The world today is a different place. There are officially more mobile devices than people. And with increasing frequency, we use these devices to manage our lives. To shop. To pay for things. To find restaurants and hail cabs. To network with our friends and family. Many of these same technologies have made their way into the enterprise. And they are transforming the way we work. Business networks, for instance, are changing the way we discover, connect, and collaborate with our trading partners. They are giving us access to insights and intelligence that allow us to make better, more informed decisions. They are, in essence, transforming procurement as we know it.
So what does the future hold? Here are five trends to watch:
1. Supply chains will go digital
Technology has driven a new wave of productivity by digitizing key financial and business processes and enabling collaboration across the organization. This trend will continue as best-in-class organizations leverage business networks to create a digital community of partners executing coordinated processes in a more organized and informed way than in the past.
2. Collaboration will increase – and fuel innovation
Many companies have taken steps to improve the efficiency and effectiveness of their supply chain operations by automating key processes such as procurement, orders, invoicing, and payment. And with good reason. Research shows that companies who have embraced digital strategies are seeing real value, boosting revenue more than nine percent, market valuation more than 12%, and profitability by over 26%.
Led by procurement, many of these companies will take things to the next level and enable new processes that drive more collaborative, intelligent, and transparent ways of operating. Processes like dynamic discounting that allow them to secure discounts that can be reinvested in research and development and funding to expand their business. Contingent workforce management through which they can identify and manage highly specialized resources needed to develop that next-generation product. Or joint solution development where they get innovations that enhance their products, and their partners get something they can market to others in the industry.
3. Business will run simpler
Personal networks from Facebook to Twitter have made it simple for consumers to shop, share, and consume in new and more informed ways. Business networks provide an equally easy and scalable way for companies to discover, connect, and collaborate with the trading partners and resources they need to operate in today’s dynamic world. Procurement will tap into these networks to create a simple, consumer-like experience where, with just a few clicks, they can shop for goods and services, place and manage orders and pay for them electronically, and view and manage spend across all major categories through a single, connected platform. With network-based spot buying they can also improve the purchasing process for low-value, non-sourced items and eliminate maverick buying.
4. Lines will blur
Fueled by digital technologies, procurement will take the lead in integrating business processes and collaborating across functions in entirely new ways that drive value. Chief procurement officers (CPOs) will, for instance, engage in helping to manage the financial supply chain, turning payables into a profit center because they have real-time visibility into whether an invoice is okay to pay and whether it has been matched against purchase orders and contracts. Or extending days payable outstanding to improve the overall balance sheet while at the same time offering early payment discounts to suppliers to mitigate both financial and supply risk. According to Future of Procurement, a recent survey conducted by Oxford Economics, 54% of executives say that procurement managing accounts payable is significantly changing the way the function operates.
5. Procurement will get smarter
Like their social counterparts, business networks house incredible amounts of insights and data. Procurement will unleash the power of this information to optimize their supply chain decisions and accelerate innovation and growth. They may, for instance, access performance ratings on potential trading partners along with recommendations from the community to determine who to do business with. Or detect risk across the multi-tier supply chain based on world events and geo-political risk factors. They might combine in-the-moment demand data with historic trends to predict stock outages before they happen and direct replenishment. Or gain real-time insights into invoice approval status to more efficiently manage cash.
In today’s global and connected economy, digital supply chains are the on ramp to innovation and success. And if you want to be among the winners, you need to get on the highway and go fast. Start today by re-imagining your supply chain. Develop digital strategies that allow you to proactively evolve ahead of the competition. Employ comprehensive solutions that support the entire source-to-settle process and create value for all parties involved in it.
Don’t just think about the future, see it and shape it to your advantage.
Emily Rakowski is Global Vice President for Ariba, an SAP Company.
The Digitalist Magazine is your online destination for everything you need to know to lead your enterprise’s digital transformation.
Read the Digitalist Magazine and get the latest insights about the digital economy that you can capitalize on today.
About Emily Rakowski
Emily Rakowski is the Global Vice President, Audience Marketing and Demand Management, SAP Ariba. She is an entrepreneurial and passionate marketing and demand management executive with 20+ years leadership experience in the software and consulting industries.
