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Digital Insurance: Partnering For The Game Of Life

Kai Görlich and Christian Jaerschke

Digitalization is a threat – and an opportunity – for one of our oldest industries. As the insurance industry faces upstart competitors and challenging economics, they must reinvent their businesses to survive.

Life is inherently risky. It’s only natural that human beings invented insurance to help mitigate some of the disastrous losses that we were bound to encounter. The earliest insurance contracts were marine policies written in Genoa, Italy, in 1347. The first known company that sold insurance to the public was Hamburger Feuerkasse (still in business today), which was established in 1676 to provide fire insurance. The insurance industry grew right along with the industrialization of the world. Today, we can insure the most important areas of our lives: health, wealth, employment, and property to name just a few.

Insurance companies have taken steps to eliminate risks over the years. Some were instrumental in demanding innovations such as fire hydrants and building sprinkler systems and, in many cases, require them before underwriting a given risk. However, the focus for the last several centuries has still been on responding to loss rather than preventing it.

But the insurance industry is on the verge of a fundamental shift – driven by increased digitization, more advanced analytics capabilities, and disruptive threats from new entrants and business models. Insurance companies are not only facing rising costs, intense competition, a challenging regulatory environment, and increasing customer expectations. But they also have the opportunity to harness technology and data in novel ways to create entirely new business models that proactively manage – and even eliminate – risks for customers.

From threat to opportunity

The insurance industry is under pressure, but it also has access to a number of technologies that could enable better customer service and expand their businesses beyond traditional offerings. Advancements in computing are helping companies in all industries transform Big Data into actionable insights. And the universe of available data is expanding every day thanks to increasingly cost-effective sensors, wearable computing options, and other technologies capable of providing a stream of ambient intelligence about the world.

The number of Internet-connected devices and sensors (14.8 billion as of February) will grow to 50 billion by 2020, according to Cisco. And Intel predicts that there will be 200 billion Internet-connected things in 2030. But that is not all that will impact the industry over the coming years:

  • The speed of analytics will intensify thirty-fold by 2030, with 95% of queries answered in mere milliseconds, according to SAP estimates.
  • By 2020, the digital data universe will grow to 44 zettabytes, and brings the potential to be analyzed and used to generate insight will grow from 22% to 37%, according to EMC and IDC.

The industry can harness these technologies not only to improve internal operations and productivity, but also expand their revenue-generating opportunities and improve the customer experience. While insurers are already harnessing automation to improve efficiency and experimenting with new business models in the areas of product development (micro-insurance) distribution (Web and mobile) and pricing (micro-segmentation), more dramatic shifts are ahead. According to Prof. Professor Dr. Fred Wagner from the Institut für Versicherungslehre in Leipzig, Germany, the future of insurance is not just insurance but has to be expanded beyond risk and investments.

Lessons from the outside

Insurance companies must disrupt themselves before they are disrupted  – embracing innovation in the areas of customer experience, product and service development, and risk management. And some promising new entrants are already beginning to shake up the industry.

  • New York-based Lemonade, with investors including Aleph and Sequoia Capital, wants to become the world’s first peer-to-peer property and casualty insurance provider. The company recently hired a chief behavioral officer who previously worked with Intuit to motivate people to save more and Intel on worker productivity.
  • Sure, launched in 2014, is a so-called episodic insurance company that will offer on-demand accident, life, property, casualty, and warranty policies. Its first product is flight insurance that travelers can purchase up to the time they take off that ends when they land. The company’s app also features analytics called Robo-Broker, which uses information about customer behavior to recommend products.
  • Trov, another mobile on-demand insurance platform that offers insurance for specific products (a new racing bike or digital camera) for a specific amount of time, is currently available in Australia with plans to launch in the U.S. in 2017.
  • Canopy Health Insurance says it offers consumers simpler, cheaper healthcare coverage by featuring just five individual plans and a smaller network of core providers.
  • Zoom+, which began as a chain of retail healthcare clinics in the Pacific Northwest, recently expanded into an integrated healthcare delivery system with on-demand insurance options aimed at tech- and health-savvy consumers. Its digital platform offers online scheduling, paying, and access to results for X-ray, ultrasound, and CT scans and its newest primary care clinics aimed at optimizing human performance using food, movement, and relationship as medicine. It also offers in-person assessments and labs with a board-certified naturopathic physician and ongoing medical coaching through video and e-mail.

