Why 2018 Is The Year Of The Personal Cloud

Dilip Khandelwal

I recently asked my Amazon Echo’s Alexa what the future of cloud computing is. Alexa paused, with her blue lights blinking, and then said, “Sorry, I don’t know that one.” She could, however, give me a thorough definition of cloud computing and confidently point out that the word “future” can have different meanings, which she then diligently recited until I stopped her.

It’s not the first time that Alexa and I have had different opinions about how we see the world. Or the first time that I stumped her. But overall, I have become very fond of Alexa. Voice-enabled personal assistants like Alexa, Siri, Google Assistant, and Cortana show the power of the enterprise cloud and the cloud’s ability to deliver new services, like artificial intelligence (AI), to our home and workplace.

Cloud grows up

Now, in the beginning of its second decade, cloud computing has outgrown its “punk image” as a disruptor. Defined by the industry research and advisory firm Gartner as “a style of computing in which scalable and elastic IT-enabled capabilities are delivered as a service using Internet technologies,”cloud computing has reached a high level of maturity on many fronts.

Most companies have adapted a cloud-first approach as the foundation of their IT strategy. The cloud has become the ubiquitous driving force for companies’ digital transformation and a catalyst for product innovation. Services delivered via the cloud today range from analytics to the Internet of Things (IoT) to AI, creating new frontiers for innovation.

High expectations

The cloud continues to evolve in 2018. This year, companies will have higher expectations for the business outcomes from a more intelligent and optimized cloud. We will see advancements in better interoperability in a multi-cloud environment, more advanced container-based development platforms, and the emergence of industry-specific clouds.

Data from IoT devices can be analyzed at the edge of the network with edge computing, or as IDC defined it, from a “mesh network of micro data centers that process or store critical data locally and push all received data to a central data center or cloud storage repository, in a footprint of less than 100 square feet.”

In my conversations with CIOs, I hear about two recurring needs: better convenience and security. That’s why I think that in 2018 we will see the rise of the personalized cloud.

Cloud infrastructure requirements

Companies want the benefits and strength of an intelligent cloud along with control over their own data. They also want flexibility: the flexibility to house their data in the type of data center of their choice, in their preferred geography, and the ability to consolidate their data on specific public cloud “hyperscalers” like AWS, Microsoft Azure, or Google Platform. This will be irrespective of which application they are operating.

On top of it, acquisitions put enterprises under pressure to consolidate data from multiple companies into their data centers. Integrating different business units requires multiple technology stacks to run in the same data center. As a result, companies are investing heavily into their data centers to keep up with the ever-changing requirements. They need their own, personalized solution.

According to Forrester, the managed cloud can provide the speed to market and scalability of a public cloud, yet with enhanced security through its various deployment options. The analyst firm also predicts that “exciting new private cloud technology stacks and fresh partnerships between infrastructure vendor stalwarts and upstart cloud-native companies bring the power and energy of elastic, on-demand cloud services to the enterprise data center.”

Let’s go back to my conversation with Alexa for a moment. Out of the millions of Siri, Google Assistant, and Cortana users worldwide, I may have been one of very few to ask that question. We all use technology for different purposes, and have different expectations.

This is the same in the enterprise. With the new maturity level of cloud computing, companies now have highly personalized demands on what to expect from their cloud to stay competitive.

In 2018, we will talk more about the managed personalized cloud as a secure passage for a company’s digital transformation.

This story also appears on SAP Innovation Spotlight.


Dilip Khandelwal

About Dilip Khandelwal

Dilip is the President of SAP HANA Enterprise Cloud (SAP HEC) and the Managing Director of SAP Labs India. In addition, he heads the Enterprise Cloud Services department. His global team ensures that SAP solutions run best in the Cloud, on-premise and in hybrid landscapes. He is a member of the SAP Global Executive Team reporting into the Executive Board. Dilip was recognized by The Economic Times as a ’40 under 40’ leader, India’s prestigious award for the top young business leaders.

Building The Big Data Warehouse, Part 3: Overcoming Challenges

Barbara Lewis

Part 3 in the “Big Data Warehouse” series.

Welcome to Part 3 of our series on the Big Data warehouse. Part 1 covered why enterprises are looking to create a Big Data warehouse, and Part 2, the key elements of a Big Data warehouse. This discussion covers how to overcome the particular challenges of creating a Big Data warehouse. Since the challenges of the enterprise data warehouse aspects of the Big Data warehouse are often well understood and addressed, this discussion will focus on the newer and rapidly evolving aspect of the Big Data warehouse – the Big Data part of the architecture.

