For the last year or two a fairly lackluster debate has – hmm, raged isn’t the word, perhaps wafted – over the financial services industry: Will banks be displaced by fintech firms with better and easier to use technology? Or will individuals and corporations stick with nice, stable, secure banks with the huge columned headquarters downtown and slick branches in the ‘burbs?
Now Oliver Wyman, the global consultancy, has wandered into the fray to suggest, just in time for Davos, that the solution is modularity – financial services are becoming modular.
“New technology is making it easier for customers to buy from multiple product providers,” the firm announces in a whitepaper, Modular Financial Services: The New Shape of the Industry. “The number of financial products used by the average customer is increasing. We call this modular demand.”
“Modular financial services are emerging at different speeds across markets. Currently, banking in the US is more modular than in Europe and Asia. Property & casualty insurance has become more modular than life insurance. Now, the modular industry structure will go deeper and spread to new markets,” said partner Oliver Wyman and co-author, Matt Austen. “Since the crisis, most firms have focused on optimizing their existing, integrated business model. Now, the industry is going to move towards a new, modular structure.”
Admirably enough, Oliver Wyman waits until the third paragraph of its paper before introducing the terribly familiar “seamless.”
“Financial services firms are using more third party suppliers. Providers of specialist services, back office processes, and risk capital can now seamlessly plug into a supply chain. New entrants have new, focused business models. We call this modular supply.”
Although the “modular” branding may be new, the idea isn’t especially. I recently wrote about Currency Cloud and Quicken Loans’ Rocket Mortgage, which link to other partners and platforms to deliver their services. Loan companies such as Lending Club and Lenddo have tapped new sources beyond FICO for rating borrowers. They then partner with individuals, banks, foundations, hedge funds, and pensions to provide the loans.
Oliver Wyman expects that fintechs, banks, or other established financial institutions will benefit from a modular financial services model.
“Distribution will become dominated by digital ‘platforms’ that can steer demand to any supplier, allowing new product providers to proliferate. Regulatory changes, particularly around customer data, will also weaken financial firms’ hold on their customers.”
Modularizing forces are not unopposed, however.
“Large integrated financial services firms continue to enjoy advantages, including their existing customer relationships, secure at-scale operations and the fixed costs of regulatory compliance.”
In Europe and Asia, bank customers are more likely to hold most of their financial accounts – credit cards, loans, and mortgages – with one bank.
However, if they are going to compete as modular firms, financial firms will have to replace their costly, inflexible legacy infrastructure – which could cost billions and may require suspending dividends for one to three years, says Oliver Wyman, but it will allow them to develop new services.
Some new banks, like Fidor in Germany, provide services from outside providers, like Currency Cloud, through an API.
Oliver Wyman warns that in a modular architecture, no one firm owns the customer, although financial firms may no longer have much choice.
“Customer loyalty to financial institutions has been eroding since the 1990s, with the advent of monolines, direct banks, and direct insurance,” says the report, which neglected to mention one of the largest forces in the industry – online mutual fund providers that have taken hundreds of billions that might once have resided at banks. In addition to investments, and increasingly automated or hybrid automated/personal advice, firms like Charles Schwab, Fidelity, and Vanguard offer checking accounts.
A few fintech companies I have talked with in the last couple of weeks think banks have an advantage in terms of convenience and efficiency – customers would rather go to one place for a variety of financial services than use multiple providers. The Oliver Wyman report disputes this:
“The digital revolution has reinforced this trend by massively reducing search costs for customers. What once would have taken hours of phoning providers or visiting branches now takes a few moments in front of a computer or mobile phone looking at an aggregator platform or price comparison site.”
The report also looks at the importance of a large, stable deposit base, and notes that monoline credit card companies like Capital One and MBNA grew until the mid-2000s when their ability to fund lending through securitization hit a wall. Capital One acquired Hibernia National Band and North Fork Bank and MBNA was bought by Bank of America.
“Cards have thus gone full circle and are now part of integrated financial institutions.” The consultancy draws two lessons from this:
Modularization can be cyclical rather than secular; forces that encourage it may come and go (in this case, capital market liquidity).
