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Autonomous Vehicles: Accelerating Into The Mainstream

Dan Wellers and Larry Stolle

In just one year, self-driving cars have gone from the theoretical to the imminent. Major manufacturers are leaping into development. The U.S. Department of Transportation issued new rules in September giving the federal government broad oversight over vehicles operated by software rather than by humans. Discussion of the technology’s pros and cons has become a staple in publications as mainstream as the New York Times. But now that the first fatality has occurred, we need to take a closer look at the implications and challenges of autonomous vehicles.

At the moment, the focus is on self-driving versions of today’s automobiles. However, autonomous vehicles also include taxis, buses, delivery vans, long-haul trucks, and more. Any vehicle that meets the fundamental need to move passengers and/or cargo from point A to point B may soon include multiple exponential technologies – machine learning and artificial intelligence, sensors, drones, cybersecurity, and at some future point, super materials – that allow it to pilot itself under some, if not all, circumstances.

What’s driving how we’re driving?

Whether autonomous vehicles will hit the road is no longer a question; some of them already have, and others, like the autonomous trucks being developed by Otto, a startup recently acquired by Uber, are on their way. What we need to be asking now is when self-driving cars will change lanes from experiments and curiosities to the mainstream – and how long it will be before they dominate. These factors will heavily influence the answers:

Technological limits. So far, machines can’t duplicate the sophisticated, intricate choreography that human drivers perform every day. Nor can they correct for catastrophic human error. The Tesla in June’s fatality failed to “see” a white truck against a bright sky, but it wasn’t designed to operate without any human intervention. The driver failed to override it, reportedly because he was not only speeding, but distracted by watching a movie on the car’s dashboard video screen. While current technology can achieve something very near autonomy, the last few steps still promise to be a big leap, in part because we don’t yet know where to draw the line between requiring human intervention and forbidding it. That line will shift as we become more comfortable with the technology and as technology itself advances. Moreover, security will have to become a higher priority as vehicles become just another set of endpoints in the Internet of Things.

The so-called “Trolley Problem.” The technological limits of self-driving vehicles are inextricably entwined with this iconic thought exercise, which asks whether it’s better to do nothing and let a runaway trolley hit five people on its track, or to actively participate in killing a single person standing on another track by pulling the lever that diverts the trolley. Self-driving vehicles will force us to confront similar ethical issues, and soon. They will have to learn what to do when a collision is unavoidable, but what will we teach them about whose safety to prioritize? Children over the elderly? Pedestrians over other drivers? The people in the vehicle over everyone else? Or should we simply instruct them to minimize injuries and deaths, even if their own passengers end up among the casualties?

Infrastructure limits. Current infrastructure is made for vehicles with human drivers. It’s going to need an overhaul if we hope to achieve the efficiency autonomous vehicles promise us. Our current infrastructure is crumbling in some areas and will need to be adapted in the short term to carry higher volumes of traffic. Traffic lights, signals, and signage may have to be enhanced (perhaps with their own sensors and beacons) so autonomous vehicles can detect them even in the brightest sunlight. Parking spaces and parking structures will need retooling to make it possible for cars to come and go as they’re summoned and allow payment when no human is present to feed a meter. On a positive note, if mass adoption of driverless vehicles reduces both the number of vehicles and the between-vehicle spacing that’s needed for safe operation, we could finally redesign roads to take up less space and free up real estate for more attractive purposes.

Mapping issues. Current autonomous vehicle technology requires highly detailed maps. Not every road is mapped to the necessary detail, and roadwork can make any map obsolete overnight. We’ll need either to raise our mapping game or develop GPS technology that includes enhanced geolocation features and isn’t entirely reliant on maps.

Resistance to change. Although an estimated 10 million driverless cars are expected to be on the road by 2020, many drivers – whether out of love of driving or a broader aversion to being told what to do – may fiercely resist any pressure to adopt them. This will change as the technology develops and is proven safe, especially as older adults realize that an autonomous car can drive them when they no longer have the reflexes, vision, or strength to drive themselves. After a generation or two grows up with self-driving cars as the norm, manually operated cars are likely to be the purview of collectors, hobbyists, and other niche enthusiasts.

