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The (R)evolution of PLM, Part 3: Using Digital Twins Throughout The Product Lifecycle

John McNiff

In Part 1 of this series we explored why manufacturers must embrace “live” PLM. In Part 2 we examined the new dimensions of a product-centric enterprise. In Part 3 we look at the role of digital twins.

It’s time to start using digital twins throughout the product lifecycle. In fact, to compete in the digital economy, manufacturers will need to achieve a truly product-centric enterprise in which digital twins guide not only engineering and maintenance, but every business-critical function, from procurement to HR.

Why is this necessary? Because product lifecycles are shrinking. Companies are managing ever-growing streams of data. And customers are demanding product individualization. The only way for manufacturers to respond is to use digital twins to place the product – the highly configurable, endlessly customizable, increasingly connected product – at the center of their operations.

Double the insight

Digital twins are virtual representations of a real-world products or assets. They’re a Top 10 strategic trend for 2017, according to Gartner. And they’re part of a broader digital transformation in which IDC says companies will invest $2.1 trillion a year by 2019.

Digital twins aren’t a new concept, but their application throughout the product lifecycle is. Here are key ways smart manufacturers will leverage digital twins – and achieve a product-centric and model-based enterprise – across operations:

Design and engineering: Traditionally, digital twins have been used by design and engineering to create virtual representations for designing and enhancing products. In this application, the digital twin actually exists before its physical counterpart does, essentially starting out as a vision of what the product should be. But you can also capture data on in-the-field product use and apply that to the digital twin for continuous product improvement.

Maintenance and service: Today, the most common use case for digital twins is maintenance and service. By creating a virtual representation of an asset in the field using lightweight model visualization, and then capturing data from smart sensors embedded in the asset, you can gain a complete picture of real-world performance and operating conditions. You can also simulate that real-world environment for predictive maintenance. Let’s say you manufacture wind turbines. You can capture data on rotor speed, wind speed, operating temperature, ambient temperature, humidity, and so on to understand and predict product performance. By doing so, you can schedule maintenance before a crucial part breaks – optimizing uptime and saving time and cost for a repair.

Quality control: Just as digital twins can help with maintenance and service, they can predictively improve quality during manufacturing. You can also use digital twins to compare quality data across multiple products to better understand global quality issues and quickly visualize issues against the model. And you can apply data collected by maintenance and service to achieve ongoing quality improvements.

Customization: As products become more customizable, digital twins will allow design and engineering to model the various permutations. But digital twins can also incorporate customer demand and usage data to enhance customization options. That sounds obvious, but in the past it was very difficult to incorporate customer input into the manufacturing process. Let’s say you sell high-end custom bikes. You might allow customers to choose different colors, wheels, and other details. By capturing customer preferences in the digital twin, you can get a picture of customer demand. And by capturing customer usage data, you can understand how custom configurations affect product performance. So you can offer the most reliable options or allow customers to configure your products based on performance attributes. You can also visualize lightweight representations of the twin without the burden of heavyweight design systems and parameters.

Finance and procurement: In our custom-configured bike example, different configurations involve different costs. And those different costs involve not only the cost of the various components, but also the cost for assembling the various configurations. By capturing sales data in the digital twin, you can understand which configurations are being ordered and how configuration-specific revenues compare to the cost to build each configuration. What’s more, you can link that data with supplier information. That will help you understand which suppliers contribute to product configurations that perform well in the field. It also can help you identify opportunities to cost-effectively rid yourself of excess supply.

Sales and marketing: The digital twin can also inform sales and marketing. For instance, you can use the digital twin to populate an online product configurator and e-commerce website. That way you can be sure what you’re selling is always tied directly to what you’re engineering in the design studio and what you’re servicing in the field.

Human resources: The digital twin can even extend into HR. For example, you can use the digital twin to understand training and certification needs and be sure the right people are trained on the right product features.

One twin, many views

Digital twins should underlie all manufacturing operations. Ideally you should have a single set of digital twin master data that resides in a central location. That will give you one version of the truth, and with “in-memory” computing-based networks plus a lightweight, change-controlled model capability, you’ll be able to analyze and visualize that data rapidly.

