Sections

The Da Vinci Code To The Internet of Things (IoT)

Richard Howells

Over the holiday, I spent a great afternoon at the Boston Museum of Science, which is currently running an exhibition called “Da Vinci – The Genius.” The exhibition brings to life the genius of Leonardo da Vinci as an inventor, scientist, engineer, architect, sculptor, and artist.

On display were interactive and life-size machine inventions, built by Italian artisans, who worked from Leonardo’s codices. Among other things they have built: the first concepts of a car, bicycle, helicopter, parachute, scuba, a forerunner to the modern military tank, and an ideal city.

Da Vinci was, without a doubt, an innovator, someone way ahead of his time. Leonardo carries instant, universal recognition – both as a name and as the figure known for ushering in a groundbreaking era of science and discovery. I can only imagine what he would think of how some of his concepts have been realized, enhanced, and empowered in today’s world of connected products, assets, and vehicle fleets. Or how they would be implemented across the Internet of Everything by connecting infrastructures, markets, and people in today’s world.

Here are a few of Leonardo’s inventions and ideas, and how they have evolved over the years:

From self-propelled cart to connected car

Hundreds of years before the motorized vehicle, Leonardo da Vinci designed a self-propelled cart that was powered by coiled springs and controlled by steering and brake capabilities.

In today’s world, that primitive design can be seen in connected cars, which are tracked by hundreds of sensors to run real-time analysis on steering, breaking, and other systems to predict performance issues and defects.

From an ideal city concept to an actual ideal city

Da Vinci’s ideal city concept was a result of a plague that killed off nearly a third of the population of Milan. Leonardo wanted to design an ideal city with greater communications, services, and sanitation to prevent the future spread of such diseases. His ideal city concept included wider streets that would be cleaned via a paddlewheel system and underground waterways that would carry away garbage.

Today, connectivity is making da Vinci’s vision of an ideal city a reality. With the rise of automation, cities can improve the environment, health, and safety of urban areas while benefiting from smoother traffic flows, more orderly parking, and the increased management of local buildings, lighting, public spaces, yards, ports, and other infrastructure forming the modern-day urban landscape.

From a robotic knight to robotics in the workplace

Da Vinci also incorporated a series of pulleys, weights, and gears to design what many believe to be the first robot. His robotic knight was designed for a pageant in Milan. It consisted of a knight suit fitted with gears and wheels and connected to a pulley-and-cable system that would enable the figure to sit down, stand up, move its head, and lift its visor.

Robotics today are commonplace throughout warehouses and manufacturing facilities around the world. We have drones and driverless vehicles that deliver goods, warehouses that run completely “lights out,” and production facilities that can be controlled and configured in a “to-order” capacity, as supply chain experts strive for a market of one.

From helicopters and flying machines to connected assets and fleets

Leonardo da Vinci was so far ahead of his time as an innovator, it’s incredible to imagine what he could have dreamed up if he were around today – particularly with access to Big Data and real-time insight into nearly every product manufactured, every piece of equipment used, every vehicle in motion, every building in use, and every person on the planet, at any given time.

IoT makes literally everything connected or connectable, from the products we make to the people that use them:

  • Connected products means new insights throughout the product lifecycle, from design, to production, to delivery, to the end of a product’s life.
  • Connected assets can be tracked, monitored, analyzed, and maintained proactively to reduce operational and maintenance costs and increase equipment uptime.
  • Connected fleets of vehicles, robots, forklifts, and autonomous vehicles can be monitored, maintained, and optimized to improve services, safety, visibility, and service quality.
  • Connected infrastructure can improve digital operational intelligence of physical infrastructure systems, construction, and energy grids enabling improved service, efficient operations, and compliance and risk mitigation.
  • Connected markets can optimize rural and urban areas to better enable new production and enhance assets, space, and our natural resources.
  • Connected people enable more insightful, collaborative work roles, health management, and smart-home environments by connecting people and communities and providing better, more personalized lifestyle experiences.

As Leonardo da Vinci once said, “It had long since come to my attention that people of accomplishment rarely sat back and let things happen to them. They went out and happened to things.”

Maybe he had a vision for how we should leverage things – way before the rest of us.

