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What Should You Consider When Embarking On An Advanced Analytics Journey?

Paul Pallath

Financial advisor talking to customer --- Image by © Ocean/CorbisIn last week’s Predictive blog, I introduced four main considerations that organizations need to keep in mind when they’re beginning that journey. Today I’ll cover them in more detail.

1. How do we measure business value and return on investment?

An advanced analytics solution must make a measurable impact. If not, the solution doesn’t get noticed, never mind appreciated. This holds even more true, if the return on investment (ROI) can’t be realized as a significant opportunity to drive business growth or new market opportunities.

Take the example of a marketing campaign. The ROI is in having the intelligence to target the customers who are likely (if persuaded) to buy your product rather than finding customers who would have bought the product without any marketing required.

An advanced analytics solution will be short-lived if it creates a “wow” effect, but nothing else.  The solution must generate recurrent value, revenue, and business opportunities.

2. How do we use advanced analytics effectively?

For your business, good questions to ask at the start of the journey are:

  • Is the enterprise truly digital?
  • Is there a single source of truth of all the data that is generated/captured by various functions of the enterprise?

These questions are important considerations. Why? Because businesses often approach advanced analytics in an ineffective manner.

Remember, advanced analytics drive value to every business function, be it marketing, finance, human resources, and so on. However, enterprise functions want often to embed advanced analytics into their business workflow and embark on advanced analytics initiatives in silos. Though there is value in doing so, the results can be underwhelming.

This is because they’re using adoptions of various technologies, methodologies, practices to address the use cases that might exists— but without an enterprise-wide vision for advanced analytics. Therefore, walls rather than bridges are built between the various functions.

The problem becomes self-perpetuating. With increasing adoption of advanced analytics solutions in various business units, the business as a whole finds it difficult to consolidate all the activities into a central initiative and have proper discipline and governance.

The solution is to create the vision and execute it across all functions— even if the pilot starts from one or two activities. The functions must agree that advanced analytics is an enterprise-wide mission. Leadership must demonstrate belief in an analytics-driven business that it is going to provide competitive advantage. In this way, advanced analytics becomes a true company asset.

3. Is advanced analytics just another technology project?

Advanced analytics is not just another technology project. If considered to be a technology project, the business understands only the technical feasibility and not its business impact.

As mentioned, an advanced analytics initiative is the means by which a business gains a competitive advantage. It follows that outcomes provide the data to help make well-informed decisions.

A lesser or confined approach is a step in the wrong direction. There is no ROI associated with technology-only thinking, because no tangible results are expected as an outcome. An initiative to embrace advanced analytics must be inseparable from your business strategy.

4. Is Big Data equal to high-quality insight?

Big Data is not equal to high-quality insight.  A traditional business approach is to think, “We’ve captured huge amounts of data, but how do we  make sense of it?” This is a wrong start.

The right approach is to start with a business question in mind. That way you can ask if the data that you have is sufficient enough to provide the answer.

These are several pieces of the puzzle that need to be put together for one to find meaningful, actionable insights from the data. This is, after all,  the quest that we embarked on.

As we now know, advanced analytics is about business change, insight, and value.

“The combination of some data and an aching desire for an answer does not ensure that a reasonable answer can be extracted from a given body of data.” –Sunset Salvo. The American Statistician 40 (1).

For more on advanced analytics, see Are You Planning To Embark On An Advanced Analytics Journey?

 

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Paul Pallath

About Paul Pallath

Paul Pallath is the Chief Data Scientist & Director, Advanced Analytics, at SAP. He has over 20 years of experience in applying machine learning to various domains like Financial Trading, Hot Spot Clustering , Consumer & Retail Analytics and Internet of Things.

Real-Time Data Transforms Political Journalism, But Context Remains Vital

John Graham

The runup to the 2016 U.S. election is being covered in interesting new ways by the political media, with analysis of Big Data and real-time opinion polling offering journalists much deeper insight than ever before. The trend of “data journalism” is peaking as the media embraces advanced technologies that allow them to deliver a new breed of numbers-driven, fact-based journalism.

The tools being used for data journalism open up possibilities for fresh perspectives, more in-depth reporting, and new stories behind the numbers that have never been seen before. Traditional journalists are beginning to see how data journalism can complement their reporting, and the U.S. election is serving as an ideal testing ground. Political reporters are lapping up the improved data literacy and access to objective analysis, which is helping to make their reports more thorough and informative.

Consequently, American voters are becoming digital voters. They have access to real-time, data-driven information and public sentiment, which is empowering them with broader insight. They’re relying on this to help them make up their minds before they cast their vote, and it’s given many voters a renewed interest in becoming informed citizens able to make an educated choice.

