Predict And Prevent To Protect And Prosper

Justin Bean

The merging of our physical and digital worlds is accelerating faster than ever. It’s become increasingly clear that connectivity will shape our future lives and the cities we live in. We’re already seeing the Internet of Things reshaping our communities and fundamentally changing the way we interact with one another. As connectivity increases, there will be more interaction of technology on the backend helping with everything from city planning efforts to enhancing the wellbeing of citizens. We may not always see it, but we will certainly be affected by it.

I was recently on a panel discussing the topic of smart cities – specifically, how IoT is helping to advance public safety technology. Dr. Alison Brooks, research director of Smart Cities and Public Safety at IDC, and James Alfano, global lead solutions expert for Public Security and Intelligence at SAP, were also guests on the show.

Our discussion during Game-Changing Smart Cities of the Future touched on several topics around smart city safety, but there was one prevailing conclusion: We need to shift from a strategy of reacting to one of preventing.

Technology can help us accomplish this goal, and more

Industry experts are now predicting that 70% of the world’s population will live in cities by 2050. We’re looking at rapid urbanization that is beginning to cause additional challenges to the modern city – there are constrained resources to meet growing demand for services, government complexities due to the increasing population, and citizens with new digital expectations of what the city should be providing and what a city should be held accountable for.

Public safety has to be top of mind for civic leaders, as well as for service providers, workers, visitors – anybody who lives in or visits cities. It’s also not enough for just governments to be held responsible for keeping people safe – it should be a collective effort of local businesses and organizations as well. These organizations can justify their investments in technology, knowing that many of the solutions that keep people safe can also be leveraged to provide business and operational intelligence that can help businesses and economies thrive.

The definition of future success is being shaped by many factors: the need for greater safety and stability, the debate over security and privacy, and how to counter new threats like cyber-attacks that are continuing to climb to the top of national agendas, both in the United States and abroad. These factors tend to take immediate priority over longer term goals like fighting climate change and long-term economic resiliency.

That’s because public safety is a foundational requirement for thriving societies. While most cities understand how critical the issue of public safety is, they are also faced with the need to find ways to make improvements in a more cost-effective way. Technology advancements are now closing the gap, pushing citizens and stakeholders to have a more collaborative conversation with our elected officials and city governments about what needs to be done. But this does not mean that other priorities need to be ignored – cities that deploy advanced technology solutions like IoT and predictive analytics to fight crime can often leverage these same technologies to better understand traffic, environmental threats, and even the flows of people throughout a retail district in order to improve life over the long term.

Data is perhaps our greatest resource to turn these insightful conversations into actionable results. The problem, however, is that we aren’t using it to its full potential. We have enormous amounts of data – big or small, from sensors, cameras, or business systems – you name it. We are living in an era where we have become data rich. Our cities are increasing their data wealth because many have adopted systems to increase efficiency and safety – but these systems alone aren’t enough. We are gathering more and more data, but our level of actionable insight remains minimal. We are data rich, but insight poor.

How can we change this? How can our cities become as rich with holistic insight as they are with data?

The first step is to break down data silos – we need to get disparate systems connected and talking to one another. This is one of the greatest challenges we face today. Integration can unlock the insights we need to create smarter, safer, healthier, and more efficient communities.

Bringing together data from various agencies (police departments, fire departments, transit, utilities, etc.) and unifying their current systems would be a powerful first step in identifying and addressing societal challenges. It would enable us to look holistically at what the causes are and the factors that contribute to them, then to test out the different activities, policies, and programs to address them. From parking and traffic congestion, to combating crime and improving public safety, environmental sustainability, and resiliency, to education and transportation – all areas of a city ecosystem can benefit from this level of data sharing. Once we start sharing data, we lay the foundation for analytics and data mining to gain tangible civic and business insights.

In cities around the world, we’re seeing this kind of integration now starting to happen, but it could be accelerated. We are deploying more sensors than ever before; people have powerful communication devices in their pocket with access to real-time information. We have the ability to bring these factors together to create safe and thriving societies.

For example, crime tracking is now a reality. There are platforms that are leveraging machine learning to crunch a variety of informational data together in order to find patterns that humans would otherwise miss. Crime can be impacted by several variables, including weather, proximity to public transportation hubs, 911 calls, and social media. This information, while all valuable, is often too much for a human to parse through and unearth behavior patterns and predictions.