Expertise is not all. The statistical marketing skill of the monolines was ultimately trumped by the greater advantage of having a large and stable source of funds from retail depositors.
The growth of online lending and uncertainty about where the Fed will take interest rates have led some observers of the online lenders to ask if they will survive a changing rate environment.
Looking ahead, Oliver Wyman expects event-based platforms that can broadly support something like buying a house from end to end; commerce platforms for both consumers and businesses with credit, cash advance, trade finance, and FX; and comprehensive personal financial management that can dynamically switch between savings and lending and keep insurance updated to cover any new purchases. The consultants also expect financial services aimed at particular business segments like property managers or importers, and expansion of affinity platforms, presumably moving beyond checks with your favorite football team.
“We believe it is realistic for new business models to capture $150-250 billion of existing revenues. Whether this accrues to new entrants will depend upon the willingness of existing providers to develop alternative models and challenger brands.”
Oliver Wyman’s managing partner for financial services, Ted Moynihan, added: “Even if we do not expect a completely modular financial services sector, the way customers buy financial services and how firms deliver them is going to be transformed.”
The fast-paced world of digital marketing is changing too quickly for most companies to adapt. But staying up to date with the latest industry trends is imperative for anyone involved with expanding a business.
Here are five trends that have shaped the industry this year and that will become more important as we move forward:
Email marketing will need to become smarter
Whether you like it or not, email is the most ubiquitous tool online. Everyone has it, and utilizing it properly can push your marketing ahead of your rivals. Because business use of email is still very widespread, you need to get smarter about email marketing in order to fully realize your business’s marketing strategy. Luckily, there are a number of tools that can help you market more effectively, such as Mailchimp.
Content marketing will become integrated and more valuable
Content is king, and it seems to be getting more important every day. Google and other search engines are focusing more on the content you create as the potential of the online world as marketing tool becomes apparent. Now there seems to be a push for current, relevant content that you can use for your services and promote your business.
Staying fresh with the content you provide is almost as important as ensuring high-quality content. Customers will pay more attention if your content is relevant and timely.
Mobile assets and paid social media are more important than ever
It’s no secret that mobile is key to your marketing efforts. More mobile devices are sold and more people are reading content on mobile screens than ever before, so it is crucial to your overall strategy to have mobile marketing expertise on your team. London-based Abacus Marketing agrees that mobile marketing could overtake desktop website marketing in just a few years.
Big Data for personalization plays a key role
Marketers are increasingly using Big Data to get their brand message out to the public in a more personalized format. One obvious example is Google Trend analysis, a highly useful tool that marketing experts use to obtain the latest on what is trending around the world. You can — and should — use it in your business marketing efforts. Big Data will also let you offer specific content to buyers who are more likely to look for certain items, for example, and offer personalized deals to specific groups of within your customer base. Other tools, which until recently were the stuff of science fiction, are also available that let you do things like use predictive analysis to score leads.
Visual media matters
A picture really is worth a thousand words, as the saying goes, and nobody can deny the effectiveness of a well-designed infographic. In fact, some studies suggest that Millennials are particularly attracted to content with great visuals. Animated gifs and colorful bar graphs have even found their way into heavy-duty financial reports, so why not give them a try in your business marketing efforts?
A few more tips:
Always keep your content relevant and current to attract the attention of your target audience.
Always keep all your social media and public accounts fresh. Don’t use old content or outdated pictures in any public forum.
Your reviews are a proxy for your online reputation, so pay careful attention to them.
Much online content is being consumed on mobile now, so focus specifically on the design and usability of your mobile apps.
Online marketing is essentially geared towards getting more traffic onto your site. The more people visit, the better your chances of increasing sales.
The Digitalist Magazine is your online destination for everything you need to know to lead your enterprise’s digital transformation.
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About Sunny Popali
Sunny Popali is SEO Director at www.tempocreative.com. Tempo Creative is a Phoenix inbound marketing company that has served over 700 clients since 2001. Tempos team specializes in digital and internet marketing services including web design, SEO, social media and strategy.
As 2015 winds down, it’s time to look forward to 2016 and explore the social media and content marketing trends that will impact marketing strategies over the next 15 months or so.