But not all business functions care about the entire data set. You need to deliver the right data to the right people at the right time. Design and engineering requires one set of data, with every specification and tolerance needed to create and continuously improve the product. Sales and marketing requires another set of data, with the features and functions customers can select. And so on.

Ultimately, as the digital product innovation platform extends the dimensions of traditional PLM, at the heart of PLM is an extended version of the digital twin. In future blogs we’ll talk about how you can leverage the latest-generation platform from SAP, based on SAP S/4HANA and SAP’s platform for the Internet of Everything, to achieve a live, visual, and intelligent product-centric enterprise.

Learn how a live supply chain can help your business, visit us at SAP.com.

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John McNiff

About John McNiff

John McNiff is the Vice President of Solution Management for the R&D/Engineering line-of-business business unit at SAP. John has held a number of sales and business development roles at SAP, focused on the manufacturing and engineering topics.

Is It Time For Auditors To Get Out Of Control?

Bruce McCuaig

Speaking recently at the IIA GRC conference, I began by asking the audience to raise their hands to indicate if they or their departments had provided opinions on:

  • Internal control effectiveness
  • Risk management effectiveness
  • Compliance effectiveness
  • Loss management practices

With  very few exceptions, internal controls were the sole focus.

I began my presentation by suggesting it was time for internal auditors to get out of control—they were adding no value there, and their presence was desperately needed elsewhere.

Audit resources wasted, or worse

A number of recent studies indicate that stakeholders expect more value from internal audit. Other studies have found that internal auditors focus on core operational activities rather than strategic risks.

It’s hard to come to any conclusion other than audit resources are misplaced.

What’s the mission of internal audit? According to the IIA, it is “to enhance and protect organizational value by providing risk-based and objective assurance, advice, and insight.”

  • Is it likely that internal audit can add value by focusing on control-intensive business processes?
  • Have internal auditors adopted automation, embraced technologies, and transformed their practices for assessing control effectiveness?
  • Has the internal auditing profession applied technology in a meaningful way?
  • Do audit standards even require the use of technology?

By focusing on internal control effectiveness, internal auditors are 1) contributing to the problem by assuming accountability management should own, and 2) preventing progress.

Is it possible that the time has come for internal auditors to step aside from their focus on internal control? Is it possible to meet stakeholder expectations to add value by focusing on non-value-adding activities?

Years ago, when you drove to a gas station, your car was automatically “audited” by the gas station attendant. Your tire pressure was manually tested. Your oil level and possibly your transmission fluid and radiator were visually inspected.

Today these controls are all automated. Can we do the same for controls in business?

Control is a management problem, not an audit problem

In his recent blog, How to Do Your Internal Audit Risk Assessment, Norman Marks, a former colleague at SAP and a long-time practitioner with whom I often disagree, makes some of the same points and comes to a similar conclusion.

Some years ago I was on the board of a midsize public sector organization. Due to the nature of the business, our finance and accounting team could not produce reliable financial statements on a timely basis. The board wrestled with the problem. We had a number of proposals to perform risk assessments and other consulting services. Finally, we came to a conclusion.

Yes, there were complexities in producing our financial statements, but they weren’t unusual. We decided that if our finance head could not find a way to meet the board’s needs, we would find someone who could. It wasn’t a control problem or an accounting problem. It was a management problem. We changed the management and the problem was solved in 60 days.

In most of our core business systems (procure to pay, billing systems, payroll), inventory systems, and even information technology, I would suggest that greater than 95% of things that could go wrong are known. To me, in those core systems, we have a management problem, not a control problem, if risks can’t be managed.

Dashboards, not dipsticks

How can auditors help?

  • Internal auditors can consult on practices to automate controls and practices in our core business processes in such a way that traditional audits aren’t necessary.
  • Internal auditors can promote and teach control self-assessment and control design practices.
  • Internal auditors can provide opinions on the quality of management control assessments.

Worse yet, is internal audit hampering the automation of controls by continuing its focus? Are there better things for internal auditors to do?

“Skate to where the puck is going to be…”

Wayne Gretzsky used this philosophy to explain his success as a hockey player. It’s also apt advice for internal auditors. Internal auditors are skating not to where the puck is, but to where the puck was yesterday.

