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My Business, My Sleepless Nights

Jennifer Schulze

tired moonlighting employeeCan I afford public or private school for my family? Should we purchase that larger home next year? These are the types of questions small and midsized business owners must ask themselves. The division between personal and professional is often unclear and the business consumes most of their day – and their future. Much is at stake. Given this, what can a business owner do to ease the burden of their worries?

Don’t supersize me – and then charge me for it

In the modern world, a variety of tools are available to help a small business run smoothly. But small business owners often draw boundaries between what they view as tools for “big business” and the tools they believe are relevant to their needs. Even the naming of some of these tools can be confusing: How can software known as enterprise resource planning (ERP), for example, be a good fit for a one-person operation, or even a 10-person operation?

Instead of viewing business software as a single entity, it’s helpful to frame it as a collection of individual tools. An enterprise-level business may be able to afford the whole tool chest at once, but small businesses often don’t need quite so much right away. Instead, they focus on being able to purchase technology in “bite size” pieces, choosing tools as needed, rather than all at once.

While software isn’t a cure-all for a small business, at the end of the day it simply helps life run more smoothly by making it easier to manage finances, employees, and the never-ending regulatory changes, among other concerns. The freedom to mix and match is key, using only the useful tools with no obligation to explore the entire world of enterprise-level software right away.

Compliment my “gut feel” decision making

Software is often the tool that allow a business owner to keep their personal and professional worlds separate, while still ensuring both worlds are well managed. Whether it’s bookkeeping, taxes, marketing, or more, being able to turn to software that holds objective, unbiased information is a blessing in many forms. It provides data-based decision making and encourages less reliance on the inconsistent “gut feeling” that so many business owners have relied on for years.

In particular, it’s vital to be able to turn to data that has no relation to the often-subjective world that characterizes a business owner’s personal life. Whether a business manager or employee is calm or upset, stressed or relaxed, tools will present clear, unbiased information, making it easier to pivot back into the objective business owner mindset and make smart decisions.

Notably, midsized business are often better at making use of technology than small businesses, as research shows that only 18% of small business owners use Big Data analytics to make informed decisions, in comparison to 57% of midsized businesses. This indicates that small business owners may feel overwhelmed at the prospect of using technology tools, when they should embrace the potential for that technology to make their lives easier, instead.

By leveraging the right tools, and using them in the right way, business owners are able to find some solstice in their decision making. Since the risk of success sits squarely on their shoulders, any comfort they can find via greater insight and data, as well as tools build for them (that can grow with them) are key. They allow for planning and data to accompany the gut feel. Together, these are a powerful combination.

“Every time I get another data point, I’ve added another piece to the jigsaw puzzle, and I’m closer to seeing the answer. And then, one day, the overall picture suddenly comes to me.”- Joel Pittman, founder, MTV

The Digital Economy isn′t on the horizon. It′s here. Learn how to use it to your advantage in The Digital Economy: Disruption, Transformation, Opportunity.

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About Jennifer Schulze

Jennifer Schulze is Vice President of marketing for SAP. In her role, she manages customer marketing as part of the office of the COO. She has over 15 years of technology marketing and management experience and is a small business owner in the San Francisco Bay area.

There Is No Question If Digitalization Will Disrupt Healthcare; The Question Is HOW

Susan Rafizadeh

“We do healthcare like [it’s] the Middle Ages.

That’s how the current state of healthcare was described at the first SAP Personalized Medicine Forum, July 6-7, 2016, in Bonn, Germany. To some, this statement may sound extreme. However, when considering SAP’s goals of providing high-quality healthcare to patients on a global level, the sentiment changes from one of perceived extremity to one of truth.

The main challenge with getting healthcare out of the Middle Ages is that doctors usually don’t have a holistic view of a patient’s personal and respective situation. Complex diseases like cancer require an individual approach to every patient, according his or her exact biological and lifestyle traits. According to representatives from political groups, healthcare organizations, health insurance, biotech, and pharma companies at the Personalized Medicine Forum, digitalization has the potential to leap this hurdle.  Now, the big question lies in how the medical world will get there.

