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Is HR Embracing Social Networking Technology? [Infographic]

Jen Cohen Crompton

Is HR really embracing social networking within companies? While it seems HR departments and other talent management professionals are actively using external social media technologies to recruit and research their candidates, they aren’t as accepting and/or understanding of their employees using the same technologies for internal use.

According to an April 2012 online study, State of Social Technology and Talent Management, commissioned by SilkRoad and delivered to professionals in human resources and other talent management disciplines, 75 percent of the 290 respondents feel their company is behind the curve when embracing and implementing internal and external social networking technology. The overarching goal of the study was to find out how organizations were using (or not using) social technology and how they were managing talent through this function.

The study reviewed both internal and external technologies – internal referring to the use of collaboration tools, and external referring to sites that exist outside the company, specifically Facebook and Twitter. The study found that 67 percent of the companies surveyed have adopted some form of the technology, or plan to adopt in the near future.

So why did most of the respondents feel their company was behind the curve even with a 67 percent adoption?

A deeper look into the study reveals that companies are using these technologies for the following processes: recruiting and hiring (64 percent); learning and development (54 percent); onboarding and offboarding (43 percent); innovation (37 percent); performance management (34 percent); and don’t know/other (21 percent). This shows that recruiting and hiring is the main function of HR social technology, and that using social technology internally for performance management has some room for growth. Also, top management is the main objective of internal social networking technology. The goal is to “drive new, innovative ideas through collaboration.”

The downfall of these technologies and the willingness of HR to comply with use is that they are finding barriers to entry (and effective implementation and execution) due to the worry that employees will misuse time on the system (46 percent); upper management is not perceiving a clear need (44 percent); there is a lack of budget (42 percent); and there are concerns about system security (42 percent). Because of these constraints, companies are noticing that social technology efforts are non-existent, not well-organized and/or poorly explained to facilitate proper and effective use.

So overall, HR is somewhat embracing social networking technology, but are lacking in the execution and implementation, which could be due to the barriers put in place by the C-Suite or because funds are unavailable.

Below is an infographic, created by Compliance and Safety, to help understand the key findings of this study.

HR Must Embrace Social [Infographic]

 http://blogs.sap.com/innovation

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About Jen Cohen Crompton

Jen Cohen Crompton is a SAP Blogging Correspondent reporting on big data, cloud computing, enterprise mobility, analytics, sports and tech, and anything else innovation-related. When she's not blogging, she can be caught marketing, using social media and/or presenting at conferences around the world. Disclosure: Jen is being compensated by SAP to produce a series of articles on the innovation topics covered on this site. The opinions reflected here are her own.

What Gen Z’s Arrival In The Workforce Means For Recruiters

Meghan M. Biro

Generation Z’s arrival in the workforce means some changes are on the horizon for recruiters. This cohort, born roughly from the mid-90s to approximately 2010, will be entering the workforce in four Hiring Generation Z words in 3d letters on an organization chart to illustrate finding young employees for your company or businessshort years, and you can bet recruiters and employers are already paying close attention to them.

This past fall, the first group of Gen Z youth began entering university. As Boomers continue to work well past traditional retirement age, four or five years from now, we’ll have an American workplace comprised of five generations.

Marketers and researchers have been obsessed with Millennials for over a decade; they are the most studied generation in history, and at 80 million strong they are an economic force to be reckoned with. HR pros have also been focused on all things related to attracting, motivating, mentoring, and retaining Millennials and now, once Gen Z is part of the workforce, recruiters will have to shift gears and also learn to work with this new, lesser-known generation. What are the important points they’ll need to know?

Northeastern University led the way with an extensive survey on Gen Z in late 2014 that included 16- through 19-year-olds and shed some light on key traits. Here are a few points from that study that recruiters should pay special attention to:

  • In general, the Generation Z cohort tends to be comprised of self-starters who have a strong desire to be autonomous. 63% of them report that they want colleges to teach them about being an entrepreneur.
  • 42% expect to be self-employed later in life, and this percentage was higher among minorities.
  • Despite the high cost of higher education, 81% of Generation Z members surveyed believe going to college is extremely important.
  • Generation Z has a lot of anxiety around debt, not only student loan debt, and they report they are very interested in being well-educated about finances.
  • Interpersonal interaction is highly important to Gen Z; just as Millennials before them, communicating via technology, including social media, is far less valuable to them than face-to-face communication.