Insurance companies face significant indirect competition as well. Customer expectations are influenced by their experiences in other areas and with companies in other industries such as Amazon, Facebook, and Apple. They want frictionless, personalized, and relevant experiences.

However, insurers can adopt best practices from other industries. Take the automotive industry, for example. Customers can configure the cars they choose to buy, making it easy for the prospect to purchase a complex, customized product. Insurers also have the opportunity to do the same with products to manage new and complex risks. They can take a page from retailers’ approaches to omnichannel management. Like companies in the travel and hospitality industries, insurers can explore compelling offerings and pricing as well as loyalty program management. They can apply banking approach to personal finance management and offer insurance customers personal risk management services. According to Prof. Wagner, insurers should follow the innovation strategies from other industries and build their own innovation frameworks with partners from different industries to create new ideas and business models outside the classical insurance business.

Smart insurance: building richer customer relationships

Moreover, insurance companies can expand beyond traditional products and interactions and become a more involved – and more positive – presence in their customers’ lives and, in the process, redefine their business models.

In the traditional insurance paradigm, insurers interact with their customers less than 1% of the time – when the product is purchased and a claim is submitted. These companies are missing opportunities to build better relationships with customers during the other 99% of the time and to sell them value-added products and services. By increasing the customer touch points, they can also shift the focus from paying for a loss, which is a process that can often be complex and frustrating for customers, to helping customers live better lives.

Using more detailed data, insurance companies can remind customers on their smartphones that it’s time, for example, to service their vehicle or suggest ski insurance for a customer who often hits the slopes. Companies can offer digitally enabled premium services such as financial coaching or retirement planning. They can partner with other companies to offer discounted products and services, including all-weather tires or fitness trackers, that will ultimately improve their customers risk profiles. They can use data and analytics to improve their core insurance offerings to deliver context-based offers that are relevant to customers, simpler insurance products, and smarter pricing. The emerging sensor ecosystem opens up opportunities for new “pay-as-you-drive” (usage-based) or “pay-how-you-live” (outcome-based) pricing strategies that consumers demand. Insurers can also explore entirely new insurance product categories, such as peer-to-peer insurance.

Progressive was an early leader in the usage-based auto insurance space offering the opportunity to receive discounts based on their driving habits by delivering data through a telematics device installed in a consumer’s car. Late last year, the company announced it was developing a mobile app to automatically monitor and measure drivers’ data – such as time of day, mileage, and hard braking – to earn discounts without the connected device. At the end of each trip, the mobile app will give drivers personalized information, including a star-based rating, a data summary, a map of their drive, and tailored driving tips, to help improve their score. There is growing interest in the use of dashboard cameras to gather accident data and encourage safer driving.

State Farm Insurance has partnered with ADT offers homeowners discounts on smart home security and monitoring systems that help measure in-home risk. Aviva Ventures, the venture capital arm of the UK insurer, invested in Cocoon, maker of an internet-connected home security device. AXA is partnering with Samsung on the development of a secured connected car ecosystem that will encourage safer driving and help consumers get access to better insurance rates.

American International Group Inc. (AIG) announced it is investing in an early stage startup developing advanced analytics and wearable devices to improve worker safety. The $58 billion insurance company invested in a startup called Human Condition Safety, which is using artificial intelligence and data modeling to help workers, their managers, and worksite owners prevent injuries in some of the highest risk settings such as manufacturing, energy, warehousing and distribution, and construction. The system correlates data from a worksite environment with historical data on workplace incidents and determines how best to prevent injuries before they happen. Workplace accidents kill one person and injure 153 others every 15 seconds. Instead of simply providing benefits to clients after these tragedies occur, AIG is looking to cognitive computing to partner with its customer to reduce those risks as well.

