What's New

Many Utilities Believe Cyberattacks Could Bring Down the Electric Distribution Grid in the Next Five Years, Accenture Research Finds

Accenture News - Wed, 10/04/2017 - 02:59
NEW YORK Oct. 4, 2017 – Almost two-thirds (63 percent) of utility executives believe their country faces at least a moderate risk of electricity supply interruption from a cyberattack on electric distribution grids in the next five years. This figure, included in Accenture’s (NYSE: ACN) new report, Outsmarting Grid Security Threats, part of the Digitally Enabled Grid research program, rises to 76 percent for North American utilities executives.
Categories: What's New

Building schools in Guatemala to empower a new generation

Capgemini News - Tue, 10/03/2017 - 21:30

Guatemala is a small Central American country with one of the lowest education rates in the world. With a staggering 40% of their children unable to complete elementary school, education is an unparalleled priority in Guatemala. In a country where good education can open a wealth of opportunities, Capgemini Guatemala is ensuring that children from disadvantaged environments and homes have a fighting chance to improve their future.

Dozens of dedicated employees from Capgemini Guatemala have been working relentlessly to build schools with adequate facilities in Ciudad Quetzal on the outskirts of Guatemala City. This is not just about volunteering through fundraising, but also through actual physical work to construct the schools. And the volunteers have never been happier! Their efforts have already positively impacted the lives of over 350 children, and they are all set to take this number higher.

This is the second time that Capgemini has put their backs and energy into a school building project. The success of the first school they constructed in 2016 has further cemented their belief that they are making a difference, and while work continues on the second school, they already have their sights set on a third.

Watch the video to learn more about how our colleagues in Guatemala have been making an amazing and greatly appreciated difference to the society in Ciudad Quetzal.

Across the world, Capgemini team members have been participating in a global fund raising challenge that involves jogging, walking, running, cycling to accumulate kilometers using a specially designed app. Thousands have participated. The Guatemala school building project is one of three educationally based endeavors to support underserved communities. Called “Move Fifty,” the initiative is part of our 50th anniversary celebrations.

Visit the Capgemini50 dedicated website to learn more.

Categories: What's New

Digital Transformation Is About How, Not What

SAP News - Tue, 10/03/2017 - 14:30
A digital transformation study from SAP, supported by Oxford Economics, reveals that 84% of those surveyed say digital transformation is critical to their survival in the next five years. But only three percent have completed company-wide transformation efforts.

The survey of more than 3,000 executives in 17 regions around the world is one of the largest of its kind.

The top 100 or so companies that report the most transformation say they’re getting big benefits: 85% say they’ve seen increased market share (versus 41% for the rest), and 80% say their efforts have increased profitability (versus 53% for the rest). They also expect to see more revenue growth over the next two years than the rest of the organizations surveyed.

The leading organizations turn out to be different from the others in four key ways:

They are focused on true transformation. Ninety-six percent of the leaders say digital transformation is a core business goal, compared to 61% percent of all others.

The leaders have a “digital engagement first” rather than a “product first” mindset, with greater emphasis on changing the company as a whole by improving digital skills and increasing employee engagement.

This is clear when it comes to how they’ve restructured their business for innovation: five times more of the leaders say they have changed their operations as a direct result of digital transformation, and seven times more report a significant impact on how they work with their partner ecosystems.

They transform customer-facing functions first. Almost all digital transformation efforts attempt to improve the customer experience. But almost twice as many of the leaders cite customer empowerment as a key global trend, and 92% report that they have mature digital transformation strategies and processes in place to improve the customer experience versus just 22% for all others.

The efforts of the leaders pay off: three times as many have seen significant or transformational value from digital transformation in customer satisfaction and engagement.

They are talent-driven. Leaders spend more both on hiring and on retraining workers than their peers. They are also significantly more likely to favor flatter, more agile organizational structures.

All this leads to a virtuous circle of new digital talent acquisition: More than three times as many of the leaders say their employees are more engaged thanks to digital transformation and are more likely to say that these efforts make it easier to attract and retain talent.

They invest in next-generation technology using a bi-modal architecture. The leaders, perhaps unsurprisingly, are ahead in technology maturity, but it’s mainly about new ways of working.

Ninety-three percent of the leaders say technology is critically important to retaining competitive advantage, compared with 72% of others. They are investing more heavily in Big Data and analytics (94 percent versus 60 percent), and the Internet of Things (76 percent versus 52 percent). And 50% percent of them are already working with artificial intelligence and machine learning, compared to seven percent of all others.

But the key difference is how that they apply that technology: 72% of leaders say that a bi-modal IT architecture—involving a mix of front-end and back-end infrastructure, able to operate at multiple speeds for multiple business needs—is important to digital transformation, compared to 30% of others.