The focus should be adding value by assessing strategic risks, by providing advice and assurance on compliance, and by assessing loss management practices.

These all require an understanding of and a focus on the future of the business, not the past.

I would add, it’s management’s job to handle the puck today. Let them do it.

What do you think? As always, I’m interested in your comments.

For more on this subject

At SAP we have developed an experimental and free iOS app for iPads that is intended to assist internal auditors and others develop appropriate strategies and use appropriate tools. You can download the SAP GRC Strategy Selector App.

Finally, I recommend you watch this recent Compliance Week webcast by Honeywell outlining their internal audit department’s “One View of Risk” initiative.

This article, GRC Tuesdays: Is It Time for Auditors to Get Out of Control?, originally appeared on the SAP BusinessObjects Analytics blog and has been republished with permission.

Follow SAP Finance online: @SAPFinance (Twitter)  | LinkedIn | Facebook YouTube

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Bruce McCuaig

About Bruce McCuaig

Bruce McCuaig is the director of Solution Marketing, Governance Risk and Compliance at SAP. His specialties include Enterprise Risk Management, Governance, Management Consulting and Strategy.

Digital Transformation In Finance: How The Whole Corporation Benefits

Thomas Zipperle

Digitization is changing the world at a dizzying pace. Fueled by data, technology, and billions of online connections among people, partners, devices, data, and processes, companies of all sizes and industries – whether they know it or not – are engaged in a continuous cycle of reinvention. Digital business models are disrupting entire markets. Industry boundaries are expanding across adjacent spaces. Consumers demand commerce experiences that are frictionless, seamless, and easier. Even product consumption, enabled by technology, is evolving from traditional ownership to shared, pay-as-you-go models.

Success in this increasingly digital economy depends on a company’s openness and ability to reshape itself digitally. Everyone sitting around the executive boardroom table certainly needs the insight and expertise to take advantage of the opportunities that this data-driven, hyperconnected environment offers. However, the spotlight is shining brightly on the office of the CFO because the finance function and its ERP systems must be at the heart of every digital strategy.

Surrounding evolution brings opportunity to deliver more value

Over the last five years, my finance organization, which focuses on our Southeast Asia operations, has gone through a series of changes. Simplification of our core ERP system decreased our data footprint globally from 7.1TB to 1.2GB as we pulled our finance information into a single ledger and eliminated data aggregates with an in-memory database. Real-time reporting from the highest level of aggregation and down to the individual invoice document is making it possible now for the company to run on live data and one single source of truth

All of our efforts proved to be incredibly successful when CFO Innovation recently awarded me the “CFO of the Year 2016: Excellence in Technology Award,” which shows how well SAP is leveraging its own technology in its finance organization. However, it’s not only large multinational companies like SAP that need to go to such lengths in digitizing their finance processes.

While many CFOs know digital transformation can make the finance function more productive, there is still one thing of which many are unaware. Digital adoption helps the finance organization simplify and improve its engagement with the rest of the company and drives direct benefits – no matter the size, industry, or region it serves.

Here are four cases that demonstrate how digital transformation in finance provides a platform for delivering greater business value for the whole enterprise.

1. Direct access to real-time data for everyone

The adoption of an app-store-like report repository allows every employee to see what data is available within the company and can help them complete their daily tasks or make decisions. This effort significantly streamlines access to information and empowers employees to run real-time reports in an easy-to-understand format that matches the business need. However, to limit access to those who have a real business need and restrict access to confidential and insider relevant information, a state-of-the-art access-control system has been put in place to ensure all access is appropriately approved.

This approach removes the risk of finance teams becoming the bottleneck to information or creating outdated and irrelevant reports and decks. Rather than churning out regular or ad hoc reports, finance teams can now focus on their role as a business partner and information broker and help ensure that the right high-quality data is available at the right time.

2. Accounts payable automation

 The optimal state of an accounts payable organization is no-touch with error-free invoices and no queries back to suppliers. While most CFOs view this scenario as just a dream, today’s invoice automation technology can make this possible. Through cloud-based end-to-end invoice management solutions and procurement networks, finance organizations can digitize 100% of their invoices to accelerate billing approval, improve compliance, and achieve new levels of processing performance.

