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Real Time Is Key To Furthering Digital Agility

Kevin Benedict

It seems we read daily about the increasing role and importance of information, data analysis, and data security on politics, national security, and global economies.

More data is being generated than ever before, and successful companies are investing in business analytics and Big Data solutions to mine it for competitive advantages. There is a new sense of urgency today as businesses realise data has a shelf life, and the value of it diminishes rapidly over time. In an always-connected world where consumers and their needs are transient, timing is everything, and a special type of data is needed: real-time data. In order to capture competitive advantages and contextual relevance before data expires, enterprises must deploy information logistics systems (ILS) that deliver on the potential fast enough to exploit it.

Mobile consumers are impatient and demand instant results. IT infrastructures must be able to support real-time mobile and Internet of Things (IoT) interactions, and this requirement will increase as mobile commerce is predicted to grow to 47% of all e-commerce by 2018. Supporting real-time information requires not only real-time IT environments, but also digital transformation across the entire organisation. In order to succeed, businesses must react to location-based and time-sensitive information while it is still contextually relevant.

Data is the lifeblood of e-commerce, mobile commerce, and increasingly physical stores where the digital and physical worlds are rapidly converging. As commerce rapidly shifts online and to mobile, the success of products, brands, and companies are increasingly dependent on data and systems that consume it in order to support the demand for more personalised digital experiences. How an organisation makes sense of data, protects it, and disseminates it is a complex and challenging issue.

Data strategies

Data strategies and the execution of them will determine the market winners of the future, and the future is now. Businesses are learning from Amazon, Google, Facebook, Netflix, and others that effective data and analytics strategies are the secret to success in digital markets. Information dominance is now the strategic business goal.

In the book Code Halos, authors Malcolm Frank, Paul Roehrig, and Benjamin Pring describe how the revolution today in data and analytic strategies are impacting industry structures in “consistent and violent patterns.” In another recent study, 82% of investors and equity analysts believe industries are being disrupted by innovations in data and analytics. They believe these innovations will alter competitive dynamics.

In addition to investments in IT, achieving real-time operational tempos in a enterprise takes rethinking business models, organisational structures, and business processes. It requires new ways of thinking and employee training. Supporting the tempo of real-time operations is a daunting task many will fail to prioritise, and they will suffer as a consequence.

The winners of a digital tomorrow will invest in four key areas:

  1. the quality and speed of their information logistics systems
  1. supporting real-time operational tempos
  1. business agility
  1. the ability to use real-time contextually relevant data to personalise digital user experiences

Businesses that embrace digital transformation will optimise their organisational structures and business models to support the operational tempos required by a mobile and connected world. Increasingly mobile and connected device users demand real-time responses. To support real-time responses requires an enterprise to move beyond “human time” and into the realm of “digital time.” Humans as biological entities operate at a pace governed by the sun, moon, and the physical requirements to keep our carbon-based bodies alive. These requirements and mental limitations make scaling humans beyond these time-cycles impossible without augmentation. Augmentation takes the form of intelligent process automation, artificial intelligence, and algorithms. These types of augmentation technologies have the advantage of being able to work 24/7, 365 days a year, and don’t (as yet) ask for holidays off.

Once an organisation is capable of supporting real-time tempos, and can support the personalised interactions mobile users demand, the challenge becomes business agility.

Agility is the speed at which a business can recognise, analyse, react, and profit from rapidly changing consumer demands in a hypercompetitive market. Businesses that can accurately understand customer demand and their competition, and then respond faster, will soon dominate those that are slower. The military strategist John Boyd called these competitive advantages, “getting inside of your competitor’s decision and response curves.” This means your actions and responses are occurring at a pace that surpasses your competitions’ ability to comprehend it.

Sub-optimal information logistics systems and the glacial operational tempos of yesteryear will not succeed in today’s or tomorrow’s world, and company valuations have already begun to reflect this. One-third of investors and equity analysts surveyed believe that good data and analytics strategies are rewarding companies with higher valuations. Gartner’s Douglas Laney has even coined the phrase “infonomics” to describe how information, as a new asset class, can be measured to estimate its impact on company valuations.

To succeed in the digital future, CIOs must implement innovative data strategies and information logistics systems capable of winning in a real-time world where contextually relevant, instant, and personalised experiences are required. They must develop company cultures where change is viewed as an opportunity. They must digitally transform their businesses to operate at real-time tempos and move beyond “human-time” limitations to algorithm supported “digital-time.” They must understand that rapidly changing digital consumer behaviours mandate companies operate in a more agile manner capable of rapid responses to new opportunities and competitive threats.

