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Tag Archives: Thought

Thought for the Day

August 15, 2020   Humor

H/T TXNick

Let’s block ads! (Why?)

ANTZ-IN-PANTZ ……

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A PERFECT EXAMPLE OF THE LIBERAL THOUGHT PROCESS

May 13, 2020   Humor

Let’s block ads! (Why?)

ANTZ-IN-PANTZ ……

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Why Enterprise Mobility Is More Transformational Than We Ever Thought

July 6, 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 Why Enterprise Mobility Is More Transformational Than We Ever ThoughtData 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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What Is Thought Leadership And Why Do You Need It?

May 23, 2017   SAP

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.

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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.

sap Q217 digital double feature1 images4 What Is Thought Leadership And Why Do You Need It?

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.”

sap Q217 digital double feature1 images10 1024x572 What Is Thought Leadership And Why Do You Need It?

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.

sap Q217 digital double feature1 images11 1024x572 What Is Thought Leadership And Why Do You Need It?

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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New password guidelines say everything we thought about passwords is wrong

April 19, 2017   Big Data
 New password guidelines say everything we thought about passwords is wrong

When I recently discovered a draft of new guidelines for password management from NIST (the National Institute of Standards and Technology), I was amazed about the number of very progressive changes they proposed.

Although NIST’s rules are not mandatory for nongovernmental organizations, they usually have a huge influence as many corporate security professionals use them as base standards and best practices when forming policies for their companies. Thus, another fact I was surprised about was a lack of attention to this document, finalized March 31, from both official media and the blogosphere. After all, those changes are supposed to affect literally everyone who browses the Internet

Here is a quick look at the three main changes the NIST has proposed:

No more periodic password changes. This is a huge change of policy as it removes a significant burden from both users and IT departments. It’s been clear for a long time that periodic changes do not improve password security but only make it worse, and now NIST research has finally provided the proof.

No more imposed password complexity (like requiring a combination of letters, numbers, and special characters). This means users now can be less “creative” and avoid passwords like “Password1$ ”, which only provide a false sense of security.

Mandatory validation of newly created passwords against a list of commonly-used, expected, or compromised passwords. Users will be prevented from setting passwords like “password”, “12345678”, etc. which hackers can easily guess.

So why haven’t we seen any coverage of the changes considering how much of a departure they are from previous advice — and considering every average user is going to be affected? I think there are several reasons for the radio silence.

First, many people now suffer from password fatigue. Users are tired of and disappointed with password rules. They are forced to follow all these complex guidelines, remember and periodically change dozens or hundreds of different passwords, and yet we still hear about an enormous number of security breaches caused by compromised passwords. Users, especially less sophisticated ones, seem to have reconciled themselves to this situation and perceive it as a matter of course, so no one believes it can be improved.

Second, we’ve seen a widespread introduction of MFA (multi factor authentication), also known as two factor authentication, which supposedly pushes the password problem to the background. Let me remind you that unlike traditional authentication by password (“something you know”), MFA requires a second factor like “something you have” (hardware token, mobile phone) or “something you are” (usually biometric such as fingerprint or face recognition). Indeed, if my account is protected by a reliable second factor such as a one-time code texted to my iPhone or generated on demand by my Yubikey, why should I care about passwords anymore? I can just use the same password I remember on every account that is protected by MFA. Unfortunately, this assumption is only partially true because MFA is reliable only when both factors are secure.

Finally, more diligent users these days have access to a large variety of password management software, both commercial and freeware, which can significantly improve user experience and security. With password management software, I only need to remember one password that unlocks my personal “password vault”, so I don’t have to worry about all the complexity rules or frequent password changes; my password manager will generate, store, and enter a secure random password every time I need one. However, there are still scenarios when we cannot use password manager (unlocking our phone, computer, or door, for example).

So are these changes NIST is proposing still relevant and important? Of course they are. Despite the desperate attempts of many security startups to introduce new authentication methods, passwords are here to stay for awhile, if not forever, and millions of people around the world will appreciate even small improvements in user experience and security.

Slava Gomzin is author of the book Hacking Point of Sale (Wiley, 2014) and Bitcoin for Nonmathematicians (Universal Publishers, 2016). He is VP of Information Security and Technology at Pieces Technologies, a health tech startup. Previously he was Director of Information Security at Parkland Center for Clinical Innovation (PCCI) and was a security and payments technologist at Hewlett-Packard, where he helped create products that are integrated into modern payment processing ecosystems using the latest security technologies. He blogs about information security and technology at www.gomzin.com.