Technology can now automate this work for law enforcement, acting as a force multiplier to improve staffing and patrols – and even help prevent crimes before they happen. As advanced as this sounds, it is only the first step. When approaching crime, we should think about it in several timescales to figure out a solution to solve the challenge once and for all:

  • Incident – How can we prevent an incident or crime from occurring, or if it does, give our officers the best technology to neutralize the situation quickly and keep everyone (including themselves) safer? This includes providing situational awareness through video and IoT, along with better collaboration tools and predictive analytics.
  • Individual – Imagine all of the wasted human potential because individuals have, for one reason or another, chosen crime over more productive pursuits, or faced abuse and situations that shattered their potential. How can we leverage data and share what works to help people find their way and overcome obstacles? The Last Mile is a great example of this. It’s a program that trains prisoners to code. In a prison with a 65% recidivism rate, the rate for people that participate in this program is only seven percent.
  • Societal – There is a three-year old out there today who will someday commit a violent crime and be sent to prison. How can we create a society in which this person will not feel compelled to do so? The kind of society with the opportunity and support they need to thrive in the world will require safety, convenient access to the economy, information to educate themselves for success, and community support that human beings instinctively need to feel esteem and satisfaction in their lives.

This may seem like a gargantuan task, but it is not impossible. There are many peaceful societies around the world that do not face similar levels of crime as the United States. There are also many that are much more dangerous than the U.S. Unifying our cities across data silos to help them learn from each other is one key step toward helping us achieve this goal. The more we start to embrace data as a strategic partner in public safety and urban planning, the better positioned we’ll be to take a proactive rather than reactive approach, and create the world that we all want to live in.

For more on how technology is shaping local communities, see Running Future Cities on Blockchain.


Justin Bean

About Justin Bean

Justin Bean is the Director of Smart City Solutions Marketing at Hitachi Insight Group, which brings IoT solutions to market with the mission of social innovation. Justin has worked with startups and fortune 500s that are applying IoT and other disruptive technologies to improve our lives and cities. He has worked on projects in the US, Japan and South Africa that include smart parking, electric vehicles, renewable energy, blockchain, machine learning and 3D printing. He was the recipient of the 2015 THINK Prize in association with renowned innovation and design firm IDEO for the Financial Empowerment Challenge, holds an MBA in sustainable management, and resides in San Francisco, California.

How To Invest With Social, Environmental, And Economic Impact

Christine Susanne Mueller and Nastassia Ostrowski

Many companies, including SAP, are participating in the new Livelihoods Carbon Fund to help protect the climate, restore ecosystems, and improve people’s lives.

Peru: At night, temperatures in the Andes plummet, and it is bitterly cold. Fireplaces can produce toxic smoke, which can have disastrous effects on people’s lungs and eyes. They also consume a lot of wood, which accelerates deforestation in the region. Since the Livelihoods Carbon Fund’s collaboration with the Instituto Trabajo y Familia (Institute for Work and Family, or ITYF) in 2016, 30,000 families have been given more efficient stoves, enabling them to heat their homes and cook meals using 60% less wood while also reducing smoke and emissions. The 14-year project will help approximately 150,000 people, save 1 million tons of CO2, and create jobs. Arqímedes Huamani, project supervisor and bricklayer for ITYF, says, “It makes me proud to improve the daily lives of so many families.”

Indonesia: During the last 20 years, wide areas of the coastal forest in North Sumatra been cleared for palm oil plantations and rice fields, making the coastal villages vulnerable to climatic hazards like the tsunami in 2004. The Livelihoods Fund supports reforestation of the mangroves, which provide natural shelter from cyclones, tidal waves, and floods. The ecological benefits of the project – to plant trees that absorb CO2 and protect biodiversity – are complemented by educational activities and credits for locals to set up businesses that use products derived from mangrove plants. The project has planted 5,000 hectares of mangrove forest and 18 million trees, to capture 2 million tons of CO2 over 20 years. The local economy benefits from the increase of shrimps, fish, and crabs. The project has improved the lives of 20,000 people in 39 villages, whose incomes have grown by around 20%.

Kenya: The region around Mount Elgon is home to 2 million people. Deforestation and farming have increased erosion of fertile land. Farmers are caught in a vicious cycle, applying ever more exhaustive methods while continuously earning less with lower yields. The Livelihoods Fund has partnered with Vi Agroforestry and Brookside Dairy to train farmers in agroforestry and crop diversification, leading to a 30% rise in crop yields. The effort also anticipates a 30x increase in the amount of milk produced after five years. Brookside, a major dairy in the region, has committed to purchase the milk during the next ten years. Farmer Christine Musasia says, “In the future, I expect big things. To educate my children and become self-reliant.”

