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What Exactly Is Big Data?

Michael Matzer

You’ve heard it mentioned everywhere from the board room to the break room, but you’re still wondering, what IS big data exactly? We provide the ultimate overview.

Photo: istockphoto.com

Photo: istockphoto.com

Big data is the term that market researchers have adopted to refer to what Gartner describes as “high-volume, high-velocity, and/or high-variety information assets that require new forms of processing to enable enhanced decision making, insight discovery and process optimization.” The big question on the minds of IT specialists and managers is: What challenges does big data pose? And where exactly does it come from?

According to predictions made by network specialist Cisco in May 2012, the volume of Internet data will quadruple between 2011 and 2016 to 1.3 zettabytes, or 1,300,000,000,000,000,000,000 bytes, per year. In the same period, the number of Internet-connected devices will double to 19 billion, says Cisco. These will be used by 3.4 billion people –almost half of the global population.

But where do these huge volumes of data come from? Some of it originates from conventional transactions. Another source is wireless WLAN data traffic, which, according to Cisco, will account for about half of all data traffic by 2016.

In Germany, where every member of the population will be using five Internet-connected devices by 2016, mobile data traffic is set to increase 21-fold between 2011 and 2016, from 18 to 394 petabytes (PB), or 394,000,000,000,000,000 bytes, per month. By these calculations, mobile data traffic will outgrow fixed-data traffic three-fold in a five-year period. Moreover, video data traffic will comprise 63% of mobile traffic by 2016, compared with its current share of 44%. Faster broadband connections and suitably powerful end devices, such as surveillance cameras, will foster this development.

Where the data comes from

Mobile devices, WLAN, social networks, sensors, and machines – they all generate the kind of mass data that market researchers refer to as big data. But, depending on where the data comes from, its characteristics can vary significantly. This is an important point to bear in mind if you want to get information out of data and turn that information into insight.

According to Gartner, the volume of data traffic is growing by 59% every year. “Today’s information-management disciplines and technologies are no match for this pace of growth,” says Mark Beyer, Research Vice President at Gartner. “Information managers need to completely rethink their approach to data processing by planning for all dimensions of information management.”

Herein lies the problem

While big data certainly presents a problem in terms of storage and analysis, the actual problem, according to Gartner, lies in spotting meaningful patterns within the data that can help companies make better decisions.

The search for meaningful data is hampered by the way in which the data is structured, because this causes difficulties for existing IT systems. Relational databases (RDBMS), which support virtually all core processes, are very good at storing structured transaction data in rows and columns and giving easy access to it. This is because transaction data consists chiefly of data in fields that each have a single data attribute such as a numeric or alphanumeric value. Often the data even describes itself by means of so-called “metadata”.

But where does a relational database store an e-mail that only consists of a header and a text? And how does it store – not to mention analyze – a tweet or Facebook message?  Clearly, either traditional databases require new tools for analyzing data with multiple structures or users need to deploy other databases that are better suited to the job at hand.

Available tools

When it comes to data that already has a degree of structure, the tools are already available. Call-center records, for example, consist of standardized forms that are filled out by call-center agents. These have a prescribed structure that is relatively simple to search through. Web shops, on the other hand, use tools that log users’ mouse-clicks as they browse a web page and create what is known as a “clickstream”. Large companies have been storing clickstreams in data warehouses for years and analyzing them in the hope of recognizing the kinds of patterns that Gartner is referring to.

The results of these analyses give customer-facing departments useful information about how they could improve their advertising, marketing, sales campaigns, and even their product development. This is because the logged mouse-clicks usually provide a very clear picture of where users’ preferences lie and which products or product features do not interest them at all. This kind of analysis is at its most valuable when it reveals new trends that initially appear as statistical outliers. These give companies the potential to develop innovative products that could transform them into trend-setting market leaders.

Why handling big data will be a core skill

Whatever an enterprise’s big data plans are, they should definitely be long-term ones. “The ability to handle extremely large data volumes,” predicts Yvonne Genovese, Vice President and analyst at Gartner, “will become a core skill in businesses and organizations. Increasingly, they will be looking to use new forms of information – such as text, context, and social media – to identify decision-supporting patterns. This is what Gartner calls a Pattern-Based Strategy.”