After procurement sends a purchase order (PO) to the vendor providing the best quote, the buyer approves goods receipt, sends an invoice, and triggers payments automatically or after any approvals are given. Finance employees are no longer involved in the accounts payable process. Instead, POs and invoices are matched automatically, and no paper-based invoices are processed.

As the process becomes more productive and efficient. The rest of the business is better equipped to make more strategic purchase decisions and quickly take advantage of supplier offers.

 3. A convenient, accurate, and compliant travel and expense system

 A cloud-based travel and expense solution can provide employees with a fully integrated experience. This approach starts with the planning and booking of a trip and ends with reporting and submitting incidental expenses, all of which can be done on a mobile device. All ticket and hotel expenses are automatically added to a trip, and incidental expenses can be added by taking a picture of the receipt with a mobile device. There is no need to sort through piles of receipts or stick them to a piece of paper.

This process can easily reduce the amount of time employees spend on this nonproductive task by 50% and increases transparency on travel spend across the corporation. A fully cloud-based solution even makes it easy for CFOs to outsource the auditing process, which provides additional economies of scale and enables moving this task to low-cost locations.

4. Lower DSO with increased partnership between finance and sales

Receivables managers are under constant pressure to keep days sales outstanding and bad-debt write-offs low while maintaining a high level of customer service. While the receivables team is critical in reaching this objective, account executives (AEs) who deal with the customer daily play a key role; they are likely to be aware of any issues long before invoices become overdue.

To support this, all AEs have direct access to the accounts receivable situation of their assigned customers through an easy-to-read customer financial fact sheet. This allows AEs to see the customer’s payment history, payment terms, or any unpaid invoices. It also allows them to directly interact with the accounts receivable team in case any disputes or issues arise.

Staying ahead of the digital curve

While past innovation in finance was rather limited to analytics (especially during the last five years), software vendors have brought solutions to the market that target the administrative processes of the organization. CFOs and their finance teams must be at the forefront of understanding and adopting technologies around cloud services, Big Data, the Internet of Things, and business networks to leverage their potential.

Because finance systems are at the core of each enterprise, CFOs not only need to evaluate these areas in terms of increased transparency and efficiency in their own function. They also must help ensure that systems are highly integrated with any other systems and processes to leverage the full benefits and avoid the creation of a disparate landscape across the company.

To learn more about how finance executives can empower themselves with the right tools and play a vital role in business innovation and value chain, please visit the SAP finance page for additional research and valuable insights.

For more insights from other organizations on the benefits of the latest technologies, download the infographic and read the report, Making the Business Case: Real CFOs Discuss the Benefits of SAP S/4HANA Finance.

Follow SAP Finance online: @SAPFinance (Twitter)  | LinkedIn | Facebook YouTube

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Thomas Zipperle

About Thomas Zipperle

Thomas Zipperle is CFO for SAP South East Asia, based in Singapore. As a business partner to the South East Asia management team, he supports SAP’s strategic growth in these emerging markets, provides them with relevant information on the state of the business, as well as decision support using SAP’s latest analytics tools. In his role, Thomas is also a distinguished speaker at many customer conferences, CFO roundtables, and events, where he shares his view on these latest innovations and showcases how SAP runs SAP. In 2016 Thomas was awarded the “CFO of the Year Award for Excellence in Technology” by CFO Innovation. Thomas has over 16 years of professional experience in Finance and Operations and holds a master’s degree in Business Administration from the University of Mannheim/Germany.

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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Donuts, Content Management and Information Governance

Ina Felsheim

I was on vacation for two weeks, which was awesome, and my girls mainly wanted to do two things:

I had my own list of projects, too. The big one was installing glass tile on the kitchen backsplash. (Grout everywhere. That’s all I’m saying.)

After two weeks of glorious holiday, I sat down to take stock. The old technical writer in me came creeping out, and I began to count how many sets of instructions we followed over the course of the two weeks—more than 15, definitely. And the amazing thing? They were all right. Every. Last. One. From proper application of fabric paint to proper frying temperature for homemade donuts, to putting together a shoe rack that came in 20 pieces.

