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Digital Transformation: Reimagining The World, Industry By Industry

Pat Bakey

The world as we know it is continually changing, and one of the fundamental drivers is digital transformation. Person by person, company by company, and industry by industry, a new reality is evolving.

The global economy is undergoing a digital transformation as well, and it’s happening at breakneck speed. Consequently, established business models no longer work, and previously successful business networks are rapidly disintegrating while industry boundaries evaporate. New, powerful players are emerging and shaking up the status quo as products get smart and consumers get even smarter.

What does that mean to the everyday person like you and me? It means imagining the world differently—because we must, and because we can.

Re-imagining industry

To see how the world can be imagined, let’s look at the agricultural industry—one that we can relate to because we all need food to survive.

One of the ambitious objectives of the United Nations Sustainable Development Goals (SDGs) is to eliminate hunger by 2030. However, with an estimated 9 billion people living on earth by 2050, this goal will not be possible unless we start re-imagining how food is produced today. In fact, a report from the Food and Agriculture Organization of the United Nations says that to feed the entire world population in 2050, food production must increase by 70%.

That means that the soybean farmer in Iowa as well as the cashew farmer in Africa must do things differently. And they can, thanks to digital transformation and new business models, such as precision farming, which combines a variety of technologies to enable farmers to increase production, optimize investments, and maximize returns.

Feeding the world is an attainable reality

For the agricultural industry—which consists of more than one billion workers worldwide—precision farming is a bold step. But now, farmers in even the most remote parts of the world can maximize yields like never before. They can also minimize irrigation, labor, and energy usage while intelligently using fertilizers, herbicides, and pesticides that may cause harm to the environment. They can produce better food, more economically and more efficiently.

It’s advancements like this that will end world hunger. In fact, the International Food and Policy Research Institute recently reported that agricultural technologies could increase global crop yields by as much as 67% percent while cutting food prices nearly in half by 2050.

Precision farming in action

Big Data, mobile, supply chain, and cloud technologies are key enablers for precision farming. Here are a few examples of how these tools are helping farmers around the globe.

  • Gaining new insights. Farmers are using Big Data from the Precision Agriculture Hub, which connects the world’s biggest agricultural businesses, farmers, and suppliers to farm smarter. Through technology solutions and the supply chain and network of F4FAgriculture, farmers can gain insights on which crops to plant where and when. They can also learn what pesticides and fertilizers to use; how upcoming weather patterns will affect their crops; and where the best market prices are. With this critical data, they can maximize their yields, optimize sales, and help feed more people.
  • Learning new ways to farm. The African Cashew Initiative works to help over 300,000 small-scale farmers increase cashew productivity and income in five African countries (Benin, Burkina Faso, Côte d’Ivoire, Ghana, and Mozambique). By offering training programs, materials, and access to mobile business applications, these farmers are learning the best way to bring their product to market too. They can more efficiently forecast and plan, connect to the best buyers, and implement advanced marketing strategies.
  • Increasing sustainability. In northern Ghana, the StarShea Network is helping rural women learn more efficient ways to harvest and process shea nuts and butter. The network, with more than 15,000 members, provides information technology, education, and microfinancing to its members so they can conduct business independently and sustainably. For instance, through mobile technology, these women have access to the current market prices so they can sell their products competitively to global customers. They also have the technology to scan personalized barcode labels on each shea nut sack to track individual production and storage details. 

SAP is helping the world re-imagine itself

The vision and purpose of SAP is to help the world run better and improve people’s lives. We are committed to accelerating our customers’ digital transformation and we challenge them to reimagine their operational processes, business models, and the way they interact with the world.

We are also committed to the United Nations SDGs, including improving the health of the world by ending hunger – because we must, and we can.

To learn more about precision farming initiatives from SAP, visit here.

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Pat Bakey

About Pat Bakey

Pat Bakey is the president of Industry Cloud for SAP. He is responsible for the industry cloud footprint, which covers 25 industries globally, the finance and extended supply chain lines of business and the go-to-market execution of SAP Business Suite 4 SAP HANA (SAP S/4HANA). By offering prescriptive cloud road maps by industry and lines of business, the Industry Cloud organization serves every customer in every cloud model (private, public, and hybrid), for any business size, anywhere in the world, enabling SAP’s customers to approach their digital transformation through an industry lens.

