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The Speed Of Now Has Never Been Faster – And People Skills Never More Essential

Matthias Heiden

The new year always hits me like a hammer. Even though the rhythm is always the same, the pace of each year seems faster than ever.

The business doesn’t care too much about the year-end processes of CFOs; we have closing, audit, and review processes to complete, but we’re in immediate demand at the start of the year. For instance, since performance in 2015 impacts the 2016 budgets (as discussed in my last blog), CFOs need to refine the budgets so the business is focusing on the proper targets for the coming year. It’s striking how difficult it is every year to land in Q1, strike the right balance, and keep up the pace.

A number of factors drive this ceaselessly increasing speed, and I’m sure other parts of the business feel it as well. Expectations are higher and the demands harder to meet. With the increased transparency available, the capital markets are ever more anxious to get information more quickly and benchmark performance against other companies. It’s all happening in parallel to the responsibilities that come with the new year.

The region I work in, Middle and Eastern Europe, faces significant challenges – currency fluctuations, sanctions with Russia and political changes in Poland, the refugee crisis and its impact on the economy – and there are many more. All of these uncertainties increase the volatility that already exists.

The need for CFOs to support the team

This is making a major impact on the workforce – an emergence of the need for human interaction and human management at the same time. People say technology can do many things. I believe that’s certainly true, but technology can’t replace the need to be physical and visible, to provide the leadership to help people deal with change and its increasing velocity. This is why I’ve been on airplanes time and again since this year began.

People throughout organizations are voicing the need for personal exchange and guidance, to be reassured, to gain acknowledgment that they’re not alone in this experience. They know the changing role of finance, and they want to have a senior manager who can help them deal with the changes, on an emotional and operational level. They desire a strong leadership role from finance.

While you as CFO can’t do everything alone, you must be part of the change management. As CFO, you need to support the team and help lead the change processes in the direction you’re going, selling the vision and helping your team become involved at every stage. This is a growing leadership challenge.

As I conclude my first year as CFO for Middle and Eastern Europe at SAP, I’m discovering that my experiences have reinforced what I’d previously learned: that the CFO needs to stay as close to the business as possible, with the greatest business acumen possible, because there will always be challenges and surprises.

2015 turned out to be a terrific year. One result of our business performance is that we won “Region of the Year 2015,” an enormous honor at SAP. I learned about the enormous stamina and endurance required of the team, which remained committed to closing business opportunities to the very end. We stayed close to our customers and open to discussions about their concerns, and we did a really good job as a team addressing those concerns.

I learned that while not everything was perfect, that wasn’t really so important. If you were to ask colleagues in the other regions, they’d likely give the same answer. And they’d also agree: The velocity of business continues to grow, and each year eclipses the years before. Hang on – it’s quite a ride!

To learn more about how finance executives can empower themselves with the right tools and play a vital role in business innovation and value chain, review the SAP finance content hub, which offers additional research and valuable insights.

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Matthias Heiden

About Matthias Heiden

Dr. Matthias Heiden, senior vice president, regional CFO, Middle and Eastern Europe (MEE), is responsible for the field finance organization of MEE. In this role, he supports the organization in managing P&L, continuously driving strategic finance transformation initiatives initiated by Corporate Finance together with the other regional CFOs. This team helps improve business-related processes and supports the Market Unit CFOs in their role as business facilitator and transformation agent.

The CFO Role In 2020

Estelle Lagorce

African American businessman looking out office window --- Image by © Mark Edward Atkinson/Blend Images/CorbisThe role of the CFO is undergoing a serious transformation, and CFOs can expect their role to continue to evolve, according to a recent CFO.com article by Deloitte COO and CFO Frank Friedman.

In the futurist article, Friedman says one of the biggest factors that will contribute to the CFO’s significant change over the next five years is technology.

Digital technology is obviously expected to drive change in high-tech companies, but Friedman says it’s industries outside of the tech sectors that are of particular interest, as they struggle to understand how to grasp and harness the digital capabilities available to them.

Working with high tech in low-tech industries

Five years from now, a finance team may be defined by how well it uses technology and innovative business tools, regardless of what industry it’s in. The article outlines some examples of ways that digital technology will increasingly be used by CFOs in “non-tech” sectors:

  • Predictive analytics: CFOs in manufacturing companies can forecast results and produce revenue predictions based on customer-experience profiles and current demand, instead of comparing to previous years as most companies still do today.
  • Social media and crowdsourcing: You may not think CFOs spend a lot of time on social media or crowdsourcing sites, but these methods can actually expedite finance processes, such as month-end responsibilities of the finance organization.
  • Big Data: CFOs already have a lot of data at their fingertips, but in 2020 they will have even more. CFOs in both tech and non-tech sectors who understand how to use that data to make valuable, informed decisions, can strategically guide their company and industry in a more digitally oriented world.

To do this, Friedman says CFOs can lead the way by addressing some critical areas:

  1. Know the issues: Gather the key questions that leaders expect Big Data analytics to answer.
  1. Make data easily accessible: Collect data that is manageable and easy to access.
  1. Broaden skills: The finance team needs people with the skills to understand and strategically interpret the data available to them.

