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Disrupt With Digitization

Sven Denecken

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

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

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

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

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

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

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

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

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

Lesson #2: Unprecedented levels of business agility

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

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

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

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

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

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

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

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

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

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

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

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

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

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Sven Denecken

About Sven Denecken

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

5 Reasons Manufacturing SMEs Need Cloud More Than Ever

Lindsey LaManna

The business environment for sales teams in manufacturing and engineering industries is increasingly demanding: IT infrastructures have become complex, whilst everyday sales activities claim simplicity and ease. Additionally, CEOs demand a clear overview of incoming opportunities and the business process.

So how do cloud solutions help sales teams in manufacturing and high-tech industries to survive?

Before looking into the specifics of cloud solutions for sales teams, we need to understand the current situation:

Outsourcing of IT has failed

In the last decade, there was a major shift in IT operations. Starting in the late 90s, companies optimized their IT budget by shifting personal costs to project costs via outsourcing. Furthermore, decisions of “buy vs. build” often were made in favour of building software. At this time, standard software often did not exactly match the companies’ demands, and they preferred a custom-tailored solution.

The outsourcing and custom-tailored solutions approach of the past is now becoming a legacy of many companies. As a result, IT is sitting on outdated solutions that are either expensive to maintain or are not maintained at all. Upgrade projects often fail since they do not offer additional benefits to the business. At the same time, sales people are left to standard office software such as Microsoft Excel to price quotes and calculate discounts, and to MS Word, which is prone to errors and lacks efficiency, to create proposals.

However, the rise of software-as-a-service changed the availability of new specialized software as well as the IT operations model dramatically. Chances arise even for small or medium enterprises (SMEs) to support their sales people in a way that is not only affordable but also the least disruptive in regards of their current sales practices. 

As business becomes more demanding, the role of IT grows more strategic than ever

In the past, IT was more focused on operating internal systems and developing custom solutions – or managing outsourced teams. Being faced with fast-paced business environments and CEOs demanding transparency and control over business processes, their mandate is becoming more strategic. Nowadays CIOs are key to strengthen and optimize business processes and thus take over a more consultative role, which supports the business owners’ decision-making process.

Performing this shift in responsibilities, CIOs must also balance and reorganize their financial and human resources to cater for more consultative workloads. Cloud software can facilitate this shift as the operations, maintenance, and support responsibilities move to specialists, which are rented in the sense of software-as-a-service (SaaS). At the same time, the incurring costs are shared among the customers of a software product and shift from CapEx (Capital Expenditures) to OpEx (Operational Expenditures) via a monthly or yearly subscription fee.

TCO comparison cloud vs. traditional software

Centralized internal systems do not work for sales people on the road

Another trend that emerged during the last decade—not only in manufacturing—was to centralize major systems like orders and material being maintained in one ERP system. This worked out nicely for personnel with a fixed work place, but is a problem for mobile people such as the sales force.

Opening up central systems for external access to provide sales reps with relevant information where they need it poses many security and data compliance risks. IT departments try to cover this issue by management of the internal network, reverse proxies, and VPN. However, secure operations of complex network setups remain complex, and users often experience these approaches as slow and cumbersome. Sales reps depend on fast and reliable access to sales relevant data anywhere and at any time.

Cloud applications can be the answer as they offer standardized, securely managed ways to synchronize or access internal systems data like ERP data and make these accessible in the cloud. Managing bandwidths and network to the users are both off the shoulders of IT, and become part of the SaaS package.

Collaboration needs vs. ad-hoc processes

Collaboration is Key in manufacturing_web
Nowadays, especially in manufacturing, the value chain of a company with several locations can appear scattered, hence hard to support by IT. Sales reps are located where the customers are, whilst manufacturing premises are built and operated in lower-cost areas. Nevertheless, the need for collaboration between sales reps, manufacturing sites, and engineering experts is key to produce marketable solutions in an engineering-to-order scenario and to produce accurate customer quotes.

Supporting this essential part of the business process is no major feature of traditional software, but rather managed externally. Employees use email or other collaboration tools and applications. These gap fillers do not only pose security risks to sensitive data, they also don’t help structuring the often ad-hoc initiated collaboration process. Mailboxes are clogged, data gets lost, and IT finds themselves surrounded by a jungle of shadow IT.