Current mileposts 

Google and Tesla, which currently dominate the discussion of autonomous vehicles, are taking different paths to the technology. Tesla is working on perfecting its autopilot while reminding drivers (not always successfully, as June’s accident proves) that the technology is meant to assist rather than replace them. Google, by comparison, is simply designing test vehicles under the assumption that human drivers will make mistakes. Its experimental self-driving cars lack brake pedals, accelerators, and steering wheels; they’re packed with impact-absorbent foam and plastic windshields; and their top speed is a far from blistering 25 mph. Of course, these won’t hit the U.S. market as long as the government requires human controls in vehicles, but they suggest where we might be heading.

Traditional car manufacturers are also picking up the pace. Toyota, currently the world’s largest car maker, is creating an onboard system that only takes over when the human driver makes a mistake. BMW, Ford, and Nissan are also steering their research toward creating autonomous vehicles, while Spanish automaker SEAT has partnered with Samsung and SAP to present “a new concept for the connected car.” Built on the SAP Vehicles Network, this currently includes a smartphone app that integrates the car key and lets owners start, stop, and share access to the vehicle remotely, as well as a different app that lets drivers find, reserve, and pay for a parking space using fingerprint verification, both features that are also building blocks for greater autonomy.

Similarly, SAP has introduced the SAP Vehicle Insights app, which gathers driving and maintenance information about individual vehicles and entire fleets. This type of information will become relevant to managing fleets of self-driving cars as well. 

Merging into a driverless world

When Henry Ford introduced the Model T in 1908, it may have been an early stage in the automobile’s evolution, but it created a mass market and gave rise to many of the rules of the road we still observe today. Similarly, we’re further down the road to autonomous vehicles than many of us realize. In fact, automated driver assistance systems like those that automate braking, alert drivers to hazards on the road, or show what’s in blind spots have already brought us a long way.

We still lack the central “brain” to control all the other automated systems, the heuristic learning and decision-making abilities to take the place of the human driver, and the infrastructure necessary to enable truly autonomous operations, and even as we solve these issues, new ones will almost certainly come speeding toward us. Yet the value, safety, efficiency, improved capital utilization, and other benefits of self-driving vehicles will far outweigh the costs of continuing to develop them – so buckle up, because we’ve got an exciting ride ahead.

Download the executive brief Speeding Toward a Driverless Future

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

Listen to a Special Edition broadcast about The Future of Cars on SAP’s Coffee Break with Game-Changers.

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Transform Or Die: What Will You Do In The Digital Economy?

Scott Feldman and Puneet Suppal

By now, most executives are keenly aware that the digital economy can be either an opportunity or a threat. The question is not whether they should engage their business in it. Rather, it’s how to unleash the power of digital technology while maintaining a healthy business, leveraging existing IT investments, and innovating without disrupting themselves.

Yet most of those executives are shying away Businesspeople in a Meeting --- Image by © Monalyn Gracia/Corbisfrom such a challenge. According to a recent study by MIT Sloan and Capgemini, only 15% of CEOs are executing a digital strategy, even though 90% agree that the digital economy will impact their industry. As these businesses ignore this reality, early adopters of digital transformation are achieving 9% higher revenue creation, 26% greater impact on profitability, and 12% more market valuation.

Why aren’t more leaders willing to transform their business and seize the opportunity of our hyperconnected world? The answer is as simple as human nature. Innately, humans are uncomfortable with the notion of change. We even find comfort in stability and predictability. Unfortunately, the digital economy is none of these – it’s fast and always evolving.

Digital transformation is no longer an option – it’s the imperative

At this moment, we are witnessing an explosion of connections, data, and innovations. And even though this hyperconnectivity has changed the game, customers are radically changing the rules – demanding simple, seamless, and personalized experiences at every touch point.

Billions of people are using social and digital communities to provide services, share insights, and engage in commerce. All the while, new channels for engaging with customers are created, and new ways for making better use of resources are emerging. It is these communities that allow companies to not only give customers what they want, but also align efforts across the business network to maximize value potential.

To seize the opportunities ahead, businesses must go beyond sensors, Big Data, analytics, and social media. More important, they need to reinvent themselves in a manner that is compatible with an increasingly digital world and its inhabitants (a.k.a. your consumers).