For more insight on aligning your digital and in-store sales and marketing (and why it’s essential to your customers’ satisfaction), see Our Digital Planet: See It, Click It, Touch It, Buy It.

This article originally appeared on the Guardian Media & Tech Network’s Digital Business hub and was syndicated with the author’s permission.

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Kevin Benedict

About Kevin Benedict

Kevin Benedict is senior analyst at Cognizant's Center for the Future of Work, SAP mentor alumnus, speaker, writer, and mobile and digital strategies expert. He is a popular keynote speaker who, in the past three years, has shared his insights into mobile and digital strategies with companies in 17 different countries. Benedict has over 30 years of experience working with enterprise applications, and he is a veteran mobile industry executive. He wrote the forward to SAP Press' best-selling book on enterprise mobility, "Mobilizing Your Enterprise with SAP," and has published over 3,000 articles. Find him on Twitter @krbenedict.

Collaboration Improves Information Security

Andrew Storm

Over time, information security can become one of the more frustrating items on a leader’s docket. You can do all of the right things, hire a team of experts, purchase high-quality systems, partner with top-tier vendors, and allocate plenty of dollars, but in the end, it’s hard to show your investment has positively affected your company.

Sure, your product and systems are secure, but your company’s overall efficiency remains relatively unchanged, or perhaps has declined. Your operational costs have soared, your development life cycle has lengthened and your company’s strategic goals are not being met.

Further, your work environment feels more tense than ever. Departments across the company aren’t quite sure what the security team does, so they pretend it doesn’t exist at all. Engineering, for example, will set up its own virtual private network without first running it by your security experts, and things will get really awkward six months later once they find out.

Your security team also feels frustrated because, like you, they want to hit their goals. It’s no coincidence the average chief information security officer leaves his or her job every 18 months. It’s hard to feel professionally satisfied when you’re constantly underperforming and struggling to prove your worth.

It’s time to stop spinning your wheels and wasting your money. Take a more holistic approach to information security; one where specialized experts and department leaders work in collaboration to create systems and processes that drive efficiency, productivity, and revenue.

The value of collaboration

The traditional corporate mindset needs to shift. Departments can no longer be viewed as separate entities in their own solar systems. This siloed approach simply has no place in a modern business; it’s now crucial for leaders to ensure each member of every team understands the company’s overarching strategy and how their individual contributions factor into the bigger picture.

Not everyone will know the intricacies of information security, but all departments, not just your development, IT, operations and security teams, should have a stake in it. Everyone can contribute unique insights, perspectives and guidance, and when that happens, your security experts’ jobs become so much easier.

Your finance leaders, for example, should play a major role in budgetary governance. It’s one thing to ensure enough money is devoted to security and the team stays within its budget, but assessing whether the funds are spent wisely brings collaboration to a new level. With a bird’s-eye view of the entire company’s expenditures, the finance department can identify where systems, vendors, and processes overlap, thus helping leaders trim fat, save money, boost efficiency and embrace lean security.

Your marketing and sales teams are the front-facing figures that put your company’s life on the line every time they interact with clients. Explaining that your digital products have features and functionality that meet customers’ needs is only half the battle; building trust from a security standpoint and assuring clients their personal information will remain private and secure is essential to the modern sales process. And further, sales professionals know every client can be a lifelong partner who refers business to your brand. One security breach could not only dissuade a lead from converting, but it could also reverberate throughout a network of potential partners.

Last, and perhaps most important, your human resources department should foster a security-minded culture across the whole company. This involves recruiting the right candidates and instilling the right values in them during onboarding. Making sure new employees deeply understand and embrace the value of secure digital systems is key to developing the collaborative culture your company needs.

Instilling a collaborative security culture

Culture change is, by far, one of the most difficult things to implement as a leader. It definitely doesn’t happen overnight, or even over the course of a year. A widespread culture change takes years of commitment.

Here are three strategies to get you started on the right foot:

Highlight the successes of others

Take a look at some of the most successful companies in the world that are on the cutting-edge of delivering digital products. Put together a presentation that shows your team what they do well, highlighting the key role collaboration plays in their processes. Once employees see the best and brightest companies in the world embrace a collaborative security approach, they’ll be much more willing to follow suit.

Netflix is an example I love to use here. Though its core competency is streaming your favorite TV shows and movies, the company also has a tremendous track record of writing and releasing high-quality, secure software collaboratively. Some products, like Simian Army and Chaos Monkey, specifically exist to help companies boost their fault tolerance, minimize the internal impact of system failures, and limit the fallout these issues have on customers. One look at Netflix’s commit logs, and you’ll see that thousands of people hours go into the creation of their software offerings, with several departments playing a central role.