The tech-savvy CFO

The role of today’s CFO has already expanded to include strategic corporate growth advice as well as managing the bottom line. In 2020, Friedman says expectations placed on the CFO are presumed to be even greater, and CFOs will likely need a much more diverse, multidisciplinary skill set to meet those demands.

The article details several traits and skills that CFOs will need in order to keep up with the pace of digital change in their role.

  1. Digital knowledge: CFOs must be tech-savvy in order to capitalize on technical innovations that will benefit their company and their industry as a whole.
  1. Data-driven execution: CFOs will need the ability to execute company strategy and operations decisions based on data-driven insights.
  1. Regulatory compliance: Regulations continue to be more stringent globally, so CFOs will need to be proficient at working closely with regulators and compliance systems.
  1. Risk management: With the growing global economy comes increased cyber and geopolitical risks worldwide. The CFOs of 2020, especially those in large multinational organizations, will need to have the expertise to monitor and manage risk in areas that may be unforeseen today.

The future CFO’s well-rounded resume

By 2020, the CFO role will require much more than just an accounting background. According to Deloitte’s Frank Friedman, “CFOs may need to bring a much more multidisciplinary skill set to the job as well as broader career experiences, from working overseas to holding positions in sales and marketing, and even running a business unit.”

So if you’re a current or aspiring CFO, you have five years to round out your resume with the necessary skills to be ready for the digitally driven role of the CFO in 2020.

The above information is based on the CFO.com article What Will the CFO Role Look Like In 2020?” by Deloitte COO & CFO, Frank Friedman – Copyright © 2015 CFO.com.

Want to learn more about best practices for transforming your finance organization? View the SAP/Deloitte Webinar, “Reshaping the Finance Function”.

For an in-depth look at digital technology’s role in business transformation, download the SAP eBook, The Digital Economy: Reinventing the Business World.

To learn more about the business and technology factors driving digital disruption, download the SAP eBook, Digital Disruption: How Digital Technology is Transforming Our World.

To read more CFO insights from a tech industry perspective, read the Wall Street Journal article with SAP CFO Luka Mucic: Driving Insight with In-memory Technology.

Discover 7 Questions CFOs Should Ask Themselves About Cyber Security.

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Estelle Lagorce

About Estelle Lagorce

Estelle Lagorce is the Director, Global Partner Marketing, at SAP. She leads the global planning, successful implementation and business impact of integrated marketing programs with top global Strategic Partner across priority regions and countries (demand generation, thought leadership).

Get Your Payables House In Order

Chris Rauen

First of 8 blogs in the series

Too many organizations ignore the business potential from streamlining accounts payable operations. In a digital economy, however, this may represent one of the best opportunities to improve financial performance and boost the bottom line.

In its recent report, ePayables 2015: Higher Ground, the research and advisory firm Ardent Partners made a strong case for accounts payable transformation. “In 2015, more AP groups are accelerating their plans to transform their operations and scale to new heights,” states the report.

The digital makeover

From a payables perspective, how you go about fixing outdated procure-to-pay (P2P) practices is much like the decision to improve an aging home. Do you tear your house down and build a new one, or leverage as much of the existing structure as you can and begin a major home improvement project?

There is, of course, a third option. Take no action and make calls to plumbers, electricians, roofers, and other specialists as needed before the house falls apart altogether. While few organizations would consider a “triage” strategy the best option to address deficiencies in P2P operations, many still do. (Just don’t share that with your CFO.)

This blog post is the first in a series that will examine options for upgrading procure-to-pay processes from outclassed to best-in-class. Continuing to focus time and effort on managing transactions just doesn’t make sense. With today’s business networks, organizations have new ways to collaborate with suppliers and other partners to buy, sell, and manage cash.

Automation handles low-value activities, eliminating data entry, exception management, and payment status phone calls. That leaves more time for benchmarking operations, monitoring supplier performance, expanding early payment discounts, and improving management of working capital – the kinds of things that can dramatically improve business performance.

Where do you start?

To begin, you have to recognize that getting your payables house in order is much more than a process efficiency initiative. While cost savings from e-invoicing can be 60% to 80% lower than paper invoicing, there’s much more to the business case.

Improving contract compliance and expanding early payment discounts are other components of a business case for P2P transformation. According to various procure-to-pay research studies and Ariba customer results, the cost savings from getting your payables house in order are conservatively estimated to be $10 million per billion collars of spend. We’ll break down these ROI components in greater detail in future posts on this topic.

The value of alignment

Another important first step, validated by the Ardent Partners report, is getting procurement and finance-accounts payables in alignment. As this is a holistic process, you’ll need to make sure that both organizations are in sync, and you have support from upper management to make it happen.

Now, back to the question: Do you approach a payables makeover to support P2P transformation as a tear-down or a fixer-upper? If your procurement-accounts payable teams are out of alignment, your P2P processes are predominantly paper, and decentralized buying leaves little control over spend, you’re looking at a tear-down to lay the foundation for best practices payables. We’ll share a blueprint with you in the next post in this series.

Chris Rauen is a solution marketer for Ariba, an SAP company. He regularly contributes to topics including e-invoicing and dynamic discounting as well as the value of collaborating in a digital economy. 

