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How HR Teams Can Use Cloud Technology For A Competitive Advantage

Meghan M. Biro

Nothing kills a good mood like a pile of administrative paperwork—a mark of many interactions with the human resources department. But it doesn’t need to be that way. Paper-based systems for everything from onboarding to attendance to payroll are moving to the cloud using new systems that can help HR work better.

A vast majority of businesses are already using cloud technology in one way or another; in fact, one industry watcher observed that it’s quickly becoming the norm. Including HR in the transformation can automate and streamline critical functions like payroll and benefits administration—a move that can save money, improve productivity, and cut the risk of human error.

And there’s another advantage: The wealth of information collected through a cloud-based system can give you broad organizational insights and a distinct advantage over your competition.

Find (and hold onto) better talent with cloud apps

It goes without saying that recruiting is one of HR’s biggest responsibilities. Recruiters already rely on various software and processes to find talent and assess skills—but until now, they’ve always been somewhat cumbersome and flawed.

The recruiting process has traditionally meant sifting through candidate information manually, which occupies hours of precious time. Human error, miscommunication, and disorganization are among the possible issues that can inadvertently lead to poor hiring decisions—and poor hiring decisions lead to a tremendous waste of money.

Cloud solutions have the power to change that. In the battle for top talent, HR departments can use the benefits cloud computing offers as a digital “magical charm” to stay ahead of the competition. For example, instead of an email or paper-driven process, applicants submit their resumes via the Internet and integrated analytics can assess their potential.

As another example, pre-employment testing evaluates a candidate’s personality and skills as they pertain to a particular position. Cloud-based software allows recruiters to access both passive and active candidates, storing the information in an easy-to-access database. Later, that information can be used to track, measure, and generate reports on the applicants. Plus, the whole process can be automated, reducing tedious paperwork and leaving room for more important matters.

Make your business more attractive to employees

Workplace transparency is just as important to employees as it is to employers: They want to understand their paycheck and benefits, quickly and at their convenience. Traditionally, tasks, like adjusting a W2 status or figuring out savings and spending account contributions, meant a call to HR—and more paper pushing for them. It was time-consuming, and they could only do during work hours when someone was available to help.

Today, cloud-based solutions put information directly into the hands of employees, letting them view and update it as needed. One payroll provider found that nearly 40 percent of registered mobile users access their pay information using mobile apps.

These platforms also make administrative tasks more efficient. For instance, if a worker wants to schedule time off, all they have to do is hop on the app and send a request. If they’re going to be late, they can send a message with the click of a button.

Making administrative tasks more efficient and more accessible to employees isn’t just about convenience; it streamlines HR and can be considered a company perk—which is always good for recruiting.

Mobile technology strengthens your brand and reputation

One of the biggest benefits of cloud-based services is the ability to collect and organize a vast amount of data that can then be used to strengthen a business’s operations.

For example, many HR departments track a variety of signals to measure brand continuity and employee engagement. With cloud apps, this analysis is simplified so organizations can measure the results of their programs internally and externally.

Brand reputation is essential to business success. In fact, a recent study by Glassdoor found that 84 percent of respondents would consider leaving a job if a company with a better corporate reputation offered them one. Clearly, it’s critical for companies to ensure their brand is consistent, and cloud apps help monitor that. Whether it’s a collaboration app like Salesforce, a training app like Learnsmarter, or one of the many capital management apps available, cloud technology puts business-critical information into your hands.

HR teams who want to gain a competitive edge—whether by improving employee engagement or strengthening their recruiting efforts—should strongly consider leveraging the power of cloud apps. With so many different functions, inherent scalability, and unbeatable efficiency, these apps are the juggernauts of the corporate world—and they’re on your side.

For more insight on where HR tech is headed, see Will Technology Replace HR In 2016?

The post How HR Teams Can Use Cloud Technology for a Competitive Advantage appeared first on Millennial CEO.