A cloud solution can tackle this challenge when the respective collaborative processes are backed deeply into the system itself. Collaborative processes such as information exchange, collaborative quote inputs by different parties, document sharing, and approval processes happen directly in the software and on the objects that run the business processes rather than externally; for example, via email. Leveraging the numeral integration capabilities of a reasonable cloud solution, the users such as sales reps and managers can enjoy seamless integration with email programs like Microsoft Outlook.

Usability is key for sales people

As reality shows, IT cannot force users to stick to outdated software with insufficient features to support their everyday business challenges. Furthermore, users also stop using given software or tools if the usability is not satisfactory. The so-called “consumerization of business software” describes this trend and explains the resulting behaviour: Sales people go back to using pen and paper, look for easy-to-use applications running on personal devices like tablets, or don’t document their sales process at all. This is not only inefficient and non-scalable for an entire company, it also leads to a lack of transparency of the sales process, and managers are unable to pull production forecasts or make accurate revenue predictions.

Cloud software usually covers only a small business process in comparison to the much bigger on-premise suites of the past. This enables cloud solutions to focus on the specific requirements of users like sales reps and sales managers. Hence, usability is backed into the DNA of cloud companies, and this term does not only cover design and interaction patterns, but also general performance and response times which are essential in fast-paced business environments. The integration of existing workflows via Excel uploads and Excel, Word, or PDF downloads further help to increase the user adoption rate, thereby increasing productivity.

The 5 reasons to shift sales operations to the cloud

Summarizing the main challenges that CIOs in manufacturing and engineering businesses face nowadays, the following chances for a shift of the sales operations to the cloud arise:

1.  For SMEs, cloud solutions are an affordable and least disruptive way to replace legacy custom solutions or error-prone and inefficient gap-filling solutions.

2.  With the software-as-a-service model, IT operations, maintenance, and support can be shifted to experts whilst costs are moved from CapEx to OpEx, and the total cost of ownership (TCO) decreases drastically.

3.  Cloud solutions relieve the negative effects of data centralization for sales reps on the road, and deliver relevant sales knowledge to them in a reliable manner and under high security standards.

4.  A reasonable cloud software not only fulfills collaboration needs amongst the users, but also deeply backs collaboration processes into the system and ensures compliance with security standards and internal company approval structures.

5.  A strong focus on usability in cloud solutions leads to a high user adoption rate, and thus enables an efficient sales process in the organisation.

outdated software user experience vs. high usability in cloud

Confronted with the role shift for IT departments from operations to strategic responsibilities – a fast-paced and highly competitive business environment and a highly demanding user group – CIOs of today need to invest in cloud solutions if they want to keep up. Leveraging smart and lean cloud solutions that limit costs and multiply productivity can be seen as a road to business success for SMEs in manufacturing and engineering industries.

This article originally appeared on the In Mind Computing Blog.

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Lindsey LaManna

About Lindsey LaManna

Lindsey LaManna is a Marketing Manager at SAP. Her specialties include social media marketing, marketing strategy, and marketing communications.

Data: The Foundation Of Real-Time Digital Business

R “Ray” Wang

From Big Data to small data, the digital world measures and values every interaction. Digital technology enables every touch point, click, conversation, picture, and byte of digital exhaust to be used to improve decision-making. In fact, data provides the foundation for success in a real-time digital business. This is why organizations must carefully design a data strategy as the first step in digital transformation.

To get started, successful organizations map out a data-to-decisions framework (see Figure 1). This framework uses all types of upstream and downstream data (for example, structured, unstructured, big, small, and contextual) to align with business processes, creating information flows. From order to cash, procure to pay, campaign to lead, hire to retire, and incident to resolution, context is applied to information flows.

In the next step, algorithms apply context attributes such as role, relationship, weather, product, geo-spatial location, time, sentiment, and even intent to the information flows. The bigger the data set, the more opportunities for algorithms to find patterns of insight. The goals are to ask questions of the data and expose patterns of insight, using performance, deduction, inference, and prediction.

Traditionally, most systems stop after discovering insight. In a digital business, though, insight powers the ability to guide decision-making. By using the ability to take actions based on data, organizations can consider how to identify the next best actions, make recommendations, suggest risk mitigation, and even suggest that no actions be taken. By designing a data-to-decisions framework, organizations gain the ability to build a digital business and enable real-time business.