Putting the human into the middle

To understand the dynamics of health and illnesses, physicians and researchers need access to more data. How do genomes, proteomes, metabolomes, different medications, behavior, and the environment influence health or a disease? How does a patient’s state evolve over the long term after he or she leaves the hospital? Are there any similar cases in the world we could learn from? With wearables and electronic health records, a big part of the desperately needed data could be generated already – it’s just a matter of sharing and connecting the data in an intelligent way that reveals medical insights.

A first move into this direction is CancerLinQ, a nonprofit subsidiary of the American Society of Clinical Oncology (ASCO). CancerLinQ connects and analyzes real-world cancer data from electronic record sources and combines the expertise of oncologists with Big Data analytics technology in one platform.

Fearless collaboration

This means that the technology to generate, aggregate, and analyze Big Data on health is readily available. It is simply a matter of achieving what Uppsala University’s Magnus Peterson called “fearless collaboration.” There is not only the technical challenge of different data sources and formats, but also the issue of silos within the minds of researchers and institutions. When asked about was the preferred solution to the problem of unwillingness to share data, attendees pointed to the “carrot-and-stick” approach. Using this method, data would be willingly shared among researchers and other medical professionals if it was gained through public monetary support.

Of course there is also the other side, the patient. Most patients who suffer from fatal diseases would likely be willing to share their health data for R&D purposes – for the benefit of themselves and for future generations. But there may be other cases when patients are more protective of their sensitive personal health data. And here the solution again lies within the key message of the forum: consistently put the patient at the center, and go from there.

Putting the data and the power into patients’ hands

While some issues were divisive among the audience, one aspect all members could agree on was that data security and privacy should be a priority when considering collaboration on health data. In that sense, the patient should be the sole owner of all of their personal data and ultimately the one who determines what it be used towards.

It is pretty understandable if a patient does not want to measure and share health data for free. It is the responsibility of care providers, insurance companies, and the life-science industry to provide a real, tangible value for patients as a reward. The patient needs to understand what the data means and which conclusions can be drawn for the patient’s health and for a broader patient group. With that, patients can make educated decisions on what data to share with whom and when, and acceptance to do so will grow.

This would help the ultimate goal to move the conversation towards improved health rather than focusing on curing sickness only – which is the current status quo of modern medicine. One example that proves that prevention is better (and more cost-efficient) than the cure is Gesundes Kinzigtal, which translates as “healthy Kinzig Valley.” With its focus on health prevention, the German healthcare management company receives an anticipated payment by insurers and gets a premium if total heathcare costs are lower than traditional approaches – which, according to its website, seems to work out pretty well!

That being said, thinking from the patient perspective, giving them the power to collect and share health data in real time, and collaborating across institutions on medical research will lead to a big step forward towards value-oriented healthcare. Through this approach, the unexpected may be uncovered and it may contribute to developing new ways to focus on health – ideally before illness beats us.

For insight on how the Internet of Things is affecting healthcare in comparison to other industries, see the whitepaper The Internet of Things and Digital Transformation: A Tale of Four Industries.

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Susan Rafizadeh

About Susan Rafizadeh

Susan Rafizadeh is the director of Global Marketing for Life Sciences Industries at SAP. In her role, she writes publications on innovation topics in the life sciences industry covering pharmaceuticals, medical devices, and biotech as well as looking after life sciences marketing across the globe. Before joining SAP, she led the Customer Relations team at TechniData, a company offering IT solutions for sustainability; here she was responsible for building and developing relationships with future and existing customers. She has more than 15 years’ experience in marketing and communications in various roles, including journalism, being an analyst in an economics institute and content creation and production for specialized conferences, mainly focusing on highly regulated industries.

No Digital For A Day. That’s How Some Businesses Still Work

Anja Reschke

Imagine going an entire week – or even a day – without using any digital devices. No smartphone, no tablet, no laptop, no access to apps that you have come to rely on daily. Could you do it? For many people, I doubt it.

People rely so heavily on their digital devices these days that many people wouldn’t know how to function without them. And yet many people still expect their business to continue functioning without digitalization!