Identify and address your internal roadblocks

information security

Some people at your company will likely see no problem with your current siloed approach to security. Perhaps lack of efficiency and wasted money aren’t directly affecting their day-to-day lives, and they’ll resist any major changes you attempt (and stall the process).

Do these sour grapes need to be managed out of their roles because they’re stubbornly holding the whole company back, or does an entire reorganization of the company’s structure need to happen? Do you really need separate operational, development and security teams “managing” your digital ecosystem from their own silos, or are there obvious opportunities for consolidation? Addressing these roadblocks is key and requires a leader to make some tough decisions.

Embrace cross-training

Perhaps you’ve already tried to break down the silos in your company by removing all of the cubicles, creating an open floor plan, and organizing more company gatherings in an attempt to forge interdepartmental relationships in which people naturally explain their roles to one another. But in the end, all you end up with is an office that’s noisier than ever, yet still isn’t collaborating.

Collaboration flows freely when teams are full of cross-functional, highly skilled individuals who understand one another’s roles. I’m not saying everyone needs to be an expert in everything; specialization is still important. But your developers should certainly know something about information security, and your security people should have a baseline understanding of what it takes to design and develop a product.

Again, Netflix does a tremendous job of ensuring its departments interact regularly and understand one another. Whether it’s organizing meetups between teams or instilling processes that keep all involved teams happy, the company clearly recognizes the value of fostering cross-departmental tolerance.

Information security should include everyone, not just your tech gurus. Often, creating this culture of collaboration requires a fresh set of eyes, so consider partnering with an outside security consultant that can expertly assess your company from top to bottom, find areas ripe for improvement, and help you change your organization’s DNA.

It may take several years, but once information security transforms into a collaborative undertaking, it will no longer be a costly, inefficient, and frustrating part your company. Instead, it will be a key driver of internal productivity, client confidence, and company-wide prosperity.

For more insight on information security, see 5 Effective Cybersecurity Steps Every CFO Should Know.

The post Collaboration Improves Information Security appeared first on Switch & Shift.

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Delivering On Transformational Innovation

R “Ray” Wang

Digital disruption is more than just a technology shift. It’s about transforming business models and changing how people engage with each other. To succeed, we can’t just look at the latest cool set of technologies of the day. Leaders must think more boldly about reinventing business models.

Many of these new business models depend on insights, derived from data analysis and interpretation. These models will create new experiences, broker insights, and deliver new networks to monetize insight. Moreover, a series of network economies will accelerate digital transformation and improve engagement while creating new ways for people to interact.

One of the biggest opportunities for monetizing digital business will come from insight streams derived from Big Data. These insights will come from both the most obvious and the least-likely sources. For example, obvious sources are usually existing transactional systems, data within the data warehouse, and other known structured sources.

Least-likely sources reveal things such as the amount of power consumed, water used, visitors into the building, foot traffic on the sidewalk, and density of the parking lot. These sources may seem mundane and useless to most of us, but large insight brokers will take that data to deliver contextually relevant information and reveal things such as workforce performance , customer satisfaction, and product quality. The goal is to use context signals applied to this information to create market and business differentiation.

New business models are emerging for digital businesses, with three that stand out in adoption and maturity. The first focuses on using data to create differentiated offerings. The second involves brokering this digital information. The third is about building networks to deliver data where it’s needed, when it’s needed.

Here is a look at how each type of business model works:

