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Why You Need An In-Memory Action Plan

Timo Elliott

Get Ahead With An In-Memory Action Plan

This is a third post of a series based on a SAP-sponsored breakfast meeting organized in Sydney earlier this year with speaker Donald Feinberg, Gartner VP and Distinguished Analyst explaining the “Nexus of Forces”: social, mobile, cloud and information.

In the first two posts, Donald covered why in-memory is disrupting everything, and why every organization will be running in-memory in 15 to 20 years time, and the business impacts of the new in-memory computing possibilities.

In this post, Donald explains why you should have an in-memory action plan — and how to create one.

These comments are based on my notes taken from the speech, formatted for legibility.


Why you need an in-memory action plan

Why You Need An In-Memory Action Plan

You need to change the way you look at IT infrastructure, applications, and the infrastructure that’s running those applications. Truly, with some of these new technologies like in-memory technology, there are no barriers, things that you can’t do. Words like “no we can’t do it” start to go away.

I’m not going to tell you it’s going to be cheap, I’m not going to tell you there’s not going to be bumps in the road as you’re doing it, but things that you really thought were not possible are possible now. Period.

What do you do in your organization to start to adopt or use some of the in-memory technologies? You are going to spend money on this. Whether the TCO is less or not, you still have to build your skills, you still have to buy applications, you still have to buy the technology and infrastructure and things like that.

Why You Need An In-Memory Action Plan

Build a business case first. Show the value of what you’re going to do. The return on investment may be long or may be short. We recommend short at first. Small projects with quick return on investment will get you more projects that are bigger and have a greater impact on the company. But you have to prove it first – that’s the key.

Assign a small team of people to look at this. Most companies don’t have a research and development organization in IT (the big ones do). But there’s no reason you can’t have one person looking at the things that are possible with the new technologies, looking at how they can make your current applications more efficient, or start to change how you use them.

So set up a CTO or department of the CTO that has somebody in there who’s just looking at the stuff that’s out five years or ten years from now, so that you will be ready to start to do projects with it when it matures to the level of risk that you’re willing to take.

Always do a POC, proof of concept. Do not just assume that because it looks good on paper it’s going to work for you. You need to test it with your data, with your applications, with your people.

Brainstorming. A lot of people don’t realize that your business unit people are much more IT-aware than they have ever been before. Brainstorm with them on what some of these things can happen, in the business, and how they can make use of it. Who has the budget today? IT? Or the business unit? So if you don’t do this, they’re going to do it anyway, and they’re going to implement the technology without IT. The big disadvantage is that the company doesn’t get the broad skill base that is necessary, and that technology is not shared across the business units. It’s much better to keep it in IT, not because you order it so, but because you are moving along in these new ways, with the business units and what they need.

If you believe what I’m telling you about in-memory technology, as being part of your future, it’s not too early to start to define a strategy for how in-memory is going to enter into your organization and be used.

You may decide that part of the strategy is “we’re going to wait two years to let it mature”. That’s fine, but start looking now at where it can fit and when within the organization, so that you’re prepared and ready to accept it when it comes along. If you’re an early adopter, start tomorrow. If you’re more risk-adverse, next year, the year after.

But at least understand the strategy for how this is going to fit in your organization, because as we believe, it IS coming, whether you want it or not, so you may as well start now to look at a strategy for where it’s going to fit in the future.

Questions and Answers

What would you reply to somebody who said “I’ve already got enough problems in my organization already”?

From a short-term standpoint, I can’t disagree with that.

But some of the new architectures and the in-memory technologies can maybe help you with some of the issues that you have today.

It depends on what the issues are. One of the issues a lot of people have is speed: my applications don’t run fast enough. So maybe there’s in-memory technology that can speed that up. Or maybe moving it to mobile will make it run faster.

Looking at the nexus of forces and looking at technology as a solution to some of your problems may actually help you short-term.

Cloud – maybe cloud can save you some time. I’ll give you a simple example: how are your development costs? Use the public cloud for that. Let your developers develop on an Amazon AWS.

Why is that good? Your people don’t have to set up the development environment. You make a phone call, and you have it. When the project’s done, you make another phone call, and not pay for it any more. You don’t have to go out and buy a server that then you’ve got to figure out what to do with after the development project is done. So there’s a place where cloud immediately can help you.

So some of this new technology is mature enough to solve some of your problems. And then, when you start putting your head together with the business units and start to have an impact on the competitiveness and the bottom line of the organization, that’s where you can really make a difference. Some of this technology may enable you, if you’re a retailer, to turn your inventory one more time a year. Is there any retailer that doesn’t want to do that? And not be out of something when somebody wants it?

If the business unit wants to be an early adopter, but the IT unit is risk-averse and conservative, how does the business user drive this change?