Learn more about the SAP Leonardo Innovation Portfolio and how you can get started to understand the value of IoT for your business.

(Public domain image via Wikimedia Commons)

Comments

About Richard Howells

Richard Howells is a Vice President at SAP responsible for the positioning, messaging, AR , PR and go-to market activities for the SAP Supply Chain solutions.

Will The Collaborative Economy Completely Reimagine Tomorrow's Big Business?

Daniel Newman

Today, the largest car rental and hospitality companies are Uber and Airbnb, respectively. What do they have in common? Let’s see — neither of them own physical possessions associated with their service, and both have turned a non-performing asset into an incredible revenue source.

Don’t be surprised, because this is the new model for doing business. People want to rent instead of own, and at the same time, they want to monetize whatever they have in excess. This is the core of the sharing economy. The concept of earning money by sharing may have existed before, but not at such a large scale. From renting rooms to rides to clothes to parking spaces to just about anything else you can imagine, the sharing economy is rethinking how businesses are growing.

What’s driving the collaborative economy?

The sharing economy, or the collaborative economy, as it’s also called, is “an economic model where technologies enable people to get what they need from each other—rather than from centralized institutions,” explains Jeremiah Owyang, business analyst and founder of Crowd Companies, a collaborative economy platform. This means you could rent someone’s living room for a day or two, ride someone else’s bike for a couple of hours, or even take someone’s pet out for a walk—all for a rental fee.

Even a few years ago, this sort of a thing was unthinkable. When Airbnb launched in 2008, many people were skeptical, as the whole idea seemed not only irrational, but totally stupid. I mean, why would anyone want to spend the night in a stranger’s room and sleep on an air bed, right? Well, turns out many people did! Airbnb moved from spare rooms to luxury condos, villas, and even castles and private islands in more than 30,000 cities across 190 countries, and rentals reached a staggering 15 million plus last year.

What is driving this trend? Millennials definitely play a role. Their love for everything on-demand, plus their frugal mindset, makes them ideal for the sharing economy. But the sharing economy is attractive to consumers across a wide demographic, as it only makes sense.

How collaborative economy is reshaping the future of businesses

Until recently, collaborative-economy startups like Uber and Airbnb were looked upon as threats. Disuptors to any marketplace are usually threatening, so this isn’t surprising. Established businesses that were accustomed to the way things had always been did (and still do) rail against companies like Uber or AirBnB, yet consumers seem to love them. And that’s what matters. Uber has faced many harsh criticisms, yet it continues to provide more than a million rides a month.

We are living in an era of consumer-driven enterprise, where consumers are at the helm. Perhaps this is the biggest reason why the collaborative economy is here to stay. No matter what industry, companies are trying to bring customers to the fore. A collaborative business model allows customers to call the shots. A great example is the cloud, which relies on resource sharing and allows users to scale up or down according to their needs.

Today, traditional businesses are participating in a collaborative economy in different ways. Some are acquiring startups. General Motors, for example, invested $3 million to acquire RelayRides, a peer-to-peer car sharing service. Others are entering into partnerships like Marriott, which partnered with LiquidSpace, an online platform to book flexible workspaces. Other brands, like GE, BMW, Walgreens, and Pepsi are also stepping into the collaborative-economy space and holding the hands of startups instead of competing with them.

Changes in the workplace

Remote work and telecommuting has taken off as companies become more comfortable with the idea of people working outside their offices, and cloud technology is enabling that. Now, let’s look at the scenario from the lens of the sharing economy. With companies looking to find temporary resources that can meet the fast-changing demands of the business, freelancers could replace a large chunk of full-time professionals in future. Why? Because at the heart of this disruptive practice lies the concept of sharing human resources.

As companies set out to temporarily use the services of people to meet short- and medium-term goals, it’s going to completely change the way we build companies. Also, as we have seen through the growth of companies like Airbnb and Uber, it’s going to change the deliverables that companies provide. With demand changing and technology proliferating at breakneck speed, it’s not just important that businesses start to see and adopt this change; it’s imperative because companies that over-commit to any one thing will find themselves obsolete.

When it comes to workplaces, so much is happening today that it’s impossible to predict where things are ultimately headed. But one thing is for sure: The collaborative economy is not going anywhere as long as our priorities are built around better, faster, more efficient and cost-effective.