Once a data-to-decisions foundation is established, organizations can think about how they can apply the framework to augment decision-making. Successful leaders start by putting together a list of questions they seek answers for. They prioritize that list and then begin addressing these questions within the data-to-decisions framework. The secret to success is not what answers can be provided, but what questions should be asked. Successful organizations learn how to ask questions that have never been asked before, sometimes by employing techniques such as design thinking.

Figure 1: Use the data-to-decisions framework to drive real-time business
Data-real time

With mastery of data to decisions, organizations eventually will move from real-time to right-time models. Real-time immediately provides data to decisions as requested, resulting in a data deluge. Unfortunately, real time on its own may not be fast enough. Organizations may need to anticipate when data should be delivered. Why? Real time describes the speed at which the transformation from data to decisions must occur. Right time is about the precision that relevant, contextual information can provide once cognitive capabilities are applied to the data-to-decisions framework. In other words, right-time systems ensure organizations see what they need to see before they even know they need it.

So where do you begin?

1. Start by identifying the questions your organization seeks to answer.
2. Ask what traits make up the most valuable products, employees, customers, and suppliers. These traits drive the questions around what context matters.
3. Determine the information flows and business processes that drive context.
4. Understand the people and devices touched to provide the next level of journey mapping.
5. Apply the data sources and channels of data to recommendation engines and decision frameworks.

After taking these 5 steps, you can then start creating big data business models powered by insight. Digital technologies, data, and algorithms should all be aggressively used to create business models that take advantage of insights. Visibility, relevance, and immediacy will come from these insights-based business models. The goals are to simplify the complexity of decision making and enable real-time digital business.

Learn more how real-time business is impacting companies like yours.

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R “Ray” Wang

About R “Ray” Wang

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

Unlock Your Digital Super Powers: How Digitization Helps Companies Be Live Businesses

Erik Marcade and Fawn Fitter

The Port of Hamburg handles 9 million cargo containers a year, making it one of the world’s busiest container ports. According to the Hamburg Port Authority (HPA), that volume doubled in the last decade, and it’s expected to at least double again in the next decade—but there’s no room to build new roads in the center of Hamburg, one of Germany’s historic cities. The port needed a way to move more freight more efficiently with the physical infrastructure it already has.

sap_Q216_digital_double_feature1_images1The answer, according to an article on ZDNet, was to digitize the processes of managing traffic into, within, and back out of the port. By deploying a combination of sensors, telematics systems, smart algorithms, and cloud data processing, the Port of Hamburg now collects and analyzes a vast amount of data about ship arrivals and delays, parking availability, ground traffic, active roadwork, and more. It generates a continuously updated model of current port conditions, then pushes the results through mobile apps to truck drivers, letting them know exactly when ships are ready to drop off or receive containers and optimizing their routes. According to the HPA, they are now on track to handle 25 million cargo containers a year by 2025 without further congestion or construction, helping shipping companies bring more goods and raw materials in less time to businesses and consumers all across Europe.

In the past, the port could only have solved its problem with backhoes and building permits—which, given the physical constraints, means the problem would have been unsolvable. Today, though, software and sensors are allowing it to improve processes and operations to a previously impossible extent. Big Data analysis, data mining, machine learning, artificial intelligence (AI), and other technologies have finally become sophisticated enough to identify patterns not just in terabytes but in petabytes of data, make decisions accordingly, and learn from the results, all in seconds. These technologies make it possible to digitize all kinds of business processes, helping organizations become more responsive to changing market conditions and more able to customize interactions to individual customer needs. Digitization also streamlines and automates these processes, freeing employees to focus on tasks that require a human touch, like developing innovative strategies or navigating office politics.

In short, digitizing business processes is key to ensuring that the business can deliver relevant, personalized responses to the market in real time. And that, in turn, is the foundation of the Live Business—a business able to coordinate multiple functions in order to respond to and even anticipate customer demand at any moment.

Some industries and organizations are on the verge of discovering how business process digitization can help them go live. Others have already started putting it into action: fine-tuning operations to an unprecedented level across departments and at every point in the supply chain, cutting costs while turbocharging productivity, and spotting trends and making decisions at speeds that can only be called superhuman.