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Industry Thought Leaders Predict Big Changes for the Supply Chain by 2020

March 12, 2017   NetSuite

Posted by Marissa Kinsley, Manufacturing and Distribution Industry Marketing Lead

Two days, three keynotes and 2,800 attendees — the recent Modern Supply Chain Industry%20Leaders%20Predict Industry Thought Leaders Predict Big Changes for the Supply Chain by 2020Experience conference held in San Jose, Calif., had one primary purpose: to bring together the strongest minds in supply chain to uncover and address the top challenges facing today’s supply chain leaders. From the Internet of Things and predictive analytics to big data, new technologies are touching every part of the business – particularly the supply chain.

But what are the implications for businesses? What exactly is changing in supply chain? Here’s what thought leaders in the industry predicted at the conference:

  • By 2020, over 50 percent of manufacturing supply chain models will benefit from investment in new technologies and 50 percent of manufacturers will be using cognitive computing and artificial intelligence as well as advanced analytics for planning and long term forecasting.
  • Ecommerce will be imperative for business expansion and will help to drive growth in the supply chain. Roughly 90 percent of product businesses will be using B2B and B2C ecommerce in the next 10 years, and 50 percent of manufacturing supply chains will reach the end consumers directly for increased profitability.
  • Businesses will need to prioritize improving business processes and streamlining the supply chain to most effectively reduce costs and improve efficiencies, drive growth, and improve the customer experience for long-term business success. The supply chain is the lifeblood of the business, and those that choose to innovate and invest their time into it will have the opportunity to not only succeed, but rather thrive in our increasingly connected business world.
  • Moving to the cloud will be the natural choice for any business managing or preparing to manage a modern, digital supply chain. The cost advantages of a cloud solution – from decreased investment in software development to lower maintenance costs – are only part of the equation driving businesses to the cloud.
  • Businesses leveraging innovative process changes, tech transformations and cloud applications – or some combination of these – will be the ones to get ahead. Supply chain management will be no exception. The businesses to innovate and capitalize on new technologies will thrive, while those who do not will become obsolete.

Regardless of whether these predictions are 100 percent accurate, one thing is certain – continuing the same supply chain strategy is not the answer. Change is inevitable, especially in an age when technology is constantly advancing and businesses are innovating and iterating day after day. With this in mind, as hard as investing in your supply chain may seem, can your business really afford to stay the same?

For more on what NetSuite can do to transform the supply chain, read this story on how Epec Engineered Technologies transformed into an agile, innovative manufacturer with a remarkable five-day turnaround from product design to customer delivery.

Posted on Wed, March 8, 2017
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18 Predictions For 2017 From Finance Thought Leaders In A Digital And Automated World

December 14, 2016   SAP
274985 low jpg srgb 18 Predictions For 2017 From Finance Thought Leaders In A Digital And Automated World

Digitization continues amok. New business models and processes are being introduced across all industries; new competitors are emerging that can natively take advantage of these new business models and are disrupting entire industries. Continued volatility is driving the demand for real-time, detailed information. Technology innovation continues at breakneck speed. While 2016 saw the application of blockchain to financial processes and the adoption of artificial intelligence to drive more automation and efficiencies in finance. What is that going to mean in 2017? Can finance organizations keep up with the technology opportunities?

Once again, we asked finance thought leaders to share their predictions for finance in a digital world. Their insights for 2017 include increased real-time insight, collaboration, automation, and partnership with the business. Take a look back at the predictions for 2016 and see how they differ from the following predictions for 2017 from finance thought leaders.

1. David Axson, managing director, CFO & Enterprise Value, Accenture Strategy – @DavidAxson, @AccentureTech

2016 was the year digital finance went mainstream. Recent Accenture research shows increasing adoption rates for technologies such as cloud –76%; predictive analytics – 67%; in-memory 65%; and artificial intelligence – 62%. In 2017, CFOs will pivot to ensure that they have the right talent to fully exploit these new schools. Accountants will be less in demand, while data scientists, sociologists, and mathematicians will be joining the ranks of finance professionals.

2. Eliseo Belmonte, managing director, SAP Finance Transformation, Accenture Technology – @EliseoBelmonte, @AccentureTech

Digital technologies elevate finance to a new role in the business, disrupting traditional ways of working, demanding a change in talent and differentiation through insights. By 2022, half of the current finance functions will no longer be relevant, and half of the required capabilities are nonexistent today or are just emerging. 2017 needs to be the year for “raising the new finance talent.”