However, the rise of data-driven journalism brings with it a potential pitfall for media organizations and readers alike. Digital information overload will bring about a fatigue around numbers if reporting quantity becomes more highly valued than quality. Having access to mountains of data is a huge benefit, but a reporter still has to be a journalist first to ensure they’re not getting buried under the numbers and missing the stories.

In other words, a political journalist still needs to be a politico, not just a statistician. They could fall into the trap of placing too much importance on meaningless correlations as indicators of voter sentiment, losing their grasp on what made them a great political reporter in the first place. As data gets bigger, this will become harder to resist. So they need to become experts in making Big Data small—rather than obsessing over the numbers, obsessing over figuring out what they really mean. In doing that, they have an unprecedented opportunity to make people more informed rather than simply overwhelming with them a series of conflicting data sets.

Some media organizations are already tackling the challenge of remaining relevant in a world of information overload. Using big data and visualizations, they are making great strides in making data journalism more accessible to reporters, politicos, and voters, which is proving its worth in giving political reporting a new lease of life.

Reuters’ Polling Explorer tool is an example of how this is being done, offering up customizable data visualizations focusing on the biggest talking points in the U.S. leading up to the election. It’s an entirely new scale of public opinion measurement, presented in a way anyone can understand and use, while enabling Reuters to usher in its own improved brand of accurate, fact-based, and timely journalism.

We can see the true potential of using real-time data analysis to measure up-to-the-minute public opinion in one poll on the most important problem facing the US today. Immediately after the Paris attacks in November, terrorism skyrocketed way above the economy as the number-one issue, rising sharply again straight after the December San Bernardino attack. For Reuters, this is just one of many examples of their greatly increased ability to find outliers in the data.

Reuters Polling Explorer runs on SAP HANA, an in-memory data platform that allows Reuters to access and analyze 100 million survey responses for quicker and more efficient reporting of public opinion.

For more on data analytics in today’s media environment, see How Big Data Is Changing The News Industry.

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John Graham

About John Graham

John Graham is president of SAP Canada. Driving growth across SAP’s industry-leading cloud, mobile, and database solutions, he is helping more than 9,500 Canadian customers in 25 industries become best-run businesses.

Smart Machines Create Markets For Cyber-Physical Advances

Marion Heindenreich

Today, industrial machines are more intelligent than ever before. These intelligent machines are changing companies in many ways.

Why smart machines?

Mobile networked computers were a key breakthrough for making smart machines. Big Data allows machines and computers to store information and analyze complex patterns. Cloud computing offers broad access to information and more storage.

These computerized machines are both physical and virtual. Some call them “cyber-physical” machines. Technology lets them be self-aware and connected to each other and larger systems.

Businesses change their approaches

Intelligent machines allow companies to innovate in many areas. For one, the value proposition for customers is evolving. Businesses now model and plan in different ways in many industries.

Makers of industrial machines and parts work in new ways within the organization. Engineering now partners with mechanical, electronic, and software staff to develop new products. Manufacturing now seamlessly ties what happens on the shop floor to the customer.

Service models are changing too. Scheduled and reactionary servicing of machines is fading. Now intelligent machines track themselves. Machines detect problems and report them automatically. Major problems or failures are predicted and reported.

A data mining example

One good industrial example is mining, which can be dangerous and difficult. As ores become scarce, the costs of mining have increased.

“Smart machines” started in mining in the late 1990s. Software and hardware let remote users change settings. Operators moved hydraulic levers from a safe distance. Sensors observed performance and diagnosed issues.

Data cables connected machines to computers on the surface. Continuous and remote monitoring of the machines grew. Over time, embedded sensors helped improve monitoring, diagnostics, and data storage.

The technology means workers only go underground to fix specific issues. As a result, accident and injury risk is lower.

New wireless technology now lets mining companies connect data from many mine sites. Service centers access large amounts of data and can improve performance. Maintenance is prioritized and equipment downtime is reduced.

Opportunity abounds

For companies the time is now. Today, mobile “connected things” generate 17% of the digital universe. By 2020 that share grows to 27%.

You might not be investing in this so-called “Internet of Things” (devices that connect to each other). But it’s a good bet your competitors are. A December 2015 study reported 33% of industrial companies are investing in the Internet of Things. Another 25% are considering it.

There are risks

This new dawning era of manufacturing is exciting. But there are concerns. Cyber attacks on the Internet of Things are not new. But as the use of intelligent machines grows, the threat of cyber attacks in industry grows.