The organization structures used to implement technology are also different, with leaders overwhelmingly favoring a more centralized approach. They are three times more likely to have a dedicated group for digital transformation or implement change using existing business-focused IT organizations. The others are much more likely to see efforts scattered across operations, finance, and other functional units.

What’s Next?

So far, as Pat Bakey put it at SAP Leonardo Live, “We see sparks of innovation happening, but we haven’t seen the big bonfires of opportunity.”

It’s time for IT organizations to accelerate digital transformation beyond small “islands of innovation” and adopt a more holistic, centralized approach. Most companies, faced with digital transformation, are changing what they do, but not how they do it.

What sets the leaders apart is that they have internalized the need to transform how they think as well as what they do—to create a digital mindset across the organization.

Digital transformation will determine the future of business, so it cannot be incremental or be considered independently from the operating model. In the end, what the survey shows most clearly is that companies that treat digital transformation as another IT project will not just fall behind. They will fail.

As Vivek Bapat, senior vice president and global head of Marketing Strategy and Thought Leadership at SAP, puts it: “The next two years will be a key inflection point, which will separate the digital winners from those left behind. Digital transformation is no longer a choice, it’s an essential driver of revenue, profit and growth. Executives need to move from simply understanding the high stakes, to activating complete end-to-end execution across their business. This requires innovative breakthrough technologies, investing in digital skills, and retraining the existing workforce.”

Timo Elliott is an innovation evangelist at SAP.

This story previously appeared on The Digitalist.

Categories: What's New

Taking R from Prototype to Production

Capgemini News - Tue, 10/03/2017 - 12:35
The Problem: anticipating bugs

R is a wildly popular language in the Data Science community. The language’s success has come from the provision of a deep and broad statistical tool kit in a free, open-source language with a large community behind it. Some indexes of programming language popularity even place the R language as high as fifth, below C++, Python, Java and C[1].

Getting data cleaned, into a model, and producing results can be achieved quickly in R. It is one of the language’s greatest assets, but this comes at a cost; making an R program that works every time is an arduous task. This is a critical feature of a code base that is to be put into production.

Say I wanted to forecast a time series which I have in an Excel (.csv) format, all I would need to do is this:

library(forecast) test.data <- ts(read.csv("time_series.csv"), frequency = findfrequency(test.data)) forecast.result <- forecast(auto.arima(test.data)) plot(forecast.result)


In just four lines of code I have created a simple program that can forecast a time series with no parameter specification. As a reward for my short labour I get a fancy plot with an accurate forecast:

Forecast of Air Passenger data in the US

Say this code gets put into production, with a new data set provided each day. What would happen to the program should a value go missing and the data provided contains the word “missing” for the first value in the data with all other values remaining the same? This is enough to break our program, as the following result is produced:

The forecast model now uses completely different values due to a single missing value in the data

Not only is the forecast completely wrong, the values of the time series values are totally incorrect. Worse still, the program hasn’t given an error message or even a warning, it has simply assumed all is well and provided a totally invalid result.

Why has this happened? The character value “missing” means that R has read the entire data set as a text values rather than numeric values. By default, R converts text to a data type known as factor, which assigns a number to each unique data point. The model has used this index, not the actual time series, for the forecast.

This shows how a prototype in R can fail when brought into production. While these are not problems unique to R, they are problems that are harder to solve in R.

Problems such as these are hard to avoid and fix in R due the nature of the language. There is no static typing outside of the S4, most CRAN packages lack unit testing, the unit tests CRAN packages must pass have significant problems[2], parallel implementations only work on some platforms and the S3 and S4 object orientated functionality is clunky. Purdue’s study of the language said it best:

“For robust code, one would like to have less ambiguity and would probably be willing to pay for that by more verbose specifications, perhaps going as far as full-fledged type declarations. So, R is not the ideal language for developing robust packages.”[3]

Serious questions follow form that statement. Can R provide business value in the long run? What are the best use cases of R? Why is R so popular? Should you even use R?

To the last question, I would say yes, emphatically. A language where you can get a result quickly is highly valuable. I’ll address how you can future proof that result below.

The Solution: adopt best practices early

The previous section talked about some of the weaknesses of the R language, showing the ease at which you can build prototypes which are feature rich but break easily in production. Here, I’ll show how by using good practices from the beginning solves the problems that R presents and make it an invaluable tool when bringing a product to market.

In my experience, it is possible to take R beyond the prototyping stage by using good practices from the beginning. These are practices that are found across all computing languages but are all too often left behind in R code.