3. Thack Brown, general manager and global head, SAP’s Line of Business Finance, SAP – @thackbrown, @SAPFinance

On the upside, 2017 will see three major changes becoming mainstream for finance:

  • Financial results will become available to companies on an as-needed basis, not on the basis of month-end close.
  • Dynamic planning processes will replace the antiquated annual budget cycle.
  • Technology will drive significant automation in back-office processes, and the first solid use cases for artificial intelligence will become mainstream.

On the downside, finance organizations are facing the daunting challenges caused by accounting standard changes (IFRS 9, 15, 16); country-by-country reporting; auditor scrutiny of transfer pricing; FCPA; and other regulatory burdens that will come into effect or face heightened enforcement.

4. Gary Cokins, founder, Analytics-Based Performance Management LLC – @GaryCokins

In 2017 the finance function will realize the impact on its profession from machine learning, artificial intelligence (AI), smart process automation (SPA), and cognitive software computing. The accelerating and disruptive “digital revolution” will adversely impact an organization’s competitiveness. Organizations must either “disrupt” or “be disrupted.” Companies often fail to recognize disruptive threats until it is too late. And even if they do, they fail to act boldly and quickly enough. The exponential growth of digital devices connected to the Internet creates both opportunities and challenges for enterprises. This requires a paradigm shift in thinking to embrace “digital transformation” for protection.

5. Mark Dudgeon, global SAP CTO, IBM – @MarkPDudgeon, @IBMSAPAlliance

CFOs are seeing the barriers between industries collapse and feeling increased competition from outside and within their industries. These trends will continue to challenge enterprises globally in 2017. Financial leaders can no longer afford to rely solely on traditional data sources to understand, anticipate, and communicate the state of the business. With significant advancements in technologies such as advanced analytics and cognitive computing, companies can now integrate and analyze information across and outside the enterprise in real time. Technology enablers can also improve agility by optimizing operation processes, which is critical to achieving desired business outcomes, vision, and growth.

6. Nilly Essaides, senior research director & EPM Advisory Practice, The Hackett Group

CFOs are being asked to do (a lot) more with less – for example, digitize finance quickly but without massive disruption. In parallel, CFOs’ scope is growing to include managing risk and its ultimate financial impacts, while taking on more and more – and ever-changing – compliance requirements across the entire business, not just finance. (In many businesses, we actually see IT returning to the CFO’s scope!)  Seems like the traditional Catch-22. I predict, though, that forward-thinking CFOs will see the opportunity in these mediocre economic times to consolidate gains, evolve their businesses into the digital economy, and streamline those consolidated operations by simplifying, analyzing, and automating.

7. Miles Ewing, principal, Deloitte Consulting LLP – @mw_ewing, @DeloitteSAP

Finance stands at a crossroads where new technology offers the chance to dramatically improve automation and efficiency, allowing finance to step forward as the analytic engine for companies. If finance fails to make this jump, other functions could increasingly take the lead role in analytics, leaving finance with a limited role as a business adviser.

8. Neil Krefsky, senior marketing director, Finance Line of Business Solutions, SAP – @Krefsky

In the wake of digital everything, one of the most interesting outputs in finance is rise of the role “chief finance transformation officer.” The growing use of automation, digital disruption, and increased business partnering is driving the progression of this new executive role. CFOs and finance organizations will increasingly acknowledge the need for this leadership position requiring a transformational skill set that converges equal know-how in finance and IT. These new leaders will drive tremendous efficiencies in finance’s traditional tasks, but also be pioneers of cutting-edge technology that amplifies business foresight across the entire enterprise.

9. Chris Horak, global VP, Solution Marketing, SAP – @choirshark

The digital economy is as different from “classic” business as an Uber-summoned Tesla is from a horse-drawn buggy. 2017 will be the year when finance professionals realize that the digital revolution is not just about making fully existing systems and processes better and faster. While that is necessary in many cases, it is not sufficient to capitalize on the opportunity of digital economy. As the saying goes: “The electric lightbulb was not invented by trying to optimize the candle.” Finance teams that can draw on the power of a single source of truth for real-time reporting, governance, compliance, and predictive insight will be at the forefront of the digital frontier. They will discover that their unique value to the business is not about looking backwards, but charging forwards with innovation and new business models.

