(Re)Programming Life

Kai Goerlich

We live in the Anthropocene era; human activity is very clearly the foremost impact on Earth. Today we require the resources of 1.6 Earths to survive, and the most moderate estimates suggest that, if current trends continue, we’ll need the equivalent of two Earths to support us.

At the same time, we are perfecting the ability to alter our ecosystems at the most fundamental level – DNA and RNA – that could theoretically reverse some of the damage we’ve done, or at least stem the continuing loss of biodiversity and habitat. Both are seen as posing great risk for our future, according to the World Economic Forum’s Global Risk Report. Of course, our quickly advancing genomic capabilities come with some difficult ethical questions.

However, gene editing will also introduce new possibilities for companies to create new lines of revenue and protect existing ones by making it possible to protect biodiversity, more safely manage ecosystem loss, and sustain agricultural production. A recent article in Nature points out that genome editing, for example, “allows much smaller changes to be made to DNA compared with conventional genetic engineering,” which might prove more palatable to the public and regulators.

The DNA revolution

The discovery in 1958 by James Watson, Francis Crick, and Rosalind Franklin of DNA as the primary building block of genetics had a major impact on how we study and interact with the world around us. The focus shifted from the analysis of plant and animal anatomy and exploring nature to the examination of life at the micro level. Over the following decades, humans have developed a comprehensive understanding of molecular biology.

Once the roles of DNA and RNA became clear – DNA stores the information of life and RNA translates the code and regulates the translation – it was only a matter of time before we figured out how to take on the role of programmers as well. When Kary Mullis discovered a way to relatively quickly synthesize DNA with polymerase chain reaction (PCR) technology (also called molecular photocopying) in 1983, the race was on.

The Human Genome Project sequenced the first full human genome in 2003. At that time, it took the collaboration of 20 universities working for 13 years and spending roughly $3 billion to do it. Thanks to high-throughput computing and massively parallel sequencing technologies (NG), sequencing speed has more than doubled every two years and costs have continued to drop (the field is advancing faster than Moore’s Law). Last September, Veritas Genetics announced $1,000 full-genome sequencing, including interpretation, for participants in the Personal Genome Project, and it’s just a matter of time before individuals can get their genomes sequenced for $100 or less.

“What we observe is a turning point in life sciences and medicine. Today our ability to generate massive amounts of biological data of any species and individual is ahead of our capabilities to interpret this vast amount of information. Working as a researcher, or even as a clinician, can feel like listening to all symphonies from Haydn to Shostakovich in parallel and trying to make sense out of it. Creating standards to annotate and exchange the data, finding the right algorithms and analytics to turn those curated data into insights will be a major challenge in the near future,” says Dr. Péter Adorján, principal expert, Precision Medicine at SAP.

Engineering life

Sequencing genomes is one thing, editing genes in living organisms is a different thing altogether. For the past 15 years, we have possessed techniques to edit human DNA by using a disabled virus (known as a viral vector) to deliver new genetic data to a cell. However, the introduction of foreign genomic materials into cells is an imprecise process and comes with a number of logistical drawbacks.

Then along came CRISPR/Cas9. Discovered in 2005, CRISPR/Cas9 is a naturally occurring immune system found in a wide range of bacteria. In a biological version of “cut-and-paste” CRISPR is able to snip out a short sequence of an invading virus’ DNA and, when invaded again, use this sequence to bind to the virus DNA and cut it at a specific part of the sequence. Less than a decade after its discovery, scientists figured out how to harness CRISPR/Cas9 for genome editing.

The approach is currently being tested for treating disease and could soon be used to treat a wide range of disorders. Once CRISPR is fully tested, it could be used to remove faulty genomes in embryos, basically eradicating those genomes from the gene pool. Theoretically, this form of gene editing should improve the safety of gene modifications; changes could be better planned, executed, and reviewed.

“The accuracy of the CRISPR method is simply stunning. The resulting medicine will improve outcomes and reduce side effects for many gene-based healthcare problems,” says Dr. Adorján. “If it holds its promises, it will probably change medicine within 10 years more than what we have observed in the last 50 years. But the methodology will raise fundamental ethical issues of how we cope with genetic optimizations of embryos or modifying germline cells, which would impact not only the individual but all subsequent generations as well.”