  • Adopting a coding standard early
    Follow a common syntax, the google R style guide is a good starting point. If the project likely to be large implement it as a package sooner rather than later. Avoid using packages with similar functionality, for instance avoid using both data.table and dplyr, use one or the other. This is particularly important when working in teams.
  • Unit tests
    The loosely typed structure of R code lends itself to packages breaking easily when altering code. Unit tests provide good protection against this, and can easily be implemented when your code is structured as a package.
  • Recognizing good packages from bad
    Getting a package onto CRAN merely requires that your code passes check, which is a pretty low benchmark. Reading through the package code and understanding how each package works under the hood is good practice.
  • Breaking apart components for later development in other languages
    Breaking apart your code into functions, even when only scripting, makes the code modular and more manageable as the project scales. It also means that more computationally intensive components can be handed over to other languages with RCPP or rJava.
  • Using R as a wrapper for other languages
    If you know C++ or Java, then it may be more effective to write larger ecosystems in those languages. This way R can act as an effective wrapper for others to quickly implement your code on other systems. This has been done very effectively by some R packages written by Google and Facebook (see BOOM and prophet respectively).

[1] http://spectrum.ieee.org/computing/software/the-2016-top-programming-languages

[2] http://stackoverflow.com/questions/9439256/how-can-i-handle-r-cmd-check-no-visible-binding-for-global-variable-notes-when

[3] Morandat, Floréal, et al. “Evaluating the design of the R language.” European Conference on Object-Oriented Programming. Springer, Berlin, Heidelberg, 2012.

Categories: What's New

Accenture and Duck Creek Technologies Jointly Create Digital and Emerging Technology Solutions for Insurers

Accenture News - Tue, 10/03/2017 - 09:59
NEW YORK; Oct. 3, 2017 – Accenture (NYSE: ACN) and Duck Creek Technologies, a leading provider of P&C insurance software and services, have created several new digital and emerging technology solutions that will be available for demonstration at InsureTech Connect, the world’s largest insurance technology conference, in Las Vegas on October 3-4, 2017. Onsite demonstrations include advanced telematics, data analytics and blockchain solutions for insurers. 
Categories: What's New

Machine Learning at SAP: Learning Field for All

SAP News - Tue, 10/03/2017 - 09:30
The machine learning teams at SAP locations in Singapore, Walldorf, and elsewhere are bursting at the seams.

For Dirk Jendroska, head of Machine Learning Strategy and Operations at SAP, “Machine learning is a growth area for SAP and our team has an increasingly vital role to play.” The majority of SAP’s machine learning experts are driving the topic out of Singapore, Palo Alto, Brisbane, Ra’anana, Potsdam, and Walldorf.

SAP’s machine learning experts are driving the topic from locations worldwide

“But if we want to achieve our vision of making all SAP solutions intelligent,” says Jendroska, “we’ll need to draw on the expertise of everyone in Development, wherever they’re located.”

Three Ways to Implement a Machine Learning Strategy

Daniel Dahlmeier, responsible for Sales and Service in the Machine Learning team, explains how there are three ways for SAP to implement its machine learning strategy: “At the top of the pyramid are SAP’s own applications — new machine learning products like SAP Cash Application and SAP Brand Impact — that we sell as independent items on the price list.”

The level below consists of business services, built by the team and either integrated into the SAP solutions to make them intelligent or made available for purchase by customers on SAP Cloud Platform. “To take an example: SAP Service Ticket Intelligence is integrated into SAP Hybris Cloud for Service. But customers that do not run SAP Hybris Cloud for Service can also buy the business service as an API and integrate into their own application.”

The third level is SAP Leonardo Machine Learning Foundation, a portfolio that SAP presented at this year’s SAPPHIRE NOW. As SAP’s machine learning platform deployed on SAP Cloud Platform, SAP Leonardo Machine Learning Foundation offers SAP teams and customers “the technical basis on which to add machine learning logic to their solutions,” according to Dahlmeier.

Key to Success of SAP S/4HANA in the Cloud

Matthias Haendly of SAP S/4HANA Cloud Product Management describes machine learning as “vitally important,” not least for the success of SAP S/4HANA in the cloud. Because, he continues, customers expect the next generation of ERP to take process efficiency to a new level.

Machine intelligence can improve all kinds of process steps, and machine learning — like predictive analytics, to which machine learning is closely related — has the power to make these improvements happen. At the same time, it is also crucial to show customers that “SAP understands new technologies and is making them usable for its customers.” That is another reason why SAP is focusing intensely on machine learning, adds Haendly.

Phoebe Poon from the Business Development team confirms that “the topic of machine learning changes the perception of SAP and opens up new conversations with customers. Once they understand we are working in that area as well, many companies are very interested to learn about what we do with machine learning on the business side.”

“Some can’t wait to start co-innovation projects with us,” says team member Niveditha Hari. These customers are very open to new things: “They know we are reliable and that they can benefit from this more open relationship with us.”

Open to Lifelong Learning

Machine learning will impact the role of the software developer, too. According to Markus Noga, head of the Machine Learning team at the SAP Innovation Center Network, developers have always had a reputation for their open attitude to lifelong learning: “Whether it’s a new language, new operating systems, or a paradigm shift like the cloud, developers work in a fast-paced technological environment. I want to encourage every single one of them to embrace and experiment with machine learning ‒ to get a feel for what this technology can and cannot do.”