10. Rodger Howell, partner, PwC’s Strategy& – @rodger_howell, @pwc_LLP

Finance is rethinking the approach to automate reporting. Many companies are skipping best-in-class software and using robotics process automation to automate their existing processes and approach, resulting in increased speed to process information, improved quality, reduced costs, and minimized human errors. It’s on fire! Once they have the processes automated, then they are looking at moving them to best-in-class software to drive additional benefit.

11. Tony Klimas, Principal, Global PI Finance Leader, EY Advisory Services – @tonyklimas, @EY_SAP

“The focus on automation, robotics, and advanced analytics will continue to drive digital disruption in the finance and accounting space. These technologies are accelerating the pace of change and will eventually become a “new normal.” At the same time, even newer technologies like blockchain and artificial intelligence are starting to appear and will have significant influence on the way businesses serve their customers and stakeholders. It’s an exciting time and one in which traditional business operating models that have been with us for the last century are going to look quite different in the near future.”

12. Kevin McCollom, global VP, GRC Go-to-Market, SAP – @SAPTradeGeek

Digitalization of the finance function will speed up the synchronization of streams of financial and non-financial data. Being able to view data across functional divides will drive finance’s ability to run advanced analytics and develop a big-picture view of how changes in the business affect financial results and vice versa. Finance will need to acquire technology architectures designed to integrate multiple platforms and data types into a single repository. At the most advanced level, new integrated planning systems should be able to connect data from factory floor machines all the way to the income statement. By crossing departmental barriers, new cloud planning solutions are enabling advanced analysis. For example, finance can ask how a change in product design will cascade through the cost structure and affect margins. These new tools often come with self-service capabilities, allowing business partners to test the financial impact of multiple scenarios.

13. Richard McLean, regional CFO, Asia-Pacific and Japan, SAP

Open digital technologies will continue to support finance transformation. Transformation is accelerating in terms of companies and people needing investment decisions, as well as the development and implementation of new business models. This will require increased automation and simplification to drive process efficiencies, increased analytics to provide high-speed business insight to drive better business decision-making, and, finally, better collaboration so business connects in a much more seamless way.

14. Martin Naraschewski, VP, LOB Finance Solutions, SAP

The awareness for digital finance transformation is growing in the broader finance community, [and] 2017 will be all about making this transformation real, driven by three major adoption trends. First, finance will move to real-time analytics based on a renewal of back-end system architecture via in-memory platforms. Adoption paths will be individualized and vary from system upgrades to partial landscape harmonization to greenfield re-implementations. Finally, customers will seek rapid cost reductions via aggressive process automation. This will be through a mixture of dedicated automation solutions that leverage novel digital architectures that support robotic process automation tools that provide fast ROI at the expense of increased system complexity. In addition, 2017 will be the year for machine learning and predictive methods to make their way into finance.

15. Phuong Nguyen, SAP S/4HANA finance initiative lead, Capgemini Center of Excellence, Capgemini –@phuongnguyen_1, @CapgemniConsul

2017 is the year for CFOs to take advantage of real-time insights and faster close thanks to digital finance with the capability to process information with high speed and huge [volumes] of data. The combination of both factors (high speed and Big Data) will help finance leaders operate, where accountants will be more and more supported by ERP systems that become more intelligent day after day. This change is the start of a new era where the relationship between man and machine is going to be drastically amended.

16. Colin Sampson, SVP and Ambassador for the Asia-Pacific and Japan Region, SAP

CFOs will continue to be more involved in IT decisions as they understand that technology will help them deliver on the business strategy and support innovation. It is critical that finance departments reimagine their processes to drive transformation and realize the benefits of a digital world; they cannot continue to do more of the same. Finally, adoption of cloud technologies will continue to be more ubiquitous.

17. Henner Schliebs, global VP, Audience Marketing, SAP S/4HANA and Finance, SAP – @hschliebs

2017 will be the year that finance professionals eventually understand the massive opportunity that modern technology provides. They will stop optimizing and re-engineering existing processes (a bad process quicker is still a bad process) based on old technology limitations, but rather design optimal processes that leverage the limitless possibilities available now. Accounting is becoming a continuous exercise, reporting is in the moment, forward-looking planning is nimble and supports dynamic resource allocation, fraud and risk management are embedded in all processes, and, finally, automation and robotics will find their way into the finance department more than ever. It will be a new world that uses methodologies of the personal life and brings it into the finance profession.

