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Disrupt With Digitization

Sven Denecken

Innovation requires reimagined processes – and the CIO needs to lead this transformation.

Enterprises today must be prepared for the transformation that the digital economy is forcing upon them.

Now, you might think, “Another digital buzzword article.” Well, yes, some dismiss it as a buzzword, but the challenge for many has just started. But let’s not look at only the problems; there are opportunities if seized right – and you can win big.

For example, competing for new business, or even exploring a new revenue stream by creating a new business model, are things you need to look out for constantly – and for sure you can learn from startups, because that is what they do: challenge the status quo. In a fast-moving digital economy, the window to capture these opportunities closes quickly; companies that are unprepared to pounce when occasions arise will likely get stuck on the road to irrelevance. In my job as product manager, my team constantly screens such opportunities, as innovation needs to be weighted fast and implemented via co-innovation even faster if there is a chance of success – and it must also be adapted fast if reality kicks in.

Successful companies need to be willing to change: They must assess whether they are truly in a position to reinvent business processes every day, not just every generation. And here is where the modern CIO comes in. Yes, digital officers arise at every corner of every industry, and they are needed ambassadors or agents of change. But today I think we should be clear: If every company will soon be a “software” company (which I very much believe, as data will rule the world) you need a modern chief information (and innovation) officer to help business and the company board of directors to make this change happen.

Here are 3 key lessons we have learned from the CIOs we constantly speak with during our co-innovation work. (Of course, there are also many lessons we learn from CIOs who are not embracing it – but will they still be CIO next year?)

Lesson #1: Four trends to check if you are on track

As I stated earlier, there are four inescapable trends are creating the pressures that shape today’s digital transformation:

  • The empowered customer: Whether your customers are Generation Z consumers or multi-national conglomerates, they all share one vitally important characteristic: Each demands to be treated as a unique segment of one. You have no choice but to meet that expectation.
  • Competitive and regulatory pressures: Transparency is a necessary part of business today, and that means competitors and regulators alike can dissect any business process. Staying ahead of the former and meeting the standards of the latter requires operational excellence and accountability at every step in the value cycle.
  • Globalization: More businesses today must be prepared to go global in order to remain relevant. Expanding into new markets can no longer be done effectively with costly, infrastructure-heavy international build-outs. Enterprises need a pay-as-you-go strategy with scalable capacity, which can be adjusted rapidly to meet market conditions in any region.
  • Technological progress: The tide of innovations and discoveries is unrelenting. Businesses must be agile enough to quickly adopt new strategies, and be steered by insightful, knowledgeable leadership that can sort winning inventions from dead-end novelties.

Lesson #2: Unprecedented levels of business agility

The need for an unprecedented level of business agility to match the rapid pace of innovation and transformation present in business is not restricted to a particular industry. Rather, we see entire markets, including transportation, logistics, and e-commerce, being reinvented on a seemingly daily basis. For any industry in which the production, shipment, and transaction of a product is still relevant, transformation supported by digitization is fast becoming a necessity.

Pressures to reshape the business using a digital template are likewise common across the industry spectrum. Companies — both in the business-to-business (B2B) and business-to-consumer (B2C) worlds — expect personalized interactions as a “segment of one,” which necessitates individualization of products and services, and freedom of choice. The business has no choice but to meet these demands — on the platform the customer chooses — or risk losing customers to a competitor.

The common solution that addresses these pressures is agility, and the way to achieve that agility is with a flexible, digital core at the heart of every organization that can meet the demands presented by increasing across-the-board disruption.

In my presentations I often state why we need to talk about a digital core: As long as something is produced (even if it is a service), as long as something is delivered or shipped, and as long as something will be paid – there is a need for a core. It is as simple as that. Every CIO surely knows that end-to-end processes often start at the edges or with systems of engagement, but they are of limited value if they do not connect with the core – the heart that makes your company run.

Now building on top of this, with a digital core, organizations can do far more than simply meet these pressures at a minimum level of success. They can pivot in near real time to capitalize on innovations in areas such as cloud, Big Data, and business network connectivity to completely transform the business, whether it’s to keep up with the growing influence of emerging topics such as the Internet of Things (IoT), 3D printing, or augmented reality, or to defend against new competitors launching up all around them.