“I don’t do digital”

Why is it so difficult to get people on board for digital transformation in the enterprise when they already rely so much on their digital devices in other areas of their life? Well, people often fear change, and the pace of change in the digital economy is accelerating so quickly that it’s hard for many people – and most companies – to keep up.

The whole idea of digital disruption can create supporters and detractors throughout your value chain. But remember that every company – with all of its complexity, silos, doubters, and other obstacles to innovation – is competing against a growing number of agile startups with active supporters taking direct aim at your industry, so you should take action sooner rather than later. As many have said before: disrupt yourself or be disrupted.

Ride the digital wave

The digital economy is gaining incredible momentum, and the digital tsunami is expected to continue flooding most industries, according to the SAP eBook The Digital Economy: Reinventing the Business World. In the next five years, there are forecast to be more than 6 billion smartphones in use and 50 billion smart things connected to the Internet. The numbers are staggering. Based on this information, it’s not surprising that more than 80% of executives expect to incorporate more digital or virtual interaction with customers, according to the latest IBM Global C-suite Study. That compares to only 8% with plans to incorporate more face-to-face interaction.

So a growing number of business leaders are finding it difficult to ignore these numbers and realize they must transform their company to be successful in the digital economy. If you’re not one of them, how do you expect your business to compete against the rest?

Take action to develop a corporate digital strategy

The unparalleled speed of technological change makes it challenging for many business leaders to grasp all of the implications and full potential of the digital economy, but having a digital strategy is essential. Now is the time to take action, because according to SAP’s Digital Economy eBook, the following fundamental technologies have matured and hit scale together.

  1. Hyperconnectivity: IoT technology using sensors, the Internet, and mobile devices is changing the way companies develop, partner, sell, and service their offerings. Hyperconnectivity is also shaping the way people buy and consume products and services.
  1. Supercomputing: Enterprise systems are shifting from a single cost-performance approach to two distinct paths: in-memory computing and distributed computing.
  1. Cloud computing: The cloud is disrupting business models in many sectors, as it levels the playing field for access to required software and infrastructure and makes information more accessible from anywhere.
  1. Smarter world: Sensors, predictive models, augmented reality, cognitive computing, and 3D printing are creating new outcome-based results.

With all of these technological elements at play, it is easy to see why technology is the biggest driver topping the list for CEOs in the IBM study when it comes to external factors influencing the enterprise. Shouldn’t it be topping your list too?

Convince people to support your digital vision (trick them if you have to!)

Even with all of the benefits of going digital, it can still be challenging to gather support for a corporate digital strategy. If you have trouble convincing the digital doubters and naysayers in your business to embrace your digital vision, just ask them to go without their digital devices for a day, and see if they can actually do it. You might hear a few grumbles, but this little exercise could be enough to convince them that your company can’t function for long without digital technology either.

For an in-depth look at how the digital era is affecting business, download the SAP eBook “The Digital Economy: Reinventing the Business World.”

To learn more about the driving forces behind digital transformation download the SAP eBook “Digital Disruption: How Digital Technology is Transforming Our World.”

For a closer look at one industry in the midst of digital transformation, download the SAP eBook Connected Care: The Digital Pulse of Global Healthcare.

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About Anja Reschke

Anja Reschke is the Senior Director of Strategic Ecosystem Marketing at SAP. She is responsible for the development of joint strategic marketing plans, programs, and activities, with global strategic services and technology partners.

Unlock Your Digital Super Powers: How Digitization Helps Companies Be Live Businesses

Erik Marcade and Fawn Fitter

The Port of Hamburg handles 9 million cargo containers a year, making it one of the world’s busiest container ports. According to the Hamburg Port Authority (HPA), that volume doubled in the last decade, and it’s expected to at least double again in the next decade—but there’s no room to build new roads in the center of Hamburg, one of Germany’s historic cities. The port needed a way to move more freight more efficiently with the physical infrastructure it already has.

sap_Q216_digital_double_feature1_images1The answer, according to an article on ZDNet, was to digitize the processes of managing traffic into, within, and back out of the port. By deploying a combination of sensors, telematics systems, smart algorithms, and cloud data processing, the Port of Hamburg now collects and analyzes a vast amount of data about ship arrivals and delays, parking availability, ground traffic, active roadwork, and more. It generates a continuously updated model of current port conditions, then pushes the results through mobile apps to truck drivers, letting them know exactly when ships are ready to drop off or receive containers and optimizing their routes. According to the HPA, they are now on track to handle 25 million cargo containers a year by 2025 without further congestion or construction, helping shipping companies bring more goods and raw materials in less time to businesses and consumers all across Europe.