FItsense is a Singapore-based startup building a data analytics platform aimed at helping data insurance companies personalize life and health insurance for customers with wearables. By taking the data from a user’s wearable (like Fitbit or Jawbone), insurance providers can make more accurate risk perditions based on actual data and reward users who live healthier lifestyles with lower insurances costs and premiums.

Your new life coach

Successful insurance providers will move beyond underwriting and loss management to become life partners for their consumers – using data, technology, and strategic partnerships to provide simple, friendly, personal, relevant, and holistic products and services.

Using real-time analytics and risk assessment, companies could provide fully customized insurance based on individual use charged by the hour and activity and click. Insurance products might include service add-ons from partners aimed at reducing risk. The financial and claims management aspects of the business will be largely automated or outsourced, taking a back-seat to value-added, customer-facing interactions. The future will be focused less on paying for loss than preventing it, which will result in closer and potentially more valuable customer relationships.

Just as the first insurance carriers transformed the risks of the shipping industry or wood homes into viable marine and fire insurance businesses centuries ago, insurance companies today have the power to turn disruption risk into an opportunity for more expansive, profitable, and customer-centric business models. With this approach, the can work closely with partners and technology innovators to help their customers better manage life’s complexities.

Download the executive brief A New Paradigm for the Insurance Industry.

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

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Data Analysts And Scientists More Important Than Ever For The Enterprise

Daniel Newman

The business world is now firmly in the age of data. Not that data wasn’t relevant before; it was just nowhere close to the speed and volume that’s available to us today. Businesses are buckling under the deluge of petabytes, exabytes, and zettabytes. Within these bytes lie valuable information on customer behavior, key business insights, and revenue generation. However, all that data is practically useless for businesses without the ability to identify the right data. Plus, if they don’t have the talent and resources to capture the right data, organize it, dissect it, draw actionable insights from it and, finally, deliver those insights in a meaningful way, their data initiatives will fail.

Rise of the CDO

Companies of all sizes can easily find themselves drowning in data generated from websites, landing pages, social streams, emails, text messages, and many other sources. Additionally, there is data in their own repositories. With so much data at their disposal, companies are under mounting pressure to utilize it to generate insights. These insights are critical because they can (and should) drive the overall business strategy and help companies make better business decisions. To leverage the power of data analytics, businesses need more “top-management muscle” specialized in the field of data science. This specialized field has lead to the creation of roles like Chief Data Officer (CDO).

In addition, with more companies undertaking digital transformations, there’s greater impetus for the C-suite to make data-driven decisions. The CDO helps make data-driven decisions and also develops a digital business strategy around those decisions. As data grows at an unstoppable rate, becoming an inseparable part of key business functions, we will see the CDO act as a bridge between other C-suite execs.

Data skills an emerging business necessity

So far, only large enterprises with bigger data mining and management needs maintain in-house solutions. These in-house teams and technologies handle the growing sets of diverse and dispersed data. Others work with third-party service providers to develop and execute their big data strategies.

As the amount of data grows, the need to mine it for insights becomes a key business requirement. For both large and small businesses, data-centric roles will experience endless upward mobility. These roles include data anlysts and scientists. There is going to be a huge opportunity for critical thinkers to turn their analytical skills into rapidly growing roles in the field of data science. In fact, data skills are now a prized qualification for titles like IT project managers and computer systems analysts.

Forbes cited the McKinsey Global Institute’s prediction that by 2018 there could be a massive shortage of data-skilled professionals. This indicates a disruption at the demand-supply level with the needs for data skills at an all-time high. With an increasing number of companies adopting big data strategies, salaries for data jobs are going through the roof. This is turning the position into a highly coveted one.

According to Harvard Professor Gary King, “There is a big data revolution. The big data revolution is that now we can do something with the data.” The big problem is that most enterprises don’t know what to do with data. Data professionals are helping businesses figure that out. So if you’re casting about for where to apply your skills and want to take advantage of one of the best career paths in the job market today, focus on data science.

I’m compensated by University of Phoenix for this blog. As always, all thoughts and opinions are my own.

For more insight on our increasingly connected future, see The $19 Trillion Question: Are You Undervaluing The Internet Of Things?

