Sections

The Internet of Things Enables Precision Logistics (And Could Save The Planet Too!)

David Stephenson

A degree of precision in every aspect of the economy that was impossible before the IoT is one of my fav memes. That’s in part because it should encourage companies that have held back from IoT strategies to get involved now (because they can realize immediate benefits in lower operating costs, greater efficiency, etc.), and also it brings with it so many ancillary benefits, such as reduced environmental impacts (remember: waste creation = inefficiency!).

 Zero Marginal Cost Society

Zero Marginal Cost       Society

I’m reminded of that while reading Jeremy Rifkin’s fascinating Zero Marginal Cost Economy, which I got months ago for research in writing my own book proposal and didn’t get around to until recently.  I’d always heard he was something of an eccentric, but, IMHO, this one’s brilliant.  Rifkin’s thesis:

“The coming together of the Communications Internet with the fledgling Energy Internet and Logistics Internet in a seamless twenty-first-century intelligent infrastructure, “the Internet of Things (IoT),” is giving rise to a Third Industrial Revolution. The Internet of Things is already boosting productivity to the point where the marginal cost of producing many goods and services is nearly zero, making them practically free.”

Tip: When the marginal cost of producing things is nearly zero, you’re gonna need a new business model, so get this book!

At any rate, one of the three revolutions he mentioned was the “Logistics Internet.”

I’m a nut about logistics, especially as it relates to supply chain and distribution networks, which I see as crucial to the radically new “circular enterprise” rotating around a real-time IoT data hub. Just think how efficient your company could be if your suppliers — miles away rather than on the other side of the world — knew instantly via M2M data sharing what you needed and when and delivered it at precisely the right time. Or if the SAP prototype vending machine notified the dispatcher, again on a M2M basis, so that delivery trucks were automatically re-routed to the machine that was most likely  to run out first!

I wasn’t quite sure what Rifkin meant about a Logistics Internet until I read his reference to the work of Benoit Montreuil, “Coca-Cola Material Handling & Distribution Chair and Professor” at Georgia Tech, who, as Rifkin puts it, closes the loop nicely in terms of imagery:

“.. just as the digital world took up the superhighway metaphor, now the logistics industry ought to take up the open-architecture metaphor of distributed Internet communication to remodel global logistics.

Montreuil elaborates on the analogy (and incidentally, places this in the context of global sustainability, saying that the current logistics paradigm is unsustainable), and paraphrases my fav Einstein saying:

“The global logistics sustainability grand challenge cannot be addressed through the same lenses that created the situation. The current logististics paradigm must be replaced by a new paradigm enabling outside-the-box paradigm enabling meta-systemic creative thinking.”

Wooo: meta-systemic creative thinking! Count me in!

Montreuil’s answer is a “physical Internet” for logistics, which he says is a necessity not only because of the environmental impacts of the current, inefficient system (such as 14% of all greenhouse gas emissions in France), but also its ridiculous costs, accounting for 10% of the US GDP according to a 2009 Department of Transportation report!  That kind of waste brings out my inner Scotsman!

Rifkin cites a variety of examples of the current system’s inefficiency based on Montreuil’s research:

  • Trucks in the US are, on average, only 60% full, and globally the efficiency is only 10%!
  • In the U.S. they were empty 20% of miles driven
  • U.S. business inventories were $1.6 trillion as of March, 2013 — so much for “just in time.”
  • Time-sensitive products such as food, clothing, and medical supplies are unsold because they can’t be delivered on time.

Montreuil’s “physical Internet” has striking parallels to the electronic one:

  • Cargo (like packets) must be packaged in standardized module containers
  • Like the internet, the cargo must be structured independently of the equipment so it can be processed seamlessly through a wide range of networks, with smart tags and sensors for identification and sorting (one of the first examples of the IoT I wrote about was FedEx’s great SenseAware containers for high-value cargo!)

With the Logistics Internet, we’d move from the old point-to-point and hub-and-spoke systems to ones that are “distributed, multi-segment, intermodal.” A single, exhausted, over-worked (and more accident-prone) driver would be replaced by several. It’s a  little counterintuitive, but Montreuil says that while it would take a driver 240 hours to get from Quebec to L.A.under the current system; instead 17 drivers in a distributed one would each drive about 3 hours, and the cargo would get there in only 60 hours.

Under the new system, the current fractionated, isolated warehouse and distribution mess would be replaced by a fully integrated one involving all of the 535,000 facilities nationwide, cutting time and dramatically reducing environmental impacts and fuel consumption.