The implementation and operational challenges of Big Data

Big Data solutions like Hadoop and/or Spark-based platforms are attractive to many organizations because they can cost-effectively store and process extremely large volumes of heterogeneous data (text files, video, audio, machine logs, and structured data like transaction information). However, Big Data solutions like Hadoop and Spark pose unusual challenges regarding infrastructure deployment, scaling, and successful ongoing operations. These particular challenges must be taken into consideration when deciding the ideal deployment model for incorporating Big Data into the enterprise data environment.

Big Data deployment models

There are three common methods of Big Data solution deployment:

  1. On-premises, do-it-yourself deployment and operations. The DIY approach requires procurement and provisioning of a scale-out cluster for Hadoop and Spark, as well as installing and configuring Hadoop and other ecosystem components. This approach is resource-intensive in terms of both capital costs and up-front and ongoing human resource costs. IT, and often the data science team, is heavily involved in deployment, upgrades, security implementation, and ongoing operations. The ongoing operations burden is not trivial. Big Data platforms need to be regularly tuned to ensure consistently high performance over time, especially as data volumes scale. Ignoring or diminishing the Big Data operations responsibility inevitably results in painfully slow, ineffective, or nonfunctional data projects.
  1. Infrastructure-as-a-service, with do-it-yourself operations. This approach includes getting generic cloud servers from a provider such as Amazon Web Services or Microsoft Azure and then running a Hadoop and/or Spark platform on top. IT is responsible for configuring the clusters and providing the operational team required to run the solution, as well as providing resources to implement and maintain supporting software. Some infrastructure-as-a-service providers also offer services that perform the initial Hadoop setup for users, such as Amazon EMR or Microsoft HDInsight. However, the critical responsibility of ongoing operations remains the purview of the IT team. Since the operational responsibility is both crucial to success and time intensive, this approach also requires heavy involvement from IT and a well-qualified user community.
  1. Fully managed Big-Data-as-a-service. This is a cloud-delivered service officering that includes computing infrastructure optimized for Hadoop and Spark; a complete Big Data software platform; and the ongoing operational support required to minimize job failure, scale the solution, ensure that solution updates are tested and applied, resolve resource conflicts, and perform ongoing tuning. The vendor also ensures adequate security measures for the customer.

Key aspects of an ideal Big Data solution

In order for a Big Data architecture to be effective, the ideal solution will be capable of the following:

  1. Minimizing the “time to value” of the organization’s Big Data initiatives, such as fraud detection, customer 360, IoT projects, and more
  1. Providing optimized performance on an ongoing basis, to ensure that service requirements are consistently met
  1. Scaling elastically based on actual compute and storage demands, so that capacity is maximized and cost is minimized
  1. Reducing the organization’s ongoing operational burden, so that valuable IT and data science resources are spent on the higher-value aspects of projects that drive the business forward

While some organizations will find that they can achieve this ideal on-premises, there are strong reasons to consider a hybrid cloud or cloud-only environment in order to achieve Big Data goals.

The next blog in this series will explore each of these aspects in greater detail, outlining the pros and cons of the various deployment approaches.

Learn more:


Barbara Lewis

About Barbara Lewis

Barbara Lewis is the VP of Marketing for SAP Cloud Platform Big Data Services and a thought leader in SAP’s Big Data practice, with expertise in cloud, Big Data solutions, data landscape management, Internet of Things (IoT), analytics, and business intelligence. Barbara led the launch of SAP Data Hub, the latest Big Data offering from SAP, and is active in SAP’s Big Data Warehousing initiative.

Building The Big Data Warehouse: Part 2

Barbara Lewis

Part 2 in the “Big Data Warehouse” series

In the first part of this four-part discussion on the Big Data warehouse, we covered why enterprises are looking to create a Big Data warehouse that unites information from Big Data stores and enterprise data stores. Here in part 2, we’ll cover the key elements of a Big Data warehouse and which issues enterprise technology leaders should keep in mind as they evaluate options.

What is a Big Data warehouse?

A Big Data warehouse is an architecture for data management and organization that utilizes both traditional data warehouse architectures and modern Big Data technologies, with the goal of providing rapid analysis across a broad range of information types. While analytics can certainly be run exclusively on Big Data repositories or on enterprise data repositories, it is the combination of the two types of repositories into a unified data architecture that distinguishes a Big Data warehouse.