The impact on society and business could be profound and broad. In healthcare, gene editing is already showing progress in treating diseases such as curing chronic infection with hepatitis B and addressing the shortage of organs for transplants, for example. A group of scientists in San Diego used gene editing to create a population of mosquitoes resistant to spreading malaria. As an article in Chemistry World stated: gene editing is now “more than just a science – it’s big business too.” The genome editing market is expected to reach $3.5 billion by 2019, according to Markets and Markets. DuPont is already growing in greenhouses corn and wheat plants edited with CRISPR in an effort to make drought-resistant corn and improve wheat yields. The company’s vice president for agricultural biotechnology has predicted that gene editing will introduce a new wave of products and profits. Novartis is working with gene-editing startups on using CRISPR for engineering immune cells and blood stem cells and as a research tool for drug discovery.

Such advances are likely decades off, but they raise important ethical questions that we will have to answer, since such editing could impact not only the host organism, but the larger ecosystem, for better or for worse. For example, how might a genetically edited mosquito population impact the rest of the ecosystem? While these new tools will provide us with novel ways of managing our impact on the world around us – say, solving world hunger or reversing climate change – and create new business opportunities, there are risks.

Beyond gene hacking

The future of digital biology will not play out only at the molecular level, though. It will advance in the context of the larger world. Because ecological systems are complex, fragile networks, even the smallest changes can have a dramatic impact. That means the gene editing alone will not be enough to better deal with humanity’s impact on the world.

But genomics technology isn’t advancing in isolation.

As we’ve pointed out in previous Digital Futures posts, our world will be increasingly populated with sensors and the advanced computing power to collect and analyze the data they produce. By linking our growing wealth of biological data with rapidly advancing sensor-facilitated data, research organizations and companies could develop a more complete understanding of our environment, from rainforests to oceans and agricultural systems, at the macro level as well.

Researchers are already developing chip-scale sensors that can placed unobtrusively in the environment to measure molecular changes that could be used for such purposes as real-time monitoring of environmental pollutants, detecting toxic leaks in an industrial plant, or detecting disease by analyzing a patient’s breath. The data from such advanced sensors could also enable researchers and organizations to model and measure the impact of changes at the molecular level on larger ecosystems, and vice versa, with applications for everything from environmental sustainability to biomedicine. That intelligence will put scientists and businesses in a much better position to manage humanity’s impact on the Earth and the economy, our own health, and even help to deal with ethical questions about the impact of gene editing.

Businesses in healthcare and those with high ecological footprints, like agriculture, fishing, wood, mining, and oil & gas, could use modern sensor and genome technology to improve their risk assessment, act more sustainably, and potentially find new business ideas as well.

In order to get to that point, we’ll need to take three key steps. First, we must digitize our existing and growing understanding of life on Earth – all the existing biological, paleontological, and geological collections we’ve gathered over the centuries  in order to make them more easily accessible. Then, using the power of sensors and analytics, we can begin to scan the environment to gather critical data on our ecosystems and the impact we have on them. Finally, using gene sequencing, we can begin to explore the changes we might make by editing things at the molecular level and simulate the outcomes on a macro scale.

A designer future?

Where will these advances take us? There are a number of possible scenarios.

  1. Limited, regulated usage: We might see a future where we would simply fix molecular flaws and allow gene editing in only very specific contexts in the healthcare industry. While technology for fast and effective DNA sequencing and editing would continue to advance, the applications would be available to a niche of professionals only. We might enable gene editing to create certain designer plants to cope with climate change, for example, but that application would be highly regulated.
  1. A hybrid approach: Broader acceptance of complex gene editing would allow us to more significantly alter the natural world, editing known life forms and perhaps designing new ones. Gene editing would still be preserved for professionals. Healthcare would embrace a hybrid approach of classical medicine and gene editing. Mankind would begin to experiment with ecosystem engineering based on advanced insight and study, generating ethical controversy and long-term disputes. Some regulations would emerge in sensitive areas.
  1. Wide acceptance: In a world where IT and technology are entirely democratized and gene editing is widely accepted, we could wake up to a second creation. In this scenario, gene editing would be allowed with little restriction, with toolkits available to consumers and professionals. The healthcare industry would apply gene editing on a grand scale, and designer plants and animals would become commonplace. But, thanks to an increasingly advanced understanding of how nature operates on a macro and micro level, we could better understand and manage the consequences.