Projects in agroforestry, mangrove restoration, and rural energy not only help reduce carbon emissions, they also bring income and meaning to the lives of thousands of people in the regions most affected by climate change.

1 million beneficiaries; 130 million trees planted; 10 million tons of CO2 eliminated; 9 active projects in Africa, Asia, and Latin America; and 40 million euro invested: These are only some of the successful numbers of the Livelihoods Carbon Fund. Created by 10 European companies, the fund aims to finance large-scale development projects, improving livelihoods of communities directly exposed to the consequences of climate change and helping them restore and preserve their ecosystems.

The Livelihoods Fund’s innovative business model connects communities by creating win-win situations for both investors and fund beneficiaries. It leverages the carbon economy to create social impact and thus contributes to the 17 United Nation Global Goals for Sustainable Development, including “Good Health and Well-Being” (UN SDG 3), “Decent Work and Economic Growth” (UN SDG 8), “Live Below Water” (UN SDG 14), and others.

Companies involved in the fund, such as Crédit Agricole SA, Danone, La Poste, Hermès, Michelin, and SAP, do not receive a financial return on investment, but rather carbon credits to offset their carbon emissions. Livelihoods Carbon Fund, therefore, is a great opportunity for companies to compensate those carbon emissions they can neither avoid nor reduce and become a carbon-neutral company. It also is a great way for companies to contribute to UN Global Goals such as UN SDG 13: “Climate Action,” UN SDG 7: “Affordable and Clean Energy,” UN SDG 15: “Live on Land,” and UN SDG 10: “Reduced Inequalities.” Because of its high social and environmental value, the projects funded are also supported by many governmental and nongovernmental development organizations, such as the French Development Agency (AFD) and the International Union for Conservation of Nature (IUCN).

Motivated by the outcome of the first fund, eight companies involved in the Livelihoods Carbon Fund decided to establish a second Livelihoods Carbon Fund. Partnering with other companies has always been key to the Livelihoods concept, as it helps to scale the impact and share investment risks – heading for long-term success and even bigger impact. The new fund aims to improve another two million lives in developing countries in Africa, South America, and Asia, and prevent 25 million tons of CO2 within the next 20 years. It will also encourage other companies and investors to join, with the goal of collecting 100 million euro starting in 2018. Like the first project, the resulting carbon credits will be certified by the Gold Standard (created by the WWF) and the Verified Carbon Standard.

At the launch of the second Livelihoods Carbon Fund on December 11th, 2017, Brune Poirson, Secretary of State to the French Minister for Ecological and Inclusive Transition, highlighted the great solitary and environmental value of the innovative investment model, “giving sense to money” and accelerating the combat of climate change.

To learn more about Livelihoods Funds, visit
Twitter: @livelih00ds


Christine Susanne Mueller

About Christine Susanne Mueller

As Global Sustainability Transformation and Change manager at SAP SE, Christine Susanne Müller is responsible for sustainability related change initiatives, stakeholder management and employee engagement. She coordinates and aligns SAP's contributions to the UN Sustainable Development Goals. Since 2001, Christine Susanne has worked in various change management and communications roles at SAP on a local, regional, and global level.

Nastassia Ostrowski

About Nastassia Ostrowski

Nastassia Ostrowski joined the sustainability team as an intern in October 2017. In her role, she co-organizes the Sustainability Champions Network and supports change management and communication activities linked to SAP’s engagement for the 17 United Nation Global Goals. For more information, see also

Air Cover For The Endangered

Rick Price

David Allen sits in a semi-dark trailer, eyes on a computer screen. He is scanning an image from an aerial drone skimming the treetops at the Dinokeng Game Reserve in South Africa. It is searching for elephants. A cloud of dust appears in the camera’s field of view – the plane banks toward it, and there! A big bull, ears flapping with each step, a tracking collar around his neck. The ERP Air Force has found its target, with the aim not to destroy, but to save the creature from his nemesis, the human poacher.

The link to poverty

The group behind this – a non-governmental organization called Elephants, Rhinos, & People or ERP, was founded in 2014 by to preserve and protect Southern Africa’s wild elephants and rhinos through alleviating rural poverty. A look at some numbers shows the link between poverty and threats to the elephants and rhinos.