A digital core is an enabling platform for transformation and innovation, but what are its hallmarks? We find five key characteristics that make up a digital core:

  1. A digital core provides the enterprise with the capability to drive and anticipate business outcomes in real time.
  2.  It integrates the business seamlessly across all value chain processes such as client interaction, administration, production, and research and development.
  3.  The digital core increases efficiency by automating processes and distributing responsibility for customer insights across an intelligent business network.
  4. It increases effectiveness by converting signals in business data into tangible action, essentially bringing Big Data to the size and scale needed to turn insight into action for the everyday user.
  5. The digital core increases enterprise agility by elevating each employee’s view of the organization.

So how can the modern CIO help to disrupt with digitization?

Here is the modern CIO’s plan for success: They prioritize day-to-day operations that were formerly siloed lines of business to have complete visibility into the entire core business of the enterprise. Finance, sales, and manufacturing can then act in concert, basing decisions on the same information in real time. This is where the company wins big, and this is how the modern CIO will drive change for the better and help their company win in the digital economy.

Successful CIOs know that the race to digitization is on. Until recently, many of the clients I spoke with were still questioning the need for digitizing the enterprise. Now, they want to know the most efficient route to get there. And while SAP’s digital core S/4HANA Enterprise Management is certainly a monumental milestone, clients are surprised to discover that arriving at a digital core is not as difficult as it might seem to enable this level of transformation.

A digital core helps any business run faster and simpler, so getting there should not be as complicated as the siloed line-of-business applications and redundancies a business leaves behind.

Want more insight on digitization? See The Digitized Core At The Heart Of Reimagined Business.

Looking forward to your feedback! Follow me for the latest updates: @SDenecken (link to Twitter account).

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Sven Denecken

About Sven Denecken

Sven Denecken is Senior Vice President, Product Management and Co-Innovation of SAP S/4HANA, at SAP. His experience working with customers and partners for decades and networking with the SAP field organization and industry analysts allows him to bring client issues and challenges directly into the solution development process, ensuring that next-generation software solutions address customer requirements to focus on business outcome and help customers gain competitive advantage. Connect with Sven on Twitter @SDenecken or e-mail at sven.denecken@sap.com.

Collaboration, Agility, And The Digital Business CIO

Allan Adler

In today’s business climate, organizations are under pressure to deliver digitally enabled and digitally driven performance improvement and growth. In order to address these opportunities and transform, innovate, and rapidly deliver digital business outcomes, companies must react to new information and insight in real time and decrease time from ideation to implementation.

CIOs will play an essential role in this process. To be effective, they must engage and partner with their line-of-business and CXO peers and adopt collaborative and agile approaches to innovation, including creative problem solving, opportunity identification, and solution ideation methodologies and agile, lean development processes that speed up time to value and time to scale.

At Digital Bridge Partners (DBP), we have seen these trends and their impacts in our work with clients. They are also corroborated in a recent study conducted by the Economist Intelligence Unit – with input from SAP and DBP – which surveyed more than 800 business and IT leaders throughout the world, highlighting the importance of IT’s role in digital transformation. Here’s a sample survey question:

1

One of the survey respondents, Geraldine Calpin, chief marketing officer at Hilton Worldwide, highlights how her company’s CIO, Bill Murphy, collaborates to deliver transformation:

“What I believe I need – and get – from Bill and his team are firstly, to challenge business concepts a little; secondly, to provide the technical feasibility and strategy elements around business objectives; and thirdly, to develop a plan for delivery that he and his team can realistically deliver on.”

Realizing the vision established in a company’s overall digital business strategy requires close alignment between IT and business and involves cross-functional teams leveraging Build-Measure-Learn (BML) models to ideate, design, test, and deliver innovative minimum viable products.

2

Without strong CIO/CXO/LOB collaboration to facilitate hypothesis testing and rapid, iterative BML cycles that connect action and outcome, the agile approach will break down.

Another of the study’s respondents, CEO Daniel Hartert of Bayer Pharmaceuticals, has found that a collaborative and agile approach is essential to Bayer’s innovations in digital farming:

“When you want to build a solution that creates real value for farmers, you have to be very innovative. You have to build stuff that no one’s ever built before, and for that you need to have an integrated team of IT and business people who have the freedom to experiment and test out pilots.