But developers are not the only ones who need machine learning expertise. It is highly relevant for the Digital Business Services area too, as business developer Christian Boos is keen to emphasize.

Colleagues in DBS need to be able to support customers that are looking to integrate machine learning business services into their solutions. By fulfilling this role, they could potentially create a feedback loop to Development “in cases where DBS or partners want to install a business service that is not yet available as a standard service on SAP Leonardo Machine Learning Foundation.”

This is how machine learning becomes a learning field for all.

Categories: What's New

Accenture Collaborates with Veeva Systems to Advance Regulatory Information Management for the Life Sciences Industry

Accenture News - Tue, 10/03/2017 - 09:29
NEW YORK; Oct. 3, 2017 – Accenture (NYSE: ACN) today announced it is working with Veeva Systems (NYSE: VEEV), a leader in cloud-based software for the global life sciences industry, to bring Veeva Vault RIM – a transformative regulatory information management solution – to biopharma companies. The collaboration will help enable the industry to bring new medicines to patients faster. 
Categories: What's New

Artificial Intelligence and the Healthcare Ecosystem—Part One

Capgemini News - Tue, 10/03/2017 - 09:00

Artificial Intelligence (AI) is a hot topic. The technology is emerging from its more traditional academic/back-office orientation and is becoming more mainstream. Many of the leading publications such as the Economist, the Financial Times, the Wall Street Journal, the New York Times, and the BBC are publishing AI-related content on a more frequent basis. World governments and leaders are now commenting on the technology—the Chinese government announced a plan to invest billions in the technology, with the goal of moving China to the forefront of AI by 2025. Vladimir Putin recently stated that whoever masters AI will rule the world. Elon Musk believes unregulated AI is a threat to the world. Others, such as Gary Kasparov, have a more positive view of AI’s potential contribution to the world.

The purpose of this blog is to share knowledge and engage in discussion with regards to the application of Artificial Intelligence (AI) across the healthcare ecosystem. The first several posts are intended to help establish a baseline understanding of AI. Over time, the focus will shift to topics such as: AI as an enabler for industry transformation; industry incumbents partnering with or acquiring technology assets; new firms and/or new technology entering the market; emerging use cases such as how AI may affect drug discovery and development; and the general evolution and maturation of the technology. The blog will remain relevant to healthcare ecosystem oriented topics.

This blog will focus more on the business application of the technology and not so much on the technology itself. Articles on artificial intelligence and machine learning are often times heavily weighted on math and technology. This is understandable given the subject matter. There will be some technical discussion, but these will be limited in scope and for the purpose of furthering high-level understanding. Links will be provided for those who seek additional, more detailed understanding of the more technical aspects.

I am doing this because I am fascinated by the potential of AI across the healthcare ecosystem. We are on the cusp of truly remarkable changes in the way we think of healthcare and how we deliver healthcare. The next couple decades will be really freaking cool.

Why now?

AI is emerging from its traditional roots primarily in academia and is becoming a mainstream business tool. Historically, AI was the focus of the more technically astute people in academia and/or the financial services community. Wall Street was an early adopter of the technology. Quants have long been valued for their ability to write complex trading algorithms. Approximately 50% of all US equity trading is executed via high frequency trading (algorithms)1.

The technology is now beginning to mature and proliferate across a much wider cross-section of the economy. Organizations that leverage AI will most likely find themselves with a competitive advantage relative to those who fail to understand and leverage the technology. Industry disruption is happening at a more rapid pace. Those that fall behind may find it difficult to close the competitive gap.

A brief history of Artificial Intelligence (AI)

The ideas associated with AI are not new. The concept of non-human objects being programmed to mimic human-like capabilities has been around since the Greeks. Homer wrote of mechanical assistants waiting on the gods at dinner.2

The more modern concept of AI dates to the 1950s. In 1950, Alan Turing proposed what has become known as the Turing test—can a computer communicate well-enough with a human to convince the human that it (the computer), too, is human.

The term artificial intelligence was coined in 1956 at a conference at Dartmouth College. The mid-1950s ushered an era of optimism. Many of that era’s leading scientific minds attended the Dartmouth conference and contributed to the early advancement of the technology.2 Despite the early optimism, achieving artificially intelligent systems proved to be a challenge. Waves of enthusiasm were followed by troughs of disillusionment throughout the 1950s, 60s, 70s, and 80s.

Interest in AI began to pick-up again in the late-1990s when IBM’s Deep Blue defeated the Russian Chess Grandmaster Gary Kasparov. Kasparov detailed this experience in his recently released book “Deep Thinking: Where Machine Intelligence Ends and Human Creativity Begins.” This is a good book and will be added to the recommended reading list. Kasparov believes AI will have a positive impact on society.