According to, rhino horn is worth up to hundreds of times the per-capita income in poor rural areas of Southern Africa. On the global black market, it can fetch up to $80,000 per kilogram. Depending on the species, a rhino horn can weigh three to four kilos, so one horn can command more than $300,000 – an unimaginable fortune to most people there.

Grim statistics

The result is carnage. Wild African elephants are being killed at the rate of four an hour. At the end of 2016, the population was 352,000, according to Group Elephant. Rhinos are in far worse shape: The total population as of this writing is 19,682 Southern White Rhinos and 5,042 Black Rhinos for a total of 24,724 in all of Africa. The government of South Africa says 529 rhinos were poached in that nation alone in the first half of 2017. ERP says all in all, three rhinos are killed every day. Any effort to preserve this species must give people an alternative to poaching and a stake in the animals’ survival, through jobs and economic opportunities like tourism. Conservation initiatives also have to make the most of scarce resources, and that requires cutting-edge technology.

Covering a lot of ground

These are big animals, they can move quickly, and their stomping grounds are large. Dinokeng alone encompasses more than 45,000 acres. It would be impractical to hire enough people to keep tabs on every elephant and every rhino there every moment of every day. Inside the reserve, they are safer, but if they break out into the surrounding suburban area, just north of Pretoria, they face threats, including exposure to poachers who might not risk the reserve itself. ERP works with Dinokeng to patrol the fences and help keep the animals in, but there’s a limit to what they can accomplish. That’s where the ERP Air Force, the tracking collars, and Big Data come in.

Eyes in the sky

Back in the trailer, the GPS collar monitoring system has alerted drone pilot David Allen that the bull is getting too close to the fences. The drone follows the elephants as they approach the limits of the reserve. The operator can then direct rangers in SUVs or a helicopter to nudge the herd away from the boundary. That reduces the poachers’ ability to kill the animals – outside Dinokeng, there is less protection. The drones will also watch for poaching within the reserve, and ERP hopes to use them for other initiatives.

Big Data to protect big creatures

ERP uses cutting-edge digital technology to save these wild elephants and rhinos, both by keeping them in safe areas and by alleviating the poverty of the local population. All of that requires the ability to wrangle huge amounts of data quickly, from knowing where the elephants are, to routing the people to them. A group of technology companies, including SAP and its partner EPI-USE, have collaborated with ERP to build a system capable of collecting, organizing, storing, and retrieving that information.

Next on the roadmap is predictive analytics: As EPI-USE’s Jan van Rensburg said in May 2017, “We rely upon analytics in the background, a platform where we can predict where poaching will happen. And we use the data that we build up and store in our in-memory database so that we can more efficiently use the drones, and send them to the spots where poachers are more likely to be. And when a poacher is found by the drone, we can dispatch a ranger to deal with it.”

A weapon to save, not kill

So for ERP, Big Data and the ability to use it in real time have become a weapon in the fight to save endangered elephants and rhinos, while helping people in poverty create new ways to earn a living and turn away from poaching.

Want to know more about how ERP uses Big Data and drones to save endangered wild Elephants and Rhinos through alleviating rural poverty? Watch the video.


Rick Price

About Rick Price

Rick Price is an Emmy Award-winning journalist who now works at SAP, where he tells stories of customers' digital transformation.

Hack the CIO

By Thomas Saueressig, Timo Elliott, Sam Yen, and Bennett Voyles

For nerds, the weeks right before finals are a Cinderella moment. Suddenly they’re stars. Pocket protectors are fashionable; people find their jokes a whole lot funnier; Dungeons & Dragons sounds cool.

Many CIOs are enjoying this kind of moment now, as companies everywhere face the business equivalent of a final exam for a vital class they have managed to mostly avoid so far: digital transformation.

But as always, there is a limit to nerdy magic. No matter how helpful CIOs try to be, their classmates still won’t pass if they don’t learn the material. With IT increasingly central to every business—from the customer experience to the offering to the business model itself—we all need to start thinking like CIOs.

Pass the digital transformation exam, and you probably have a bright future ahead. A recent SAP-Oxford Economics study of 3,100 organizations in a variety of industries across 17 countries found that the companies that have taken the lead in digital transformation earn higher profits and revenues and have more competitive differentiation than their peers. They also expect 23% more revenue growth from their digital initiatives over the next two years—an estimate 2.5 to 4 times larger than the average company’s.