This strategy, Genovese continues, is a major driving force behind the big data trend. It relies on using the full range of dimensions in the search for meaningful patterns, and its results provide the basis for modeling new business solutions that allow companies to adapt to changing market conditions. “The cycle of searching, modeling, and adapting can be completed in various media, such as a social media analysis or in context-oriented calculation models.”

“Worldwide, companies invested 3.38 billion euros in big data projects and services in 2011,” reports Steve Janata, a consultant with Experton Group. The market for new solutions will grow by 36% per year between 2011 and 2016 and, in Germany alone, some 350 million euros will be invested in Big Data in 2012. According to Experton, this makes the market for big data one of the fastest-growing segments in the IT industry and a driving force in the IT economy as a whole.

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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.

Why Manufacturers Must Run Live

Harry Blunt

Whether you label it digitization or digitalization, the digital economy is rewriting the rules of business. In this new environment, companies of all sizes must operate their businesses differently while freeing themselves from the constraints of the past.

It’s a time in business where the consumer is king, and access to information and product choice is everywhere. Manufacturers will only survive and thrive if they can Run Live.

As a manufacturer, what can you achieve by running live?

When manufacturers Run Live, they can operate without boundaries, in the moment at speed, with unique and actionable business insights. Running a live manufacturing business restores the balance of sales influence between the manufacturer (seller) and the consumers (buyers) they service. Manufacturers that Run Live do so with more customer insight and less business complexity. They operate with far greater innovation, speed, and predictability, all of which is required to successfully compete in today’s highly disruptive digital economy.

While a digital business is filled with possibilities, it can be equally unsettling and chaotic. It is important that companies Run Live if for no other reason than to bring added order and control to a business environment that is largely characterized by business disruption.

When companies are able to run their operations live with predictable recurring revenues and costs, they are far more profitable and less susceptible to being victimized by market changes. Successful companies have historically always balanced the need to generate more recurring revenue with reduced operating costs. What is new is the demand for improved live business agility and an enhanced level of customer insight and business ecosystem interaction, which are now required to ensure companies can continue to run with predictable results at optimal operating costs.

A company’s focus toward innovation and improving operating efficiencies must become increasingly outwardly focused, starting first with the customer and then extending into the manufacturer’s business ecosystem. Trying to manage corporate innovation and operating efficiencies within department silos, or even within a company’s four walls, is a dated business operating model that won’t work to service an outwardly driven and customer-centric digital economy.

Put customers at the core of your live business

To meet the demands of innovating and operating cost-efficiently in the digital economy, manufacturers must begin with an external view of the world, and that view must always begin with the customer.

Manufacturers must service their customers and run their operations as live, digitized extended supply chains, because while the world has become more connected, it is also far more interdependent. How well a company manages its risks and opportunities around these live, digital interdependencies has a direct impact on the company’s ability to service its clients and on its potential recurring revenues and operating costs.

To achieve differentiated customer value and true operating efficiencies in managing these digital interdependencies, manufacturers must deliver superior customer experiences and operational excellence in four key areas:

  1. Customer-centricity: Mastering “end-to-end” omnichannel commerce from initial order engagement through demand response and same-day product delivery
  1. Individualized products: Having the flexibility to design and manufacture to a lot size of one at mass-production cost efficiencies
  1. Resource scarcity: Developing and safeguarding people talent and assets while ensuring sustainable and compliant products and operations
  1. Sharing economy: Leveraging business networks and digital connectivity to further empower innovation and operating efficiencies throughout the extended business ecosystem

Continue your education on live business and the extended supply chain

On June 14–15, over 500 attendees from small and large manufacturers will gather in Lombard, Illinois, to discuss how leading manufacturers are driving transformational change by leveraging a live and digitized extended supply chain.

Learn how 3D printing, the Internet of Things, cloud computing, business networks, and the SAP S/4HANA platform are providing manufacturers with the digital core and solutions they need to reinvent and reimagine their businesses.

With keynotes and presentations from leading industry analysts and SAP experts, customer case studies, and solution demonstrations, the forum will help you come away with the knowledge you require to build a customer-centric, live manufacturing business that delivers greater innovation and a more predictable and sustainable future.

