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3 Surprising Reasons Why Social Collaboration Should Be Part Of Your 2016 Sales Strategy

Roger Noia

Even though 2016 just started, it’s obvious that the digital economy is changing the world around us. And if there is one area of your business that is most affected, it’s your sales operations. For decades, sales reps have accurately targeted qualified buyers that are ready to select a product and finalize the purchase. They could lead the potential customer through the purchase journey – one step at a time. Thanks to the Internet and social network, those “simpler” times are a thing of the past.

Sales processes have accelerated to the point where it’s difficult to see who is considering your products, services, and competitors. In fact, CEB reported that the average buyer is 57% done with the purchase decision process even before their first interaction with a sales rep or channel. Plus, there’s no real customer loyalty since brands comprise only 12% of their customer’s mindshare during the buying experience.

In essence, the digital economy has made the sales process more complicated and less transparent. However, it can also fix this common problem. With a commitment to digital transformation, sales organizations can provide multiple touch points that make the brand more accessible to every existing and potential customer throughout the customer experience.

How can sales teams adjust to this highly digital world? According to The Total Economic Impact™ Of SAP Jam, a March 2015 commissioned study conducted by Forrester Consulting on behalf of SAP, social collaboration may be the right first step.

Did you know sales deals close 9% faster with social collaboration?

One question, one delay, or one miscommunication can shut down an entire deal at a moment’s notice. To avoid this situation, sales reps need to access expertise, information, and customer data together in one place at all times. With an average of seven people scattered across business areas and geographies involved in a single deal, a collaborative team approach powered by a Web-based, mobile-enabled social collaboration platform can help win new business.

Forrester’s research indicates that a reduction of one week (9%) in time to close new business results in $9.63 million in new deals over three years. The average time required to close a deal decreased from 13 weeks to 12 weeks, which enabled sales professionals to close more deals per year. Furthermore, with an average of seven people working on every deal, that saved time means increased productivity for those workers.

How your sales team can benefit from social collaboration: Say goodbye to the painstaking, time-consuming process of gathering information through email, phone, and the Internet! All of this information is now a click away. As a result, your team can close deals one week sooner – leading to more sales and higher win rates.

Did you know social collaboration reduces onboarding and training costs by 13%?

The sales organization is known to be a source of high turnover. Whether the reason is low earnings or disengagement, proper onboarding and training are a key part of lowering that rate. But at the same time, the business needs reps in the field as soon as possible and closing profitable deals.

In the composite analysis, Forrester found that social collaboration reduces onboarding and training costs by 13% – a savings of nearly $1.7 million. This advantage is attributed to the creation of a community where new hires engage with one another, work together on onboarding activities, and receive support from experts in other departments.

How your sales team can benefit from social collaboration: When sales reps are supported with expertise anytime and anywhere, they are liberated and empowered. With direct access to the intellectual power of the entire organization, they can avoid common pitfalls, mitigate potential risks, and strengthen their sales acumen. And this can create a scenario where reps meet or exceed their quotas every quarter and effectively close more deals.

Did you know social collaboration can help you resolve customer issues 10% faster? 

In every business, the customer experience is everything. And this is most likely the case for your sales reps. Nothing is worse than having a customer who is unhappy with your products and services and unwilling to purchase more or looking to go elsewhere.

Using social collaboration for customer service, employees can quickly locate the best experts and information across the company to answer any need. They can also access a complete customer view, including service and sales histories, and quickly gather the right team to handle escalations of any degree of difficulty. Through its composite analysis cited above, Forrester found that this capability leads to a 10% faster resolution of customer and internal issues with an associated annual benefit of approximately $384,600.

How your sales team can benefit from social collaboration: Improving this side of the customer experience can also dramatically impact the success of your sales reps. By connecting service agents with critical customer information such as a pending deal or ongoing sales activities, the customer service and sales functions can work together to make sure the customer remains happy and identify ways to accelerate the close of the deal.

Real-time transparency, access to information, and communication

For years, organizations have struggled to collaborate in the most efficient way without getting lost in email chains and outdated spreadsheets. And for sales, this scenario can spell disaster. By centralizing collaboration to streamline and connect business processes, sales operations can hasten the advancement of sales opportunities, decision making, and understanding of customer needs.