But the market is grading on a steep curve: this same SAP-Oxford study found that only 3% have completed some degree of digital transformation across their organization. Other surveys also suggest that most companies won’t be graduating anytime soon: in one recent survey of 450 heads of digital transformation for enterprises in the United States, United Kingdom, France, and Germany by technology company Couchbase, 90% agreed that most digital projects fail to meet expectations and deliver only incremental improvements. Worse: over half (54%) believe that organizations that don’t succeed with their transformation project will fail or be absorbed by a savvier competitor within four years.

Companies that are making the grade understand that unlike earlier technical advances, digital transformation doesn’t just support the business, it’s the future of the business. That’s why 60% of digital leading companies have entrusted the leadership of their transformation to their CIO, and that’s why experts say businesspeople must do more than have a vague understanding of the technology. They must also master a way of thinking and looking at business challenges that is unfamiliar to most people outside the IT department.

In other words, if you don’t think like a CIO yet, now is a very good time to learn.

However, given that you probably don’t have a spare 15 years to learn what your CIO knows, we asked the experts what makes CIO thinking distinctive. Here are the top eight mind hacks.

1. Think in Systems

A lot of businesspeople are used to seeing their organization as a series of loosely joined silos. But in the world of digital business, everything is part of a larger system.

CIOs have known for a long time that smart processes win. Whether they were installing enterprise resource planning systems or working with the business to imagine the customer’s journey, they always had to think in holistic ways that crossed traditional departmental, functional, and operational boundaries.

Unlike other business leaders, CIOs spend their careers looking across systems. Why did our supply chain go down? How can we support this new business initiative beyond a single department or function? Now supported by end-to-end process methodologies such as design thinking, good CIOs have developed a way of looking at the company that can lead to radical simplifications that can reduce cost and improve performance at the same time.

They are also used to thinking beyond temporal boundaries. “This idea that the power of technology doubles every two years means that as you’re planning ahead you can’t think in terms of a linear process, you have to think in terms of huge jumps,” says Jay Ferro, CIO of TransPerfect, a New York–based global translation firm.

No wonder the SAP-Oxford transformation study found that one of the values transformational leaders shared was a tendency to look beyond silos and view the digital transformation as a company-wide initiative.

This will come in handy because in digital transformation, not only do business processes evolve but the company’s entire value proposition changes, says Jeanne Ross, principal research scientist at the Center for Information Systems Research at the Massachusetts Institute of Technology (MIT). “It either already has or it’s going to, because digital technologies make things possible that weren’t possible before,” she explains.

2. Work in Diverse Teams

When it comes to large projects, CIOs have always needed input from a diverse collection of businesspeople to be successful. The best have developed ways to convince and cajole reluctant participants to come to the table. They seek out technology enthusiasts in the business and those who are respected by their peers to help build passion and commitment among the halfhearted.

Digital transformation amps up the urgency for building diverse teams even further. “A small, focused group simply won’t have the same breadth of perspective as a team that includes a salesperson and a service person and a development person, as well as an IT person,” says Ross.

At Lenovo, the global technology giant, many of these cross-functional teams become so used to working together that it’s hard to tell where each member originally belonged: “You can’t tell who is business or IT; you can’t tell who is product, IT, or design,” says the company’s CIO, Arthur Hu.

One interesting corollary of this trend toward broader teamwork is that talent is a priority among digital leaders: they spend more on training their employees and partners than ordinary companies, as well as on hiring the people they need, according to the SAP-Oxford Economics survey. They’re also already being rewarded for their faith in their teams: 71% of leaders say that their successful digital transformation has made it easier for them to attract and retain talent, and 64% say that their employees are now more engaged than they were before the transformation.

3. Become a Consultant

Good CIOs have long needed to be internal consultants to the business. Ever since technology moved out of the glasshouse and onto employees’ desks, CIOs have not only needed a deep understanding of the goals of a given project but also to make sure that the project didn’t stray from those goals, even after the businesspeople who had ordered the project went back to their day jobs. “Businesspeople didn’t really need to get into the details of what IT was really doing,” recalls Ferro. “They just had a set of demands and said, ‘Hey, IT, go do that.’”

Now software has become so integral to the business that nobody can afford to walk away. Businesspeople must join the ranks of the IT consultants.