If you figure out that a particular solution has real potential, and you want to put it into production, then you’re talking about processing and storing huge amounts of data on behalf of a customer base that will expect it to be available 24/7, 365 days a year. It makes more sense to me to have people involved from the very start of an idea who understand the IT infrastructure that will be needed and how it should be managed.”

Adopting a lean-startup model also allows teams to break free of organizational gravitational forces and establish a new culture of agile innovation. However, the new approach is disruptive and CIOs can expect extensive pushback. In most cases the new agile teams need to be protected from organizational pushback and reversion to business as usual.

For more, read our entire article, “The Agile CIO” (registration required) or visit us at Digital Bridge Partners.

Learn more about Digitizing IT.

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Allan Adler

About Allan Adler

Allan Adler is managing partner of Digital Bridge Partners, where he develops and executes digital business innovation, value networks and ecosystems, and go-to-market strategies for leading industrial and technology companies including SAP. Allan’s work and thought leadership help clients refine their innovation cultures and practices, build profitable business ecosystems, and develop winning go-to-market strategies.

Deciding What To Change Is Key To Successful ERP Implementations

Larry Perlov

In my previous post, I reviewed why answering “Why?” is critical before starting an ERP implementation project. Many companies fail to meet their objectives because they don’t set any. It’s hard to achieve value if you don’t know what you want to accomplish. Once you determine why your organization should move ahead with an ERP implementation, you need to consider very carefully what exactly you need to change.

What do we need to change for our ERP?

During this step, many companies try to take on too much. Once they’ve decided they need to change, they assume that the best thing to do is to change as much as possible – especially since an ERP license typically comes with a lot of functionality options included. Companies make the decision to implement the many options available to them, rather than critically considering what needs to change on a systematic basis.

More scope does not necessarily mean more benefit – just like more ingredients in a meal does not mean it will taste better. Moreover, added scope in an ERP implementation almost always results in more costs, time, and risks for an organization. Rather than try and implement every possible module associated with an ERP solution, companies should have a guiding principle to achieve the “minimal viable scope” – the minimum solution needed to achieve the value a company desires. By looking to achieve the minimal viable scope, a company can align a solution with its desired outcomes while reducing risks associated with implementing added capabilities that are not needed or required.

The best way to determine the minimal viable scope is to consider the Value Cascade, as depicted in Figure 1. The business outcomes identified at the top of the figure set the tone (focus) for the rest of the cascade. In order to achieve the desired outcomes, companies should define the list of business capabilities which are necessary and sufficient.

Further, to achieve each business capability, companies should identify all the necessary functional capabilities (i.e., the process capabilities needed to deliver each business capability efficiently and effectively, the technical capabilities needed to automate or enable the processes, and the people capabilities needed to support both process and technical capabilities). Once each of these has been defined in an ERP implementation, the company will be able to define the scope of the technology (in this case, the ERP solution) needed to deliver to the technical capability requirements.

Figure 1:

Larry Blog #2

Making the tough decision of what not to implement right now may not be popular with those business stakeholders who are feeling some pain at present, but it is critical to ensure that each phase is as simple and lean as possible yet provides the components necessary to deliver the desired outcome of each phase.

It is important to remember that with ERP, it is usually easier to add more sophistication to a simple solution later on than it is to reduce the complexity of a complex solution. Furthermore, since not all business outcomes are needed right away, most companies can define the timeline desired or required to achieve the outcomes. This timeline also facilitates the phasing of the capabilities required to deliver those outcomes. We call this timeline the “capability roadmap,” which defines when we expect the various business and functional capabilities to be delivered.

For more about the keys to a successful ERP implementation, please check out our new thought leadership paper, Creating a Recipe for Success:  Questions to guide the development of a first class ERP solution.

 

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Larry Perlov

About Larry Perlov

Larry Perlov, president of Illumiti Corp, is a veteran of the high-tech and consulting sectors with over 20 years of international executive-level experience in SAP, ERP, and IT infrastructure solutions.

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.