18. David Williams, VP, Global Product Marketing, Analytics (EPM & GRC), SAP – @daveswilliams

As finance continues to increase its focus on providing forward-looking insight to support the business, more investment will be made to automate what can be automated for greater efficiency while at the same time putting the technology in place to simulate actions and better predict future outcomes. Machine learning and artificial intelligence will be further embedded into the fabric of financial management systems, allowing finance teams to quickly sift through the increasing volume of data that the digital economy is creating, get the insights they need, and ask the questions they want in the moment without having to be a data scientist.

Do you agree? Let me know your predictions @jucubiss.

CFO Research surveyed 1,500+ finance professionals to find out how their careers are changing in the digital economy. Read the research Thriving in the Digital Economy: The Innovative Finance Function to discover nine trends shaping the future of financial management.

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NetSuite Customers, Solution Providers, ISV Partners, and Industry Thought Leaders to Converge at NetSuite’s Sydney SuiteConnect

September 9, 2016   NetSuite
websitelogo NetSuite Customers, Solution Providers, ISV Partners, and Industry Thought Leaders to Converge at NetSuite’s Sydney SuiteConnect

Posted by Lee Thompson, Senior Vice President and General Manager, Asia Pacific and Japan, NetSuite

Last year, the Australian government updated its Cloud Computing Policy, requiring non-corporate entities to adopt a cloud-first approach. That requirement part was significant. The last version of the policy, released in 2013, merely encouraged the adoption of cloud, providing guidelines to streamline procurement and migration, so the latest update demonstrated the momentum and importance of the cloud in Australia.

Indeed, the market for cloud computing in Australia continues to mature, with industry analyst firm IDC’s CloudView 2016 survey finding that 67 percent of Australian organisations are embracing the cloud for more than one or two small applications.

Some Australian businesses have seen the advantages of running their business on a cloud-based suite. STM Bags has fueled its international growth by adopting NetSuite OneWorld for CRM, demand and supply planning, inventory and order management, document management, warehouse management, invoicing and payroll and financial consolidation. Similarly, Seeing Machines, an innovative Australian company that makes sophisticated facial recognition technology to track fatigued and distracted drivers, basketball sensors that help NBA and college teams improve their shooting and technology for 3D laptops, replaced MYOB with NetSuite and has seen 13 percent growth while expanding its product offerings.

For years, NetSuite has been leading the cloud charge in the region. A pioneer in the cloud with more than 30,000 companies, organizations, and subsidiaries in more than 100 countries, NetSuite has a unique perspective on cloud adoption and a long history of successful deployments in Australia.

That perspective and the experiences of some leading Australian brands will be on display at the Annual SuiteConnect in Sydney on Thursday, 29 September. The event will feature:

  • A keynote address by Jason Maynard, NetSuite’s EVP of Strategy and Corporate Development, outlining how cloud is the last computing architecture.
  • New research results from analyst firm Frost & Sullivan, which surveyed 363 small business executives in Australia, Hong Kong, New Zealand, the Philippines and Singapore and identified prime opportunities for growth.
  • A roadmap of NetSuite’s future plans
  • Focused breakout sessions with industry-specific content on retail, services and software, wholesale distribution and manufacturing as well as small business and enterprise tracks.

SuiteConnect will offer NetSuite customers, prospects and partners a real-world view of the trends, challenges and benefits of cloud computing in Australia and around the globe. To be held at the Hilton Hotel in Sydney on Thursday 29 September, the event offers a unique opportunity to network with and learn from the leading brands who have transformed their businesses in the cloud in Australia and around the globe.

Register here to reserve your space.

Posted on Thu, September 8, 2016 by NetSuite filed under

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Comedian Katt Williams- Thought To Be Freed- Is STILL In Custody!

March 11, 2016   Humor

Comedian Micah “Katt” Williams, who thought he would be able walk out of a Hall County jail cell Wednesday, was still in custody Thursday morning.

Williams, who was arrested for the second time in recent days Tuesday after being accused of threatening bodyguard Corey Dixon, was granted $ 60,000 bond during a Wednesday hearing.

Attorneys for Williams, 44, said he would surrender his passport while out on bond. As a condition of the bond, a Hall judge said Williams could not possess weapons, drugs or alcohol of any kind.

But the bond allowance didn’t stick.