In the past, the port could only have solved its problem with backhoes and building permits—which, given the physical constraints, means the problem would have been unsolvable. Today, though, software and sensors are allowing it to improve processes and operations to a previously impossible extent. Big Data analysis, data mining, machine learning, artificial intelligence (AI), and other technologies have finally become sophisticated enough to identify patterns not just in terabytes but in petabytes of data, make decisions accordingly, and learn from the results, all in seconds. These technologies make it possible to digitize all kinds of business processes, helping organizations become more responsive to changing market conditions and more able to customize interactions to individual customer needs. Digitization also streamlines and automates these processes, freeing employees to focus on tasks that require a human touch, like developing innovative strategies or navigating office politics.

In short, digitizing business processes is key to ensuring that the business can deliver relevant, personalized responses to the market in real time. And that, in turn, is the foundation of the Live Business—a business able to coordinate multiple functions in order to respond to and even anticipate customer demand at any moment.

Some industries and organizations are on the verge of discovering how business process digitization can help them go live. Others have already started putting it into action: fine-tuning operations to an unprecedented level across departments and at every point in the supply chain, cutting costs while turbocharging productivity, and spotting trends and making decisions at speeds that can only be called superhuman.

Balancing Insight and Action

sap_Q216_digital_double_feature1_images2Two kinds of algorithms drive process digitization, says Chandran Saravana, senior director of advanced analytics at SAP. Edge algorithms operate at the point where customers or other end users interact directly with a sensor, application, or Internet-enabled device. These algorithms, such as speech or image recognition, focus on simplicity and accuracy. They make decisions based primarily on their ability to interpret input with precision and then deliver a result in real time.

Edge algorithms work in tandem with, and sometimes mature into, server-level algorithms, which report on both the results of data analysis and the analytical process itself. For example, the complex systems that generate credit scores assess how creditworthy an individual is, but they also explain to both the lender and the credit applicant why a score is low or high, what factors went into calculating it, and what an applicant can do to raise the score in the future. These server-based algorithms gather data from edge algorithms, learn from their own results, and become more accurate through continuous feedback. The business can then track the results over time to understand how well the digitized process is performing and how to improve it.

sap_Q216_digital_double_feature1_images5From Data Scarcity to a Glut

To operate in real time, businesses need an accurate data model that compares what’s already known about a situation to what’s happened in similar situations in the past to reach a lightning-fast conclusion about what’s most likely to happen next. The greatest barrier to this level of responsiveness used to be a lack of data, but the exponential growth of data volumes in the last decade has flipped this problem on its head. Today, the big challenge for companies is having too much data and not enough time or power to process it, says Saravana.

Even the smartest human is incapable of gathering all the data about a given situation, never mind considering all the possible outcomes. Nor can a human mind reach conclusions at the speed necessary to drive Live Business. On the other hand, carefully crafted algorithms can process terabytes or even petabytes of data, analyze patterns and detect outliers, arrive at a decision in seconds or less—and even learn from their mistakes (see How to Train Your Algorithm).

How to Train Your Algorithm 

The data that feeds process digitization can’t just simmer.
It needs constant stirring.

Successfully digitizing a business process requires you to build a model of the business process based on existing data. For example, a bank creates a customer record that includes not just the customer’s name, address, and date of birth but also the amount and date of the first deposit, the type of account, and so forth. Over time, as the customer develops a history with the bank and the bank introduces new products and services, customer records expand to include more data. Predictive analytics can then extrapolate from these records to reach conclusions about new customers, such as calculating the likelihood that someone who just opened a money market account with a large balance will apply for a mortgage in the next year.