Most important for companies, and looping back to my precision meme, Montreuil points out that an open supply network allows firms to reduce their lead time to near zero if their stock is distributed among some of the hundreds of distribution centers that are located near their final buyer market. And once we have more 3-D printing, the product might actually be printed out near the destination. How cool is that?

Trucking is such an emblematic aspect of the 20th-century economy, yet as with the neat things that Union Pacific and other lines are doing with the 19th-century’s emblematic railroads, they can be transformed into a key part of the 21st century “precision economy” — but only if we couple IoT technology with “IoT thinking.”

Now let’s pick up our iPads & head to the loading dock!

I’ll be addressing this subject in one of my two speeches at the SCM2016 Conference later this month. Hope to see you there! 

 

Comments

Coffee Machines Brew Industry Disruption: Digital Twins Emerge In 2017

Susan Galer

How fast can your coffee machine accelerate business growth? Of all the demos I saw at SAP TechEd Barcelona, digital twins was among the most fascinating.

Opportunities explode and industries implode when everyday items like coffee machines power a direct conversation between customers, companies and suppliers, not only crunching high-volume, actionable data in real time, but also looking into the future.

It’s not surprising that digital twins made it into Gartner Research’s top 10 trends for 2017. Those analysts predict hundreds of millions of things will be represented by digital twins within three to five years.

Above, Thomas Kaiser, senior vice president of IoT at SAP, talked with me about how the smartest companies are using digital twin technology to shake up the status quo. Featured is a clip of Ian Kimball of SAP demonstrating the amazing power of digital twins using a connected coffee machine at SAP TechEd Barcelona.

How digital twins disrupt

A digital twin is a virtual representation of a process, product, or service. While companies have been using digital twins for years, it’s only with the Internet of Things (IoT) that they’ve become cost-effective.

Using software on a cloud-based platform, digital twins pull together and analyze data companies can use to monitor and head off repairs and other problems before they occur. They can look into the future, simulating scenarios to uncover new opportunities for delighting customers. The data is deep and broad, encompassing business content like the customer’s name, exact street location of their coffee machines, and service level agreements. Information is also contextual and, of course, from sensors. The digital twin replicates everything about the machine’s operation history, from how many cups and what type of coffee people are drinking, to the precise temperature of the milk and amount of steam pressure used to brew each pour.

Think of digital twins as a combination of your smartest product technician coupled with advanced machine monitoring capabilities plus predictive and preemptive analytics. The measurable gains for companies are astounding. By 2018, IDC predicts companies investing in IoT-based operational sensing and cognitive-based situational awareness will see 30 percent improvements in the cycle times of impacted critical processes.

Four steps to get started

When I talked with SAP’s Thomas Kaiser, senior vice president of IoT, at SAP TechEd, he told me about the hottest industries using digital twins, and what companies can realistically expect. After the event, he added these thoughts to what we covered in my video interview.

“Digital twins are becoming a business imperative, covering the entire life cycle of an asset or process and forming the foundation for connected products and services,” said Kaiser. “Companies that fail to respond will be left behind. Those that embrace digital twins have the opportunity to better understand customer needs, continuously improve their products and services, and even identify new business models that give them competitive advantage.”

Digital twins are becoming a business imperative, forming the foundation for connected products and services.

Kaiser recommended four steps to get started with digital twins, noting that while these steps are easy to list, they can require significant effort to achieve. First, integrate smart components into new or existing products. Second, connect the products/services to a central (cloud-based) location with streaming, Big Data, in-memory, and analytics capabilities to capture sensor data and enrich it with business and contextual data. Third, constantly analyze the data to identify areas for improvements, new products or even new business models. Fourth, use digital insights to create new services that transform the company — disrupt before your business is disrupted.

The coffee machine on stage at SAP TechEd may have looked like every other one, but quietly brewing behind it is a world of innovative difference. As for that question about how fast your coffee machines can fuel growth, using digital twin technology, it’s a potent brew of fresh insights fueling innovation with tremendous business outcomes.

For more on digital twin technology, see Leveraging Digital Twins To Breathe New Life Into Your Products And Services.

Follow me @smgaler

Images via SAP

Comments

How Can IoT Help Retailers?

Sarah McMullin

“The system says that there are 3 shirts in the store, but…” How many times have you gone into a store and asked an associate to look up something, and after the search (which can also take some time), the associate scratches their head and claims the item’s in the store, according to the system, but they still can’t find it?