Hall sheriff’s spokeswoman Nicole Bailes said Williams had already violated the condition of his previous bond. Law enforcement officials seized seven weapons and drugs while executing a search warrant Tuesday.

The added charge delayed Williams’ release.

“Williams will have to see a judge before being released,” Bailes said Thursday. “That date is not known at this time.”

The incident that led to the comedian’s most recent arrest happened Feb. 28.

Hall prosecutors accused Williams of not only threatening to kill his bodyguard but also ordering an acquaintance to shave the victim and hit him with a bat. Defense and prosecuting attorneys gave accounts of what they said happened that day during the Wednesday hearing.

Dixon said when he refused to commit crimes under Williams’ orders in Atlanta, he was taken back to the comedian’s home in the Harbor Point subdivision in Gainesville and assaulted.

One acquaintance, Tatiana Smith, 24, allegedly choked Dixon and assaulted him with a baseball bat on Williams’ order.

Dixon was hospitalized for a day with internal and external injuries, a prosecutor said.

Deputies executed a search of Williams’ home Tuesday in the 3500 block of Lake Breeze Lane. During the search, investigators found large quantities of marijuana and several firearms, Bailes said.

Williams was charged with aggravated assault, terroristic threats, false imprisonment and felony possession of marijuana.

Tatiana Smith was arrested on multiple charges that included aggravated assault, misdemeanor marijuana possession and possession of the drug Alprazolam. She was not granted bond.

A second acquaintance, Lena Smith, 40, was arrested on a charge of felony possession of marijuana. She was granted $ 5,000 bond.

During the hearing, Williams’ attorney asked why his client was allowed to walk free from Feb. 28 to his arrest Tuesday if he was such a threat.

Deputies had an answer for the attorney.

“Obviously, Mr. Williams has been in the news quite a bit, and we wanted to preserve the integrity of our investigation,” Bailes told Channel 2 Action News. “When we initially received this, we wanted to follow up with this. A lot of our witnesses live out of state.”

The alleged assault is believed to have happened the day before Williams was arrested on a separate misdemeanor battery charge in Hall. Williams was arrested Feb. 29 after a store employee at Leslie’s Pool Supplies on Dawsonville Highway told police the comedian punched him during an argument. Williams, who told TMZ he did what he had to do after the employee made a racist remark, was released on $ 5,000 bond in that incident.

That same week, Williams and about 15 members of his entourage and security team were accused of physically attacking and stealing cellphones from five women visiting Atlanta for the weekend.

He was also recently accused of fighting agitators in Los Angeles — which he told TMZ was self-defense — and bum-rushing a stage and punching a man at a rap show in Philadelphia.

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“The Occasional Unexpected Weirdness Of Machine Thought Might Also Be A Teaching Moment”

August 8, 2015   Humor

kasparov615 e1438984108616 “The Occasional Unexpected Weirdness Of Machine Thought Might Also Be A Teaching Moment”

When Garry Kasparov was defeated by Deep Blue, a key breaking point was his mistaking a glitch in his computer opponent for a human level of understanding. These strange behaviors can throw us off our game, but perhaps they can also shed light. In “Artificial Intelligence Is Already Weirdly Human,” David Berreby’s Nautilus article, the author believes that neural-network oddities, something akin to AI meeting ET, might be useful. An excerpt:

Neural nets sometimes make mistakes, which people can understand. (Yes, those desks look quite real; it’s hard for me, too, to see they are a reflection.) But some hard problems make neural nets respond in ways that aren’t understandable. Neural nets execute algorithms—a set of instructions for completing a task. Algorithms, of course, are written by human beings. Yet neural nets sometimes come out with answers that are downright weird: not right, but also not wrong in a way that people can grasp. Instead, the answers sound like something an extraterrestrial might come up with.

These oddball results are rare. But they aren’t just random glitches. Researchers have recently devised reliable ways to make neural nets produce such eerily inhuman judgments. That suggests humanity shouldn’t assume our machines think as we do. Neural nets sometimes think differently. And we don’t really know how or why.

That can be a troubling thought, even if you aren’t yet depending on neural nets to run your home and drive you around. After all, the more we rely on artificial intelligence, the more we need it to be predictable, especially in failure. Not knowing how or why a machine did something strange leaves us unable to make sure it doesn’t happen again.

But the occasional unexpected weirdness of machine “thought” might also be a teaching moment for humanity. Until we make contact with extraterrestrial intelligence, neural nets are probably the ablest non-human thinkers we know.•

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