Germany --- Germany, Lower Bavaria, Man training English Springer Spaniel in grass field --- Image by © Roman M‰rzinger/Westend61/CorbisTo keep data models accurate, you have to have enough data to ensure that your models are complete—that is, that they account for every possible predictable outcome. The model also has to push outlying data and exceptions, which create unpredictable outcomes, to human beings who can address their special circumstances. For example, an algorithm may be able to determine that a delivery will fail to show up as scheduled and can point to the most likely reasons why, but it can only do that based on the data it can access. It may take a human to start the process of locating the misdirected shipment, expediting a replacement, and establishing what went wrong by using business knowledge not yet included in the data model.

Indeed, data models need to be monitored for relevance. Whenever the results of a predictive model start to drift significantly from expectations, it’s time to examine the model to determine whether you need to dump old data that no longer reflects your customer base, add a new product or subtract a defunct one, or include a new variable, such as marital status or length of customer relationship that further refines your results.

It’s also important to remember that data doesn’t need to be perfect—and, in fact, probably shouldn’t be, no matter what you might have heard about the difficulty of starting predictive analytics with lower-quality data. To train an optical character recognition system to recognize and read handwriting in real time, for example, your samples of block printing and cursive writing data stores also have to include a few sloppy scrawls so the system can learn to decode them.

On the other hand, in a fast-changing marketplace, all the products and services in your database need consistent and unchanging references, even though outside the database, names, SKUs, and other identifiers for a single item may vary from one month or one order to the next. Without consistency, your business process model won’t be accurate, nor will the results.

Finally, when you’re using algorithms to generate recommendations to drive your business process, the process needs to include opportunities to test new messages and products against existing successful ones as well as against random offerings, Saravana says. Otherwise, instead of responding to your customers’ needs, your automated system will actually control their choices by presenting them with only a limited group of options drawn from those that have already received the most
positive results.

Any process is only as good as it’s been designed to be. Digitizing business processes doesn’t eliminate the possibility of mistakes and problems; but it does ensure that the mistakes and problems that arise are easy to spot and fix.

From Waste to Gold

Organizations moving to digitize and streamline core processes are even discovering new business opportunities and building new digitized models around them. That’s what happened at Hopper, an airfare prediction app firm in Cambridge, Massachusetts, which discovered in 2013 that it could mine its archives of billions of itineraries to spot historical trends in airfare pricing—data that was previously considered “waste product,” according to Hopper’s chief data scientist, Patrick Surry.

Hopper developed AI algorithms to correlate those past trends with current fares and to predict whether and when the price of any given flight was likely to rise or fall. The results were so accurate that Hopper jettisoned its previous business model. “We check up to 3 billion itineraries live, in real time, each day, then compare them to the last three to four years of historical airfare data,” Surry says. “When consumers ask our smartphone app whether they should buy now or wait, we can tell them, ‘yes, that’s a good deal, buy it now,’ or ‘no, we think that fare is too expensive, we predict it will drop, and we’ll alert you when it does.’ And we can give them that answer in less than one second.”

When consumers ask our smartphone app whether they should buy now or wait, we can tell them, ‘yes, that’s a good deal, buy it now’.

— Patrick Surry, chief data scientist, Hopper

While trying to predict airfare trends is nothing new, Hopper has told TechCrunch that it can not only save users up to 40% on airfares but it can also find them the lowest possible price 95% of the time. Surry says that’s all due to Hopper’s algorithms and data models.

The Hopper app launched on iOS in January 2015 and on Android eight months later. The company also switched in September 2015 from directing customers to external travel agencies to taking bookings directly through the app for a small fee. The Hopper app has already been downloaded to more than 2 million phones worldwide.

Surry predicts that we’ll soon see sophisticated chatbots that can start with vague requests from customers like “I want to go somewhere warm in February for less than $500,” proceed to ask questions that help users narrow their options, and finally book a trip that meets all their desired parameters. Eventually, he says, these chatbots will be able to handle millions of interactions simultaneously, allowing a wide variety of companies to reassign human call center agents to the handling of high-value transactions and exceptions to the rules built into the digitized booking process.