Of course Gen Z is still very young, and their opinions as they relate to future employment may well change. For example, reality is that only 6.6% of the American workforce is self-employed, making it likely that only a small percentage of those expecting to be self-employed will be as well. The future in that respect is uncertain, and this group has a lot of learning to do and experiences yet ahead of them. However, when it comes to recruiting them, here are some things that might be helpful.

Generation Z is constantly connected

Like Millennials, Gen Z is a cohort of digital natives; they have had technology and the many forms of communication that affords since birth. They are used to instant access to information and, like their older Gen Y counterparts, they are continually processing information. Like Millennials, they prefer to solve their own problems, and will turn to YouTube or other video platforms for tutorials and to troubleshoot before asking for help. They also place great value on the reviews of their peers.

For recruiters, that means being ready to communicate on a wide variety of platforms on a continual basis. In order to recruit the top talent, you will have to be as connected as they are. You’ll need to keep up with their preferred networks, which will likely always be changing, and you’ll need to be transparent about what you want, as this generation is just as skeptical of marketing as the previous one.

Flexible schedules will continue to grow in importance

With the growth of part-time and contract workers, Gen Z will more than likely assume the same attitude their Millennial predecessors did when it comes to career expectations; they will not expect to remain with the same company for more than a few years. Flexible schedules will be a big part of their world as they move farther away from the traditional 9-to-5 job structure as work becomes more about life and less about work, and they’ll likely take on a variety of part time roles.

This preference for flexible work schedules means that business will happen outside of traditional work hours, and recruiters’ own work hours will, therefore, have to be just as flexible as their Gen Z targets’ schedule are. Companies will also have to examine what are in many cases decades old policies on acceptable work hours and business norms as they seek to not only attract, but to hire and retain this workforce with wholly different preferences than the ones that came before them. In many instances this is already happening, but I believe we will see this continue to evolve in the coming years.

Echoing the silent generation

Unlike Millennials, Gen Z came of age during difficult economic times; older Millennials were raised in the boom years. As Alex Williams points out in his recent New York Times piece, there’s an argument to be made that Generation Z is similar in attitude to the Silent Generation, growing up in a time of recession means they are more pragmatic and skeptical than their slightly older peers.

So how will this impact their behavior and desires as job candidates? Most of them are the product of Gen X parents, and stability will likely be very important to them. They may be both hard-working and fiscally savvy.

Sparks & Honey, in their much quoted slideshare on Gen Z, puts the number of high-schooler students who felt pressured by their parents to get jobs at 55 percent. Income and earning your keep are likely to be a big motivation for GenZ. Due to the recession, they also share the experience of living in multi-generational households, which may help considerably as they navigate a workplace comprised of several generations.

We don’t have all the answers

With its youngest members not yet in double digits, Gen Z is still maturing. There is obviously still a lot that we don’t know. This generation may have the opposite experience from the Millennials before them, where the older members experienced the booming economy, with some even getting a career foothold, before the collapse in 2008. Gen Z’s younger members may get to see a resurgent economy as they make their way out of college. Those younger members are still forming their personalities and views of the world; we would be presumptuous to think we have all of the answers already.

Generational analysis is part research, but also part theory testing. What we do know is that this second generation of digital natives, with its adaption of technology and comfort with the fast-paced changing world, will leave its mark on the American workforce as it makes its way in. As a result, everything about HR will change, in a big way. I wrote a post for my Forbes column recently where I said, “To recruit in this environment is like being part wizard, part astronaut, part diplomat, part guidance counselor,” and that’s very true.

As someone who loves change, I believe there has never been a more exciting time to be immersed in both the HR and the technology space. How do you feel about what’s on the horizon as it relates to the future of work and the impending arrival of Generation Z? I’d love to hear your thoughts.