In 2011, IBM once again demonstrated the potential of AI when its Watson system won the quiz show Jeopardy. Watson’s success on Jeopardy coupled with a successful marketing campaign is helping to expose the capabilities of AI to a much wider audience. The technology is emerging from its traditional academic orientation and is becoming more accepted by the mainstream.

How do we define Artificial Intelligence (AI) and why the recent resurgence?

There is no single absolute definition of AI. For the purpose of this blog, AI is defined as: the capability of a machine (non-human) to replicate intelligent human behavior and human decision-making capabilities. AI should have the ability to perform as well or better than a human when performing a task.

Recent advances in technology and access to large amounts of data are enabling the resurgence of AI.  Hardware and software are becoming ever-increasingly more powerful, less expensive, and easier to access. This enables the processing of large data sets quickly and cost effectively. The amount of data we produce doubles every year. As much data was produced in 2016 as was produced from the beginning of time through 2015. Data is instrumental in helping AI systems learn. The more information available for processing, the more the AI system can learn, and the more accurate it becomes. AI is beginning to mature to the point where it can learn without human interaction. For example, Google’s Deep Mind taught itself how to play and win Atari games.4

What is Machine Learning?

Artificial intelligence (AI) consists on numerous subfields including natural language processing (NLP), reasoning and knowledge representation, perception, and machine learning. Machine learning is one of the more important components of artificial intelligence. It is being used to enhance our everyday experiences via artificially intelligent machines and interfaces. Amazon’s Echo, Apple’s Siri, and Google’s Assistant are a couple of the more well-known products that leverage machine learning.

Machine learning can be applied to a variety of situations; however, it is often used to predict behavior.  Credit scoring is a well-known application of machine learning. When someone applies for a loan, a credit card, or a mortgage, the applicant is generally asked a series of questions. This information is combined with input from the applicant’s credit history and fed into a predictive model. This model generates the credit score.

Target marketing is another frequent application of machine learning. Marketing departments will leverage insights based on a series of attributes such as:  age, web-browsing history, income, purchase history, location, etc. to predict if the person may be interested in a product or not. This prediction can be used to decide whether or not to extend a promotional offer. Likewise, target marketing can be used to determine how much a person may be willing to pay for a particular product or service. Personalized pricing strategies can be implemented via this insight.

Machine learning has numerous use cases across the healthcare ecosystem. For example, the technology can be applied in preventative health programs. Machine learning can be used to assess a person’s –omic (genome, proteome, metabolome, microbiome) data along with other data sources such as the person’s electronic medical record to predict the likelihood of developing diseases such as diabetes or heart disease. Individuals who demonstrate a high propensity for the disease can be addressed with proactive intervention—e.g., the implementation of lifestyle changes or the prescription of preventative therapies.

Thank you for taking the time to read the first of what will be many posts on this topic. I hope you found the content informative.  Future instalments will include topics such as—why use artificial intelligence and machine learning; an overview of the technology and models; an overview of the leading AI companies and what they are working on; ethical considerations; how to get started with AI, etc. Please reach out to me at any time if you have any questions, comments, or would like to participate in future posts.


  1. Chaparro, Frank.  “CREDIT SUISSE: Here’s how high-frequency trading has changed the stock market” Business Insider.  March 20, 2017.
  2. Buchanan, Bruce. “A (Very) Brief History of Artificial Intelligence” AI Magazine Volume 26 Number 4. 2006.
  3. Moor, James. “The Dartmouth College Artificial Intelligence Conference:  The Next Fifty Years” AI Magazine Volume 27 Number 4.  2006.
  4. Helbing, Dirk; Frey, Bruno; Gigerenzer, Gerd; Hafen, Ernst; Hagner, Michael; Hofstetter, Yvonne; van den Hoven, Jeroen; Zicari, Roberto; Zwitter, Andrej. “Will Democracy Survive Big Data and Artificial Intelligence?” Scientific American. February 2017.
Categories: What's New

2017 University Graduates Cling to Human Elements Despite Digital Fluency, According to Accenture

Accenture News - Tue, 10/03/2017 - 08:45
NEW YORK; Oct. 3, 2017 – A new study by Accenture (NYSE: ACN) shows that three in four new university graduates believe their education prepared them for today’s digital workforce. Despite their digital native prowess, only 19 percent of these new graduates prefer web communications tools, while nearly four in ten (39 percent) favor face-to-face interactions with colleagues. 
Categories: What's New

Accenture Innovation Challenge Launches to Inspire Young Innovators to Create Disruptive Business and Tech for Good Solutions

Accenture News - Tue, 10/03/2017 - 04:00
BENGALURU, INDIA; Oct. 3, 2017 – Accenture (NYSE: ACN) has launched the Accenture Innovation Challenge to inspire and catalyze technology innovation among college students across India. The Challenge invites full-time graduate, under-graduate and post-graduate students to imagine and develop innovative ideas that can create value for people and businesses and to discover their own innovation potential.
Categories: What's New

The Open Business Data Lake Standard, Part III

Capgemini News - Mon, 10/02/2017 - 14:06

In my first and second blog about the “Open Business Data Lake Conceptual Framework (O-BDL)” I introduced its background, concept and characteristics. In this third part I want to discuss the capabilities of an O-BDL.