Are you interested in learning more about social collaboration? Check out The Total Economic Impact™ Of SAP Jam, a March 2015 commissioned study conducted by Forrester Consulting on behalf of SAP.

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Roger Noia

About Roger Noia

Roger Noia is the director of Solution Marketing, SAP Jam Collaboration, at SAP. He is responsible for product marketing and sales enablement for our dedicated sales team as well as the broader SAP sales force selling SAP Jam.

Level-Up Your Customer Experience: Lessons From The Gaming Industry

Mark de Bruijn

From the first teaser trailer to the final level, the gaming industry demonstrates that it knows exactly how to engage and cultivate the interest (and loyalty) of its customers. And, it’s a goldmine: there are an estimated 1.5 billion gamers, and this year revenue from the global games industry will be around $100 billion.

So, what can other industries learn from this example? Developers and game designers are under huge pressure to create a new experience each time, one that gamers are willing to pay for. Competition in the ceaselessly growing industry is enormous. For every hit, there are countless games no one plays. This constant “survival of the fittest” has a major advantage – many games offer a customer experience that has been perfected down to the finest detail.

Here are five ways gaming takes customer engagement to the next level.

1. Promotion

Popular franchises like Pokémon, Grand Theft Auto, and Battlefield are constantly preoccupied with building a relationship with their customers. Real fans know well in advance that they want to buy a game. They are teased with new features, sneak peeks, trailers, artwork, events, and promotional campaigns. Early birds get discounts and unique content, like new levels and rare weapons.

Games companies understand that customer retention is an ongoing process. And they know that it’s easier and cheaper to sell something to an existing customer than to a new one. Franchises are strong brands with recognizable characters that appeal to the gamer. It’s only a matter of galvanizing the fans into action at the right moment – for example, for the holiday season.

Foster your existing customers, whet their appetites with news of your new products or services, and reward the early birds. And just keep on building a strong brand!

2. First impressions

New mobile games appear daily and can often be downloaded for free. However, research has shown that around a quarter of the apps are opened only once. For that reason, making a good impression is very important for game developers. After all, you don’t get a second chance. That’s why a game is tested exhaustively before it’s launched.

A messy or un-intuitive interface, the lack of a tutorial, long loading times, poor performance, irritating advertising, and intrusive in-app selling can motivate people to delete a game instantly. But factors outside the game also play a role, like the description of its features, general evaluations, and the amount of space needed for installation.

Developing games is a question of testing, optimizing, and testing again. And that’s how companies should also approach their customer experience: only perfect is good enough.

3. Immersion

What actually makes a game good? Tastes differ, just as with films or music. But whether it’s a futuristic shooter, a strategic RPG, on an online word game with friends, a game has to be compelling or “immersive.” There’s no magic wand for creating an immersive experience. But there are certainly elements which contribute to it:

  • Gameplay
    • Good gameplay is crucial. Some games feature deep gameplay mechanics or have a high difficulty level, others opt for addictive elements like puzzles, and still others put the emphasis on the competitive factor (who’s the best?). Whatever they do, the game’s mechanisms must entertain the player.
    • Gameplay elements enrich the customer experience. There are countless examples of gamification. For example, at Starbucks you can earn stars with purchases. The Spanish bank BBVA lets customers perform tasks to promote the use of Internet banking. And the running app Nike+ Run Club challenges sports enthusiasts to measure themselves against others.
  • Graphics and sound
    • A brilliant-looking game that’s enhanced with varied musical and sound effects creates a credible world that gamers want to plunge into. If you constantly encounter visual errors and strange animations, or you’re irritated by poor voice actors and repetitive music, it detracts from the immersion.
    • High visual production values are also a unique selling point for companies, and not only in terms of their products and services. So, for instance, a slow or ugly (mobile) website can upset the customer journey and can motivate potential customers to head to a competitor.
  • Story
    • A huge attraction in many games is the story. Interesting characters, exciting developments, and humorous dialogues ensure that the gamer feels emotionally involved and is curious to discover what will happen. The choices you make, along with the way of playing, leads to a personal experience.
    • Marketing is increasingly concerned with emotion. You persuade the modern consumer not with superlatives, but with storytelling: a good story, which he or she would like to be part of. The interaction between customer and brand is one and the same. Buying stems from that.