Forrester defines the Big Data warehouse as: “A specialized, cohesive set of data repositories and platforms used to support a broad variety of analytics running on-premises, in the cloud, or in a hybrid environment. BDW leverages both traditional and new technologies such as Hadoop, columnar and row-based data warehouses, ETL and streaming, and elastic in-memory and storage frameworks.” (Forrester, “The Next Generation EDW is the Big Data Warehouse” Yuhanna, Noel. August 29, 2016, page 6.)

Key elements of the Big Data warehouse

A Big Data warehouse architecture typically encompasses the following elements:

  • A breadth of data repositories. These include repositories for both Big Data and enterprise, structured data. A Big Data warehouse typically draws from multiple data repositories, including traditional relational databases that house structured, enterprise data; columnar data stores tailored for rapid enterprise data aggregation; and Big Data stores (such as Hadoop) that handle both unstructured and structured data in massive volumes.
  • Compute/processing. Fundamental processing can happen at multiple levels in a Big Data warehouse architecture. For example, Hadoop platforms contain processing capability that can deliver aggregated information to the enterprise relational database. Fast-turn analytical processing can also happen at a higher layer, such as using the Spark engine on Big Data. Machine learning analytics can also be applied at a higher level in the stack.
  • Data management capabilities. The data management capabilities necessary for an effective Big Data warehouse include: data integration (tying systems together), data quality (ensuring a level of cleanliness or correctness of information), data transformation (ensuring consistency of data format), data security, and data governance (ensuring compliance with appropriate policy and regulatory rules).
  • Interactive analytics. Interactive analytical capabilities include in-memory analytics, ad hoc interactions, or the ability for analysts to do self-service analytics on the underlying data.
  • Advanced analytics. In addition to traditional data analysis techniques, organizations can also add advanced analytical engines to data managed by the Big Data warehouse architecture. This includes predictive analytics, graph analytics, and spatial analytics, for example.
  • A variety of data environments. Big Data warehouses typically span a variety of data environments often combining on-premises databases, cloud data stores, and hybrid environments that have already been integrated. While it is possible for some organizations to have all on-premises environments or all cloud environments, this is increasingly unusual.

Big Data warehouse general architecture

Figure: Generic Big Data warehouse architecture. (Forrester, “The Next Generation EDW is the Big Data Warehouse” Yuhanna, Noel. August 29, 2016, page 8.)

Driving analytics and business intelligence across the organization

Generally, the goal of the Big Data warehouse is similar to the traditional goals of the enterprise data warehouse: delivering intelligence and analytics to decision-makers to drive business efficiency and effectiveness. While the goal may be the same, there is also typically a goal of making analytics and reporting more broadly available across the organization.

In order for an enterprise to remain agile and respond to emerging opportunities and threats, enterprises typically cannot afford the time delays required for decisions to be made only at the top of the organizations. As a result, to meet changing expectations regarding speed and responsiveness, companies are increasingly providing analytics and reporting tools to additional layers of management or to divisions that did not have this level of insight or autonomy before.

Key issues to keep in mind

Ease of integration. By definition, a Big Data warehouse requires the integration of a wide variety of data repositories, processing capabilities, and analytical capabilities. Thoroughly investigating the ease of integration of major components of the Big Data warehouse will be key not only to initial deployment success, but also the ongoing success of the architecture.

Extensibility. There has been rapid innovation in data management, data storage, and analytics, all happening simultaneously. Ensuring that the architecture can be easily extended to incorporate emerging technologies will be important to ensuring the ongoing relevance of the overall data architecture.

Orchestration. How easy is it to create data pipelines that cross the different elements of the data warehouse? And how easy is it to manage and update those pipelines?

With this overview of the key elements of the Big Data warehouse architecture, the next blog will cover the challenges of implementing a Big Data warehouse architecture and how they can be overcome.

Learn more


Barbara Lewis

About Barbara Lewis

Barbara Lewis is the VP of Marketing for SAP Cloud Platform Big Data Services and a thought leader in SAP’s Big Data practice, with expertise in cloud, Big Data solutions, data landscape management, Internet of Things (IoT), analytics, and business intelligence. Barbara led the launch of SAP Data Hub, the latest Big Data offering from SAP, and is active in SAP’s Big Data Warehousing initiative.

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