But that was then. Now software has become so integral to the business that nobody can afford to walk away. Businesspeople must join the ranks of the IT consultants. “If you’re building a house, you don’t just disappear for six months and come back and go, ‘Oh, it looks pretty good,’” says Ferro. “You’re on that work site constantly and all of a sudden you’re looking at something, going, ‘Well, that looked really good on the blueprint, not sure it makes sense in reality. Let’s move that over six feet.’ Or, ‘I don’t know if I like that anymore.’ It’s really not much different in application development or for IT or technical projects, where on paper it looked really good and three weeks in, in that second sprint, you’re going, ‘Oh, now that I look at it, that’s really stupid.’”

4. Learn Horizontal Leadership

CIOs have always needed the ability to educate and influence other leaders that they don’t directly control. For major IT projects to be successful, they need other leaders to contribute budget, time, and resources from multiple areas of the business.

It’s a kind of horizontal leadership that will become critical for businesspeople to acquire in digital transformation. “The leadership role becomes one much more of coaching others across the organization—encouraging people to be creative, making sure everybody knows how to use data well,” Ross says.

In this team-based environment, having all the answers becomes less important. “It used to be that the best business executives and leaders had the best answers. Today that is no longer the case,” observes Gary Cokins, a technology consultant who focuses on analytics-based performance management. “Increasingly, it’s the executives and leaders who ask the best questions. There is too much volatility and uncertainty for them to rely on their intuition or past experiences.”

Many experts expect this trend to continue as the confluence of automation and data keeps chipping away at the organizational pyramid. “Hierarchical, command-and-control leadership will become obsolete,” says Edward Hess, professor of business administration and Batten executive-in-residence at the Darden School of Business at the University of Virginia. “Flatter, distributive leadership via teams will become the dominant structure.”

5. Understand Process Design

When business processes were simpler, IT could analyze the process and improve it without input from the business. But today many processes are triggered on the fly by the customer, making a seamless customer experience more difficult to build without the benefit of a larger, multifunctional team. In a highly digitalized organization like Amazon, which releases thousands of new software programs each year, IT can no longer do it all.

While businesspeople aren’t expected to start coding, their involvement in process design is crucial. One of the techniques that many organizations have adopted to help IT and businesspeople visualize business processes together is design thinking (for more on design thinking techniques, see “A Cult of Creation“).

Customers aren’t the only ones who benefit from better processes. Among the 100 companies the SAP-Oxford Economics researchers have identified as digital leaders, two-thirds say that they are making their employees’ lives easier by eliminating process roadblocks that interfere with their ability to do their jobs. Ninety percent of leaders surveyed expect to see value from these projects in the next two years alone.

6. Learn to Keep Learning

The ability to learn and keep learning has been a part of IT from the start. Since the first mainframes in the 1950s, technologists have understood that they need to keep reinventing themselves and their skills to adapt to the changes around them.

Now that’s starting to become part of other job descriptions too. Many companies are investing in teaching their employees new digital skills. One South American auto products company, for example, has created a custom-education institute that trained 20,000 employees and partner-employees in 2016. In addition to training current staff, many leading digital companies are also hiring new employees and creating new roles, such as a chief robotics officer, to support their digital transformation efforts.

Nicolas van Zeebroeck, professor of information systems and digital business innovation at the Solvay Brussels School of Economics and Management at the Free University of Brussels, says that he expects the ability to learn quickly will remain crucial. “If I had to think of one critical skill,” he explains, “I would have to say it’s the ability to learn and keep learning—the ability to challenge the status quo and question what you take for granted.”

7. Fail Smarter

Traditionally, CIOs tended to be good at thinking through tests that would allow the company to experiment with new technology without risking the entire network.

This is another unfamiliar skill that smart managers are trying to pick up. “There’s a lot of trial and error in the best companies right now,” notes MIT’s Ross. But there’s a catch, she adds. “Most companies aren’t designed for trial and error—they’re trying to avoid an error,” she says.

To learn how to do it better, take your lead from IT, where many people have already learned to work in small, innovative teams that use agile development principles, advises Ross.

For example, business managers must learn how to think in terms of a minimum viable product: build a simple version of what you have in mind, test it, and if it works start building. You don’t build the whole thing at once anymore.… It’s really important to build things incrementally,” Ross says.

Flexibility and the ability to capitalize on accidental discoveries during experimentation are more important than having a concrete project plan, says Ross. At Spotify, the music service, and CarMax, the used-car retailer, change is driven not from the center but from small teams that have developed something new. “The thing you have to get comfortable with is not having the formalized plan that we would have traditionally relied on, because as soon as you insist on that, you limit your ability to keep learning,” Ross warns.