I’ve been around a while in this business. If there’s one thing I’ve heard over and over since I started in the 60s, it’s “IT has to communicate with the business”.  We’ve learned that lesson – that doesn’t work. Going out to dinner with your business liaison once a month and talking with them is nothing.

So one of the concepts that we came up with around twelve years ago, with respect to BI specifically, is the BI Competency Center. The reason that has worked is because it takes business people and IT people and puts them together, working together, not talking. So they make decisions together.

If I’m going to do a new project, all the business units decide what the priority project is. This is a concept that works. Some of your companies can’t afford to have full-time people in it, so you do it virtually: you have a meeting once a week. But they still manage projects, they still make buying decisions on products, they still set strategy for the company. The group should not be run by IT (which is hard to swallow sometimes) – but by the business unit. And most important: the CIO can not be the sponsor. It must be higher in the organization.

So if I’m going to have a “business technology competency center” where people from the business and the industry are going to get together to look at new technologies and where they may work, the sponsor has to be the CFO, the CEO, the Board, somebody like that. Then they will work together to do this.

Risk-adverse IT organizations are normal. You have a job to do to keep the lights on and you’re not going to do it if you take risks. It’s that simple – you’re not going to have a job if you take risks.

So how do you fit that with adopting new technology? Again, just like with the research and development with one person, you can take a couple of people from your organization as part of this “business innovation competency center”, sponsored by the CEO, so you can go hire some new people to do it if you need to, or move people over and backfill them.

They may take on a project with a business unit where you see tremendous value to the business, and you look at something that is, say, in beta. And you look at that technology to enable that business unit to be more competitive, more productive, more profitable, and it doesn’t affect the rest of your organization. You still can deliver the things you’re doing, because it’s “outside”. How do you get to that? You have to get senior management in the organization – not the IT organization – behind you. How do you get that? A small project, to demonstrate to them the value of this kind of thing.

Now one thing that comes to mind immediately: if you look at what’s happened in the past ten years with data warehousing – my area – every time there’s been a recession, database sales and data warehousing sales have dropped off. Except in the 2008/2009 worldwide recession, where every segment of IT was negative growth except DBMS, which was flat. In that environment, flat was positive.

Why? Because when the CIO came in to the CEO and said “I need more money to spend on my data warehouse” and the CEO says “are you nuts, with this economy?!”, you pointed to a flat screen on his wall that had key metrics of the business in “real-time” – for the first time, senior management, the CFO, COO, CEO, could physically see the value that information was bringing to their business.

If you can demonstrate physically to them the advantages of some new technology, then they’re going to buy into it and start to fund it. You can’t say something like “I want a new ERP package” – in an economy like 2009, that will get you fired for asking. But if you have some real strong value that you can demonstrate quickly or instantly to them, they’re going to spend money on it if they think it’s going to save money or help them. So that’s what you have to do. Lots of people say “only large companies can afford that” – but anybody can put it together with at couple of visionary people from the business units and one or two people from IT to put this together, and they can be virtual.

Pfizer is one of our BICC case studies. They have 150 people full time in the BI competency center: 75 employees and 75 consultants. Most people can’t afford to do that, and I’m not suggesting you do. But here are models in-between that make sense, that will fit in everybody’s budget.

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About Timo Elliott

Timo Elliott is an innovation evangelist and international conference speaker who has presented to business and IT audiences in over forty countries around the world. A 23-year veteran of SAP BusinessObjects, Elliott works closely with SAP development and innovation centers around the world on new technology directions. His popular Business Analytics blog at timoelliott.com tracks innovation in analytics and social media, including topics such as big data, collaborative decision-making, and social analytics. Prior to Business Objects, Elliott was a computer consultant in Hong Kong and led analytics projects for Shell in New Zealand. He holds a first-class honors degree in Economics with Statistics from Bristol University, England.

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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 will 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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Millennials Are Transforming How Finance Works

Estelle Lagorce

If you haven’t already, get used to it: Millennials are now officially the largest demographic group in the U.S. workforce. And they have very different aspirations and expectations than their fellow workers do about the nature of work.

Millennials are typically self-motivated and forward-looking. They understand the power of technology and social media, and all the different ways to use both to network and communicate with one another. So does that make them “dangerous” to how finance operates in the future? Their characteristic work style – caring deeply about workplace satisfaction, social networking, and mobile-addiction – changes the historically new-trend-and-technology-averse finance department. As a result, all financial professionals worldwide are now seeing their role and job within the corporate enterprise differently.

On the April 26, 2016 edition of Financial Excellence with Game-Changers Radio, produced by SAP’s Bonnie D. Graham, three thought leaders agreed: Yes, it could make them “dangerous.” (Listen to the episode here.)