Or even worse, you researched online and came into the store expecting to buy something and it’s not there? I have spent the better half of the year talking to retailers about inventory accuracy, and I have discovered that everyone has a magic number. You know, that number in the system that you need to see in order to BELIEVE that at least one of the items is actually there.

Why is inventory accuracy so difficult to achieve?

Inaccurate inventory is a problem that has plagued retailers for ages, and you may be shocked to find out that average inventory accuracy is only around 65%. A lack of inventory accuracy produces symptoms in stores such as over-stocking and out-of-stocks, which are actually quite costly for retailers (over $1 trillion a year).

Think about the dynamic nature of retailing. Throughout a day, a store performs many different operations other than selling in the store (which is why after-the-fact point-of-sale [POS] data is never good enough). There are store transfers. There are goods to be received that haven’t been accounted for yet. There is shrink (permanently missing items). There are e-commerce sales. The list goes on.

Improving inventory accuracy with the Internet of Things

How can IoT be used to improve inventory accuracy? First we can digitize the inventory. Let’s define the Things as sensors on individual items in inventory (like one shirt, or one shoe, or one can). These sensors can vary across a wide range of technology like beacons, RFID tags, or shelf liners that can stream raw data about inventory presence. For example, if I take something off a shelf, the shelf can send a message that an item has been moved off the shelf. Or if I take a shirt into a dressing room, the shirt can send a message that it has moved into another area. Now imagine all these items talking, constantly sending state updates per millisecond to a dynamic edge processing server sitting in a store.

What immediately happens is your inventory accuracy level rises as you are given inventory counts that are timelier than cycle counts (physical counting) could ever create. This can be done quickly, run on minimal hardware, and independent from existing systems and network connections. But is that truly retailer ROI for IoT? Knowing in-store inventory levels for a particular item is not useful if you can’t act on it. In fact, you might argue an associate could easily tell you a shelf is empty just by looking at it rather than investing in IoT technology to tell you.

More than IoT data – business of things

But what if you could combine your minimum quantity rules from SAP with an automated purchase order requisition from the store based on the total in-store count for an item? You could potentially avoid stock-outs from ever happening, not from just live inventory counts, but with combined intelligence at the store level. Or what if you could automate receiving so associates don’t need to manually scan everything, highlighting discrepancies between what was received versus the original purchase requisition automatically, freeing the associate up to make sales in the store front?


Six use cases we have identified where IoT combined with dynamic edge processing can help retailers.

Retail reality check

We have the technology today to achieve this, and we have worked on many edge processing scenarios across various industries, including retail. But is retail really ready for it? A lot of the retailers I have spoken to are just starting to dip their toe into the IoT water. They’re interested in simple use cases around improving efficiency with real-time in-store data to build the ROI case. I can appreciate that approach. But I truly believe it won’t be until we marry the insights collected at the store with the business intelligence at the headquarters that IoT will start to make real gains in retail. When retailers are ready, we will be waiting.

Learn more about how to engage your customers wherever they are in Customer Experience: OmniChannel. OmniNow. OmniWow.

Comments

Sarah McMullin

About Sarah McMullin

Sarah McMullin is the Director of Emerging Technologies at SAP, with a focus on food safety and food fraud innovations within supply chains. Her specialties include enterprise software, cloud computing, mobile devices and applications, enterprise mobility and product marketing.

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.

Comments

Tags:

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

About Andre Smith

An Internet, Marketing and E-Commerce specialist with several years of experience in the industry. He has watched as the world of online business has grown and adapted to new technologies, and he has made it his mission to help keep businesses informed and up to date.

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

Jay Tchakarov

About Jay Tchakarov

Jay Tchakarov is vice president of Product Management and Marketing at HighRadius Corporation. As part of HighRadius’ executive team, he is responsible for defining HighRadius’ Credit and A/R products and for educating the market about the value of automation and advanced technologies. He and his team work closely with sales, consultants, and customers to make sure the products address critical pain points and provide quantifiable, high-value solutions. Jay has more than 15 years of experience in software development, product management, and marketing, and numerous successful product launches. Jay graduated summa cum laude and received a Bachelor of Science in Computer Science from the University of Louisiana at Lafayette, a Master of Science in Computer Science from the University of Illinois at Urbana-Champaign, and an MBA from Rice University.

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

About Derek Klobucher

Derek Klobucher is a Brand Journalist, Content Marketer and Master Digital Storyteller at SAP. His responsibilities include conceiving, developing and conducting global, company-wide employee brand journalism training; managing content, promotion and strategy for social networks and online media; and mentoring SAP employees, contractors and interns to optimize blogging and social media efforts.

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

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.

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

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

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

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