Port of Hamburg Lets the Machines Untangle Complexity

In early 2015, AI experts told Wired magazine that at least another 10 years would pass before a computer could best the top human players at Go, an ancient game that’s exponentially harder than chess. Yet before the end of that same year, Wired also reported that machine learning techniques drove Google’s AlphaGo AI to win four games out of five against one of the world’s top Go players. This feat proves just how good algorithms have become at managing extremely complex situations with multiple interdependent choices, Saravana points out.

The Port of Hamburg, which has digitized traffic management for an estimated 40,000 trucks a day, is a good example. In the past, truck drivers had to show up at the port to check traffic and parking message boards. If they arrived before their ships docked, they had to drive around or park in the neighboring residential area, contributing to congestion and air pollution while they waited to load or unload. Today, the HPA’s smartPORT mobile app tracks individual trucks using telematics. It customizes the information that drivers receive based on location and optimizes truck routes and parking in real time so drivers can make more stops a day with less wasted time and fuel.

The platform that drives the smartPORT app also uses sensor data in other ways: it tracks wind speed and direction and transmits the data to ship pilots so they can navigate in and out of the port more safely. It monitors emissions and their impact on air quality in various locations in order to adjust operations in real time for better control over environmental impact. It automatically activates streetlights for vehicle and pedestrian traffic, then switches them off again to save energy when the road is empty. This ability to coordinate and optimize multiple business functions on the fly makes the Port of Hamburg a textbook example of a Live Business.

Digitization Is Not Bounded by Industry

Other retail and B2B businesses of all types will inevitably join the Port of Hamburg in further digitizing processes, both in predictable ways and in those we can only begin to imagine.

sap_Q216_digital_double_feature1_images4Customer service, for example, is likely to be in the vanguard. Automated systems already feed information about customers to online and phone-based service representatives in real time, generate cross-selling and upselling opportunities based on past transactions, and answer customers’ frequently asked questions. Saravana foresees these systems becoming even more sophisticated, powered by AI algorithms that are virtually indistinguishable from human customer service agents in their ability to handle complex live interactions in real time.

In manufacturing and IT, Sven Bauszus, global vice president and general manager for predictive analytics at SAP, forecasts that sensors and predictive analysis will further automate the process of scheduling and performing maintenance, such as monitoring equipment for signs of failure in real time, predicting when parts or entire machines will need replacement, and even ordering replacements preemptively. Similarly, combining AI, sensors, data mining, and other technologies will enable factories to optimize workforce assignments in real time based on past trends, current orders, and changing market conditions.

Public health will be able to go live with technology that spots outbreaks of infectious disease, determines where medical professionals and support personnel are needed most and how many to send, and helps ensure that they arrive quickly with the right medication and equipment to treat patients and eradicate the root cause. It will also make it easier to track communicable illnesses, find people who are symptomatic, and recommend approaches to controlling the spread of the illness, Bauszus says.

He also predicts that the insurance industry, which has already begun to digitize its claims-handling processes, will refine its ability to sort through more claims in less time with greater accuracy and higher customer satisfaction. Algorithms will be better and faster at flagging claims that have a high probability of being fraudulent and then pushing them to claims inspectors for investigation. Simultaneously, the same technology will be able to identify and resolve valid claims in real time, possibly even cutting a check or depositing money directly into the insured person’s bank account within minutes.

Financial services firms will be able to apply machine learning, data mining, and AI to accelerate the process of rating borrowers’ credit and detecting fraud. Instead of filling out a detailed application, consumers might be able to get on-the-spot approval for a credit card or loan after inputting only enough information to be identified. Similarly, banks will be able to alert customers to suspicious transactions by text message or phone call—not within a day or an hour, as is common now, but in a minute or less.