By now, most executives are keenly aware that the digital economy can be either an opportunity or a threat. The question is not whether they should engage their business in it. Rather, it’s how to unleash the power of digital technology while maintaining a healthy business, leveraging existing IT investments, and innovating without disrupting themselves.
Yet most of those executives are shying away from such a challenge. According to a recent study by MIT Sloan and Capgemini, only 15% of CEOs are executing a digital strategy, even though 90% agree that the digital economy will impact their industry. As these businesses ignore this reality, early adopters of digital transformation are achieving 9% higher revenue creation, 26% greater impact on profitability, and 12% more market valuation.
Why aren’t more leaders willing to transform their business and seize the opportunity of our hyperconnected world? The answer is as simple as human nature. Innately, humans are uncomfortable with the notion of change. We even find comfort in stability and predictability. Unfortunately, the digital economy is none of these – it’s fast and always evolving.
Digital transformation is no longer an option – it’s the imperative
At this moment, we are witnessing an explosion of connections, data, and innovations. And even though this hyperconnectivity has changed the game, customers are radically changing the rules – demanding simple, seamless, and personalized experiences at every touch point.
Billions of people are using social and digital communities to provide services, share insights, and engage in commerce. All the while, new channels for engaging with customers are created, and new ways for making better use of resources are emerging. It is these communities that allow companies to not only give customers what they want, but also align efforts across the business network to maximize value potential.
To seize the opportunities ahead, businesses must go beyond sensors, Big Data, analytics, and social media. More important, they need to reinvent themselves in a manner that is compatible with an increasingly digital world and its inhabitants (a.k.a. your consumers).
Here are a few companies that understand the importance of digital transformation – and are reaping the rewards:
Under Armour: No longer is this widely popular athletic brand just selling shoes and apparel. They are connecting 38 million people on a digital platform. By focusing on this services side of the business, Under Armour is poised to become a lifestyle advisor and health consultant, using his product side as the enabler.
Port of Hamburg: Europe’s second-largest port is keeping carrier trucks and ships productive around the clock. By fusing facility, weather, and traffic conditions with vehicle availability and shipment schedules, the Port increased container handling capacity by 178% without expanding its physical space.
Haier Asia: This top-ranking multinational consumer electronics and home appliances company decided to disrupt itself before someone else did. The company used a two-prong approach to digital transformation to create a service-based model to seize the potential of changing consumer behaviors and accelerate product development.
Uber: This startup darling is more than just a taxi service. It is transforming how urban logistics operates through a technology trifecta: Big Data, cloud, and mobile.
American Society of Clinical Oncologists (ASCO): Even nonprofits can benefit from digital transformation. ASCO is transforming care for cancer patients worldwide by consolidating patient information with its CancerLinQ. By unlocking knowledge and value from the 97% of cancer patients who are not involved in clinical trials, healthcare providers can drive better, more data-driven decision making and outcomes.
It’s time to take action
During the SAP Executive Technology Summit at SAP TechEd on October 19–20, an elite group of CIOs, CTOs, and corporate executives will gather to discuss the challenges of digital transformation and how they can solve them. With the freedom of open, candid, and interactive discussions led by SAP Board Members and senior technology leadership, delegates will exchange ideas on how to get on the right path while leveraging their existing technology infrastructure.
Stay tuned for exclusive insights from this invitation-only event in our next blog! Scott Feldman is Global Head of the SAP HANA Customer Community at SAP. Connect with him on Twitter @sfeldman0.
Puneet Suppal drives Solution Strategy and Adoption (Customer Innovation & IoT) at SAP Labs. Connect with him on Twitter @puneetsuppal.
Achieving quantum leaps through disruption and using data in new contexts, in ways designed for more than just Generation Y — indeed, the digital transformation affects us all. It’s time for a detailed look at its key aspects.
Data finding its way into new settings
Archiving all of a company’s internal information until the end of time is generally a good idea, as it gives the boss the security that nothing will be lost. Meanwhile, enabling him or her to create bar graphs and pie charts based on sales trends – preferably in real time, of course – is even better.
But the best scenario of all is when the boss can incorporate data from external sources. All of a sudden, information on factors as seemingly mundane as the weather start helping to improve interpretations of fluctuations in sales and to make precise modifications to the company’s offerings. When the gusts of autumn begin to blow, for example, energy providers scale back solar production and crank up their windmills. Here, external data provides a foundation for processes and decisions that were previously unattainable.