Learn more about how to take your payables to the next level of performance in Ardent Partners’ research report “ ePayables 2015: Higher Ground.”

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Chris Rauen

About Chris Rauen

Chris Rauen is a solution marketer for SAP Ariba. He regularly contributes to topics including e-invoicing and dynamic discounting as well as the value of collaborating in a digital economy.

How AI Can End Bias

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

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

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

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

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

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

Bias: Bad for Business

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

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

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

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

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

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

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

Look for Where Bias Already Exists

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

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

Code Is Only Human

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

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

Don’t Do What You’ve Always Done

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

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

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

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

Look Beyond the Obvious

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

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

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

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

Context Matters—and Context Changes

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

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

Moving Forward with AI

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

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

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

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

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

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


About the Authors:

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

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

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

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

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2017: The Year Businesses Will Learn The True Meaning Of Digital Transformation

Hu Yoshida

Over the last 10 years, the exponential growth and power of technology have brought some fascinating, if not mind-bending, opportunities. Machines talk to one another with computer-connected humans on the other end observing, analyzing, and acting on the explosion of Big Data generated. Doctors use algorithms that mine patient history or genetic information to detect possible diagnoses and treatment. Cars are programmed with data-driven precision to direct drivers to the best-possible route to their destination. And even digital libraries for 3D parts are growing rapidly – possibly to the point where we can soon print whatever we need.

With all of this technology, it is common sense to believe that productivity would also rise over the same span of time. However, according to a recent 2016 productivity report released by the Organisation for Economic Co-operation and Development (OECD), this is, sadly, not the case. In fact, most advanced and emerging countries are experiencing declining growth that is cutting across nearly all sectors and affecting both large and small firms. But more interesting is the agency’s observation that this trend does not exclude areas where digital innovation is expected to improve information sharing, communication, and finance.

See how IT can help organizations shift to real-time operations. Read the EIU report.

Although nearly 5 billion people on our planet have a computer in their pocket or their hands at any moment of the day, our digital ways have not translated into productivity gains for the enterprise. The culprit? Businesses are not changing their processes to allow that technology to reach its full potential.

Technology alone does not bring real digital transformation

Every week, I hear how companies worldwide are so excited about their digital transformation initiatives. Some are developing their own applications or executing a new digital commerce strategy. Others may decide to deploy a new analytics tool. No matter the investment, there is always great hope for success. Yet, they often fall short because the focus is typically on how technology will change the business – not how the enterprise will change to fully embrace the digital innovation’s potential.

Take, for example, a bank’s decision to allow the loan process to be initiated through a mobile app or online store. The bank may receive the information from the consumer faster than ever before, but no real benefit is achieved if it still takes three weeks to approve or decline the loan request. Technology may be changing the customer experience online, but back-office processes are unaffected. The same old ways of work are still happening, and productivity is not improving. For a digital world where everything is supposed to be automatic and immediate, a customer will inevitably turn to a competitor that will approve the loan faster.

True digital transformation requires more than technology. Companies must evolve their processes with a keen focus on outcomes, not just infrastructure. All too often, they are focused on creating this sort of digital facade where it appears to be a digital experience for the customer, but, in reality, the back-office still has not caught up to support that level of digitization.

Deep digital transformation starts with process innovation

In the coming year, most companies will look to transition to real-time analytics that drives predictive decision-making and possibly draw from the Internet of Things. While this technology presents a clear opportunity for greater insight, organizations are no better off unless they transform business processes to act quickly on them.

Traditional data processes require days to move data from one database to another, process it, and generate reports in an easy-to-understand format. In-memory computing accelerates these processes from days and weeks to hours and minutes – paving the way for transformative power by moving decision-making closer to data generation. However, no matter how fast the analysis, no benefit is realized if downstream processes and decisions do not capitalize on the resulting insight. Like the loan process I mentioned earlier, you need to make sure that the back office and front office are aligned in order to produce improved business outcomes. Legacy systems and databases may still hinder the ability to achieve faster results, unless they are aligned with in-memory analytics.

The ability to modernize core systems with technologies like in-memory computing and innovative new applications can prove to be highly transformational. The key is to integrate these new technologies into an overall business architecture to support digital transformation and deliver real business improvements.

Are you ready to transform your business? Learn 4 Ways to Digitally Disrupt Your Business Without Destroying It.

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Hu Yoshida

About Hu Yoshida

Hu Yoshida is responsible for defining the technical direction of Hitachi Data Systems. Currently, he leads the company's effort to help customers address data life cycle requirements and resolve compliance, governance and operational risk issues. He was instrumental in evangelizing the unique Hitachi approach to storage virtualization, which leveraged existing storage services within Hitachi Universal Storage Platform® and extended it to externally-attached, heterogeneous storage systems. Yoshida is well-known within the storage industry, and his blog has ranked among the "top 10 most influential" within the storage industry as evaluated by Network World. In October of 2006, Byte and Switch named him one of Storage Networking’s Heaviest Hitters and in 2013 he was named one of the "Ten Most Impactful Tech Leaders" by Information Week.