Pitfalls and Possibilities

As intelligent as business processes can be programmed to be, there will always be a point beyond which they have to be supervised. Indeed, Saravana forecasts increasing regulation around when business processes can and can’t be digitized. Especially in areas involving data security, physical security, and health and safety, it’s one thing to allow machines to parse data and arrive at decisions to drive a critical business process, but it’s another thing entirely to allow them to act on those decisions without human oversight.

Automated, impersonal decision making is fine for supply chain automation, demand forecasting, inventory management, and other processes that need faster-than-human response times. In human-facing interactions, though, Saravana insists that it’s still best to digitize the part of the process that generates decisions, but leave it to a human to finalize the decision and decide how to put it into action.

“Any time the interaction is machine-to-machine, you don’t need a human to slow the process down,” he says. “But when the interaction involves a person, it’s much more tricky, because people have preferences, tastes, the ability to try something different, the ability to get fatigued—people are only statistically predictable.”

For example, technology has made it entirely possible to build a corporate security system that can gather information from cameras, sensors, voice recognition technology, and other IP-enabled devices. The system can then feed that information in a steady stream to an algorithm designed to identify potentially suspicious activity and act in real time to prevent or stop it while alerting the authorities. But what happens when an executive stays in the office unusually late to work on a presentation and the security system misidentifies her as an unauthorized intruder? What if the algorithm decides to lock the emergency exits, shut down the executive’s network access, or disable her with a Taser instead of simply sending an alert to the head of security asking what to do while waiting for the police to come?

sap_Q216_digital_double_feature1_images6The Risk Is Doing Nothing

The greater, if less dramatic, risk associated with digitizing business processes is simply failing to pursue it. It’s true that taking advantage of new digital technologies can be costly in the short term. There’s no question that companies have to invest in hardware, software, and qualified staff in order to prepare enormous data volumes for storage and analysis. They also have to implement new data sources such as sensors or Internet-connected devices, develop data models, and create and test algorithms to drive business processes that are currently analog. But as with any new technology, Saravana advises, it’s better to start small with a key use case, rack up a quick win with high ROI, and expand gradually than to drag your heels out of a failure to grasp the long-term potential.

The economy is digitizing rapidly, but not evenly. According to the McKinsey Global Institute’s December 2015 Digital America report, “The race to keep up with technology and put it to the most effective business use is producing digital ‘haves’ and ‘have-mores’—and the large, persistent gap between them is becoming a decisive factor in competition across the economy.” Companies that want to be among the have-mores need to commit to Live Business today. Failing to explore it now will put them on the wrong side of the gap and, in the long run, rack up a high price tag in unrealized efficiencies and missed opportunities. D!

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Erik Marcade

About Erik Marcade

Erik Marcade is vice president of Advanced Analytics Products at SAP.

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5 Things Pokémon Go Taught Me About The Future Of Marketing

Madelyn Bayer

In case you haven’t been outside lately, there is a game taking over the millennial world right now – it’s called Pokémon Go.

Pokémon Go is a mobile app that you can download for iOS or Android. It’s free to download and play, but you have the option to use real money to buy in-game currency called PokéCoins. PokéCoins are used to purchase Pokéballs, the in-game item you need to catch Pokémon. The game uses your phone’s GPS to obtain your real-world location and augmented reality to bring up Pokémon characters on your screen, placing them on top of what you see in front of you. You—the digital you—can be customised with clothing, a faction (a “team” of players you can join), and other options, and you level up as you play.

On the surface, it’s a fun mobile game whose popularity is as intriguing as it is entertaining, but the superficial fun of the app has led to some real results: Developer Nintendo’s valuation has increased by an estimated $7.5 billion thanks to the game.

With results like that, this app is more than just a game, but a possible whole new realm of digital marketing. I started to research some of the key learnings from Pokémon go from a marketing perspective.