I’m pretty sure this wouldn’t have happened five years ago. The difference comes from an increased awareness in the importance of great user assistance. Without successful “use,” who’s going to evangelize your product?

Information Governance: Part of a Larger Food Pyramid

In EIM, we have a well-seasoned group of information developers. They apply information governance principles every day:

  • Create a single source of master information (in this case, product step-by-step instructions)
  • Manage versioning of master information (as product updates happen)
  • Survey end-users of the information to gauge quality, freshness, and applicability of master information
  • Establish master information Responsible, Accountable, Consulted, or Informed (RACI) models for owners, reviewers, and informed stakeholders.

Sometimes, we group this knowledge management work into other categories, like content management. However, information governance needs to also be inclusive of these activities; otherwise, how can we be successful? No one can live on donuts alone!

Does your information governance program include content management? Do you have comments about the quality of EIM user assistance (online help, PDFs, printed documentation, etc.)?

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Tiffany Rowe

About Tiffany Rowe

Tiffany Rowe is a marketing administrator who assists in contributing resourceful content. Tiffany prides herself in her ability to provide high-quality content that readers will find valuable.

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Donuts, Content Management and Information Governance

Ina Felsheim

I was on vacation for two weeks, which was awesome, and my girls mainly wanted to do two things:

I had my own list of projects, too. The big one was installing glass tile on the kitchen backsplash. (Grout everywhere. That’s all I’m saying.)

After two weeks of glorious holiday, I sat down to take stock. The old technical writer in me came creeping out, and I began to count how many sets of instructions we followed over the course of the two weeks—more than 15, definitely. And the amazing thing? They were all right. Every. Last. One. From proper application of fabric paint to proper frying temperature for homemade donuts, to putting together a shoe rack that came in 20 pieces.

I’m pretty sure this wouldn’t have happened five years ago. The difference comes from an increased awareness in the importance of great user assistance. Without successful “use,” who’s going to evangelize your product?

Information Governance: Part of a Larger Food Pyramid

In EIM, we have a well-seasoned group of information developers. They apply information governance principles every day:

  • Create a single source of master information (in this case, product step-by-step instructions)
  • Manage versioning of master information (as product updates happen)
  • Survey end-users of the information to gauge quality, freshness, and applicability of master information
  • Establish master information Responsible, Accountable, Consulted, or Informed (RACI) models for owners, reviewers, and informed stakeholders.

Sometimes, we group this knowledge management work into other categories, like content management. However, information governance needs to also be inclusive of these activities; otherwise, how can we be successful? No one can live on donuts alone!

Does your information governance program include content management? Do you have comments about the quality of EIM user assistance (online help, PDFs, printed documentation, etc.)?

Comments

Tags:

awareness

Donuts, Content Management and Information Governance

Ina Felsheim

I was on vacation for two weeks, which was awesome, and my girls mainly wanted to do two things:

I had my own list of projects, too. The big one was installing glass tile on the kitchen backsplash. (Grout everywhere. That’s all I’m saying.)

After two weeks of glorious holiday, I sat down to take stock. The old technical writer in me came creeping out, and I began to count how many sets of instructions we followed over the course of the two weeks—more than 15, definitely. And the amazing thing? They were all right. Every. Last. One. From proper application of fabric paint to proper frying temperature for homemade donuts, to putting together a shoe rack that came in 20 pieces.

I’m pretty sure this wouldn’t have happened five years ago. The difference comes from an increased awareness in the importance of great user assistance. Without successful “use,” who’s going to evangelize your product?

Information Governance: Part of a Larger Food Pyramid

In EIM, we have a well-seasoned group of information developers. They apply information governance principles every day:

  • Create a single source of master information (in this case, product step-by-step instructions)
  • Manage versioning of master information (as product updates happen)
  • Survey end-users of the information to gauge quality, freshness, and applicability of master information
  • Establish master information Responsible, Accountable, Consulted, or Informed (RACI) models for owners, reviewers, and informed stakeholders.

Sometimes, we group this knowledge management work into other categories, like content management. However, information governance needs to also be inclusive of these activities; otherwise, how can we be successful? No one can live on donuts alone!

Does your information governance program include content management? Do you have comments about the quality of EIM user assistance (online help, PDFs, printed documentation, etc.)?