According to Henner Schliebs, vice president and head of Global Finance Audience Marketing at SAP, it is not millennials themselves who are dangerous. Rather it is their work style that is being picked up by others in the finance department. As he said, “It’s not really a generational conflict. It’s a shift in society, it’s a shift in the workforce, and everybody is and should be a part of it.”

Another attitude

One of the ways millennials differ from previous generations is in their attitude toward money: They are more money-conscious. And according to John Arellano, senior manager with the Aerospace & Defense and Aviation group at Deloitte Consulting, millennials share 13 times more content about their personal finances on LinkedIn than the preceding Generation X. And this spills over into their style of financial planning.

“Many millennials have started saving for retirement in their early twenties,” John commented. “It’s really interesting how early they understand the importance of saving money and investing money for the future.”

Celina Rogers, vice president and editorial director for CFO Publishing, agreed. “I certainly think it’s interesting that millennials have such a keen interest in personal finance. And I think that feeds into the desire among some millennials to find more secure work – but also to find meaning within that work. And that’s where I think many finance functions will be challenged: to provide that sense of meaning together with the kind of security finance has offered in the past.”

In fact, millennials’ attitude towards work will put the traditional financial department under threat. Millennials want the flexibility to work when they want to – work on a Sunday, for example, and have Monday off. Henner added, “Technology advancement over the last couple of years makes this possible.”

Different expectations

The expectation from millennials that they can be more self-deterministic will undoubtedly be a challenge for many finance departments. But enlightened CFOs are embracing it, as Celina explained. “Many finance leaders see this as a tremendous opportunity. By absorbing millennials and their approach to work into finance, [leaders] can help transform their finance functions in ways that are incredibly important to the business in general.”

Millennials also expect the latest technology at work. They have grown up with the Internet and mobile phones. They want to use their own devices, not the aging company desktop. And they also expect to be able to access from anywhere the data and systems they need to do their jobs.

But according to Henner, there is something else that makes them different, and that will be a catalyst for change. “They want to see what their contribution is to the overall mission of the company and the company’s contribution to society,” he said. “This is a very drastic change in how a CFO should perceive his or her role.”

Fragile loyalty

With millennials bringing lots of fresh ideas to the table, the panel of experts believes it is important to communicate with them and incorporate their ideas. Simply seeing millennials as another resource is not enough. Millennials want their work to have meaning, which requires treating them differently; otherwise they could easily walk away from their job. As John put it, “There are a lot of great millennials in the workplace that a lot of firms need to retain.”

Gaining their loyalty requires rethinking the finance function, what its role in the organization should be, and, importantly, communicating this to everyone involved. Finance can no longer be seen as simply reporting the numbers. Finance needs to report what the company is giving back to society, from a social, environmental, and societal perspective.

Reverse mentoring

Millennials also want to work smarter, not harder. For them it’s not about a work-life balance; it’s about work-life integration. And this can also benefit other demographic cohorts in the workplace. Henner sees a kind of “reverse mentoring” happening, where the older generations are learning from millennials. For example, he observed, millennials are asking, “Why should I do a job that can be automated? If a machine can do it, why should I?”

Millennials also want to be connected to their companies 24×7 so they can work when they want to. “I think this convenience that improves our whole lives is something that we can learn from the millennials,” he continued.

The millennial CFO

So as millennials climb the corporate ladder (if there is such a thing any more) and start taking on the role of CFO, what changes do the experts think they will make?

For John, it will be technology. “With technology, you are going to see a lot of new capabilities.”

Celina added that it will happen quickly, even in the next five to ten years. “I think that the fresh perspective that millennials will bring to finance will really accelerate and drive change, if for no other reason than the value consciousness they will bring.”

For Henner, millennials are already having an effect. “They want this digitalized world because they grew up with it,” he explained. “They grew up with information all over the place and they are expecting the same within the organization [where they work].”

Prediction

So what are the experts’ future predictions regarding millennials? For John, it’s leadership. “Millennials are no longer leaders of tomorrow. They are now leaders of today. So we have to understand that, first and foremost.”

Celina pointed to the influence factor. “I see that in ten years, millennials will really dominate finance leadership. In that capacity, we’ll see tremendous improvement in the kind of enterprise technology that’s working in finance and an expansion of finance’s influence.”

And for Henner, it’s how millennials will manage the generation after them. “Given that 50% of the children going into elementary school today will have a job that does not currently exist, millennials will have to face bigger problems than we’re facing with them right now,” he says. “I wish them all the luck in the world.”

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Estelle Lagorce

About Estelle Lagorce

Estelle Lagorce is the Director, Global Partner Marketing, at SAP. She leads the global planning, successful implementation and business impact of integrated marketing programs with top global Strategic Partner across priority regions and countries (demand generation, thought leadership).