  1. Differentiation of insight creates new experiences. For the past decade or so, technology and data have brought new levels of personalization and relevance. Google’s AdSense delivers advertising that’s actually related to what users are looking for. Online retailers are able to offer — via FedEx, UPS, and even the U.S. Postal Service — up-to-the-minute tracking of where your packages are. Map services from Google, Microsoft, Yahoo!, and now Apple provide information linked to where you are. Big Data offers opportunities for many more service offerings that will improve customer satisfaction and provide contextual relevance. Imagine package tracking that allows you to change the delivery address as you head from home to office, or map-based services that link information on your fuel supply to data on the availability of gas stations. If you are low on fuel and your car spoke to your maps app, you could not only find the nearest open gas stations within a 10-mile radius, but also learn the price per gallon. I would pay a few dollars a month for a contextual service that delivers the peace of mind of never running out of fuel on the road.
  1. Brokering augments the value of insight. Companies such as Bloomberg, Experian, and Dun & Bradstreet already sell raw information, provide benchmarking services, and deliver analysis and insights with structured data sources. In a Big Data world, though, these propriety systems may struggle to keep up. Opportunities will arise for new forms of information brokering and new types of brokers that handle new unstructured, often open data sources such as social media, chat streams, and video. Organizations will mash up data to create new revenue streams. The permutations of available data will explode, leading to sub-sub- specialized streams that can tell you things like the number of left-handed Toyota drivers who drink four cups of coffee every day but are vegan and seek a car wash during their lunch break. New players will emerge to bring these insights together and repackage them to provide relevancy and context. For example, retailers like Amazon could sell raw information on the hottest categories for purchases. Additional data from business partners on weather patterns and payment volumes could help suppliers pinpoint demand signals more closely. These insight streams could be created and maintained by information brokers who could sort by age, location, interest, and other categories. With endless permutations, brokers’ business models would align by industries, geographies, user roles and other factors.
  1. Delivery networks enable the monetization of insight. To be truly valuable, information must be delivered into the hands of those who can use it, when they can use it. Content creators — the information providers and brokers — will seek placement and distribution in as many ways as possible. This creates ample opportunities for the dealers — the suppliers of the technologies that make all this gathering and exchanging of data possible. It also suggests a role for new marketplaces that facilitate the spot trading of insight and of services that allow for private information brokering.The most intriguing opportunities, though, may be in the creation of delivery networks where information is aggregated, exchanged, and reconstituted into newer, cleaner, and smarter insight streams. Similar to the cable TV model for content delivery, these new delivery networks will be the essential funnel through which information-based offerings will find their markets and be monetized. Few organizations will have the capital to create end-to-end content delivery networks that can go from cloud to devices. Today, Amazon, Apple, Bloomberg, Google, and Microsoft show such potential, as they own the distribution chain from cloud to device as well as some starter content. Telecommunications giants such as AT&T, Verizon, Comcast, and BT have an opportunity to also provide infrastructure; however, we haven’t seen them move significantly beyond voice and data services. Big Data could be their opportunity.Meanwhile, content creators — the information providers and brokers — will likely seek placement and distribution in as many delivery networks as possible. Content relevancy will emerge as a strategic competency in delivering offers in ad networks based on role, relationship, product ownership, location, time, sentiment, and even intent. For example, large wireless carriers can map traffic flows down to the cell tower. Using this data, carriers could work with display advertisers to optimize advertising rates for the most popular routes on football game days based on digital foot traffic.

While insight is one model for delivering content, other sources of content include the mix of products, services, and experiences. In this winner-takes-all digital market, success requires business models that aggregate components of network economies. The three distinct components of the network economy include:

  • Content (value):  Whether it’s a product, service, experience, outcome, or business model, the content is the value. How that content’s value is exchanged is the core tenet of the business model.
  • Network (sourcing and distribution):  How the content is sourced and distributed is the foundation of the network.  The network is only as strong as the content and the participants.
  • Dealers (enablers):  The mission of enablers is to reduce friction between content and network or to improve the experience involving content and network

Most organizations choose one of these components as the primary business model and partner with others to create a network economy. However, over time, organizations realize they need to build business models that include two or even all three of these components.

In fact, successful winners of the digital era have created competitive advantage by taking over all three components.  For example, in the consumer world, four companies have the ability to create this network economy: Apple, Amazon, Google, and Microsoft. These companies have the content, the network, and the dealer capabilities to trade on trust and identity. In the digital world, business networks will provide a foundation where sellers become buyers, buyers become partners, and partners become sellers. Welcome to the business network in a digital world.

Learn more how to drive business innovation.

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R “Ray” Wang

About R “Ray” Wang

R “Ray” Wang is the Principal Analyst, Founder, and Chairman of Silicon Valley based Constellation Research, Inc. He’s also the author of the popular business strategy and technology blog “A Software Insider’s Point of View”. With viewership in the 10’s of millions of page views a year, his blog provides insight into how disruptive technologies and new business models such as digital transformation impact brands, enterprises, and organizations. Wang has held executive roles in product, marketing, strategy, and consulting at companies such as Forrester Research, Oracle, PeopleSoft, Deloitte, Ernst & Young, and Johns Hopkins Hospital. His new best selling book Disrupting Digital Business, published by Harvard Business Review Press and globally available in Spring of 2015, provides insights on why 52% of the Fortune 500 have been merged, acquired, gone bankrupt, or fallen off the list since 2000. In fact, this impact of digital disruption is real. However, it’s not the technologies that drive this change. It’s a shift in how new business models are created.

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.