Immersion is also very important for the customer experience. The process from orientation to buying and use doesn’t only have to be smooth. It must also be absorbing, personal, and even fun.

4. Customer behavior

Game developers are extremely proficient at analyzing customer behavior. The free-to-play model with in-app purchases has accorded this a completely new dimension. How long does the average gamer play? What type of content and what offers open wallets? What notifications are effective to get you to play again?

Research has shown that just 0.19 percent of mobile gamers account for half of all revenues. They are referred to jokingly as “whales.” Games companies pull out all the stops to land these meaty fish. Sometimes digital items are even developed specially for one whale. You might wonder whether this is ethical, but that gamer is certainly given the royal treatment.

Analyzing customer behavior offers valuable insights with which you can improve the customer experience. And it helps each company determine which customers are really worthwhile.

5. Feedback

Certainly with mobile games, it’s entirely normal to ask the gamer for feedback. This has all sorts of benefits. A positive review can be just enough to persuade people to try an app. But bad reviews with complaints and criticism can also be very valuable. A developer can then see immediately what he has to change.

Even better is if the feedback arrives during the development. That’s why players of previous games are often invited to beta-test a new title – an excellent way to bind gamers at an early stage and get focused feedback from the target group. It’s also an economical way to spot bugs.

Every company benefits from feedback. Take every sign from customers seriously, both positive and negative. And let them test new products, for instance in exchange for a discount.

Business can learn lots from the games industry, but the customer experience is not a game. The biggest difference? Unfortunately your company doesn’t have an endless number of lives. Once your customers walk away, it really is game over.

Here’s another industry to model: Improving The Customer Experience: It’s Time To Look To Fintech.

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Preparing Your E-Commerce Site: 3 Pitfalls To Avoid

Jake Anderson

Using a website instead of a brick-and-mortar retail store to sell products can be a great way to make money while keeping your overhead low. No need to pay rent or utilities on a storefront and hiring people to manage it—with an e-commerce site, you can open a virtual store and begin selling your wares almost immediately.

While it is similar to opening a physical store, the mere act of creating a website won’t provide you with instant sales. Yes, your overhead is much lower and moving product from your pipeline is a more seamless event; however, there are still many things that can go wrong if you aren’t paying attention or thinking about every angle.

Here are three of the most common mistakes people make when building an e-commerce site:

Not knowing where your customers are online

As with traditional marketing, not every channel is right for your business. For example, trying to sell mass-produced plastic items on a DIY website like Etsy may not net you much success. However, if fun, hand-crafted items are your thing, Etsy would be a great place to start.

It is absolutely critical that you do your homework before jumping into e-commerce. Who buys what you’re selling? Where would they go online? Look for websites that carry items similar to the ones you create. Browse their selections and see what their Internet presence is like to get a feel for what you should be doing.

Not maximizing social media

The marketing gods blessed the world of advertisers everywhere when social media came into existence. There are many different platforms that reach different types of people, which makes reaching your target audience much easier. This does take a little bit of awareness, however — for example, methods that appeal to a typical Facebook user might not work as well on Twitter or Instagram, and vice versa.

Social media is where you can build a strong brand voice and interact directly with potential customers. Understand the difference between the platforms and be as active in your community as possible in order to better serve your customers. Many e-commerce website builders integrate the major social media platforms directly on your page.

Getting too far ahead of yourself

In the early stages of any business, it’s important to pace yourself and utilize your time and resources effectively. One of the most common mistakes is purchasing too much inventory before you know what demand is going to be—you don’t want to end up with a garage or storage unit full of product with no one around to buy! To get a sense of how much demand a particular item has, search for it on eBay or other e-tailers and see how many have sold in the last month.

Opening an e-commerce store is very nuanced and comes with many obstacles. Avoiding these three pitfalls will help lead to a successful launch of your e-commerce store.

For more on the future of e-commerce, see How Can IoT Help Retailers?

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Jake Anderson

About Jake Anderson

Awestruck by Star trek as a kid, Jake Anderson has been relentless in his pursuit for covering the big technological innovations which will shape the future. A self-proclaimed gadget freak, he loves getting his hands on every piece of gadget he can afford. Contact Jake on Twitter @_ShoutatJake.

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.