Balancing Insight and Action

sap_Q216_digital_double_feature1_images2Two kinds of algorithms drive process digitization, says Chandran Saravana, senior director of advanced analytics at SAP. Edge algorithms operate at the point where customers or other end users interact directly with a sensor, application, or Internet-enabled device. These algorithms, such as speech or image recognition, focus on simplicity and accuracy. They make decisions based primarily on their ability to interpret input with precision and then deliver a result in real time.

Edge algorithms work in tandem with, and sometimes mature into, server-level algorithms, which report on both the results of data analysis and the analytical process itself. For example, the complex systems that generate credit scores assess how creditworthy an individual is, but they also explain to both the lender and the credit applicant why a score is low or high, what factors went into calculating it, and what an applicant can do to raise the score in the future. These server-based algorithms gather data from edge algorithms, learn from their own results, and become more accurate through continuous feedback. The business can then track the results over time to understand how well the digitized process is performing and how to improve it.

sap_Q216_digital_double_feature1_images5From Data Scarcity to a Glut

To operate in real time, businesses need an accurate data model that compares what’s already known about a situation to what’s happened in similar situations in the past to reach a lightning-fast conclusion about what’s most likely to happen next. The greatest barrier to this level of responsiveness used to be a lack of data, but the exponential growth of data volumes in the last decade has flipped this problem on its head. Today, the big challenge for companies is having too much data and not enough time or power to process it, says Saravana.

Even the smartest human is incapable of gathering all the data about a given situation, never mind considering all the possible outcomes. Nor can a human mind reach conclusions at the speed necessary to drive Live Business. On the other hand, carefully crafted algorithms can process terabytes or even petabytes of data, analyze patterns and detect outliers, arrive at a decision in seconds or less—and even learn from their mistakes (see How to Train Your Algorithm).

How to Train Your Algorithm 

The data that feeds process digitization can’t just simmer.
It needs constant stirring.

Successfully digitizing a business process requires you to build a model of the business process based on existing data. For example, a bank creates a customer record that includes not just the customer’s name, address, and date of birth but also the amount and date of the first deposit, the type of account, and so forth. Over time, as the customer develops a history with the bank and the bank introduces new products and services, customer records expand to include more data. Predictive analytics can then extrapolate from these records to reach conclusions about new customers, such as calculating the likelihood that someone who just opened a money market account with a large balance will apply for a mortgage in the next year.

Germany --- Germany, Lower Bavaria, Man training English Springer Spaniel in grass field --- Image by © Roman M‰rzinger/Westend61/CorbisTo keep data models accurate, you have to have enough data to ensure that your models are complete—that is, that they account for every possible predictable outcome. The model also has to push outlying data and exceptions, which create unpredictable outcomes, to human beings who can address their special circumstances. For example, an algorithm may be able to determine that a delivery will fail to show up as scheduled and can point to the most likely reasons why, but it can only do that based on the data it can access. It may take a human to start the process of locating the misdirected shipment, expediting a replacement, and establishing what went wrong by using business knowledge not yet included in the data model.

Indeed, data models need to be monitored for relevance. Whenever the results of a predictive model start to drift significantly from expectations, it’s time to examine the model to determine whether you need to dump old data that no longer reflects your customer base, add a new product or subtract a defunct one, or include a new variable, such as marital status or length of customer relationship that further refines your results.

It’s also important to remember that data doesn’t need to be perfect—and, in fact, probably shouldn’t be, no matter what you might have heard about the difficulty of starting predictive analytics with lower-quality data. To train an optical character recognition system to recognize and read handwriting in real time, for example, your samples of block printing and cursive writing data stores also have to include a few sloppy scrawls so the system can learn to decode them.

On the other hand, in a fast-changing marketplace, all the products and services in your database need consistent and unchanging references, even though outside the database, names, SKUs, and other identifiers for a single item may vary from one month or one order to the next. Without consistency, your business process model won’t be accurate, nor will the results.

Finally, when you’re using algorithms to generate recommendations to drive your business process, the process needs to include opportunities to test new messages and products against existing successful ones as well as against random offerings, Saravana says. Otherwise, instead of responding to your customers’ needs, your automated system will actually control their choices by presenting them with only a limited group of options drawn from those that have already received the most
positive results.

Any process is only as good as it’s been designed to be. Digitizing business processes doesn’t eliminate the possibility of mistakes and problems; but it does ensure that the mistakes and problems that arise are easy to spot and fix.