The O-BDL is a platform which provides a set of common capabilities that are required or useful to create new insights from data, regardless of its purpose (descriptive, diagnostic, predictive, prescriptive, see diagram below).

To create these insights the O-BDL is divided into the following four domains:

  • Data ingestion domain
  • Discovery domain
  • Data assembly domain
  • Insights creation domain

Data ingestion domain
The goal of this domain is to ingest data/events loaded from different sources and store it as-is (i.e. “schema on read”) and make it searchable. To achieve this, metadata is extracted, classified, and indexed.

Data discovery domain
The goal of this domain is to discover possible and relevant data patterns. To achieve this, ingested data sets are searched, compared, and assembled and stored into new data sets. If needed, synthetic data is generated and added to the assembled data set or data sets residing outside of the Data Lake are added by virtualizing them. New data sets are used to define and test machine learning algorithms. The assembled data sets are stored in a format depending on the proposed usage (i.e. “schema on write”).

Data assembly domain
The goal of this domain is to prepare data sets to be used for creating insights. To achieve this, ingested data sets are searched, compared and assembled and stored into new data sets. If needed, synthetic data might be generated and added to the assembled data set. It might also be relevant to connect to data sets residing outside of the O-BDL by virtualizing them. The quality of the data is assessed and improved (cleansing, standardization, harmonization, etc.), after which the data set is stored in a format depending on the proposed usage (i.e. “schema on write”), which can be an Enterprise Data Warehouse/Data Mart, or a SQL, key-value database, document, graph, or column database. Finally the metadata is extracted, classified, and indexed and the assembled data set is made available for distribution.

Insights creation domain
The goal of this domain is to create any type of insights (i.e. descriptive, diagnostic, predictive, prescriptive). To achieve this, assembled data sets are searched for and consumed within reports, algorithms, and/or simulations. When data is used coming directly from user input, natural processing capabilities are required. The output can be visualized, distributed, or embedded into a business process (i.e. rules engine) and will be stored in a format depending on the proposed usage (i.e. –“schema on write”).
To keep track of changes made in the data between ingestion and actual use (by whom), data lineage and monitoring, as well as data authorization capabilities are part of the O-BDL. Finally, data archiving capabilities should be applied when data isn’t used anymore or when to comply to legislation rules.

In the fourth blog in this series I’ll position the O-BDL domains within the CRISP-DM (Cross Industry Standard Process for Data Mining) and compare the O-BDL with other data processing platforms.

Categories: What's New

Accenture Launches Cloud Suite for Oracle to Accelerate Clients' Move to the Cloud

Accenture News - Mon, 10/02/2017 - 11:59
NEW YORK; Oct. 2, 2017 – To answer enterprise clients’ needs for a solution that supports personalized client journeys to cloud, Accenture has launched the Accenture Cloud Suite for Oracle. As the latest innovation from the Accenture Oracle Business Group, the Cloud Suite consists of an extensive portfolio of cloud-based accelerators, assets and tools that help enable the tailored creation and delivery of the Oracle Cloud portfolio.
Categories: What's New

Innovation: Parting With Traditional Industry Trends

SAP News - Mon, 10/02/2017 - 10:30
Traditional business models are under pressure across all industries. Companies such as Knorr-Bremse, Daimler, and ARRI have already undergone a business transformation.

According to Carsten Linz, head of SAP’s Center for Digital Leadership, it’s a question of keeping up, or falling behind.

Q: In your book “Radical Business Model Transformation: Gaining the Competitive Edge in a Disruptive World,” you closely analyzed 380 different companies. What is the distinguishing factor for a company’s success?

A: Successful companies show a willingness to learn and adapt. They seize new opportunities and take calculated, strategic risks with their minds set on creating a sustainable future, rather than simply reacting to changes in the market. When it comes to digitalization initiatives, the overwhelming majority of companies today are still focused on automation and increasing efficiency. Yet a small number of pioneer companies have realized that the real improvement potential lies in transforming their sales model or even their entire business model. This allows them to create game-changing innovations and new digital revenue streams instead of merely digitalizing long-standing processes and consolidating past achievements.

Successful companies show a willingness to learn and adapt

Can you give us an example of a company that achieved greater success by reinventing itself?