The event is free is to customers. Learn more by visiting the event website: SAP Manufacturing Industries Forum 2016.

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Harry Blunt

About Harry Blunt

Harry Blunt is the NA Marketing Director for the SAP Extended Supply Chain solution portfolio. The SAP extended supply chain portfolio helps companies run as "Live" digitized businesses while managing critical interdependent business processes from initial product ideas up through product deliveries and services. Incorporating innovations like the Internet of Things, Cloud Computing, and the SAP S/4HANA operating platform, coupled with tightly integrated mobile applications and business networks, we help our customers leverage the capabilities of their entire business ecosystem to obtain greater innovation, stakeholder collaboration, and improved business performance.

Tech Helps Kenyan Women Beat Cervical Cancer

Martin Kopp

273396_l_srgb_s_glAccording to the Kenya Cancer Network, 25 out of 100,000 women in Kenya will develop cervical cancer. Of these, 70-80 percent are not detected until the later stages, as lack of awareness among Kenyan women remains a major problem. The largest barrier to care is the high cost of treatment combined with high poverty rates. In addition, there are not enough treatment centers or diagnostic equipment.

But thanks to technology, that is changing.

Heidelberg University Hospital: Digital health breaks barriers

In developed nations, healthcare is undergoing a digital transformation. We see this through the hyper-connectivity of an informed patient population. Consumers are driving industries that develop products such as wearable monitors and health aids. The supercomputing and cloud-based storage technology behind those devices enable a global healthcare community, as connectivity allows doctors to treat patients without seeing them.

Connected care is at the heart of this movement, so healthcare developments like these are a boon to people who need help but lack access. Technology such as SAP HANA uses cloud computing that enables doctors to gather information through mobile devices, giving women in Kenya access to world-class healthcare. That connection makes a huge difference in treating pregnancy-related issues.

Of course, healthcare apps do not replace the traditional relationship between doctor and patient. Rather, they allow doctors to gather information from patients via mobile platforms. Healthcare workers can then use that information to track the progress of pregnancy and share information with patients.

Connectivity is a major driver of the healthcare revolution. Patients no longer need to visit a doctor to find out what is wrong; they are already informed about their condition. App-based care deepens the traditional relationship between healthcare and consumers. That is a powerful advantage, especially in places like Kenya.

Breaking healthcare barriers

Lack of treatment facilities and diagnostic tools, limited awareness, and the high cost of healthcare, remain significant problems in Kenya. But thanks to technology, these issues are diminishing.

Even in developed countries, the cost to healthcare is an issue. It is one of the driving forces around Obamacare in the United States. In places such as Kenya, Big Data is making a difference. Heidelberg University Hospital has developed an app using SAP HANA technology that allows a deeper connection between doctor and patient. The exchange of information is critical in hyperconnected healthcare. Because the app is mobile-based, it can be deployed in remote locations to bring healthcare to millions of women anywhere there is an Internet connection.

Health and well-being: Byproducts of digital transformation

This app helps to increase the knowledge base of women in Kenya. For example, many Kenyans believe that only women who are HIV+ are at risk for cervical cancer. Dispelling that myth encourages more women seek health screenings, therefore reducing the high rates of late detection. That in turn improves treatment options and survival rates.

Also thanks to the app, more women have access to care at earlier stages of pregnancy. It becomes easier to track patient progress, allows doctors to care for more patients, and decreases the cost of healthcare.

The process works by patient engagement via the app. Patients check in, answer questions, and receive information from their doctor. The app creates a positive healthcare ecosystem via health information exchanges. In Kenya, it helps women who are at risk for cervical cancer find doctors who can treat them.

Heidelberg University Hospital is also helping women in Kenya beat cervical cancer with biomedical informatics. In nations with limited healthcare, IT changes the game. The connection between healthcare professionals and patients no longer needs to be face-to-face, and routine care does not need to be clinic-based. Technology is bringing healthcare to rural populations.

Optimized healthcare not only removes barriers to care, it also fits into cost-driven business models, offering greater value for patients and lower costs for healthcare professionals. Cloud-based electronic medical records also enable doctors to treat rural patients without leaving their office.