Data confidentiality and privacy are concerns. So too are software and hardware vulnerabilities. Exposure to attack lies not just in the virtual space but the physical too. Tampering with unattended machines and theft pose serious risk.

To address these threats, industries must invest in cybersecurity along with smart machines.

Conclusion

The potential advantages of smart machines are staggering. They can reshape industries and change how companies produce new products and create new markets.

For more information, please download the white paper Digital Manufacturing: Powering the Fourth Industrial Revolution.

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Marion Heindenreich

About Marion Heindenreich

Marion Heidenreich is a solution manager for the SAP Industrial Machinery and Components Business Unit who focuses on solution innovations like Product Costing on SAP HANA and cloud solutions, as well as providing financial and business analysis for industry business strategy definition and business planning.

The Robotics Race

Stephanie Overby

As robotic technologies continue to advance, along with related technologies such as speech and image recognition, memory and analytics, and virtual and augmented reality, better, faster, and cheaper robots will emerge. These machines – sophisticated, discerning, and increasingly autonomous – are certain to have an impact on business and society. But will they bring job displacement and danger or create new categories of employment and protect humankind?

We talked to SAP’s Kai Goerlich, along with Doug Stephen of the Institute for Human and Machine Cognition and Brett Kennedy from NASA’s Jet Propulsion Laboratory, about the advances we can expect in robotics, robots’ limitations, and their likely impact on the world.

SAP_Robotics_QA_images2400x16002

qa_qWhat are the biggest drivers of the robot future?

Kai Goerlich: Several trends will come together to drive the robotics market in the next 15 to 20 years. The number of connected things and sensors will grow to the billions and the data universe will likewise explode. We think the speed of analytics will increase, with queries answered in milliseconds. Image and voice recognition – already quite good – will surpass human capabilities. And the virtual and augmented reality businesses will take off. These technologies are all building blocks for a new form of robotics that will vastly expand today’s capabilities in a diversity of forms and applications.

Brett Kennedy: When I was getting out of school, there weren’t that many people working in robotics. Now kids in grade school are exposed to a lot of things that I had to learn on the job, so they come into the workplace with a lot more knowledge and fewer preconceptions about what robots can or can’t do based on their experiences in different industries. That results in a much better-trained workforce in robotics, which I think is the most important thing.

In addition, many of the parts that we need for more sophisticated robots are coming out of other fields. We could never create enough critical mass to develop these technologies specifically for robotics. But we’re getting them from other places. Improvements in battery technology, which enable a robot to function without being plugged in, are being driven by industries such as mobile electronics and automotive, for example. Our RoboSimian has a battery drive originally designed for an electric motorcycle.

qa_qDo you anticipate a limit to the tasks robots will be able to master as these core technologies evolve?

Goerlich: Robots will take over more and more complex functions, but I think the ultimate result will be that new forms of human-machine interactions will emerge. Robots have advantages in crunching numbers, lifting heavy objects, working in dangerous environments, moving with precision, and performing repetitive tasks. However, humans still have advantages in areas such as abstraction, curiosity, creativity, dexterity, fast and multidimensional feedback, self-motivation, goal setting, and empathy. We’re also comparatively lightweight and efficient.

Doug Stephen: We’re moving toward a human-machine collaboration approach, which I think will become the norm for more complex tasks for a very long time. Even when we get to the point of creating more-complex and general-purpose robots, they won’t be autonomous. They’ll have a great deal of interaction with some sort of human teammate or operator.

qa_qHow about the Mars Rover? It’s relatively autonomous already.

Kennedy: The Mars Rover is autonomous to a certain degree. It is capable of supervised autonomy because there’s no way to control it at that distance with a joystick. But it’s really just executing the intent of the operator here on the ground.

In 2010, DARPA launched its four-year Autonomous Robotic Manipulator Challenge to create machines capable of carrying out complex tasks with only high-level human involvement. Some robots completed the challenge, but they were incredibly slow. We may get to a point where robots can do these sorts of things on their own. But they’re just not as good as people at this point. I don’t think we’re all going to be coming home to robot butlers anytime soon.

Stephen: It’s extremely difficult to program robots to behave as humans do. When we trip over something, we can recover quickly, but a robot will topple over and damage itself. The problem is that our understanding of our human abilities is limited. We have to figure out how to formally define the processes that human beings or any legged animals use to maintain balance or to walk and then tell a robot how to do it.

You have to be really explicit in the instructions that you give to these machines. Amazon has been working on these problems for a while with its “picking challenge”: How do you teach a robot to pick and pack boxes the way a human does? Right now, it’s a challenge for robots to identify what each item is.

qa_qSo if I’m not coming home to a robot butler in 20 years, what am I coming home to?