Social tools are playing an increasingly important role in the workplace, especially for younger workers. Learn more: Adopting Social Software For Workforce Collaboration [Video].

The post What Gen Z’s Arrival In The Workforce Means For Recruiters appeared first on TalentCulture.

Image: Bigstock

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How To Find The Talent You Need To Solve Challenges That Don’t Exist Yet

Mike Ettling

Although executives, analysts, and experts regularly try to predict where business is headed, the pace of innovation continues to exceed our expectations and imagination – especially when it comes to the world of work. Not only is technology impacting how we work and interact with each other, it’s transforming what we actually do for work.People walking on office concourse --- Image by © Igor E./Image Source/Corbis

Consider this: 2 billion jobs that exist today will disappear by 2030, according to futurist Thomas Frey. 2 billion. That’s roughly 50% of all of jobs worldwide. Cathy N. Davidson, Duke University professor, backed up this prediction in her book Now You See It, noting that 65% of children entering grade school this year will assume careers that don’t yet exist.

How can you possibly plan for a future workforce in jobs we can’t today know? And how can we develop talent when we don’t what our business will need not just in a few years, but even in a few months from now?

The future of talent acquisition relies on a broad footprint enabled by technology

The dynamic of workforce mix is changing. Employees no longer fit neatly into a box, nor should they. Salaried employees. Hourly employees. Contingent employees. These categories are more fluid than ever.

As digital businesses like Uber and Airbnb have shown, the understanding of “employee” is being redefined to include people who are not employed in the traditional sense or necessarily found on the company payroll. Rather, they are customers – on the other side of the seller-buyer relationship.

This new approach does not come without risk. Once the salary-wage relationship is removed from the employer-employee equation, the degree of employee loyalty and affinity seen in the past will slowly deteriorate. This forces CHROs to adjust how to relate to their existing workforce, and as important, their future employees and the people who influence them.

To create an employer brand that is more fluid and differentiated, CHROs should consider four things:

1. Your employer brand matters whether you’re actively recruiting or not.

Your employer brand needs to be an interaction that happens consistently – whether or not you are looking for new talent to join your team at the moment. And while the brand is not the sole purview of HR, HR is in the best position to shepherd it.

2. Expand your footprint to attract the best – before they’re even in the workforce.

In our age of social media, people follow brands they admire. But here’s a secret: This also brings an opportunity for following high-performing professionals within or outside the industry as well as students of all ages who are mastering valuable skills.

As I look at my two school-aged boys, I see firsthand how their new generation – Gen Z – will create their own definition of work and career fulfillment. Pretty soon, new graduates will be less concerned about job titles and more interested in working for companies with whom they feel an affinity. And increasingly, these interactions begin long before a job search.

3. Master the science of data – no PhD required.

How many of us groan when terms like “data science” and “number crunching” get mentioned? Today’s technology is taking away the fear factor; analysing data is becoming more intuitive and delivering more valuable insights. And increasingly, the machines are doing it for us, melting processes along the way.

4. Engage before Day 1.

HR today has the tools to become less about process and more about employee engagement. Onboarding is a perfect example of how, and why it matters.

Typically, onboarding has been about providing the physical things a new employee needs to start working: security badge, laptop, desk assignment, setup of a 401k account, and payroll deductions to name a just a few. None of this generally happens until the person walks through the door on Day 1.

Now we have the ability to make onboarding a social interaction, allowing a new employee the opportunity to be engaged before they even start. HR can provide the ability for new employees to connect with their manager, along with peers who can help them better understand and navigate the organisation, and potential mentors who can help them become successful – reducing the traditional ramp up process that can take months or longer.

In today’s digital economy, it’s less about the job and more about the talent. How are you preparing?

Want more future-focuses strategies that empower your workforce? See 6 Habits Of Mind That Will Impact The Future Of Work.

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Mike Ettling

About Mike Ettling

Mike Ettling is the President of SAP SuccessFactors. He is an inspirational, visionary and highly dynamic leader with a wealth of leadership expertise, genuine business acumen, and an exemplary record driving multi-million dollar sales, marketing initiatives and transformation in a global context.