Comments

Andreas Heckmann

About Andreas Heckmann

Andreas Heckmann is head of Product Support at SAP. You can follow him on Twitter, LinkedIn, and WeChat at AndHeckmann.

Tags:

awareness

Donuts, Content Management and Information Governance

Ina Felsheim

I was on vacation for two weeks, which was awesome, and my girls mainly wanted to do two things:

I had my own list of projects, too. The big one was installing glass tile on the kitchen backsplash. (Grout everywhere. That’s all I’m saying.)

After two weeks of glorious holiday, I sat down to take stock. The old technical writer in me came creeping out, and I began to count how many sets of instructions we followed over the course of the two weeks—more than 15, definitely. And the amazing thing? They were all right. Every. Last. One. From proper application of fabric paint to proper frying temperature for homemade donuts, to putting together a shoe rack that came in 20 pieces.

I’m pretty sure this wouldn’t have happened five years ago. The difference comes from an increased awareness in the importance of great user assistance. Without successful “use,” who’s going to evangelize your product?

Information Governance: Part of a Larger Food Pyramid

In EIM, we have a well-seasoned group of information developers. They apply information governance principles every day:

  • Create a single source of master information (in this case, product step-by-step instructions)
  • Manage versioning of master information (as product updates happen)
  • Survey end-users of the information to gauge quality, freshness, and applicability of master information
  • Establish master information Responsible, Accountable, Consulted, or Informed (RACI) models for owners, reviewers, and informed stakeholders.

Sometimes, we group this knowledge management work into other categories, like content management. However, information governance needs to also be inclusive of these activities; otherwise, how can we be successful? No one can live on donuts alone!

Does your information governance program include content management? Do you have comments about the quality of EIM user assistance (online help, PDFs, printed documentation, etc.)?

Comments

Joerg Koesters

About Joerg Koesters

Joerg Koesters is the Head of Retail Marketing and Communication at SAP. He is a Technology Marketing executive with 20 years of experience in Marketing, Sales and Consulting, Joerg has deep knowledge in retail and consumer products having worked both in the industry and in the technology sector.

Tags:

awareness

Donuts, Content Management and Information Governance

Ina Felsheim

I was on vacation for two weeks, which was awesome, and my girls mainly wanted to do two things:

I had my own list of projects, too. The big one was installing glass tile on the kitchen backsplash. (Grout everywhere. That’s all I’m saying.)

After two weeks of glorious holiday, I sat down to take stock. The old technical writer in me came creeping out, and I began to count how many sets of instructions we followed over the course of the two weeks—more than 15, definitely. And the amazing thing? They were all right. Every. Last. One. From proper application of fabric paint to proper frying temperature for homemade donuts, to putting together a shoe rack that came in 20 pieces.

I’m pretty sure this wouldn’t have happened five years ago. The difference comes from an increased awareness in the importance of great user assistance. Without successful “use,” who’s going to evangelize your product?

Information Governance: Part of a Larger Food Pyramid

In EIM, we have a well-seasoned group of information developers. They apply information governance principles every day:

  • Create a single source of master information (in this case, product step-by-step instructions)
  • Manage versioning of master information (as product updates happen)
  • Survey end-users of the information to gauge quality, freshness, and applicability of master information
  • Establish master information Responsible, Accountable, Consulted, or Informed (RACI) models for owners, reviewers, and informed stakeholders.

Sometimes, we group this knowledge management work into other categories, like content management. However, information governance needs to also be inclusive of these activities; otherwise, how can we be successful? No one can live on donuts alone!

Does your information governance program include content management? Do you have comments about the quality of EIM user assistance (online help, PDFs, printed documentation, etc.)?

Comments

Henry Albrecht

About Henry Albrecht

Henry Albrecht is the CEO of Limeade, the corporate wellness technology company that measurably improves employee health, well-being and performance. Connect with Henry and the Limeade team on Twitter, Facebook and LinkedIn.

Tags:

awareness

Donuts, Content Management and Information Governance

Ina Felsheim

I was on vacation for two weeks, which was awesome, and my girls mainly wanted to do two things:

I had my own list of projects, too. The big one was installing glass tile on the kitchen backsplash. (Grout everywhere. That’s all I’m saying.)