From Waste to Gold

Organizations moving to digitize and streamline core processes are even discovering new business opportunities and building new digitized models around them. That’s what happened at Hopper, an airfare prediction app firm in Cambridge, Massachusetts, which discovered in 2013 that it could mine its archives of billions of itineraries to spot historical trends in airfare pricing—data that was previously considered “waste product,” according to Hopper’s chief data scientist, Patrick Surry.

Hopper developed AI algorithms to correlate those past trends with current fares and to predict whether and when the price of any given flight was likely to rise or fall. The results were so accurate that Hopper jettisoned its previous business model. “We check up to 3 billion itineraries live, in real time, each day, then compare them to the last three to four years of historical airfare data,” Surry says. “When consumers ask our smartphone app whether they should buy now or wait, we can tell them, ‘yes, that’s a good deal, buy it now,’ or ‘no, we think that fare is too expensive, we predict it will drop, and we’ll alert you when it does.’ And we can give them that answer in less than one second.”

When consumers ask our smartphone app whether they should buy now or wait, we can tell them, ‘yes, that’s a good deal, buy it now’.

— Patrick Surry, chief data scientist, Hopper

While trying to predict airfare trends is nothing new, Hopper has told TechCrunch that it can not only save users up to 40% on airfares but it can also find them the lowest possible price 95% of the time. Surry says that’s all due to Hopper’s algorithms and data models.

The Hopper app launched on iOS in January 2015 and on Android eight months later. The company also switched in September 2015 from directing customers to external travel agencies to taking bookings directly through the app for a small fee. The Hopper app has already been downloaded to more than 2 million phones worldwide.

Surry predicts that we’ll soon see sophisticated chatbots that can start with vague requests from customers like “I want to go somewhere warm in February for less than $500,” proceed to ask questions that help users narrow their options, and finally book a trip that meets all their desired parameters. Eventually, he says, these chatbots will be able to handle millions of interactions simultaneously, allowing a wide variety of companies to reassign human call center agents to the handling of high-value transactions and exceptions to the rules built into the digitized booking process.

Port of Hamburg Lets the Machines Untangle Complexity

In early 2015, AI experts told Wired magazine that at least another 10 years would pass before a computer could best the top human players at Go, an ancient game that’s exponentially harder than chess. Yet before the end of that same year, Wired also reported that machine learning techniques drove Google’s AlphaGo AI to win four games out of five against one of the world’s top Go players. This feat proves just how good algorithms have become at managing extremely complex situations with multiple interdependent choices, Saravana points out.

The Port of Hamburg, which has digitized traffic management for an estimated 40,000 trucks a day, is a good example. In the past, truck drivers had to show up at the port to check traffic and parking message boards. If they arrived before their ships docked, they had to drive around or park in the neighboring residential area, contributing to congestion and air pollution while they waited to load or unload. Today, the HPA’s smartPORT mobile app tracks individual trucks using telematics. It customizes the information that drivers receive based on location and optimizes truck routes and parking in real time so drivers can make more stops a day with less wasted time and fuel.

The platform that drives the smartPORT app also uses sensor data in other ways: it tracks wind speed and direction and transmits the data to ship pilots so they can navigate in and out of the port more safely. It monitors emissions and their impact on air quality in various locations in order to adjust operations in real time for better control over environmental impact. It automatically activates streetlights for vehicle and pedestrian traffic, then switches them off again to save energy when the road is empty. This ability to coordinate and optimize multiple business functions on the fly makes the Port of Hamburg a textbook example of a Live Business.

Digitization Is Not Bounded by Industry

Other retail and B2B businesses of all types will inevitably join the Port of Hamburg in further digitizing processes, both in predictable ways and in those we can only begin to imagine.

sap_Q216_digital_double_feature1_images4Customer service, for example, is likely to be in the vanguard. Automated systems already feed information about customers to online and phone-based service representatives in real time, generate cross-selling and upselling opportunities based on past transactions, and answer customers’ frequently asked questions. Saravana foresees these systems becoming even more sophisticated, powered by AI algorithms that are virtually indistinguishable from human customer service agents in their ability to handle complex live interactions in real time.