Quantum leaps possible through disruption
While these advancements involve changes in existing workflows, there are also much more radical approaches that eschew conventional structures entirely.
“The aggressive use of data is transforming business models, facilitating new products and services, creating new processes, generating greater utility, and ushering in a new culture of management,” states Professor Walter Brenner of the University of St. Gallen in Switzerland, regarding the effects of digitalization.
Harnessing these benefits requires the application of innovative information and communication technology, especially the kind termed “disruptive.” A complete departure from existing structures may not necessarily be the actual goal, but it can occur as a consequence of this process.
Having had to contend with “only” one new technology at a time in the past, be it PCs, SAP software, SQL databases, or the Internet itself, companies are now facing an array of concurrent topics, such as the Internet of Things, social media, third-generation e-business, and tablets and smartphones. Professor Brenner thus believes that every good — and perhaps disruptive — idea can result in a “quantum leap in terms of data.”
Products and services shaped by customers
It has already been nearly seven years since the release of an app that enables customers to order and pay for taxis. Initially introduced in Berlin, Germany, mytaxi makes it possible to avoid waiting on hold for the next phone representative and pay by credit card while giving drivers greater independence from taxi dispatch centers. In addition, analyses of user data can lead to the creation of new services, such as for people who consistently order taxis at around the same time of day.
“Successful models focus on providing utility to the customer,” Professor Brenner explains. “In the beginning, at least, everything else is secondary.”
In this regard, the private taxi agency Uber is a fair bit more radical. It bypasses the entire taxi industry and hires private individuals interested in making themselves and their vehicles available for rides on the Uber platform. Similarly, Airbnb runs a platform travelers can use to book private accommodations instead of hotel rooms.
Long-established companies are also undergoing profound changes. The German publishing house Axel Springer SE, for instance, has acquired a number of startups, launched an online dating platform, and released an app with which users can collect points at retail. Chairman and CEO Matthias Döpfner also has an interest in getting the company’s newspapers and other periodicals back into the black based on payment models, of course, but these endeavors are somewhat at odds with the traditional notion of publishing houses being involved solely in publishing.
The impact of digitalization transcends Generation Y
Digitalization is effecting changes in nearly every industry. Retailers will likely have no choice but to integrate their sales channels into an omnichannel approach. Seeking to make their data services as attractive as possible, BMW, Mercedes, and Audi have joined forces to purchase the digital map service HERE. Mechanical engineering companies are outfitting their equipment with sensors to reduce downtime and achieve further product improvements.
“The specific potential and risks at hand determine how and by what means each individual company approaches the subject of digitalization,” Professor Brenner reveals. The resulting services will ultimately benefit every customer – not just those belonging to Generation Y, who present a certain basic affinity for digital methods.
“Think of cars that notify the service center when their brakes or drive belts need to be replaced, offer parking assistance, or even handle parking for you,” Brenner offers. “This can be a big help to elderly people in particular.”
Chief digital officers: team members, not miracle workers
Making the transition to the digital future is something that involves not only a CEO or a head of marketing or IT, but the entire company. Though these individuals do play an important role as proponents of digital models, it also takes more than just a chief digital officer alone.
For Professor Brenner, appointing a single person to the board of a DAX company to oversee digitalization is basically absurd. “Unless you’re talking about Da Vinci or Leibnitz born again, nobody could handle such a task,” he states.
In Brenner’s view, this is a topic for each and every department, and responsibilities should be assigned much like on a soccer field: “You’ve got a coach and the players – and the fans, as well, who are more or less what it’s all about.”
Here, the CIO neither competes with the CDO nor assumes an elevated position in the process of digital transformation. Implementing new databases like SAP HANA or Hadoop, leveraging sensor data in both technical and commercially viable ways, these are the tasks CIOs will face going forward.
“There are some fantastic jobs out there,” Brenner affirms.
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!
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.
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.
The Digitalist Magazine is your online destination for everything you need to know to lead your enterprise’s digital transformation.
Read the Digitalist Magazine and get the latest insights about the digital economy that you can capitalize on today.
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.