Here are a few companies that understand the importance of digital transformation – and are reaping the rewards:

  1. Under Armour:  No longer is this widely popular athletic brand just selling shoes and apparel. They are connecting 38 million people on a digital platform. By focusing on this services side of the business, Under Armour is poised to become a lifestyle advisor and health consultant, using his product side as the enabler.
  1. Port of Hamburg: Europe’s second-largest port is keeping carrier trucks and ships productive around the clock. By fusing facility, weather, and traffic conditions with vehicle availability and shipment schedules, the Port increased container handling capacity by 178% without expanding its physical space.
  1. Haier Asia: This top-ranking multinational consumer electronics and home appliances company decided to disrupt itself before someone else did. The company used a two-prong approach to digital transformation to create a service-based model to seize the potential of changing consumer behaviors and accelerate product development. 
  1. Uber: This startup darling is more than just a taxi service. It is transforming how urban logistics operates through a technology trifecta: Big Data, cloud, and mobile.
  1. American Society of Clinical Oncologists (ASCO): Even nonprofits can benefit from digital transformation. ASCO is transforming care for cancer patients worldwide by consolidating patient information with its CancerLinQ. By unlocking knowledge and value from the 97% of cancer patients who are not involved in clinical trials, healthcare providers can drive better, more data-driven decision making and outcomes.

It’s time to take action 

During the SAP Executive Technology Summit at SAP TechEd on October 19–20, an elite group of CIOs, CTOs, and corporate executives will gather to discuss the challenges of digital transformation and how they can solve them. With the freedom of open, candid, and interactive discussions led by SAP Board Members and senior technology leadership, delegates will exchange ideas on how to get on the right path while leveraging their existing technology infrastructure.

Stay tuned for exclusive insights from this invitation-only event in our next blog!
Scott Feldman is Global Head of the SAP HANA Customer Community at SAP. Connect with him on Twitter @sfeldman0.

Puneet Suppal drives Solution Strategy and Adoption (Customer Innovation & IoT) at SAP Labs. Connect with him on Twitter @puneetsuppal.

 

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About Scott Feldman and Puneet Suppal

Scott Feldman is the Head of SAP HANA International Customer Community. Puneet Suppal is the Customer Co-Innovation & Solution Adoption Executive at SAP.

What Is Digital Transformation?

Andreas Schmitz

Achieving quantum leaps through disruption and using data in new contexts, in ways designed for more than just Generation Y — indeed, the digital transformation affects us all. It’s time for a detailed look at its key aspects.

Data finding its way into new settings

Archiving all of a company’s internal information until the end of time is generally a good idea, as it gives the boss the security that nothing will be lost. Meanwhile, enabling him or her to create bar graphs and pie charts based on sales trends – preferably in real time, of course – is even better.

But the best scenario of all is when the boss can incorporate data from external sources. All of a sudden, information on factors as seemingly mundane as the weather start helping to improve interpretations of fluctuations in sales and to make precise modifications to the company’s offerings. When the gusts of autumn begin to blow, for example, energy providers scale back solar production and crank up their windmills. Here, external data provides a foundation for processes and decisions that were previously unattainable.

Quantum leaps possible through disruption

While these advancements involve changes in existing workflows, there are also much more radical approaches that eschew conventional structures entirely.

“The aggressive use of data is transforming business models, facilitating new products and services, creating new processes, generating greater utility, and ushering in a new culture of management,” states Professor Walter Brenner of the University of St. Gallen in Switzerland, regarding the effects of digitalization.

Harnessing these benefits requires the application of innovative information and communication technology, especially the kind termed “disruptive.” A complete departure from existing structures may not necessarily be the actual goal, but it can occur as a consequence of this process.

Having had to contend with “only” one new technology at a time in the past, be it PCs, SAP software, SQL databases, or the Internet itself, companies are now facing an array of concurrent topics, such as the Internet of Things, social media, third-generation e-business, and tablets and smartphones. Professor Brenner thus believes that every good — and perhaps disruptive — idea can result in a “quantum leap in terms of data.”

Products and services shaped by customers

It has already been nearly seven years since the release of an app that enables customers to order and pay for taxis. Initially introduced in Berlin, Germany, mytaxi makes it possible to avoid waiting on hold for the next phone representative and pay by credit card while giving drivers greater independence from taxi dispatch centers. In addition, analyses of user data can lead to the creation of new services, such as for people who consistently order taxis at around the same time of day.

“Successful models focus on providing utility to the customer,” Professor Brenner explains. “In the beginning, at least, everything else is secondary.”