How to embrace this technology

Digital technology is transforming world healthcare. For practitioners, the real question is how to get on board. The first step is to go digital, including medical records. Educate your patients about the value of these changes. Train and develop a core workforce that understands digital healthcare. Streamline processes between your practice and suppliers.

Efficiency is a key part of cost reduction. Connect to the hyperconnectivity of technology.

To learn more about digital transformation for healthcare, visit 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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How Digital Supply Chains Are Changing Business

Dominik Erlebach

In the real world, customer demands change and supply disruptions happen. Consumers and collaborative business partners have new expectations. Is your supply chain agile enough to respond?

As technology expands and the Internet of Things becomes increasingly prevalent, a digitized supply chain strategy is moving into the core of business operations. Today’s high-tech companies must adapt – speed and accuracy are crucial, and collaboration is more important than ever. The right digital tools are the key to developing an extended, fast supply chain process. The evolution of the extended supply chain can be seen with McCormack and Kasper’s study on statistical extended supply chains.

Ultimately, however, everything comes down to the efficient use of data in decision making and communication, improving overall business performance, and opening a whole new world of opportunities and competitive differentiation.

The changing horizon of supply chains in technology

Supply chain management now extends further than ever before. Until recently, the task for supply chains was simple: Companies developed ideas, produced goods, and shipped them to distribution points, with cost efficiency as one of the most prevalent factors. As the supply chain evolves into extended supply chain management, however, companies must plan beyond these simple steps.

The Digitalist summarizes these new market conditions as collaborative, customer-centric, and individualized. New products need to hit markets much faster, and customers expect premium service and collaboration. Increasingly, the supply chain must meet rapidly changing consumer demands and respond accordingly. Management can now be conducted by analysis of incoming data or by exception, as discussed in a presentation by Petra Diessner. Resilience and responsiveness have clearly become differentiators in the cutthroat high-tech market. To keep up, high-tech companies must change their method of conducting business.

What is the extended supply chain?

The extended supply chain refers to widening the scope of supply chain planning and execution, not only across internal organizations, but beyond a company’s boundaries. High-tech companies must involve several tiers of suppliers, manufacturers, distributors, and customers. Some businesses also study the popularity and dynamics of a product directly, such as through mining social media data. To lead the competition, modern businesses must orchestrate the extended supply chain to keep up with consumer demands, as consumers now look for fully integrated service experiences.

What makes the supply chain work in extended form?

There are several key factors to consider when updating your supply chain model. The first, access to information, is essential to managing digitized extended supply chains and unleashes benefits that can help your company grow beyond traditional models. All companies gather information, but companies that use this information effectively will excel where others do not.

World Market Forum discusses this in its report on extended supply chains. Digitizing the supply chain process can help manage even an extended, complex network and provide key access to critical information. The success of digitization relies on gathering accurate, pertinent information in a format that can be readily applied and used.  While speed is important, it is not enough. The information itself must be applicable to the company’s needs and it must provide data that allows companies to make more informed decisions about product growth, marketing, and supply chain distribution.

Real-time information access is a primary benefit of digitized supply chains. Market trends become instantly visible with real-time data and cloud-based analytics, as Marcus Schunter notes in this analysis. This insight allows companies to plan for new products and platform development as the market shifts. Eliminating the need to wait months to access and analyze data is critical for the high-speed technology market and also allows for assessment of critical situations. Resolving problems as they occur is more efficient than post-mortem analysis and allows companies to act rather than react. In extending your supply chain, look for technological advancements that allow you to gather data and analyze it in real time.

Consolidation of digital information is another major benefit to the digitized supply chain. Localized digital information can be gathered and analyzed to form executable action plans. Data becomes part of the planning and problem-solving cycle, improving overall communication and allowing for fuller, more meaningful problem-solving and planning. With digitization, speed is a factor, but the relevancy and application of information separates successful companies from the pack. For companies looking to extend their supply chain, this is a key element when searching analysis tools and platforms.

Is your high-tech organization ready to meet the challenges of a complex market and navigate the digital economy?

To learn more about digital transformation in high-tech supply chain, visit here.

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