Download the executive brief Gene Editing: Big Science, Big Business.


To learn more about how exponential technology will affect business and life, see Digital Futures in the Digitalist Magazine.


Kai Goerlich

About Kai Goerlich

Kai Goerlich is the Chief Futurist at SAP Innovation Center network His specialties include Competitive Intelligence, Market Intelligence, Corporate Foresight, Trends, Futuring and ideation. Share your thoughts with Kai on Twitter @KaiGoe.heif Futu

Very Soon We Won’t Trust Anything Unless It’s Backed By Blockchain

Susan Galer

Most people wouldn’t set foot inside a plane with non-certified parts, bring tainted food home to their family, or hire someone with false credentials on their resume. Time was when lack of knowledge kept consumers and businesses from authenticating stuff – be it equipment, supply chains, or documents. Now blockchain is starting to emerge as fraud fighter extraordinaire. I heard several experts talk up blockchain’s potential strengths during a recent SAP Radio broadcast of Startup Focus with Game-Changers, “Blockchain, Trust, and Startups,” hosted by Bonnie D. Graham.

Ethical, compliant supply chains

The panel was united on one point: every industry can benefit from blockchain’s foundation of digital trust. Peter Ebert, senior vice president of sales and business development at Cryptowerk, expanded on a conversation I had with him during an interview at SAP TechEd, where he demonstrated a blockchain use case that that helped the pharmaceutical industry better track drugs. During the radio show, he positioned blockchain as an incredible fraud fighter supporting ethical supply chains, as well as end-to-end visibility for regulators.

“If you buy anything…you want to make sure that there’s no child labor involved, that the raw materials were sourced in a fair way, that you are not paying for a counterfeit and think it’s the real thing,” said Ebert. “Many different laws in various industries demand to know from where was something shipped, when was it delivered or returned. Blockchain automates this trust that you need to store all these events with trust embedded.”

Safety in the skies

Drew Hingorani, CEO of AI-BlockChain, recounted how an airplane crashed when a maintenance crew member put a jackscrew in the wrong place.

“The lubricant didn’t recognize the jackscrew because the actual jackscrew that was supposed to go into that spot had a different composite. But if you track the jet engine parts, which is a use case we’re talking about currently, you can actually solve that problem using blockchain technology,” said Hingorani.

Fraud fighter extraordinaire

Ebert agreed that aerospace engineering was a compelling use case for blockchain.

“You have parts that are very expensive, being swapped out of one airplane into another, and then suddenly you have a part that didn’t come from where you thought it would come from. It doesn’t comply with the quality metrics that are required, and you find yourself with your loved ones sitting in a plane where, even if it’s just five parts, they are not what they are supposed to be. And that’s very scary,” he said.

Blockchain spots fakes anywhere

Ebert thinks the same authenticity scenarios are true for enterprise software. “We see things where you look at an image, you look at a video, and you hear somebody talk and you think you know who it is and it sounds and looks completely authentic but it isn’t,” he said. “The very foundations of our trust can be shaken by [other] technologies, and blockchain can come in and can automate this trust…at some point, you will actually not trust a document that is being presented to you unless it’s anchored in a blockchain somewhere.”

Trust in blockchain

Andreas Fichter, SAP Innovation Center Network, predicted blockchain’s eventual emergence as the new standard for trust, whether tracking documents, 3D printing, or any kind of digital asset across enterprises and in the consumer space.

“Paper is dead, and everything will be digitalized and there will be a new expectation when it comes to trust in those new digital records, and in how we conduct business,” he said. “Probably we’ll get to the point where people will say, why haven’t we done this before, or why was it so complicated before?”