In this regard, the private taxi agency Uber is a fair bit more radical. It bypasses the entire taxi industry and hires private individuals interested in making themselves and their vehicles available for rides on the Uber platform. Similarly, Airbnb runs a platform travelers can use to book private accommodations instead of hotel rooms.

Long-established companies are also undergoing profound changes. The German publishing house Axel Springer SE, for instance, has acquired a number of startups, launched an online dating platform, and released an app with which users can collect points at retail. Chairman and CEO Matthias Döpfner also has an interest in getting the company’s newspapers and other periodicals back into the black based on payment models, of course, but these endeavors are somewhat at odds with the traditional notion of publishing houses being involved solely in publishing.

The impact of digitalization transcends Generation Y

Digitalization is effecting changes in nearly every industry. Retailers will likely have no choice but to integrate their sales channels into an omnichannel approach. Seeking to make their data services as attractive as possible, BMW, Mercedes, and Audi have joined forces to purchase the digital map service HERE. Mechanical engineering companies are outfitting their equipment with sensors to reduce downtime and achieve further product improvements.

“The specific potential and risks at hand determine how and by what means each individual company approaches the subject of digitalization,” Professor Brenner reveals. The resulting services will ultimately benefit every customer – not just those belonging to Generation Y, who present a certain basic affinity for digital methods.

“Think of cars that notify the service center when their brakes or drive belts need to be replaced, offer parking assistance, or even handle parking for you,” Brenner offers. “This can be a big help to elderly people in particular.”

Chief digital officers: team members, not miracle workers

Making the transition to the digital future is something that involves not only a CEO or a head of marketing or IT, but the entire company. Though these individuals do play an important role as proponents of digital models, it also takes more than just a chief digital officer alone.

For Professor Brenner, appointing a single person to the board of a DAX company to oversee digitalization is basically absurd. “Unless you’re talking about Da Vinci or Leibnitz born again, nobody could handle such a task,” he states.

In Brenner’s view, this is a topic for each and every department, and responsibilities should be assigned much like on a soccer field: “You’ve got a coach and the players – and the fans, as well, who are more or less what it’s all about.”

Here, the CIO neither competes with the CDO nor assumes an elevated position in the process of digital transformation. Implementing new databases like SAP HANA or Hadoop, leveraging sensor data in both technical and commercially viable ways, these are the tasks CIOs will face going forward.

“There are some fantastic jobs out there,” Brenner affirms.

Want more insight on managing digital transformation? See Three Keys To Winning In A World Of Disruption.

Image via Shutterstock

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Andreas Schmitz

About Andreas Schmitz

Andreas Schmitz is a Freelance Journalist for SAP, covering a wide range of topics from big data to Internet of Things, HR, business innovation and mobile.

How AI Can End Bias

Yvonne Baur, Brenda Reid, Steve Hunt, and Fawn Fitter

We humans make sense of the world by looking for patterns, filtering them through what we think we already know, and making decisions accordingly. When we talk about handing decisions off to artificial intelligence (AI), we expect it to do the same, only better.

Machine learning does, in fact, have the potential to be a tremendous force for good. Humans are hindered by both their unconscious assumptions and their simple inability to process huge amounts of information. AI, on the other hand, can be taught to filter irrelevancies out of the decision-making process, pluck the most suitable candidates from a haystack of résumés, and guide us based on what it calculates is objectively best rather than simply what we’ve done in the past.

In other words, AI has the potential to help us avoid bias in hiring, operations, customer service, and the broader business and social communities—and doing so makes good business sense. For one thing, even the most unintentional discrimination can cost a company significantly, in both money and brand equity. The mere fact of having to defend against an accusation of bias can linger long after the issue itself is settled.

Beyond managing risk related to legal and regulatory issues, though, there’s a broader argument for tackling bias: in a relentlessly competitive and global economy, no organization can afford to shut itself off from broader input, more varied experiences, a wider range of talent, and larger potential markets.

That said, the algorithms that drive AI don’t reveal pure, objective truth just because they’re mathematical. Humans must tell AI what they consider suitable, teach it which information is relevant, and indicate that the outcomes they consider best—ethically, legally, and, of course, financially—are those that are free from bias, conscious or otherwise. That’s the only way AI can help us create systems that are fair, more productive, and ultimately better for both business and the broader society.