The post Data Analysts and Scientists More Important Than Ever For the Enterprise appeared first on Millennial CEO.

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About Daniel Newman

Daniel Newman serves as the Co-Founder and CEO of EC3, a quickly growing hosted IT and Communication service provider. Prior to this role Daniel has held several prominent leadership roles including serving as CEO of United Visual. Parent company to United Visual Systems, United Visual Productions, and United GlobalComm; a family of companies focused on Visual Communications and Audio Visual Technologies. Daniel is also widely published and active in the Social Media Community. He is the Author of Amazon Best Selling Business Book "The Millennial CEO." Daniel also Co-Founded the Global online Community 12 Most and was recognized by the Huffington Post as one of the 100 Business and Leadership Accounts to Follow on Twitter. Newman is an Adjunct Professor of Management at North Central College. He attained his undergraduate degree in Marketing at Northern Illinois University and an Executive MBA from North Central College in Naperville, IL. Newman currently resides in Aurora, Illinois with his wife (Lisa) and his two daughters (Hailey 9, Avery 5). A Chicago native all of his life, Newman is an avid golfer, a fitness fan, and a classically trained pianist

When Good Is Good Enough: Guiding Business Users On BI Practices

Ina Felsheim

Image_part2-300x200In Part One of this blog series, I talked about changing your IT culture to better support self-service BI and data discovery. Absolutely essential. However, your work is not done!

Self-service BI and data discovery will drive the number of users using the BI solutions to rapidly expand. Yet all of these more casual users will not be well versed in BI and visualization best practices.

When your user base rapidly expands to more casual users, you need to help educate them on what is important. For example, one IT manager told me that his casual BI users were making visualizations with very difficult-to-read charts and customizing color palettes to incredible degrees.

I had a similar experience when I was a technical writer. One of our lead writers was so concerned with readability of every sentence that he was going through the 300+ page manuals (yes, they were printed then) and manually adjusting all of the line breaks and page breaks. (!) Yes, readability was incrementally improved. But now any number of changes–technical capabilities, edits, inserting larger graphics—required re-adjusting all of those manual “optimizations.” The time it took just to do the additional optimization was incredible, much less the maintenance of these optimizations! Meanwhile, the technical writing team was falling behind on new deliverables.

The same scenario applies to your new casual BI users. This new group needs guidance to help them focus on the highest value practices:

  • Customization of color and appearance of visualizations: When is this customization necessary for a management deliverable, versus indulging an OCD tendency? I too have to stop myself from obsessing about the font, line spacing, and that a certain blue is just a bit different than another shade of blue. Yes, these options do matter. But help these casual users determine when that time is well spent.
  • Proper visualizations: When is a spinning 3D pie chart necessary to grab someone’s attention? BI professionals would firmly say “NEVER!” But these casual users do not have a lot of depth on BI best practices. Give them a few simple guidelines as to when “flash” needs to subsume understanding. Consider offering a monthly one-hour Lunch and Learn that shows them how to create impactful, polished visuals. Understanding if their visualizations are going to be viewed casually on the way to a meeting, or dissected at a laptop, also helps determine how much time to spend optimizing a visualization. No, you can’t just mandate that they all read Tufte.
  • Predictive: Provide advanced analytics capabilities like forecasting and regression directly in their casual BI tools. Using these capabilities will really help them wow their audience with substance instead of flash.
  • Feature requests: Make sure you understand the motivation and business value behind some of the casual users’ requests. These casual users are less likely to understand the implications of supporting specific requests across an enterprise, so make sure you are collaborating on use cases and priorities for substantive requests.

By working with your casual BI users on the above points, you will be able to collectively understand when the absolute exact request is critical (and supports good visualization practices), and when it is an “optimization” that may impact productivity. In many cases, “good” is good enough for the fast turnaround of data discovery.

Next week, I’ll wrap this series up with hints on getting your casual users to embrace the “we” not “me” mentality.

Read Part One of this series: Changing The IT Culture For Self-Service BI Success.

Follow me on Twitter: @InaSAP

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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.

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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.