After two weeks of glorious holiday, I sat down to take stock. The old technical writer in me came creeping out, and I began to count how many sets of instructions we followed over the course of the two weeks—more than 15, definitely. And the amazing thing? They were all right. Every. Last. One. From proper application of fabric paint to proper frying temperature for homemade donuts, to putting together a shoe rack that came in 20 pieces.

I’m pretty sure this wouldn’t have happened five years ago. The difference comes from an increased awareness in the importance of great user assistance. Without successful “use,” who’s going to evangelize your product?

Information Governance: Part of a Larger Food Pyramid

In EIM, we have a well-seasoned group of information developers. They apply information governance principles every day:

  • Create a single source of master information (in this case, product step-by-step instructions)
  • Manage versioning of master information (as product updates happen)
  • Survey end-users of the information to gauge quality, freshness, and applicability of master information
  • Establish master information Responsible, Accountable, Consulted, or Informed (RACI) models for owners, reviewers, and informed stakeholders.

Sometimes, we group this knowledge management work into other categories, like content management. However, information governance needs to also be inclusive of these activities; otherwise, how can we be successful? No one can live on donuts alone!

Does your information governance program include content management? Do you have comments about the quality of EIM user assistance (online help, PDFs, printed documentation, etc.)?

Comments

Tags:

awareness

Donuts, Content Management and Information Governance

Ina Felsheim

I was on vacation for two weeks, which was awesome, and my girls mainly wanted to do two things:

I had my own list of projects, too. The big one was installing glass tile on the kitchen backsplash. (Grout everywhere. That’s all I’m saying.)

After two weeks of glorious holiday, I sat down to take stock. The old technical writer in me came creeping out, and I began to count how many sets of instructions we followed over the course of the two weeks—more than 15, definitely. And the amazing thing? They were all right. Every. Last. One. From proper application of fabric paint to proper frying temperature for homemade donuts, to putting together a shoe rack that came in 20 pieces.

I’m pretty sure this wouldn’t have happened five years ago. The difference comes from an increased awareness in the importance of great user assistance. Without successful “use,” who’s going to evangelize your product?

Information Governance: Part of a Larger Food Pyramid

In EIM, we have a well-seasoned group of information developers. They apply information governance principles every day:

  • Create a single source of master information (in this case, product step-by-step instructions)
  • Manage versioning of master information (as product updates happen)
  • Survey end-users of the information to gauge quality, freshness, and applicability of master information
  • Establish master information Responsible, Accountable, Consulted, or Informed (RACI) models for owners, reviewers, and informed stakeholders.

Sometimes, we group this knowledge management work into other categories, like content management. However, information governance needs to also be inclusive of these activities; otherwise, how can we be successful? No one can live on donuts alone!

Does your information governance program include content management? Do you have comments about the quality of EIM user assistance (online help, PDFs, printed documentation, etc.)?

Comments

Timo Elliott

About Timo Elliott

Timo Elliott is the VP of Global Innovation Evangelist at SAP. Over the last 25 years, I've presented to Business and IT audiences in over 50 different countries around the world, on themes such as Digital Transformation, Big Data and Analytics, the Internet of Things, the future of Digital Marketing, and the challenges of technology culture change in organizations.

Tags:

awareness

Donuts, Content Management and Information Governance

Ina Felsheim

I was on vacation for two weeks, which was awesome, and my girls mainly wanted to do two things:

I had my own list of projects, too. The big one was installing glass tile on the kitchen backsplash. (Grout everywhere. That’s all I’m saying.)

After two weeks of glorious holiday, I sat down to take stock. The old technical writer in me came creeping out, and I began to count how many sets of instructions we followed over the course of the two weeks—more than 15, definitely. And the amazing thing? They were all right. Every. Last. One. From proper application of fabric paint to proper frying temperature for homemade donuts, to putting together a shoe rack that came in 20 pieces.

I’m pretty sure this wouldn’t have happened five years ago. The difference comes from an increased awareness in the importance of great user assistance. Without successful “use,” who’s going to evangelize your product?