Bias: Bad for Business

When people talk about AI and machine learning, they usually mean algorithms that learn over time as they process large data sets. Organizations that have gathered vast amounts of data can use these algorithms to apply sophisticated mathematical modeling techniques to see if the results can predict future outcomes, such as fluctuations in the price of materials or traffic flows around a port facility. Computers are ideally suited to processing these massive data volumes to reveal patterns and interactions that might help organizations get ahead of their competitors. As we gather more types and sources of data with which to train increasingly complex algorithms, interest in AI will become even more intense.

Using AI for automated decision making is becoming more common, at least for simple tasks, such as recommending additional products at the point of sale based on a customer’s current and past purchases. The hope is that AI will be able to take on the process of making increasingly sophisticated decisions, such as suggesting entirely new markets where a company could be profitable, or finding the most qualified candidates for jobs by helping HR look beyond the expected demographics.

As AI takes on these increasingly complex decisions, it can help reduce bias, conscious or otherwise. By exposing a bias, algorithms allow us to lessen the impact of that bias on our decisions and actions. They enable us to make decisions that reflect objective data instead of untested assumptions; they reveal imbalances; and they alert people to their cognitive blind spots so they can make more accurate, unbiased decisions.

Imagine, for example, a major company that realizes that its past hiring practices were biased against women and that would benefit from having more women in its management pipeline. AI can help the company analyze its past job postings for gender-biased language, which might have discouraged some applicants. Future postings could be more gender neutral, increasing the number of female applicants who get past the initial screenings.

AI can also support people in making less-biased decisions. For example, a company is considering two candidates for an influential management position: one man and one woman. The final hiring decision lies with a hiring manager who, when they learn that the female candidate has a small child at home, assumes that she would prefer a part-time schedule.

That assumption may be well intentioned, but it runs counter to the outcome the company is looking for. An AI could apply corrective pressure by reminding the hiring manager that all qualifications being equal, the female candidate is an objectively good choice who meets the company’s criteria. The hope is that the hiring manager will realize their unfounded assumption and remove it from their decision-making process.

At the same time, by tracking the pattern of hiring decisions this manager makes, the AI could alert them—and other people in HR—that the company still has some remaining hidden biases against female candidates to address.

Look for Where Bias Already Exists

In other words, if we want AI to counter the effects of a biased world, we have to begin by acknowledging that the world is biased. And that starts in a surprisingly low-tech spot: identifying any biases baked into your own organization’s current processes. From there, you can determine how to address those biases and improve outcomes.

There are many scenarios where humans can collaborate with AI to prevent or even reverse bias, says Jason Baldridge, a former associate professor of computational linguistics at the University of Texas at Austin and now co-founder of People Pattern, a startup for predictive demographics using social media analytics. In the highly regulated financial services industry, for example, Baldridge says banks are required to ensure that their algorithmic choices are not based on input variables that correlate with protected demographic variables (like race and gender). The banks also have to prove to regulators that their mathematical models don’t focus on patterns that disfavor specific demographic groups, he says. What’s more, they have to allow outside data scientists to assess their models for code or data that might have a discriminatory effect. As a result, banks are more evenhanded in their lending.

Code Is Only Human

The reason for these checks and balances is clear: the algorithms that drive AI are built by humans, and humans choose the data with which to shape and train the resulting models. Because humans are prone to bias, we have to be careful that we are neither simply confirming existing biases nor introducing new ones when we develop AI models and feed them data.

“From the perspective of a business leader who wants to do the right thing, it’s a design question,” says Cathy O’Neil, whose best-selling book Weapons of Math Destruction was long-listed for the 2016 National Book Award. “You wouldn’t let your company design a car and send it out in the world without knowing whether it’s safe. You have to design it with safety standards in mind,” she says. “By the same token, algorithms have to be designed with fairness and legality in mind, with standards that are understandable to everyone, from the business leader to the people being scored.” (To learn more from O’Neil about transparency in algorithms, read Thinkers in this issue.)

Don’t Do What You’ve Always Done

To eliminate bias, you must first make sure that the data you’re using to train the algorithm is itself free of bias, or, rather, that the algorithm can recognize bias in that data and bring the bias to a human’s attention.

SAP has been working on an initiative that tackles this issue directly by spotting and categorizing gendered terminology in old job postings. Nothing as overt as No women need apply, which everyone knows is discriminatory, but phrases like outspoken and aggressively pursuing opportunities, which are proven to attract male job applicants and repel female applicants, and words like caring and flexible, which do the opposite.