Absolutely. But it’s important not to mistake digital transformation with business model transformation. They are two separate topics. For example, ARRI, a global company in the motion picture media industry, transformed its product offerings from analog to digital video cameras, which put it back up to global leader in the market. No changes to its well-structured business model were necessary – all ARRI needed was to digitalize its products.

Yet this is the exception. Most companies do require a more comprehensive transformation of their business. In this case, our Business Transformation Board differentiates four different types of business models, which include the product, platform, project, and solution business models. They are differentiated based on the dimensions “completeness of transaction” and “product customization,” and there are predefined approaches for transformations between these business model types. These include recommended procedures for front-end (value proposition, customer interaction), back-end (value activities not perceived by customer), and revenue mechanism transformations. Structuring the process in this way will not only lend more control over the transformation, but also enables the leadership team to use a clear common language. We also published an article on our approach in the Harvard Business Manager.

What exactly is the platform business model?

Let’s look at Daimler to illustrate this business model. The car company created a mobility platform called moovel, which in addition to its own car-sharing service Car2Go, incorporates public transportation, the mytaxi platform, bicycle rentals, and Deutsche Bahn — none of which are related to Daimler in any way. The reason for this was to create a comprehensive mobility solution on a single platform, and due to the network effect, not many alternative platforms are still out there. The winner takes it all.

We are all familiar with this effect, like when Google completely took over the search engine market, or how hotel portals are dominated by booking.com. We can therefore assume that Daimler’s aim behind launching this platform is to become number one in the market. At the same time, Daimler is reinventing itself – not by changing everything all at once, but by making successive, small changes, starting with the outer borders of the company and the boundaries of their market, which are gradually disintegrating.

Becoming a solution provider isn’t exactly easy. Doesn’t that make transforming the entire business model as a first step seem like a crazy decision?

Depending on the starting point, some companies even require a two-phase transformation. For example, Knorr-Bremse, the world’s largest provider of rail vehicle braking systems, began by shifting from a product business model for rail braking systems to a platform business that offers multiple braking systems. From this point, it went on to offer customized sub-system solutions for rail and commercial vehicles.

This two-phase transformation is a huge challenge, especially for small and midsize businesses. Yet isn’t it remarkable that we still managed to find many hidden champions who dared to take this strategic risk, and implement a full transformation despite the challenges and uncertainties? It takes a lot of courage and conviction to follow this path, but it could end up paying off better than following general industry trends, especially considering the “winner takes it all” phenomenon across the platform world.

So as companies take a completely different course from their original business, the lines between industries are blurring?

Which means that digitalization involves breaking away from traditional industry perspectives. The basic business model types will remain, but the traditional boundaries that defined them will disappear. For example, if I spontaneously decide go skiing in Switzerland for half a day during a business trip, I can book a skiing pass and an accident insurance daily using my smartphone or car ID, and will receive an additional assistance service in the event of a skiing accident. This allows insurance companies to offer entirely new service packages.

Innovation takes place predominantly at the borders where different industries intersect, and business model transformation requires companies to think from a business perspective, not just an industry perspective.

Innovation takes place at the borders where industries intersect

Everyone’s talking about digital transformation and trying to understand it and keep up with the changes. Will the hype be over in 10 to 15 years?

It’s true that the term “digital transformation” is overused these days. Yet considering that only a small group of company managers have managed to achieve a higher level of digital maturity so far, the topic will most likely remain relevant for a long time — see the latest SAP Digital Transformation Executive Study.

More importantly though, we must learn to recognize digitalization as a potential driver for business model transformations. Our customers’ CEOs often ask me to help them define a blockchain strategy. What they don’t realize is that it’s not a blockchain or a digitalization strategy the need, but a transformation strategy. Digitalization is only the tool used to achieve this. Use cases are the main focus. The various technologies merely help companies realize them. Use cases often require a combination of several different technologies to solve a problem more efficiently.

After all, digital transformation itself is not an end in itself; it is a means to drive innovation and create new revenue streams and added value for the corporation. The technologies will change, but this core function will remain.

In the end, transformation must benefit mankind. Do you have a recent personal example of this?

As I am often away on business, I decided to use a mobility platform that integrates several different means of transport to plan my latest trip. The platform booked a car-sharing service for my journey to the airport and, at the same time, reserved a parking spot right by my terminal. This saved me the taxi fare, and the effort of finding a parking spot by myself, and it was all organized through a single app with hardly any effort on my part.

Which topic should CEOs add to their next executive board meeting agenda?

I would broach the topic with a question: “When was the last time you did something for the first time?”

More information:

Dr. Carsten Linz is head of SAP’s Center for Digital Leadership, which helps SAP customers’ executive boards successfully overcome the leadership challenges of this digital era. Together with co-authors Günter Müller-Stewens and Alexander Zimmermann from the University of St. Gallen, Switzerland, Linz explains in his book “Radical Businesss Model Transformation” how companies can successfully transform their business model to gain a competitive advantage.