8. Understand the True Cost—and Speed—of Data

Gut instincts have never had much to do with being a CIO; now they should have less to do with being an ordinary manager as well, as data becomes more important.

As part of that calculation, businesspeople must have the ability to analyze the value of the data that they seek. “You’ll need to apply a pinch of knowledge salt to your data,” advises Solvay’s van Zeebroeck. “What really matters is the ability not just to tap into data but to see what is behind the data. Is it a fair representation? Is it impartial?”

Increasingly, businesspeople will need to do their analysis in real time, just as CIOs have always had to manage live systems and processes. Moving toward real-time reports and away from paper-based decisions increases accuracy and effectiveness—and leaves less time for long meetings and PowerPoint presentations (let us all rejoice).

Not Every CIO Is Ready

Of course, not all CIOs are ready for these changes. Just as high school has a lot of false positives—genius nerds who turn out to be merely nearsighted—so there are many CIOs who aren’t good role models for transformation.

Success as a CIO these days requires more than delivering near-perfect uptime, says Lenovo’s Hu. You need to be able to understand the business as well. Some CIOs simply don’t have all the business skills that are needed to succeed in the transformation. Others lack the internal clout: a 2016 KPMG study found that only 34% of CIOs report directly to the CEO.

This lack of a strategic perspective is holding back digital transformation at many organizations. They approach digital transformation as a cool, one-off project: we’re going to put this new mobile app in place and we’re done. But that’s not a systematic approach; it’s an island of innovation that doesn’t join up with the other islands of innovation. In the longer term, this kind of development creates more problems than it fixes.

Such organizations are not building in the capacity for change; they’re trying to get away with just doing it once rather than thinking about how they’re going to use digitalization as a means to constantly experiment and become a better company over the long term.

As a result, in some companies, the most interesting tech developments are happening despite IT, not because of it. “There’s an alarming digital divide within many companies. Marketers are developing nimble software to give customers an engaging, personalized experience, while IT departments remain focused on the legacy infrastructure. The front and back ends aren’t working together, resulting in appealing web sites and apps that don’t quite deliver,” writes George Colony, founder, chairman, and CEO of Forrester Research, in the MIT Sloan Management Review.

Thanks to cloud computing and easier development tools, many departments are developing on their own, without IT’s support. These days, anybody with a credit card can do it.

Traditionally, IT departments looked askance at these kinds of do-it-yourself shadow IT programs, but that’s changing. Ferro, for one, says that it’s better to look at those teams not as rogue groups but as people who are trying to help. “It’s less about ‘Hey, something’s escaped,’ and more about ‘No, we just actually grew our capacity and grew our ability to innovate,’” he explains.

“I don’t like the term ‘shadow IT,’” agrees Lenovo’s Hu. “I think it’s an artifact of a very traditional CIO team. If you think of it as shadow IT, you’re out of step with reality,” he says.

The reality today is that a company needs both a strong IT department and strong digital capacities outside its IT department. If the relationship is good, the CIO and IT become valuable allies in helping businesspeople add digital capabilities without disrupting or duplicating existing IT infrastructure.

If a company already has strong digital capacities, it should be able to move forward quickly, according to Ross. But many companies are still playing catch-up and aren’t even ready to begin transforming, as the SAP-Oxford Economics survey shows.

For enterprises where business and IT are unable to get their collective act together, Ross predicts that the next few years will be rough. “I think these companies ought to panic,” she says. D!

About the Authors

Thomas Saueressig is Chief Information Officer at SAP.

Timo Elliott is an Innovation Evangelist at SAP.

Sam Yen is Chief Design Officer at SAP and Managing Director of SAP Labs.

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The Differences Between Machine Learning And Predictive Analytics

Shaily Kumar

Many people are confused about the specifics of machine learning and predictive analytics. Although they are both centered on efficient data processing, there are many differences.

Machine learning

Machine learning is a method of computational learning underlying most artificial intelligence (AI) applications. In ML, systems or algorithms improve themselves through data experience without relying on explicit programming. ML algorithms are wide-ranging tools capable of carrying out predictions while simultaneously learning from over trillions of observations.

Machine learning is considered a modern-day extension of predictive analytics. Efficient pattern recognition and self-learning are the backbones of ML models, which automatically evolve based on changing patterns in order to enable appropriate actions.