Beyond The Millennial Stereotype: Passion, Authenticity, And Purpose

Nancy Langmeyer

I must say I’m guilty. Yes, I’m one of the many people in the world that has stereotyped millennials. I believed the research I read and thought the most common labels associated with this generation – entitled, lazy, adventure-seeking, job-hopping, and disrespectful of older colleagues – were right on the money.

And of course, those stereotypes must be true if they show up on American reality TV, right? The 2016 season of “Survivor” pitted the so-called instant-gratification, fun-loving millennials against the older, more methodical and conservative Gen Xers. Described as “old ideas versus new ideas,” the show glorified the generational differences (at least after its artful editing), such as highlighting when one millennial noted that he “will never grow up.” Now, interestingly enough, the carefree, pleasure-seeking millennials are holding their own, with one more tribe member remaining than the Gen Xers as the season nears completion (at the time of this writing). 

But wait…is this fair to this generation?

After having admitted that I believed the hype about millennials, it may be hard to imagine that I try to avoid stereotypes, but I do. I know from experience that global characterizations are nearly ever 100% correct, and shame on me for collectively believing the ones about millennials.

The “Survivor” show was a tipping point for me, as I said to myself, “Enough…the world needs to give this generation a break.” This feeling was fueled by the fact that I’ve been lucky enough to work with several millennials as they developed blogs for a series called Millennials on Purpose. The blogs focus on this generation’s view of purpose-driven business and I learned quite a bit from each person’s submission.

Now, having interacted one-on-one with these millennials over the last couple of months, I know without a doubt that like any stereotypes, the ones slapped on these young people are not one-size-fits-all labels.

Here’s what millennials really care about…

What I have learned is there are several commonly shared traits that millennials are quite proud of, but that don’t often show up in the research. For instance, the millennials I have interacted with seem to be universally proud of their innate ability to see the truth. As one millennial, Thomas Leisen says, “Millennials have really good BS-sniffers when it comes to authenticity.” Sam Yeoman, another millennial I’m working with, says his generation is “wary of anyone trying to sell us something.”

Why is this attitude prevalent? Well, because if anything fishy is detected, these self-proclaimed smartphone addicts will be the first to Google it and they will rapidly uncover the truth.

What about work-life balance, and putting life – and fun – first? According to Leisen, he said this is another myth. He and the millennials he knows are passionate about their careers, despite the fact that are often bashed for job hopping and their readiness to take the next big job offering. Again, Leisen deflects this characterization, noting that he personally would love to grow and develop his skills within one organization.

Another millennial, Jessica Gutierrez agrees, saying that she wouldn’t categorize millennials as caring more about work-life balance than about going the extra mile. “When a company gives millennials a chance,” says Gutierrez, “they will have incredible loyalty and be willing to stay longer, work harder, work smarter, and invest in their career.”

So what really makes them tick?

Beyond the hype, the myths, and the stereotyping, what are millennials really passionate about? What do they value? It’s pretty simple, when it gets right down to it – they care about things like purpose, values, and sustainability, and they want to work for businesses that are vocal about these attributes.

Kishore Kumar, a contributor to the Millennial series, says in his blog, “I believe why a business exists is almost more important than how well it is run. I also believe it is critical for businesses to find their core purpose, and then pursue it relentlessly, if they want to be successful.”

In her blog, Gutierrez says, “I believe that millennials will gravitate toward organizations that demonstrate their purpose and values in the culture and work environment. In turn, millennial employees will be influenced by their working environment and will begin to live out the purpose, values, and strategy that the organization embodies.”

And from millennial Faith Woo’s perspective, sustainability is another lens through which she and her peers can examine the impact of purpose-driven business. “Championing a cause and promoting a purpose engages and inspires millennials,” says Woo in her blog, “and I believe we value companies with a strong environmental and social record.”

I agree here and now to stop…

I personally am finished stereotyping millennials and will seek out more articles like this piece, which Gutierrez shared with me, that bust up the myths about this generation. And I now wholeheartedly believe pieces like this one, which shares research that states millennials “are loyal to companies that allow them to stay true to their personal and family values.”

Get to know a millennial today – you might just be surprised at what you find below the surface. Or, if you’d like to get to know the ones I’ve worked with, then you can meet these SAP millennials here as they personally share their passions and their insights about purpose-driven businesses.

This blog is part of our Millennials on Purpose series. To learn more about SAP’s higher purpose to help the world run better and improve people’s lives, visit sap.com/purpose.