Once humans categorize this language and feed it into an algorithm, the AI can learn to flag words that imply bias and suggest gender-neutral alternatives. Unfortunately, this de-biasing process currently requires too much human intervention to scale easily, but as the amount of available de-biased data grows, this will become far less of a limitation in developing AI for HR.

Similarly, companies should look for specificity in how their algorithms search for new talent. According to O’Neil, there’s no one-size-fits-all definition of the best engineer; there’s only the best engineer for a particular role or project at a particular time. That’s the needle in the haystack that AI is well suited to find.

Look Beyond the Obvious

AI could be invaluable in radically reducing deliberate and unconscious discrimination in the workplace. However, the more data your company analyzes, the more likely it is that you will deal with stereotypes, O’Neil says. If you’re looking for math professors, for example, and you load your hiring algorithm with all the data you can find about math professors, your algorithm may give a lower score to a black female candidate living in Harlem simply because there are fewer black female mathematicians in your data set. But if that candidate has a PhD in math from Cornell, and if you’ve trained your AI to prioritize that criterion, the algorithm will bump her up the list of candidates rather than summarily ruling out a potentially high-value hire on the spurious basis of race and gender.

To further improve the odds that AI will be useful, companies have to go beyond spotting relationships between data and the outcomes they care about. It doesn’t take sophisticated predictive modeling to determine, for example, that women are disproportionately likely to jump off the corporate ladder at the halfway point because they’re struggling with work/life balance.

Many companies find it all too easy to conclude that women simply aren’t qualified for middle management. However, a company committed to smart talent management will instead ask what it is about these positions that makes them incompatible with women’s lives. It will then explore what it can change so that it doesn’t lose talent and institutional knowledge that will cost the company far more to replace than to retain.

That company may even apply a second layer of machine learning that looks at its own suggestions and makes further recommendations: “It looks like you’re trying to do X, so consider doing Y,” where X might be promoting more women, making the workforce more ethnically diverse, or improving retention statistics, and Y is redefining job responsibilities with greater flexibility, hosting recruiting events in communities of color, or redesigning benefits packages based on what similar companies offer.

Context Matters—and Context Changes

Even though AI learns—and maybe because it learns—it can never be considered “set it and forget it” technology. To remain both accurate and relevant, it has to be continually trained to account for changes in the market, your company’s needs, and the data itself.

Sources for language analysis, for example, tend to be biased toward standard American English, so if you’re building models to analyze social media posts or conversational language input, Baldridge says, you have to make a deliberate effort to include and correct for slang and nonstandard dialects. Standard English applies the word sick to someone having health problems, but it’s also a popular slang term for something good or impressive, which could lead to an awkward experience if someone confuses the two meanings, to say the least. Correcting for that, or adding more rules to the algorithm, such as “The word sick appears in proximity to positive emoji,” takes human oversight.

Moving Forward with AI

Today, AI excels at making biased data obvious, but that isn’t the same as eliminating it. It’s up to human beings to pay attention to the existence of bias and enlist AI to help avoid it. That goes beyond simply implementing AI to insisting that it meet benchmarks for positive impact. The business benefits of taking this step are—or soon will be—obvious.

In IDC FutureScapes’ webcast “Worldwide Big Data, Business Analytics, and Cognitive Software 2017 Predictions,” research director David Schubmehl predicted that by 2020 perceived bias and lack of evidentiary transparency in cognitive/AI solutions will create an activist backlash movement, with up to 10% of users backing away from the technology. However, Schubmehl also speculated that consumer and enterprise users of machine learning will be far more likely to trust AI’s recommendations and decisions if they understand how those recommendations and decisions are made. That means knowing what goes into the algorithms, how they arrive at their conclusions, and whether they deliver desired outcomes that are also legally and ethically fair.

Clearly, organizations that can address this concern explicitly will have a competitive advantage, but simply stating their commitment to using AI for good may not be enough. They also may wish to support academic efforts to research AI and bias, such as the annual Fairness, Accountability, and Transparency in Machine Learning (FATML) workshop, which was held for the third time in November 2016.