In manufacturing and IT, Sven Bauszus, global vice president and general manager for predictive analytics at SAP, forecasts that sensors and predictive analysis will further automate the process of scheduling and performing maintenance, such as monitoring equipment for signs of failure in real time, predicting when parts or entire machines will need replacement, and even ordering replacements preemptively. Similarly, combining AI, sensors, data mining, and other technologies will enable factories to optimize workforce assignments in real time based on past trends, current orders, and changing market conditions.

Public health will be able to go live with technology that spots outbreaks of infectious disease, determines where medical professionals and support personnel are needed most and how many to send, and helps ensure that they arrive quickly with the right medication and equipment to treat patients and eradicate the root cause. It will also make it easier to track communicable illnesses, find people who are symptomatic, and recommend approaches to controlling the spread of the illness, Bauszus says.

He also predicts that the insurance industry, which has already begun to digitize its claims-handling processes, will refine its ability to sort through more claims in less time with greater accuracy and higher customer satisfaction. Algorithms will be better and faster at flagging claims that have a high probability of being fraudulent and then pushing them to claims inspectors for investigation. Simultaneously, the same technology will be able to identify and resolve valid claims in real time, possibly even cutting a check or depositing money directly into the insured person’s bank account within minutes.

Financial services firms will be able to apply machine learning, data mining, and AI to accelerate the process of rating borrowers’ credit and detecting fraud. Instead of filling out a detailed application, consumers might be able to get on-the-spot approval for a credit card or loan after inputting only enough information to be identified. Similarly, banks will be able to alert customers to suspicious transactions by text message or phone call—not within a day or an hour, as is common now, but in a minute or less.

Pitfalls and Possibilities

As intelligent as business processes can be programmed to be, there will always be a point beyond which they have to be supervised. Indeed, Saravana forecasts increasing regulation around when business processes can and can’t be digitized. Especially in areas involving data security, physical security, and health and safety, it’s one thing to allow machines to parse data and arrive at decisions to drive a critical business process, but it’s another thing entirely to allow them to act on those decisions without human oversight.

Automated, impersonal decision making is fine for supply chain automation, demand forecasting, inventory management, and other processes that need faster-than-human response times. In human-facing interactions, though, Saravana insists that it’s still best to digitize the part of the process that generates decisions, but leave it to a human to finalize the decision and decide how to put it into action.

“Any time the interaction is machine-to-machine, you don’t need a human to slow the process down,” he says. “But when the interaction involves a person, it’s much more tricky, because people have preferences, tastes, the ability to try something different, the ability to get fatigued—people are only statistically predictable.”

For example, technology has made it entirely possible to build a corporate security system that can gather information from cameras, sensors, voice recognition technology, and other IP-enabled devices. The system can then feed that information in a steady stream to an algorithm designed to identify potentially suspicious activity and act in real time to prevent or stop it while alerting the authorities. But what happens when an executive stays in the office unusually late to work on a presentation and the security system misidentifies her as an unauthorized intruder? What if the algorithm decides to lock the emergency exits, shut down the executive’s network access, or disable her with a Taser instead of simply sending an alert to the head of security asking what to do while waiting for the police to come?

sap_Q216_digital_double_feature1_images6The Risk Is Doing Nothing

The greater, if less dramatic, risk associated with digitizing business processes is simply failing to pursue it. It’s true that taking advantage of new digital technologies can be costly in the short term. There’s no question that companies have to invest in hardware, software, and qualified staff in order to prepare enormous data volumes for storage and analysis. They also have to implement new data sources such as sensors or Internet-connected devices, develop data models, and create and test algorithms to drive business processes that are currently analog. But as with any new technology, Saravana advises, it’s better to start small with a key use case, rack up a quick win with high ROI, and expand gradually than to drag your heels out of a failure to grasp the long-term potential.

The economy is digitizing rapidly, but not evenly. According to the McKinsey Global Institute’s December 2015 Digital America report, “The race to keep up with technology and put it to the most effective business use is producing digital ‘haves’ and ‘have-mores’—and the large, persistent gap between them is becoming a decisive factor in competition across the economy.” Companies that want to be among the have-mores need to commit to Live Business today. Failing to explore it now will put them on the wrong side of the gap and, in the long run, rack up a high price tag in unrealized efficiencies and missed opportunities. D!

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Erik Marcade

About Erik Marcade

Erik Marcade is vice president of Advanced Analytics Products at SAP.

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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).