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Nancy Langmeyer

About Nancy Langmeyer

Nancy Langmeyer is a freelance writer and marketing consultant. She works with some of the largest technology companies in the world and is a frequent blogger. You'll see some under her name...and then there are others that you won't see. These are ones where Nancy interviews marketing executives and leaders and turns their insights into thought leadership pieces..

Meaningful Purpose: How Sustainability Attracts Millennials

Faith Woo

I was in the seventh grade when Al Gore’s controversial documentary, An Inconvenient Truth, captivated audiences and exposed the devastating causes and effects of global warming. I remember no fewer than three of my teachers rolling the projector into class so that we could watch it.

The documentary opened our eyes to the horrors that lurked in pollution and consumption, and inspired a classroom of impressionable twelve-year-olds to adopt the few sustainable practices available to them. The message it delivered – an exciting, galvanizing call to action – resonated in many of my peers and me.

I distinctly recall feeling a sudden passion for environmentalism, though my grasp on the concept then was naturally hazy and loose. This passion has grown with me over the last decade, and remains with me to this day.

The values revolution

The fact is I am not alone: my generation of millennials has grown up with an acute awareness of environmental issues as the threat of global warming produces tangible effects. The consequences of climate change are catching up to us: melting polar ice caps, rising sea levels, and even a sickly Great Barrier Reef. It just takes one Google search of “millennials + sustainability” to conjure headlines about the remarkable interest this generation has in environmentalism.

Moreover, a so-called “values revolution” seems to be taking place among millennials, according to a study conducted by Global Tolerance. The study says that 84% of millennials “consider it their duty to make a positive difference through their lifestyle.” Similarly, an article in Business Insider shares how millennials place great value on the sustainability of a purchase, and are more willing than other generations to spend more for an environmentally friendly product.

As consumers, millennials respond to environmental purpose, and we carry this fervent attitude towards social responsibility into the workplace.

Millennials want meaningful purpose

Companies that champion a purpose beyond financial gain increase their impact with millennials. We want to engage in corporate social responsibility (CSR), and with a great wealth of knowledge on sustainability, our commitment often influences our attitudes towards our employers.

The 2016 Deloitte Millennial Survey concluded that millennials judge companies’ success on a level beyond the financial: my generation’s desire for commitments to improving the world displaces the traditional importance of profitability.

Unlike other generations, millennials actively seek employers whose environmental values align with theirs. The Global Tolerance report found 62% of millennials want to work for a company “that makes a positive impact” and 53% work harder knowing their work makes a difference to the world.

Championing a cause and promoting a purpose engages and inspires millennials, and I believe we value companies with a strong environmental and social record.

Purpose-driven business is sustainable – for all generations

Sustainability is simply another lens through which we can examine the impact of purpose-driven business on a millennial workforce. A company whose purpose in some way aligns with a millennial’s core values is a winning combination where the relationship between employer and employee becomes mutually beneficial.

However, while millennials may be driving the conversation, the effects of a sustainable purpose resonate with employees of all ages. A recent article in Harvard Business Review shows that a company’s engagement in sustainability creates a culture desirable to all employees. In fact, morale and productivity increase in employees when they feel a loyalty to their companies as a result of sustainability programs.

When a company has a purpose, whether environmental or otherwise, it sends a message to employees that their values and passions can be realized on a corporate level. For instance, my purpose and my company’s align. Working at SAP, I see firsthand that its vision and purpose is rooted in many causes, one of which is sustainability. Its dedication to creating a clean planet, combating climate change, and encouraging responsible growth is exemplar of a purpose-driven organization.

I feel lucky that I can share and channel my personal passion for a sustainable world in a professional setting. I feel as though my participation in a company that integrates sustainability “into [its] core business by embedding sustainability throughout [its] organization” adds value to society and benefits the environment. And because of this personal association, SAP has my loyalty.

This blog is part of our Millennials on Purpose series. To learn more about SAP’s higher purpose to help the world run better and improve people’s lives, visit sap.com/purpose.

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Faith Woo

About Faith Woo

Faith Woo is a millennial who likes to stay curious and challenge herself both personally and professionally. She recently graduated from McGill University with a B.A. in English Literature, and joined SAP in May 2016. Passionate about the values of sustainability and corporate social responsibility espoused by SAP, she currently works as an information developer associate, and supports a development team by creating product documentation.

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.