, anytime access that allows Minds&More’s freelance associates to access the system remotely to enter their skills, résumé, availability and time and expenses.
  • 360-degree view of customers, associates and projects, enabling enhanced customer support and service.

NetSuite SRP is the first and only cloud native solution that unites all of the ERP and professional services automation functionality to meet the business requirements and needs of both product and services companies in one single system. NetSuite SRP allows:

  • Companies to automate and manage key aspects of their business across the complete bid-to-bill lifecycle, giving them what they need to stay competitive, gain efficiency, deliver quality services and delight their clients.
  • Omni business model billing for service delivery across the services value chain from fixed fee to milestone to time-and-materials.
  • On-the-go services professionals to access real-time business information anywhere, anytime and better collaboration across the organisation.
  • Companies to gain comprehensive visibility into back-office financials and front-office services operations in one unified system to help increase project profitability, maximise billable resource utilisation and make informed, data-driven, strategic and tactical decisions with powerful and insightful business metrics.
  • Companies to address global businesses’ needs by giving them comprehensive real-time visibility, integrated financials, resource optimisation and services management from corporate to subsidiaries, down to the individual project level across geographies, currencies and tax jurisdictions.

For companies selling both services and products, NetSuite SRP supplies complete visibility and control over enterprise-wide processes with such capabilities as revenue recognition for diverse revenue streams, and unified billing across multi-model service and product delivery.

About NetSuite
In 1998, NetSuite pioneered the Cloud Computing revolution, establishing the world’s first company dedicated to delivering business applications over the Internet. Today, NetSuite provides a suite of cloud-based financials/Enterprise Resource Planning (ERP) and omnichannel commerce software that runs the business of more than 30,000 companies, organisations, and subsidiaries in more than 100 countries. 

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

For more information about NetSuite please visit www.netsuite.com.

NOTE: NetSuite and the NetSuite logo are service marks of NetSuite Inc. Third-party trademarks mentioned are the property of their respective owners.

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Teradata® Aster® Connector for Spark Drives Data Democracy

June 8, 2016   BI News and Info

Teradata (NYSE: TDC), the big data analytics and marketing applications company, today introduced the Teradata® Aster® Connector for Spark, an industry-first integration of Apache Spark analytics with Teradata Aster Analytics. The connector enables pre-built analytics functions from both solutions to be executed from Aster Analytics to form a truly multi-genre advanced analytics environment. The result is that virtually anyone who can use Aster Analytics can also run advanced analytics on Spark without the need to learn or know Scala.

The Teradata Aster Connector for Spark democratizes big data through self-service, business- focused, analytic solutions. By enabling ease-of-use for many business users, companies can more quickly identify revenue-driving insights and accelerate business performance. Specifically, the Teradata Aster Connector for Spark gives users many choices and benefits:

1) Customers can now use techniques from both Aster Analytics and Spark (example, Teradata Aster nPath®, used for pattern matching, and deep learning neural network analysis with Spark), and can choose the technique for implementation that generates the best insights upon evaluation.

2) Customers can pipeline various functions together in one workflow that can be executed in Aster Analytics. For example, a text parser function from Aster Analytics can be invoked, followed by a Spark machine learning algorithm, to support the development of an illuminating data model. This sequence can be replicated for other function types.

3) Customers can run a clustering algorithm in Aster Analytics, and a similar one in Spark, and compare results to see which approach is preferred.

“There’s huge interest in the in-memory performance and analytical capabilities of Spark, but the universe of data professionals with Spark skills and experience is still quite small as compared to those with SQL skills,” said Doug Henschen, vice president and principal analyst, Constellation Research. “Customers seek an ensemble of analytical capabilities expressed in SQL and SQL-like expressions. Moreover, they want in-memory performance and analytical capabilities while abstracting users from complex and unfamiliar Spark interfaces and coding.”

Raghu Chakravarthi, vice president of Teradata Aster Engineering, emphasized the value of use cases enabled by the Teradata Aster Connector for Spark.

“The beauty of the Teradata Aster Connector for Spark is its application for a variety of use cases in just about any industry,” said Chakravarthi. “For instance, Aster can be the repository for customer data and finance data. Once Aster pre-processes these, machine learning from Spark can be applied to create automatic credit ratings for each customer. Analysts could then use these credit ratings as one variable in a predictive model that ascertains the likelihood of, say, this customer purchasing a new automobile in the next 12 months.”

Another use case arena for the Teradata Aster Connector for Spark is The Internet of Things, where large volumes of sensor data are ingested and pre-processed using Aster Analytics. This data can be passed on to Spark for analysis using deep learning techniques. Data on home sensors and thermostats can be combined with other information such as geo-location, resident demographics, and weather conditions to determine usage patterns, to predict instrument wear and tear, and to proactively activate household appliances in response to changing environmental conditions.

Chakravarthi also noted examples in the retail sector, where customer transaction information could be passed to a clustering algorithm like k-means to create product groups – and combined with customer comment data or product reviews to create new insights. He also said financial institutions could mine interaction information to understand the distinct set of conditions that could result in churn. Using Spark to ingest parsed and transformed data from Aster Analytics, additional analytics could be run to determine churn drivers.