Many companies today depend on machine learning algorithms to better understand their clients and potential revenue opportunities. Hundreds of existing and newly developed machine learning algorithms are applied to derive high-end predictions that guide real-time decisions with less reliance on human intervention.

Business application of machine learning: employee satisfaction

One common, uncomplicated, yet successful business application of machine learning is measuring real-time employee satisfaction.

Machine learning applications can be highly complex, but one that’s both simple and very useful for business is a machine learning algorithm that compares employee satisfaction ratings to salaries. Instead of plotting a predictive satisfaction curve against salary figures for various employees, as predictive analytics would suggest, the algorithm assimilates huge amounts of random training data upon entry, and the prediction results are affected by any added training data to produce real-time accuracy and more helpful predictions.

This machine learning algorithm employs self-learning and automated recalibration in response to pattern changes in the training data, making machine learning more reliable for real-time predictions than other AI concepts. Repeatedly increasing or updating the bulk of training data guarantees better predictions.

Machine learning can also be implemented in image classification and facial recognition with deep learning and neural network techniques.

Predictive analytics

Predictive analytics can be defined as the procedure of condensing huge volumes of data into information that humans can understand and use. Basic descriptive analytic techniques include averages and counts. Descriptive analytics based on obtaining information from past events has evolved into predictive analytics, which attempts to predict the future based on historical data.

This concept applies complex techniques of classical statistics, like regression and decision trees, to provide credible answers to queries such as: ‘’How exactly will my sales be influenced by a 10% increase in advertising expenditure?’’ This leads to simulations and “what-if” analyses for users to learn more.

All predictive analytics applications involve three fundamental components:

  • Data: The effectiveness of every predictive model strongly depends on the quality of the historical data it processes.
  • Statistical modeling: Includes the various statistical techniques ranging from basic to complex functions used for the derivation of meaning, insight, and inference. Regression is the most commonly used statistical technique.
  • Assumptions: The conclusions drawn from collected and analyzed data usually assume the future will follow a pattern related to the past.

Data analysis is crucial for any business en route to success, and predictive analytics can be applied in numerous ways to enhance business productivity. These include things like marketing campaign optimization, risk assessment, market analysis, and fraud detection.

Business application of predictive analytics: marketing campaign optimization

In the past, valuable marketing campaign resources were wasted by businesses using instincts alone to try to capture market niches. Today, many predictive analytic strategies help businesses identify, engage, and secure suitable markets for their services and products, driving greater efficiency into marketing campaigns.

A clear application is using visitors’ search history and usage patterns on e-commerce websites to make product recommendations. Sites like Amazon increase their chance of sales by recommending products based on specific consumer interests. Predictive analytics now plays a vital role in the marketing operations of real estate, insurance, retail, and almost every other sector.

How machine learning and predictive analytics are related

While businesses must understand the differences between machine learning and predictive analytics, it’s just as important to know how they are related. Basically, machine learning is a predictive analytics branch. Despite having similar aims and processes, there are two main differences between them:

  • Machine learning works out predictions and recalibrates models in real-time automatically after design. Meanwhile, predictive analytics works strictly on “cause” data and must be refreshed with “change” data.
  • Unlike machine learning, predictive analytics still relies on human experts to work out and test the associations between cause and outcome.

Explore machine learning applications and AI software with SAP Leonardo.


Shaily Kumar

About Shaily Kumar

Shailendra has been on a quest to help organisations make money out of data and has generated an incremental value of over one billion dollars through analytics and cognitive processes. With a global experience of more than two decades, Shailendra has worked with a myriad of Corporations, Consulting Services and Software Companies in various industries like Retail, Telecommunications, Financial Services and Travel - to help them realise incremental value hidden in zettabytes of data. He has published multiple articles in international journals about Analytics and Cognitive Solutions; and recently published “Making Money out of Data” which showcases five business stories from various industries on how successful companies make millions of dollars in incremental value using analytics. Prior to joining SAP, Shailendra was Partner / Analytics & Cognitive Leader, Asia at IBM where he drove the cognitive business across Asia. Before joining IBM, he was the Managing Director and Analytics Lead at Accenture delivering value to its clients across Australia and New Zealand. Coming from the industry, Shailendra held key Executive positions driving analytics at Woolworths and Coles in the past. Please feel to connect on: Linkedin: Twitter: