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Big Data In Action

Michael Matzer

You’ve know where Big Data comes from, and you understand the potential it holds. Now it’s time find out how and where to use Big Data in the quickest, most useful, and most advantageous way possible.

big data in action

Photo: iStockphoto.com

“What’s special about Big Data is that it has a vast range of possible usage scenarios,” explains Holger Kisker, an analyst at Forrester Research. This means that you don’t have a single definitive business case for Big Data: the business case must fit the scenario in question. However, warns Kisker, it’s particularly important for businesses to ensure that they select measurable, business-relevant success factors. For example, “increase in the success rate of marketing campaigns” is a business-relevant success factor, whereas “improvement in customer data” is not. Market researchers including Gartner,IDC, and McKinsey have established that 90% of Big Data is unstructured. Thus, before diving into theBig Data deluge, users must consider exactly what it is they can and want to do with it.

Management consultant Wolfgang Martin has defined five main use types for Big Data:

  • Transparency: insights into ongoing business operations
  • Decision-testing: What happened (will happen) when (if) we made (make) this decision?
  • Individualization in real time: tailoring offerings and services to customer wishes in real time in order to increase customer satisfaction and reduce customer churn
  • Intelligent process control and automation
  • Innovative data-driven business models.

Applying use cases to specific industriesEnergy sector

Generally speaking, one or more of these use cases will lend itself particularly to a specific sector of industry.  “Individualization in real time”, for instance, is highly suited to the needs of the energy sector. In the future, energy suppliers will be able to use the measurement and operational data they continuously collect from smart meters to establish how their customers use energy and how they pay for it. This information could help them offer cheaper energy rates to suit individual customers’ needs.

Analyzing customer data could also help suppliers reduce customer churn and collect outstanding utility payments more effectively. Conversely, customers could urge their suppliers to use Big Data to find ways of minimizing power outages, particularly during the winter months and in rural areas.

If they are to benefit from real-time sensor data collection, energy suppliers need to have an extremely agile Big Data concept. Specifically, they need to conduct analyses quickly and incorporate the insight gained from them into their direct customer-contact processes. Wolfgang Martin refers here to “operational Big Data”, because it addresses the issue of how businesses can create an infrastructure in which everyone benefits from what is learned from Big Data.

 Analyzing market and customer data

A second aspect, called “high-resolution management”, is relevant for every single company – no matter which sector they operate in, says Martin. It answers the question, “How can we change the way we manage our businesses based on the high-resolution view that big data provides?”

“The benefits that Big Data pioneers like AmazoneBayFacebook, and Google enjoy today are chiefly in the areas of customer focus, customer relationship management (CRM), and customer experience management,” explains Martin. “Currently, the area of marketing can benefit particularly strongly from taking unstructured data from sources other than ERP and CRM systems – from FacebookTwitter, blogs, or forums – and turning the insight it delivers into competitive advantage,” adds Holger Stelz, who is the director of business development and marketing at Uniserv GmbH. Accordingly, explains Stelz, Big Data allows marketing departments to extend their 360-degree view of the customer into a 360-degree view of the entire market. It makes hidden trends visible and supplies information about customer behavior and about what companies can do to fulfill customer wishes better and more quickly.

For marketing specialists, Facebook and Twitter are the places to look if they want to know the opinions of consumers who no longer watch TV and who no longer read e-mails. To harvest these opinions, they need a suitable interface and analyses tools that can handle functions such as sentiment analysis.

Instantly react to negative analyses

Sentiment analysis acts as an early-warning device for producers of consumer goods who have just brought a new product to the market by picking up on any negative consumer responses. It can therefore indicate potential loss of revenue and customer churn.

“Once your social media monitoring is in place, you can move on to setting up your social media interaction,” adds Wolfgang Martin. If a company reacts quickly to negative analyses with a social network campaign, it can mitigate – or even prevent – any negative consequences. This, says Martin, is a major advantage in customer service and during product launches, because companies can immediately establish and foster communication with Web communities.

Furthermore, GPS and other smartphone data allow marketing specialists to create “movement profiles” that are not restricted to a city, region, or country, but that can cover the entire world. These geo-coded profiles make it possible to draw conclusions about customer behavior and attributes. However, experts like Wolfgang Martin warn of the importance of complying with data protection laws in this regard.

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michaelmatzer

About michaelmatzer

Michael Matzer is a Freelance IT Journalist. His specialties include Cloud Computing, IT security, Network Security, Big Data & Analytics (mobile, Cloud, location, social media, operational) and Data Visualisation.

How Big Data And IoT Are Transforming How We Buy And Drive Cars

Larry Stolle

In today’s tech-savvy world, most people are familiar with the idea of consumer and technical data collection. Business leaders use this data to get complete view of any given market segment. The sheer amount of business information that is currently collected has inspired the term Big Data. This Big Data means different things to different companies. It can include anything from records of retail purchases and buying patterns to product information and systematic feedback.

Many business professionals view Internet of Things (IoT) as the next big thing in the world of high technology. The online dictionary for IT professionals Webopedia defines IoT as “the ever-growing network of physical objects that feature an IP address for internet connectivity.”

The term also refers to “the communication that occurs between these objects and other Internet-enabled devices and systems.” IoT can accomplish great things in a number of industries when paired with the benefits of big data. Examples of “things” that can fall under the IoT umbrella include many consumer goods, including electronic appliances, connected security systems, household lights, speaker systems, and a full range of automotive vehicles.

Market research/analysis firm IDC recently released a study on the enormous potential of IoT and Big Data in a range of manufacturing industries. The study projected that worldwide manufacturing will generate $746 billion by 2018. This sentiment is echoed by Morgan Stanley, which estimates that IoT-driven automation in manufacturing could save $500 billion (2 to 4 percent based on a penetration rate of 50 percent).

The technological future of the automotive industry

A recent global expert survey by McKinsey & Co. on the manufacture of cars and trucks determined that automotive suppliers have big plans for Big Data and IoT. In fact, 92 percent of automotive industry leaders feel that these technologies will have a “huge impact” in the way their products are designed and manufactured. Big Data and IoT will also influence the way that automotive companies interact with, and sell to, their customer bases.

The global nonprofit Application Developers Alliance (ADA) regards automotive innovations as a microcosm of these digital technologies. According to ADA, industry experts estimate that every car will be connected in some way by 2025. It goes on to report that the market for connected vehicle technology will reach $54 billion by 2017.

Forbes also recently chimed in on this issue with the article “Big Data’s Big Impact Across Industries.” Writer Howard Balwin complied reports from the Center for Automotive Research and the Michigan news site MLive and concluded that Big Data is an “engine of innovation” and “about to get bigger for auto industry.”

Otto Schell of General Motors sums up the whole subject in a few words: “The use of data is not anymore questioned.” He goes on to report that the “entire business is changing.”

Car and truck companies are embracing the full potential of Big Data/IoT technologies. Modern digital technology within the automotive sector has already produced some stunning results. Business opportunities that are driven by Big Data and IoT include connected products and logistics that use embedded sensors to communicate.

This communication can occur among “smart” vehicles or between these vehicles and a strong digital core. These technologies can do great things, such as assisting in preventive maintenance and coordinating activities within a fleet of vehicles. They can also help detect engineering defects and determine driving patterns.

There are many other key IoT and Big Data trends in the automotive sector, including tracking customer sales and services and fostering seamless collaborations between automotive companies and high-tech companies such Google.

Perhaps the most exciting product of the marriage between automobile manufactures and IoT/Big Data technology is the self-driving car. These cars are still in development, but they stand to impact the automotive sector in profound ways. Morgan Stanley addressed the enormous potential of autonomous vehicles in its April 3, 2014 Blue Paper. The section entitled “The ‘Internet of Things’ Is Now: Connecting the Real Economy” reported that full penetration of self-driving vehicles would save the U.S. economy a total of $1.3 trillion. This figure increases to $5.6 trillion at the global level.

The Blue Paper goes on to estimate that self-driving cars and other state-of-the-art digital marvels will save the U.S. $158 billion in estimated fuel savings and $488 billion in estimated accident avoidance savings.

So while we look to big things from Big Data and IoT in the future, we cannot underestimate the exciting things that are happening right now. Otto Schell again sums things up nicely: “I think when we talk in general about cars, you see all over the place a lot of things are digitalization, connect the car.” He believes that the automotive industry must use digital technology to serve consumers today. “We give them options to go into their lives. And this option is very clearly digitalized.”

Learn more about digital transformation in the automotive industry here.

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Larry Stolle

About Larry Stolle

Larry Stolle is the senior global marketing director for the Automotive Industry at SAP. He has over 45 years of experience in the automotive industry with experience, ranging from dealerships to manufacturers and importers to technology companies such as IBM. Stolle currently holds two patents for dealer and manufacturer communications and for quality insights.

The New Digital Healthcare Patient Experience

Martin Kopp

Digitized healthcare has arrived. And it is only going to get better. Since the 1950s, information technology has had a growing influence on the healthcare industry. And today, more than three-quarters of all patients expect to use digital services in the future. That is, if they are not using them already. Healthcare consumers have become more informed and proactive.

Today, a pregnant woman can schedule a gynecology appointment electronically. Her insurance company probably offers a smartphone app to monitor her health. She can download the app and self-register. The app documents her ongoing health as she updates the profile data. And because her data is stored in the cloud, her gynecologist has immediate access to it.

These are a few examples of the important trends shaping the patient experience with digital innovation. The latest digital solutions are bringing the patient and the healthcare industry closer together. And this digital connectivity means more personalized patient care.

Digital technology is changing the role of the patient. Patients are better informed and more involved in their own health decisions. With greater access to information, they can sometimes self-diagnose certain health issues. Due to digitization, they have better communication with healthcare providers and easier access to their own test results.

Monitoring illness

Healthcare providers are better equipped to gather and analyze data. So, healthcare outcomes are faster and easier to realize. Providers can react earlier to conditions. And they can even sometimes predict medical conditions before any symptoms appear. Therapies are transforming to a more user-centric design. This is all possible because digital networking of data informs caregivers earlier and keeps them informed. We have moved past the patient’s chart as the most important source of information.

Improving wellness

The ability to predict medical conditions gives providers a tool to promote wellness. This is changing the healthcare value chain. Remote monitoring is possible, making trips to the clinic or doctor’s office less necessary. Wearable monitoring devices have changed the medial landscape. And the use of wearable devices is expected to grow. According to the McKinsey Global Institute (MGI), 1.3 billion people will be using fitness trackers by the year 2025. In some regions, this will account for up to 56% of the population. The millennial generation sums up the benefits in a word: convenience.

The blending of physical and digital realms into a common reality is referred to as the Internet of Things (IoT). The IoT makes many things possible that were only dreamed of a few years ago. It extends the reach of information technology. From remote locations, we can electronically monitor and control things in the physical world. Basically, it is the digitizing of the physical world.

With the IoT, MGI predicts a savings in healthcare treatment costs of up to $470 billion per year by 2025. But even more important is the improvement in healthcare. In addition to driving down treatment costs, this will extend healthy life spans and improve the quality of life for millions of people. And it will improve access to healthcare for those who are underserved in the present system. Plus, this extensive use of fitness tracking devices will create a multi-billion dollar industry.

Re-shaping the patient experience

The patients of today and tomorrow have more information and more options than ever before. Patients are already seeing increased value from the Big Data that healthcare professionals now have access to. Patients are more engaged in their own care. We are entering an age of personalized healthcare based on far-reaching knowledge bases.

Because of digital innovation, healthcare consumers can more easily seek relief when they are sick. They can be more involved in disease prevention and self-supported care. With patient-owned medical devices, they are connected to the Big Data of cloud computing. This cloud-based information provides proven treatments and outcomes for specific conditions.

Value chain improvements

The digital value network connects all aspects of the healthcare ecosystem in real time. This connectivity drives better healthcare outcomes that are specifically relevant to the patient. Digital innovation in healthcare improves interactions to provide personalized care based on Big Data. In that respect, you can think of it as Big Medicine for the little guy. A massive database gives healthcare providers a 360-degree view of the patient. Data is stored in the cloud and processed in the core platform.

Services and functions that this efficient system provides include medication reminders for patients. It tracks your health for you, your family, and friends. Remote home monitoring and emergency detection offer an increased level of safety and protection. Remote diagnostics can mean you stay at home instead of being hospitalized. Prediction of organ or other physical failures before they happen can save lives.

SAP software provides a single platform that brings together healthcare providers, patients, and value-added services. It offers a seamless digitization of the entire patient experience. And it provides results in real time, available to all parts of the healthcare ecosystem. This broad connectivity creates an omni-channel, end-to-end patient experience.

To learn more about Digital Transformation for Healthcare, click here.

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Martin Kopp

About Martin Kopp

Martin Kopp is the global general manager for Healthcare at SAP. He is responsible for setting the strategy and articulating the vision and direction of SAP's healthcare-provider industry solutions, influencing product development, and fostering executive level relationships key customers, IT influencers, partners, analysts, and media.

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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Digital Transformation Needs More Than Technology

Andreas Hauser

Digital transformation is a hyped-up topic these days. But it is much more than a buzzword. Technology trends like hyper-connectivity, Big Data, cloud, Internet of Things, and security provide new opportunities for companies to re-imagine their business and how they engage with their customers and users.

But what happens if you develop an amazing technical solution that people cannot use?

Let me tell you a story.

On a business trip recently, I had an experience that some of you might have also encountered from time to time. I wanted to enter the parking garage of a hotel and had to get a parking ticket to get in — sounds simple. The machine looked pretty modern. It had an integrated monitor and several buttons on the side. First I touched the screen, but nothing happened — it was not a touchscreen. Then I pressed some buttons on the side, and again, nothing happened. The rounded button at the bottom finally got me a ticket. Great technical solution … but not usable.

Endurance testing experiences like this one are actually easily preventable when taking into consideration human needs (desirability). This makes very clear that we need to connect three elements—viability, feasibility, and desirability—to be successful and remain competitive in the digital era.

Wikipedia defines digital transformation as “application of digital technology in all aspects of human society.” This is why companies with the most successful digital transformations have focused on people and applied a design-led approach.

One company that has excelled at creating a pleasant experience is Uber. Their app not only tells you how long it will take the car to arrive, but you can also watch the arrival on your mobile device. I like the user interface. But here’s what I personally like most about the Uber experience: You get out of the car, keep your mobile phone in your pocket, do nothing, pay automatically without thinking about how much you need to tip the driver, and get the receipt via e-mail.

That is the difference between simply focusing on the user interface and providing a great customer and user experience. To design and develop such a solution, you need to know what people really desire. Technology certainly plays a very important role to make this experience a reality, and you must be clear about the business model.

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Design-led digital transformation means leveraging breakthrough technology trends, re-imaging business processes and business models, and re-imaging the customer and user experience to achieve design-led innovations.

In today’s digital economy, companies understand that the experience their customers and users have must be the core focus of its brand and survival. Customers and users drive the current and future state of any business. Products and services, whether they are delivered to internal or external customers, must create a value for them and the company. Therefore, customers and users need to be an integral part—not an afterthought—of the entire product development process.

Design thinking to focus on human needs

To better understand what that experience can be, companies are using design thinking – a human-centered approach to innovation – and are putting the customer and user into the center of all activities. Design thinking focuses on human needs, problem finding, working in inter-disciplinary teams across the innovation lifecycle, and a fail-fast, fail-early approach.

My observation from about 500 customer projects is that more and more IT organizations are starting to apply design thinking within their organization. They are hiring designers to better understand the needs of their customers and users and are translating these needs into an experience design. In the past, they simply collected requirements from the business and implemented functions, features, and business processes. This was sufficient in last-decade enterprises, but consumerization of IT requires re-thinking of this approach.

Create business value with human-centered design

The goal is to create business value by engaging with customers and users throughout the end-to-end process—from discovery to design to delivery—and apply design thinking combined with agile methodologies. It is not about simply creating a cool design; rather it is all about creating business value and outcomes.

To do this, business and IT need to work hand in hand to take the company toward that single consumer’s experience.Slide2.JPG

Let’s look at an example.

As part of its business strategy, Mercedes-AMG, the sports car brand of Mercedes-Benz, aimed to increase its production drastically while keeping the excellent quality standards that have always characterized its products. In a co-innovation project, we have engaged on an intensive research plan and applied the principles of design thinking and agile software development to bring the Mercedes-AMG vision to life: a customizable collaborative planning solution that supports cross-functional competence teams and increases efficiency during the three-year production process. The solution, based on SAP HANA, provides access to relevant data in a holistic way and enables a seamless team collaboration in the remodeled process. One of the key success factors was engaging with users throughout the entire process by observing how they work and iterating on solutions with them.

Digital transformation is a journey, not just a one-time project. Ultimately, enterprises want to prepare their organization for sustainable design-led digital transformation.

So how can you embrace the human aspect of design in your digital transformation? This is our credo: Apply design thinking to engage with your customers and most importantly, with users, right from the beginning, in an iterative, user-centric design process.

If you are interested in more customer stories, check out the UX Design Services website. You can also find more information in this presentation, or check out this video recording.

This article originally appeared on SAP Business Trends.

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Andreas Hauser

About Andreas Hauser

Andreas is global head of the design and co-innovation center at SAP. His team drives customer & strategic design projects through Co-Innovation and Design Thinking. Before he was Vice President of User Experience at SAP SE for OnDemand Solutions.