O’Neil, who blogs about data science and founded the Lede Program for Data Journalism, an intensive certification program at Columbia University, is going one step further. She is attempting to create an entirely new industry dedicated to auditing and monitoring algorithms to ensure that they not only reveal bias but actively eliminate it. She proposes the formation of groups of data scientists that evaluate supply chains for signs of forced labor, connect children at risk of abuse with resources to support their families, or alert people through a smartphone app when their credit scores are used to evaluate eligibility for something other than a loan.

As we begin to entrust AI with more complex and consequential decisions, organizations may also want to be proactive about ensuring that their algorithms do good—so that their companies can use AI to do well. D!

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


About the Authors:

Yvonne Baur is Head of Predictive Analytics for Sap SuccessFactors solutions.

Brenda Reid is Vice President of Product Management for Sap SuccessFactors solutions.

Steve Hunt is Senior Vice President of Human Capital Management Research for Sap SuccessFactors solutions.

Fawn Fitter is a freelance writer specializing in business and technology.

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Next-Generation, Real-Time Data Warehouse: Bringing Analytics To Data

Iver van de Zand

Imagine the following situation: you are analyzing and gathering insights about product sales performance and wonder why a certain area in your country is doing better than others. You deep dive, slice, dice, and use different perspectives to analyze, but can’t find the answer to why sales are better for that region.

You conclude you need data that is not available in your corporate systems. Some geographical data that is available through Hadoop might answer your question. How can you get this information and quickly analyze it all?

Bring analytics to data

If we don’t want to go the traditional route of specifying, remodeling the data warehouse, and uploading and testing data, we’d need a whole new way of modern data warehousing. What we ultimately need is a kind of semantics that allows us to remodel our data warehouse in real time and on the fly – semantics that allows decision makers to leave the data where it is stored without populating it into the data warehouse. What we really need is a way to bring our analytics to data, instead of the other way around.

So our analytics wish list would be:

  • Access to the data source on the fly
  • Ability to remodel the data warehouse on the fly
  • No replication of data; the data stays where it is
  • Not losing time with data-load jobs
  • Analytical processing done in the moment with pushback to an in-memory computing platform
  • Drastic reduction of data objects to be stored and maintained
  • Elimination of aggregates

Traditional data warehousing is probably the biggest hurdle when it comes to agile business analytics. Though modern analytical tools perfectly add data sources on the fly and blend different data sources, these components are still analytical tools. When additional data must be available for multiple users or is huge in scale and complexity, analytical tools lack the computing power and scalability needed. It simply doesn’t make sense to blend them individually when multiple users require the same complex, additional data.

A data warehouse, in this case, is the answer. However, there is still one hurdle to overcome: A traditional data warehouse requires a substantial effort to adjust to new data needs. So we add to our wish list:

  • Adjust and adapt the modeling
  • Develop load and transformation script
  • Assign sizing
  • Setup scheduling and linage
  • Test and maintain

In 2016, the future of data warehousing began. In-memory technology with smart, native, and real-time access moved information from analytics to the data warehouse, as well as the data warehouse to core in-memory systems. Combined with pushback technology, where analytical calculations are pushed back onto an in-memory computing platform, analytics is brought back to data. End-to-end in-memory processing has become the reality, enabling true agility. And end-to-end processing is ready for the Internet of Things at the petabyte scale.

Are we happy with this? Sure, we are! Does it come as a surprise? Of course, not! Digital transformation just enabled it!

Native, real-time access for analytics

What do next-generation data warehouses bring to analytics? Well, they allow for native access from top-end analytics components through the data warehouse and all the way to the core in-memory platform with our operational data. Even more, this native access is real-time. Every analytics-driven interaction from an end-user generates calculations. With the described architecture, these calculations are massively pushed back to the core platform where our data resides.

The same integrated architecture is also a game changer when it comes to agility and data optimization. When new, complex data is required, it can be added without data replication. Since there is no data replication, the data warehouse modeling can be done on the fly, leveraging the semantics. We no longer have to model, create, and populate new tables and aggregates when additional data is required in the data warehouse, because there are no new tables needed! We only create additional semantics, and this can be done on the fly.

Learn why you need to put analytics into your business processes in the free eBook How to Use Algorithms to Dominate Your Industry.

This article appeared on Iver van de Zand.

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Iver van de Zand

About Iver van de Zand

Iver van de Zand is a Business Analytics Leader at SAP responsible for Business Analytics with a special attention towards Business Intelligence Suite, Lumira and Predictive Analytics.