Photo Credit: SalesBabu via Compfight cc

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About Meghan M. Biro

Meghan Biro is talent management and HR tech brand strategist, analyst, digital catalyst, author and speaker. I am the founder and CEO of TalentCulture and host of the #WorkTrends live podcast and Twitter Chat. Over my career, I have worked with early-stage ventures and global brands like Microsoft, IBM and Google, helping them recruit and empower stellar talent. I have been a guest on numerous radio shows and online forums, and has been a featured speaker at global conferences. I am the co-author of The Character-Based Leader: Instigating a Revolution of Leadership One Person at a Time, and a regular contributor at Forbes, Huffington Post, Entrepreneur and several other media outlets. I also serve on advisory boards for leading HR and technology brands.

Innovation Without Boundaries: Why The Cloud Matters

Michael Haws

Is it possible to innovate without boundaries?

Of course – if you are using the cloud. An actual cloud doesn’t have any boundaries. It’s fluid. But more important, it can provide the much-needed precipitation that brings nature to life. So it is with cloud technology – but it’s your ideas that can grow and transform your business.USA --- Clouds, Heaven --- Image by © Ocean/Corbis

Running your business in the cloud is no longer just a consideration during a typical use-case exercise. Business executives are now faced with making decisions on solutions that go beyond previous limitations with cloud computing. Selecting the latest tools to address a business process gap is now less about features and more about functionality.

It doesn’t matter whether your organization is experienced with cloud solutions or new to the concept. Cloud technology is quickly becoming a core part of addressing the needs of a growing business.

5 considerations when planning your journey to the cloud

How can your organization define its successful path to the cloud? Here are five things you should consider when investigating whether a move to the cloud is right for you.

1. Understanding the cloud is great, but putting it into action is another thing.

For most CIOs, putting a cloud strategy on paper is new territory. Cloud computing is taking on new realms: Pure managed services to software-as-a-service (SaaS). Just as legacy computing had different flavors, so does cloud technology.

2. There is more than one way to innovate in the cloud.

Alignment with an open cloud reference architecture can help your CIO deliver on the promises of the cloud while using a stair-step approach to cloud adoption – from on-premise to hybrid to full cloud computing. Some companies find their own path by constantly reevaluating their needs and shifting their focus when necessary – making the move from running a data center to delivering real value to stakeholders, for example.

3. The cloud can help accelerate processes and lower cost.

By recognizing unprecedented growth, your organization can embark on a path to significant transformation that powers greater agility and competitiveness. Choose a solution set that best meets your needs, and implement and support it moving forward. By leveraging the cloud to support the chosen solution, ongoing maintenance, training, and system issues becomes the cloud provider’s responsibility. And for you, this offers the freedom to focus on the core business.

4. You can lock down your infrastructure and ensure more efficient processes.

Do you use a traditional reporting engine against a large relational database to generate a sequential batched report to close your books at quarter’s end? If so, you’re not alone. Sure, a new solution with new technology may be an obvious improvement. But how valuable to your board will you become when you reduce the financial closing process by 1–3 days? That’s the beauty of the cloud: You can accelerate the deployment of your chosen solution and realize ROI quickly – even before the next full reporting period.

5. The cloud opens the door to new opportunity in a secure environment.

For many companies, moving to the cloud may seem impossible due to the time and effort needed to train workers and hire resources with the right skill sets. Plus, if you are a startup in a rural location, it may not be as easy to attract the right talent as it is for your Silicon Valley counterparts. The cloud allows your business to secure your infrastructure as well as recruit and onboard those hard-to-find resources by applying a managed services contract to run your cloud model

The cloud means many things to different people. What’s your path?

With SAP HANA Enterprise Cloud service, you can navigate the best path to building, running, and operating your own cloud when running critical business processes. Find out how SAP HANA Enterprise Cloud can deliver the speed and resources necessary to quickly validate and realize solid ROI.

Check out the video below or visit us at www.sap.com/services-support/svc/in-memory-computing/hana-consulting/enterprise-cloud-services/index.html.