The Teradata Aster Connector for Spark will be generally available on a global basis in the fourth quarter of 2016. This announcement is being made at the Spark Summit 2016, where Teradata is a Silver Sponsor and offering demonstrations in Booth C2.

Relevant News Links

• Learn more about multi-genre analytics with Teradata Aster Analytics

• The Teradata ASTER COMMUNITY WEB PAGE:Click here to see what’s happening

• What’s missing in your net promoter score? White Paper sheds light on CSI

• Finally! A true customer satisfaction score: More on the CSI Analytic solution

• Read all about it: Recent Northwestern University Hackathon led by Teradata Aster

 
 
 
 

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Analytics drives healthcare data storage in the cloud

May 26, 2016   BI News and Info

For many CIOs and other IT executives at hospitals and health systems, planning a storage redesign comes with multiple angles.

For example, health IT professionals must:

  • Rationalize what data files and applications will be earmarked for storage in the cloud.
  • Determine what data will remain on-premises.
  • Figure out if any new technologies will be used to process patient information that must be stored to meet regulatory requirements.

As healthcare organizations embark on new data-driven initiatives — such as population health, precision medicine and big data analytics — many medical facilities are focused on remodeling the way they store data and are looking at technologies, including cloud computing, to reduce costs and enhance performance.

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Project scope determines cloud use

The decisions around healthcare data storage in the cloud are also being shaped by the size of a hospital and the scope of its work. For instance, a hospital that conducts medical research may find the need to buy more storage technology or may opt to engage cloud services to store older research projects.

What is also evident is that storing data has to be done in a way that meets the needs and regulatory requirements of many stakeholders.

 Analytics drives healthcare data storage in the cloudMarc Staimer

“If you look at storage from a trend point of view, hospitals have multiple different clientele that IT has to satisfy,” Marc Staimer, president of Dragon Slayer Consulting in Beaverton, Ore., a company that advises health IT executives, said. On one side are patients, doctors and hospital administrators, and elsewhere “you have the regulators that are looking to make sure that the data is safe and secure under federal rules.”

The difference in hospital storage needs has also meant that there is no consistent answer to the question of how hospitals store data and what approach each one will take, Staimer said.

Patient info as part of analytics

Based on a poll of 181 health IT professionals, organizations will increasingly upload more data to the cloud. Research from a 2016 survey conducted by TechTarget in partnership with the College of Healthcare Information Management Executives shows that 32% of survey respondents said their organizations are looking to boost their investments in cloud technology in 2016.The top three areas for cloud adoption are disaster recovery (45%), storage (37%) and mobile health (35%).

An important trend occurring at healthcare organizations is the shift from using electronic health records (EHRs) to simply input and access patient data to instead using patient information for data analytics projects.

CIO discusses adoption of cloud in healthcare.

“In the past, EHRs were operating just to make sure we collected data from physicians and all of the data is stored in a single repository,” Sriram Bharadwaj, director of information services at UC Irvine Health in Orange, Calif., said. “Now hospitals are trying to figure out how the data stored in an EHR can be used to make effective decisions around the planning of care, the quality of care, redesigning care and reducing the cost of care.”

As the industry moves toward evidence-based medicine, the requirements for data analytics projects will increase. Healthcare data storage in the cloud will be increasingly prevalent for big data projects that comprise national or regional patient populations, as well as conducting data analytics on a subset of patients within a hospital, Bharadwaj, who is also chair of the Healthcare Information and Management Systems Society’s Privacy and Security Committee, said.

For many healthcare providers, embarking on the shift to host data in the cloud is a slow, deliberate process.

“Let’s say you want to redesign a care model that applies to a specific patient population, and, for example, you’re focused on changes in medication dosage. You might want extra computing power to crunch the numbers from your claims data, which you can get from the cloud,” Bharadwaj said. “IT executives are increasingly looking at the cloud for quick projects like this.”

Baby steps for storage in the cloud

UC Irvine Health, which operates cancer, trauma and stroke centers, has taken a cautious approach to storing data in the cloud and kept its critical applications, such as its EHR and financial systems, within its data center.

For many healthcare providers, embarking on the shift to host data in the cloud is a slow, deliberate process.

One hospital in the throes of transitioning applications to a cloud platform is Beth Israel Deaconess Medical Center in Boston, a major teaching facility of Harvard Medical School that serves approximately 1,250 full-time physicians. The hospital, which also conducts academic research, has 3 petabytes of data and an annual data growth rate ranging from 25% to 100%, depending on the type of data being generated, Manu Tandon, Beth Israel’s CIO, said.

Currently, Beth Israel is in the process of identifying which applications to keep at its data center and which ones to move to the cloud — and in what sequence, Tandon said. While the hospital could very well host critical technology in the cloud one day, that time has not arrived as of yet.

“We are moving to the cloud in a very measured manner,” Tandon said. “We think that certain types of storage, such as archived research projects, older patient images and old home directories, are good candidates for storage in the cloud, but we’ve decided that mission-critical applications, such as our EHRs, as well as our human resource and financial applications, will remain at our data center, for now.”

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