Information Governance: Part of a Larger Food Pyramid

In EIM, we have a well-seasoned group of information developers. They apply information governance principles every day:

  • Create a single source of master information (in this case, product step-by-step instructions)
  • Manage versioning of master information (as product updates happen)
  • Survey end-users of the information to gauge quality, freshness, and applicability of master information
  • Establish master information Responsible, Accountable, Consulted, or Informed (RACI) models for owners, reviewers, and informed stakeholders.

Sometimes, we group this knowledge management work into other categories, like content management. However, information governance needs to also be inclusive of these activities; otherwise, how can we be successful? No one can live on donuts alone!

Does your information governance program include content management? Do you have comments about the quality of EIM user assistance (online help, PDFs, printed documentation, etc.)?

Comments

Bruce McCuaig

About Bruce McCuaig

Bruce McCuaig is the director of Solution Marketing, Governance Risk and Compliance at SAP. His specialties include Enterprise Risk Management, Governance, Management Consulting and Strategy.

Tags:

awareness

Donuts, Content Management and Information Governance

Ina Felsheim

I was on vacation for two weeks, which was awesome, and my girls mainly wanted to do two things:

I had my own list of projects, too. The big one was installing glass tile on the kitchen backsplash. (Grout everywhere. That’s all I’m saying.)

After two weeks of glorious holiday, I sat down to take stock. The old technical writer in me came creeping out, and I began to count how many sets of instructions we followed over the course of the two weeks—more than 15, definitely. And the amazing thing? They were all right. Every. Last. One. From proper application of fabric paint to proper frying temperature for homemade donuts, to putting together a shoe rack that came in 20 pieces.

I’m pretty sure this wouldn’t have happened five years ago. The difference comes from an increased awareness in the importance of great user assistance. Without successful “use,” who’s going to evangelize your product?

Information Governance: Part of a Larger Food Pyramid

In EIM, we have a well-seasoned group of information developers. They apply information governance principles every day:

  • Create a single source of master information (in this case, product step-by-step instructions)
  • Manage versioning of master information (as product updates happen)
  • Survey end-users of the information to gauge quality, freshness, and applicability of master information
  • Establish master information Responsible, Accountable, Consulted, or Informed (RACI) models for owners, reviewers, and informed stakeholders.

Sometimes, we group this knowledge management work into other categories, like content management. However, information governance needs to also be inclusive of these activities; otherwise, how can we be successful? No one can live on donuts alone!

Does your information governance program include content management? Do you have comments about the quality of EIM user assistance (online help, PDFs, printed documentation, etc.)?

Comments

Bob Caswell

About Bob Caswell

Bob Caswell is Senior Product Manager of the Internet of Things at SAP.

Tags:

awareness

Donuts, Content Management and Information Governance

Ina Felsheim

I was on vacation for two weeks, which was awesome, and my girls mainly wanted to do two things:

I had my own list of projects, too. The big one was installing glass tile on the kitchen backsplash. (Grout everywhere. That’s all I’m saying.)

After two weeks of glorious holiday, I sat down to take stock. The old technical writer in me came creeping out, and I began to count how many sets of instructions we followed over the course of the two weeks—more than 15, definitely. And the amazing thing? They were all right. Every. Last. One. From proper application of fabric paint to proper frying temperature for homemade donuts, to putting together a shoe rack that came in 20 pieces.

I’m pretty sure this wouldn’t have happened five years ago. The difference comes from an increased awareness in the importance of great user assistance. Without successful “use,” who’s going to evangelize your product?

Information Governance: Part of a Larger Food Pyramid

In EIM, we have a well-seasoned group of information developers. They apply information governance principles every day:

  • Create a single source of master information (in this case, product step-by-step instructions)
  • Manage versioning of master information (as product updates happen)
  • Survey end-users of the information to gauge quality, freshness, and applicability of master information
  • Establish master information Responsible, Accountable, Consulted, or Informed (RACI) models for owners, reviewers, and informed stakeholders.

Sometimes, we group this knowledge management work into other categories, like content management. However, information governance needs to also be inclusive of these activities; otherwise, how can we be successful? No one can live on donuts alone!

Does your information governance program include content management? Do you have comments about the quality of EIM user assistance (online help, PDFs, printed documentation, etc.)?

Comments

Tags:

awareness