Goerlich: We naturally tend to imagine humanoid robots, but I think the emphasis will be on human-controlled robots, not necessarily humanshaped units. Independent robots will make sense in some niches, but they are more complex and expensive. The symbiosis of human and machine is more logical. It will be the most efficient way forward. Robotic suits, exoskeletons, and robotic limbs with all kinds of human support functions will be the norm. The future will be more Iron Man than Terminator.

qa_qWhat will be the impact on the job market as robots become more advanced?

SAP_Robotics_QA_images2400x16004Goerlich: The default fear is of a labor-light economy where robots do most of the work and humans take what’s left over. But that’s lastcentury thinking. Robots won’t simply replace workers on the assembly line. In fact, we may not have centralized factories anymore; 3D printing and the maker movement could change all that. And it is probably not the Terminator scenario either, where humanoid robots take over the world and threaten humankind. The indicators instead point to human-machine coevolution.

There’s no denying that advances in robotics and artificial intelligence will displace some jobs performed by humans today. But for every repetitive job that is lost to automation, it’s possible that a more interesting, creative job will take its place. This will require humans to focus on the skills that robots can’t replicate – and, of course, rethink how we do things and how the economy works.

qa_qWhat can businesses do today to embrace the projected benefits of advanced robotics?

Kennedy: Experiment. The very best things that we’ve been able to produce have come from people having the tools an d then figuring out how they can be used. I don’t think we understand the future well enough to be able to predict exactly how robots are going to be used, but I think we can say that they certainly will be used. Stephanie Overby is an independent writer and editor focused on the intersection of business and technology.

Stephanie Overby  is an independent writer and editor focused on the intersection of business and technology

To learn more about how humans and robots will co-evolve, read the in-depth report Bring Your Robot to Work.

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What Is The Key To Rapid Innovation In Healthcare?

Paul Clark

Healthcare technology has already made incredible advancements, but digital transformation of the healthcare industry is still considered in its infancy. According to the SAP eBook, Connected Care: The Digital Pulse of Global Healthcare, the possibilities and opportunities that lie ahead for the Internet of Healthcare Things (IoHT) are astounding.

Many health organizations recognize the importance of going digital and have already deployed programs involving IoT, cloud, Big Data, analytics, and mobile technologies. However, over the last decade, investments in many e-health programs have delivered only modest returns, so the progress of healthcare technology has been slow out of the gate.

What’s slowing the pace of healthcare innovation?

In the past, attempts at rapid innovation in healthcare have been bogged down by a slew of stakeholders, legacy systems, and regulations that are inherent to the industry. This presents some Big Data challenges with connected healthcare, such as gathering data from disparate silos of medical information. Secrecy is also an ongoing challenge, as healthcare providers, researchers, pharmaceutical companies, and academic institutions tend to protect personal and proprietary data. These issues have caused enormous complexity and have delayed or deterred attempts to build fully integrated digital healthcare systems.

So what is the key to rapid innovation?

According to the Connected Care eBook, healthcare organizations can overcome these challenges by using new technologies and collaborating with other players in the healthcare industry, as well as partners outside of the industry, to get the most benefit out of digital technology.

To move forward with digital transformation in healthcare, there is a need for digital architectures and platforms where a number of different technologies can work together from both a technical and a business perspective.

The secret to healthcare innovation: connected health platforms

New platforms are emerging that foster collaboration between different technologies and healthcare organizations to solve complex medical system challenges. These platforms can support a broad ecosystem of partners, including developers, researchers, and healthcare organizations. Healthcare networks that are connected through this type of technology will be able to accelerate the development and delivery of innovative, patient-centered solutions.

Platforms and other digital advancements present exciting new business opportunities for numerous healthcare stakeholders striving to meet the increasing expectations of tech-savvy patients.

The digital evolution of the healthcare industry may still be in its infancy, but it is growing up fast as new advancements in technology quickly develop. Are you ready for the next phase of digital transformation in the global healthcare industry?

For an in-depth look at how technology is changing the face of healthcare, download the SAP eBook Connected Care: The Digital Pulse of Global Healthcare.

See how the digital era is affecting the business environment in the SAP eBook The Digital Economy: Reinventing the Business World.

Discover the driving forces behind digital transformation in the SAP eBook Digital Disruption: How Digital Technology is Transforming Our World.

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Paul Clark

About Paul Clark

Paul Clark is the Senior Director of Technology Partner Marketing at SAP. He is responsible for developing and executing partner marketing strategies, activities, and programs in joint go-to-market plans with global technology partners. The goal is to increase opportunities, pipeline, and revenue through demand generation via SAP's global and local partner ecosystems.