Categories: What's New

Facilitating Migration to SAP S/4HANA

SAP News - Mon, 10/02/2017 - 09:30
Are you a new or existing SAP customer and looking for an easy and smooth transition to SAP S/4HANA?

SAP can support you with the following three main transition scenarios: new implementation, system conversion, or landscape transformation.

In addition, customers may want to combine some of these scenarios or have specific migration requirements beyond the scope of these options. In this case, SAP can also provide support with the service offerings from the Data Management & Landscape Transformation team.

In a series of articles, we’ll inform you about the various transition scenarios and offerings, beginning with the new implementation.

SAP S/4HANA Migration Cockpit for New Implementations

To newly implement SAP S/4HANA, you need to think about how to migrate legacy data from the source systems to SAP S/4HANA.

“We have developed the next generation data migration solution which is the SAP S/4HANA migration cockpit,” states Wolfgang Gutberlet, senior vice president, Products & Innovation. “It is easy-to-use and contains a comprehensive set of new functions for the data migration to SAP S/4HANA.”

As part of SAP S/4HANA Cloud and on premise, this migration cockpit is the tool of choice for migration to SAP S/4HANA.

Preconfigured SAP S/4HANA-Specific Migration Content

To facilitate migration, the cockpit uses predefined migration content specific to SAP S/4HANA: migration objects such as customers, suppliers, and open items to identify and transfer the data. For each migration object, the cockpit provides a template file with necessary SAP S/4HANA fields that customers fill with the source data and then upload it into the target system.

High Data Quality and Consistency Due to Automated Mapping

The cockpit contributes to high data quality and consistency by providing automated mapping between the data coming from the source systems and the structure of the target systems. This means the cockpit checks whether the values from the source systems – which you have entered in the template file – are compliant with the customizing values for SAP S/4HANA.

Flexible Migration

You can easily extend and adapt the standard migration scope delivered with the SAP S/4HANA migration cockpit to your requirements. Also, you can include custom-specific data using the inherent migration object modeler. You can either create your own migration objects from scratch or adjust standard migration objects to your needs; for example, add new fields to these objects.

Easy-to-Use and Safe Migration Processes

The migration cockpit efficiently guides you through the migration process and offers a simulation functionality. When you have uploaded the template files with source data, you get guidance through the various activities of the migration.

At first, the cockpit automatically checks the mapping between the uploaded template files and the target structure. If there are incorrect values, you can easily change them so that they fit the target values. Before the actual data migration, you can simulate the transfer without any update in the target system. When this finishes successfully, you start the data migration to SAP S/4HANA.

Outlook: End-to-End SAP S/4HANA Migration Solutions for SAP Applications

“We are currently working on more functions to facilitate and accelerate the migration,” Gutberlet articulates. “With SAP S/4HANA release 1709, we provide a staging solution on a project basis, which allows customers to connect third-party or SAP systems to SAP S/4HANA. This makes mass data transfer much quicker and easier to handle.”

“In a next step, we will deliver end-to-end SAP S/4HANA migration solutions for SAP applications. These cover various migration scenarios and include preconfigured packages for business data of SAP applications such as SAP ERP,” he continues. “Through direct connection between SAP Business Suite and SAP S/4HANA , we can extract the relevant business data using predefined selection criteria. This will help reduce customers’ efforts to extract data from SAP source systems to almost zero.”

After the extraction, the migration cockpit maps and migrates the business data to the SAP S/4HANA target system, as described above. For the testing of the migration results, we are planning additional functionality to automatically validate migration results. For example, we are including SAP standard reports to compare business data between source and target systems.

“To summarize the benefits of the SAP S/4HANA migration cockpit for the future: With the planned end-to-end SAP S/4HANA migration solutions containing preconfigured business data packages for SAP applications and automated validation of the migrated data, we will substantially lower customers’ project cost and effort,” Gutberlet concludes.

How Can We Support Your Migration to SAP S/4HANA?

If you are or know an SAP customer interested in migrating legacy data, please contact SAP_S4HANA_Migration_Cockpit@sap.com. Get more information on migration to SAP S/4HANA and about the SAP S/4HANA Value Assurance Offering.

Categories: What's New

Accenture Research Finds Lack of Trust in Third-Party Providers Creates Major Opportunity for Banks as Open Banking Set to Roll Out Across Europe

Accenture News - Mon, 10/02/2017 - 02:59
LONDON; Oct. 2, 2017 – Less than six months before the Revised Payments Service Directive (PSD2) makes open banking a reality across Europe, research from Accenture (NYSE: ACN) has found that two-thirds of consumers in the U.K. said they won’t share their personal financial data with third-party providers, giving banks the opportunity to benefit from the trust they have built with their customers over the years. 
Categories: What's New
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