Some of the upcoming trends simply indicate an intensification of current trends, however others indicate that there are new things that will have a big impact in 2016.
Take a look at a few trends that should definitely factor in your planning for 2016.
1. SEO will focus more on social media platforms and less on search engines
Clearly Google is going nowhere. In fact, in 2016 Google’s word will still essentially be law when it comes to search engine optimization.
However, in 2016 there will be some changes in SEO. Many of these changes will be due to the fact that users are increasingly searching for products and services directly from websites such as Facebook, Pinterest, and YouTube.
There are two reasons for this shift in customer habits:
Customers are relying more and more on customer comments, feedback, and reviews before making purchasing decisions. This means that they are most likely to search directly on platforms where they can find that information.
Customers who are seeking information about products and services feel that video- and image-based content is more trustworthy.
2. The need to optimize for mobile and touchscreens will intensify
Consumers are using their mobile devices and tablets for the following tasks at a sharply increasing rate:
Sending and receiving emails and messages
Researching products and services
Reading or writing reviews and comments
Obtaining driving directions and using navigation apps
Visiting news and entertainment websites
Using social media
Most marketers would be hard-pressed to look at this list and see any case for continuing to avoid mobile and touchscreen optimization. Yet, for some reason many companies still see mobile optimization as something that is nice to do, but not urgent.
This lack of a sense of urgency seemingly ignores the fact that more than 80% of the highest growing group of consumers indicate that it is highly important that retailers provide mobile apps that work well. According to the same study, nearly 90% of Millennials believe that there are a large number of websites that have not done a very good job of optimizing for mobile.
3. Content marketing will move to edgier social media platforms
Platforms such as Instagram and Snapchat weren’t considered to be valid targets for mainstream content marketing efforts until now.
This is because they were considered to be too unproven and too “on the fringe” to warrant the time and marketing budget investments, when platforms such as Facebook and YouTube were so popular and had proven track records when it came to content marketing opportunity and success.
However, now that Instagram is enjoying such tremendous growth, and is opening up advertising opportunities to businesses beyond its brand partners, it (along with other platforms) will be seen as more and more viable in 2016.
4. Facebook will remain a strong player, but the demographic of the average user will age
In 2016, Facebook will likely remain the flagship social media website when it comes to sharing and promoting content, engaging with customers, and increasing Internet recognition.
However, it will become less and less possible to ignore the fact that younger consumers are moving away from the platform as their primary source of online social interaction and content consumption. Some companies may be able to maintain status quo for 2016 without feeling any negative impacts.
However, others may need to rethink their content marketing strategies for 2016 to take these shifts into account. Depending on their branding and the products or services that they offer, some companies may be able to profit from these changes by customizing the content that they promote on Facebook for an older demographic.
5. Content production must reflect quality and variety
More and more businesses are focusing marketing efforts on content. This means that, as customers have more content to choose from, competition is going to increase significantly.
In 2016, content will remain King, with an increasing focus on variety and and quality. When companies are creating their content marketing strategies for 2016, they may wish to consider the following when they make their final decisions:
Both B2B and B2C buyers value video based content over text based content.
While some curated content is a good thing, consumers believe that custom content is an indication that a company wishes to create a relationship with them.
The great majority of these same consumers report that customized content is useful for them.
B2B customers prefer learning about products and services through content as opposed to paid advertising.
Consumers believe that videos are more trustworthy forms of content than text.
Here is a great infographic depicting the importance of video in content marketing efforts:
A final, very important thing to note when considering content trends for 2016 is the decreasing value of the keyword as a way of optimizing content. In fact, in an effort to crack down on keyword stuffing, Google’s optimization rules have been updated to to kick offending sites out of prime SERP positions.
6. Oculus Rift will create significant changes in customer engagement
Oculus Rift is not likely to offer much to marketers in 2016. After all, it isn’t expected to ship to consumers until the first quarter. However, what Oculus Rift will do is influence the decisions that marketers make when it comes to creating customer interaction.
For example, companies that have not yet embraced storytelling may want to make 2016 the year that they do just that, because later in 2016 Oculus Rift may be the platform that their competitors will be using to tell stories while giving consumers a 360-degree vantage point.
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.