  1. Keep it small and simple. Gone are the days of needing to invest in large ad campaigns and advertising budgets. How many ads did we see leading up to the Pokémon Go launch? Very few. Pokémon Go didn’t invest much into advertising because it didn’t need it – either the ad executives in charge knew that the success of the app would be dependent on the marketing and viral factors listed here, or they didn’t expect the app to be a breakout hit. Regardless, the bottom line is that you don’t need a massive advertising budget to be a great marketer; you just need to be able to connect with people. Simplicity is key: Well-designed websites, e-commerce platforms, apps, and products should welcome new users and make it extremely easy for all to get involved (a lesson learned from breakout social media apps like Instagram and Snapchat).
  1. Have an agile digital platform. If you don’t have an agile digital marketing platform, you will miss the boat. This lesson has been proven time and time again in today’s digital world. The marketing game changes faster than most brands can keep up with – but being able to react quickly to trends like this is essential. Failing fast, minimum viable product, and agile: These are fast becoming key phrases in marketing teams’ vocabulary. Whether you are launching a social campaign, a consumer app, or a large-scale marketing operation, you must be able to stand it up quickly, test it, iterate on it, and send it out quickly.
  1. Loyalty is everything. If you want to increase customer loyalty, you must reward your users for continuing to invest in your product. Pokémon Go players get bonuses and incentives for levelling up, taking on gyms, catching new Pokémon, and even walking. The thrill of finding a rare Pokémon or winning an intense battle is enough to keep users yearning for more, even through the less-active parts of the game. There are definite rewards for continued investment, and that’s what keeps users playing—sometimes at the expense of productivity. When I think of the apps I know and love, this feature is nothing new, but it is very important. Gamification and loyalty are what keep me checking in on the highly addictive Air New Zealand app, for example, tuning in each Tuesday to watch the reverse auctions grab flight seats. Creating an individualised offering to every consumer is a hot trend for retailers right now, and it may also be part of the lessons learned from Pokémon Go.
  1. Appeal to the new generation of augmented reality and virtual-reality natives. Just as Gen Y are considered digital natives because they grew up with Internet access, the emerging gen Z will be known as AR and VR natives – what feels new to us now will be the new normal for kids growing up today. That’s not to say every brand should jump on the AR or VR bandwagon. But learn from what this game has taught us: Why is this game taking over the world? What insights can be adapted to generate positive brand engagement? We have evolved past the age of disruptive placement and are now in an era of behavioral targeting. One of the biggest challenges retailers face is knowing where their customers are at any given point in time. How do they reward their customers at the point of sale? Could the next wave of retail disruption be the gamification of shopping in a virtual reality?
  1. Privacy vs. Personalisation. That old chestnut. According to the SAP New Zealand Digital Experience Report 2016, New Zealanders rated having relevant offers without infringing on privacy amongst the highest consumer experience attributes when considering importance to digital experience satisfaction. This is interesting considering the backlash concerning the data Niantic is actively collecting on Pokémon Go users. It seems this hasn’t deterred users too much; the explanation for this may lie further in the New Zealand Digital Experience report research.

Arguably, Pokémon Go ticks all the boxes when we look at the consumer-rated digital experience attributes listed below – though there may be one exception if we consider recent user safety horror stories that are starting to come out.

So what has all this taught us? It links back to the report: The better the digital experience – defined by the above attributes – the happier consumers are to give up their data. The graphs below show consumers’ willingness to give up certain personal information, depending on whether or not they have a satisfactory digital experience. As we all know, data, or information, is the currency of the future, and lessons like these raise important takeaways for all digital marketers looking to gain real consumer insights and preferences.

If you haven’t already given Pokémon Go a go, see what all the fuss is about. Whether the game is a passing fad or the newest trend of digital marketing is yet to be determined, but it offers some interesting thoughts to consider before you launch your next campaign to consumers.

For more insight on where marketing is headed, see MarTech: The Future Of Digital Marketing.

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Madelyn Bayer

About Madelyn Bayer

In my role as an Industry Value Associate at SAP Australia and New Zealand, I help organisations calculate and realise the value that new systems and technology will have on their operations. My role covers industries spanning utilities, public sector, consumer products and retail with a specific focus around customer engagement and commerce solutions and through this role I have developed a strong understanding of mega trends, cloud computing, enterprise software, the networked economy, Internet of Things, millennials and digital consumers. I am particularly passionate about creating sustainable solutions to solving world problems through technology.