With the right blend of blockchain and other emerging technologies, your supply chain can become a competitive advantage. Read The Blockchain Solution.

This blog was originally posted on the Medium Community under SAP Innovation Spotlight.


Digital Transformation And Five New Imperatives For The Paper And Packaging Industry

Jennifer Scholze

No industry has been more affected by digital disruption than paper and packaging. The challenge has been to survive this shift and leverage digital technologies in a world far less dependent on paper products than it was 10 years ago.

Uneven impact of digital disruption

The impact of digital disruption has not been felt evenly across the paper and packaging industry. Tissue, hygiene, and packaging are doing well and seeing healthy growth. The segments that have been impacted the most are newsprint, graphic, and printing paper. These segments must redefine how to create value in a digital world.

Leaders across all industries have found ways to leverage technology to solve digital challenges. Although it may seem counterintuitive to recommend that the print and packaging industry become more digital, they must to stay relevant.

Five strategies for paper and packaging success in a digital economy

  1. A healthy segment can still be improved. While healthy, packaging can grow by leveraging innovations such as artificial intelligence or augmented reality. Machine learning can help improve product quality and maintenance and is a game changer in terms of improving process automation. Augmented reality can support workers to maintain devices safely and more cost-effectively without needing to call skilled technicians.
  1. Customer collaboration can drive new business and margins for paper and packaging companies using digital tools. Using digitized information, paper and packaging companies can provide additional services based on customers’ individualized needs, such as co-development of new packaging materials or detailed tracking information for shipped goods. Technology can also be the answer to revamping channels to open up new businesses with higher margins. One example is Sappi. As part of the company’s digital transformation initiative, Sappi targeted the European markets as an opportunity for transformation of customer segments and the use of the merchant-retailer channel.
  1. Digital transformation improves logistics and operational efficiency. Paper and packaging companies have complex manufacturing and logistics challenges. These range from transportation and warehousing to process management and asset downtime. Capturing and analyzing data from machines, vehicles, or products allows better predictions, simulations, and decisions. Automation and connectivity across the plant floor reduce error rates, add speed, and cut operating costs. Analyzing sensor data from machines helps predict possible failures early and reduces unplanned downtimes.
  1. Digital tools like machine learning help make the most out of your workforce. Digital tools help employees spend less time on repetitive tasks and more time on strategic analysis and action by automating tasks. An added benefit of leveraging digital tools to create cost-effective, outcome-driven, human-machine partnership workflows across the organization is that smart automation also tends to reduce the number of errors and accidents that occur when automation isn’t built into the model.
  1. Key partnerships with technology providers are essential in the execution of this type of digital transformation: No company can be successful if it tries to tackle digital transformation alone. In the paper and packaging industry, strategic partnerships with technology providers who understand how to synthesize emerging technologies to address core business processes is a key ingredient to success. By partnering with technology solutions providers who can not only fulfill current technology needs but help them co-innovate on the business side to drive disruption (rather than just react and adapt to it), paper and packaging companies can more easily reinvent their industry for a digitally driven world where their products don’t have to be commoditized and devalued.

The paper and packaging industry was hit hard by the explosion of digital content in recent years, but new technologies open up a world of opportunities to improve operational efficiency, accelerate speed to market, become a hub of product innovation for retailers, and create more value to its customer ecosystem than ever before.

Learn how to bring new technologies and services together to power digital transformation by downloading The IoT Imperative for Energy and Natural Resource Companies. Explore how to bring Industry 4.0 insights into your business today by reading Industry 4.0: What’s Next?


Jennifer Scholze

About Jennifer Scholze

Jennifer Scholze is the Global Lead for Industry Marketing for the Mill Products and Mining Industries at SAP. She has over 20 years of technology marketing, communications and venture capital experience and lives in the Boston area with her husband and two children.

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.

Bennett Voyles is a Berlin-based business writer.

Read more thought provoking articles in the latest issue of the Digitalist Magazine, Executive Quarterly.


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: http://linkedin.com/in/shaily Twitter: https://twitter.com/meisshaily