Want more insight on today’s sharing economy? see Collaborative Economy: It’s Real And It’s Disrupting Enterprises.

This article was originally seen on Ricoh Blog.

The post Will the Collaborative Economy Completely Reimagine Tomorrows Big Business appeared first on Millennial CEO.

Photo Credit: Pedrolu33 via Compfight cc

Comments

About Daniel Newman

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

How One Business Approach Can Save The Environment – And Bring $4.5 Trillion To The World Economy

Shelly Dutton

Despite reports of a turbulent global economy, the World Bank delivered some great news recently. For the first time in history, extreme poverty (people living on less than $1.90 each day) worldwide is set to fall to below 10%. Considering that this rate has declined from 37.1% in 1990 to 9.6% in 2015, it is hopeful that one-third of the global population will participate the middle class by 2030.

For all industries, this growth will bring new challenges and pressures when meeting unprecedented demand in an environment of dwindling – if not already scarce – resources. First of all, gold, silver, indium, iridium, tungsten, and many other vital resources could be depleted in as little as five years. And because current manufacturing methods create massive waste, about 80% of $3.2 trillion material value is lost irrecoverably each year in the consumer products industry alone.

This new reality is forcing companies to rethink our current, linear “take-make-dispose” approach to designing, producing, delivering, and selling products and services. According to Dan Wellers, Digital Futures lead for SAP, “If the economy is not sustainable, we are in trouble. And in the case of the linear economy, it is not sustainable because it inherently wastes resources that are becoming scarce. Right now, most serious businesspeople think sustainability is in conflict with earning a profit and becoming wealthy. True sustainability, economic sustainability, is exactly the opposite. With this mindset, it becomes strategic to support practices that support a circular economy in the long run.”

The circular economy: Good for business, good for the environment

What if your business practices and operation can help save our planet? Would you do it? Now, what if I said that this one business approach could put $4.5 trillion up for grabs?

By taking a more restorative and regenerative approach, every company can redesign the future of the environment, the economy, and their overall business. “Made possible by the digital economy, forward-thinking businesses are choosing to embrace this value to intentionally reimagine the economy around how we use resources,” observed Wellers. “By slowing down the depletion of resources and possibly even rejuvenating them, early adopters of circular practices have created business models that are profitable, and therefore sustainable. And they are starting to scale.”

In addition to making good financial sense, there’s another reason the circular economy is a sound business practice: Your customers. In his blog 99 Mind-Blowing Ways the Digital Economy Is Changing the Future of Business, Vivek Bapat revealed that 68% of consumers are interested in companies that bring social and environmental change. More important, 84% of global consumers actively seek out socially and environmentally responsible brands and are willing to switch brands associated with those causes.

Five ways your business can take advantage of the circular economy

As the circular economy proves, business and economic growth does not need to happen at the cost of the environment and public health and safety. As everyone searches for an answer to job creation, economic development, and environmental safety, we are in an economic era primed for change.

Wellers states, “Thanks to the exponential growth and power of digital technology, circular business models are becoming profitable. As a result, businesses are scaling their wealth by investing in new economic growth strategies.”

What are these strategies? Here are five business models that can enable companies to unlock the economic benefits of the circular economy, as stated in Accenture’s report Circular Advantage: Innovative Business Models and Technologies that Create Value:

  1. Circular supplies: Deliver fully renewable, recyclable, and biodegradable resource inputs that underpin circular production and consumption systems.
  2. Recovery of resources: Eliminate material leakage and maximize the economic value of product return flows.
  3. Extension of product life: Extend the life cycle of products and assets. Regain the value of your resources by maintaining and improving them by repairing, upgrading, remanufacturing, or remarketing products.
  4. Sharing platforms: Promote a platform for collaboration among product users as individuals or organizations.
  5. Product as a service: Provide an alternative to the traditional model of “buy and own.” Allow products to be shared by many customers through a lease or pay-for-use arrangement.

To learn more about the circular economy, check out Dan Wellers’ blog “4 Ways The Digital Economy Is Circular.”

Comments

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 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

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

About Bob Caswell

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

Tags:

awareness