Connect with us on Twitter: @SAPServices

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Michael Haws

About Michael Haws

Michael Haws is the Vice President of HANA Enterprise Cloud at SAP. His specialties include Enterprise Resource Planning Software & Services, Onshore, Nearshore, Offshore--Application, Infrastructure and Business Process Outsourcing.

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Consumers And Providers: Two Halves Of The Hybrid Cloud Equation

Marty McCormick

Long gone are the days of CIOs and IT managers freely spending money to move their 02 Jun 2012 --- Young creatives having lunch and conversation. --- Image by © Hero/Corbisexisting systems to the cloud without any real business justification just to be part of the latest hype. As cloud deployments are becoming more prevalent, IT leaders are now tasked with proving the tangible benefits of adopting a cloud strategy from an operational, efficiency, and cost perspective. At the same time, they must balance their end users’ increasing demand for access to more data from an ever-expanding list of public cloud sources.

Lately, public cloud systems have become part of IT landscapes both in the form of multi-tenant systems, such as software-as-a-service (SaaS) offerings and data consumption applications such as Twitter. Along with the integration of applications and data outside of the corporate domain, new architectures have been spawned, requiring real-time and seamless integration points.  As shown in the figure below, these hybrid clouds – loosely defined as the integration of data from systems in both public and private clouds in a unified fashion – are the foundation of this new IT architecture.

hybridCloudImage

Not only has the hybrid cloud changed a company’s approach to deploying new software, but it has also changed the way software is developed and sold from a provider’s perspective.

The provider perspective: Unifying development and operations

Thanks to the hybrid cloud approach, system administrators and developers are sitting side by side in an agile development model known as Development and Operations (DevOps). By increasing collaboration, communication, innovation, and problem resolution, development teams can closely collaborate with system administrators and provide a continuous feedback loop of both sides of the agile methodology.

For example, operations teams can provide feedback on reported software bugs, software support issues, and new feature requests to development teams in real time. Likewise, development teams develop and test new applications with support and maintainability as a key pillar in design.
After seeing the advantages realized by cloud providers that have embraced this approach long ago, other companies that have traditionally separated these two areas are now adopting the DevOps model.

The consumer perspective: Moving to the cloud on its own terms

From the standpoint of the corporate consumer, hybrid cloud deployments bring a number of advantages to an IT organization. Specifically, the hybrid approach allows companies to move some application functionality to the cloud at their own pace.
Many applications naturally lend themselves to public cloud domains given their application and data requirements. For most companies, HR, indirect procurement, travel, and CRM systems are the first to be deployed in a public cloud. This approach eliminates the requirement for building and operating these applications in house while allowing IT areas to take advantage of new features and technologies much faster.

However, there is one challenge consumers need to overcome: The lack of capabilities needed to extend these applications and meet business requirements when the standard offering is often insufficient. Unfortunately, this tempts organizations to create extensive custom applications that replicate information across a variety of systems to meet end user requirements. This development work can offset the cost benefits of the initial cloud application, especially when you consider the upgrades and support required to maintain the application.

What this all means to everyone involved in the hybrid cloud

Given these two perspectives, on-premise software providers are transforming themselves so they can meet the ever-evolving demands of today’s information consumer. In particular, they are preparing for these unique challenges facing customers and creating a smooth journey to a hybrid cloud.

Take SAP, for example. By adopting a DevOps model to break down a huge internal barrier and allowing tighter collaboration, the company has delivered a simpler approach to hybrid cloud deployments through the SAP HANA Cloud Platform for extending applications and SAP HANA Enterprise Cloud for hosting solutions.

Find out how these two innovations can help you implement a robust and secure hybrid cloud solution:
SAP HANA Cloud Platform
SAP HANA Enterprise Cloud

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Marty McCormick

About Marty McCormick

Marty McCormick is the Lead Technical Architect, Managed Cloud Delivery, at SAP. He is experienced in a wide range of SAP solutions, including SAP Netweaver SAP Portal, SAP CRM, SAP SRM, SAP MDM, SAP BI, and SAP ERP.

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.