Unlock Your Digital Super Powers: How Digitization Helps Companies Be Live Businesses

Erik Marcade and Fawn Fitter

The Port of Hamburg handles 9 million cargo containers a year, making it one of the world’s busiest container ports. According to the Hamburg Port Authority (HPA), that volume doubled in the last decade, and it’s expected to at least double again in the next decade—but there’s no room to build new roads in the center of Hamburg, one of Germany’s historic cities. The port needed a way to move more freight more efficiently with the physical infrastructure it already has.

sap_Q216_digital_double_feature1_images1The answer, according to an article on ZDNet, was to digitize the processes of managing traffic into, within, and back out of the port. By deploying a combination of sensors, telematics systems, smart algorithms, and cloud data processing, the Port of Hamburg now collects and analyzes a vast amount of data about ship arrivals and delays, parking availability, ground traffic, active roadwork, and more. It generates a continuously updated model of current port conditions, then pushes the results through mobile apps to truck drivers, letting them know exactly when ships are ready to drop off or receive containers and optimizing their routes. According to the HPA, they are now on track to handle 25 million cargo containers a year by 2025 without further congestion or construction, helping shipping companies bring more goods and raw materials in less time to businesses and consumers all across Europe.

In the past, the port could only have solved its problem with backhoes and building permits—which, given the physical constraints, means the problem would have been unsolvable. Today, though, software and sensors are allowing it to improve processes and operations to a previously impossible extent. Big Data analysis, data mining, machine learning, artificial intelligence (AI), and other technologies have finally become sophisticated enough to identify patterns not just in terabytes but in petabytes of data, make decisions accordingly, and learn from the results, all in seconds. These technologies make it possible to digitize all kinds of business processes, helping organizations become more responsive to changing market conditions and more able to customize interactions to individual customer needs. Digitization also streamlines and automates these processes, freeing employees to focus on tasks that require a human touch, like developing innovative strategies or navigating office politics.

In short, digitizing business processes is key to ensuring that the business can deliver relevant, personalized responses to the market in real time. And that, in turn, is the foundation of the Live Business—a business able to coordinate multiple functions in order to respond to and even anticipate customer demand at any moment.

Some industries and organizations are on the verge of discovering how business process digitization can help them go live. Others have already started putting it into action: fine-tuning operations to an unprecedented level across departments and at every point in the supply chain, cutting costs while turbocharging productivity, and spotting trends and making decisions at speeds that can only be called superhuman.

Balancing Insight and Action

sap_Q216_digital_double_feature1_images2Two kinds of algorithms drive process digitization, says Chandran Saravana, senior director of advanced analytics at SAP. Edge algorithms operate at the point where customers or other end users interact directly with a sensor, application, or Internet-enabled device. These algorithms, such as speech or image recognition, focus on simplicity and accuracy. They make decisions based primarily on their ability to interpret input with precision and then deliver a result in real time.

Edge algorithms work in tandem with, and sometimes mature into, server-level algorithms, which report on both the results of data analysis and the analytical process itself. For example, the complex systems that generate credit scores assess how creditworthy an individual is, but they also explain to both the lender and the credit applicant why a score is low or high, what factors went into calculating it, and what an applicant can do to raise the score in the future. These server-based algorithms gather data from edge algorithms, learn from their own results, and become more accurate through continuous feedback. The business can then track the results over time to understand how well the digitized process is performing and how to improve it.

sap_Q216_digital_double_feature1_images5From Data Scarcity to a Glut

To operate in real time, businesses need an accurate data model that compares what’s already known about a situation to what’s happened in similar situations in the past to reach a lightning-fast conclusion about what’s most likely to happen next. The greatest barrier to this level of responsiveness used to be a lack of data, but the exponential growth of data volumes in the last decade has flipped this problem on its head. Today, the big challenge for companies is having too much data and not enough time or power to process it, says Saravana.

Even the smartest human is incapable of gathering all the data about a given situation, never mind considering all the possible outcomes. Nor can a human mind reach conclusions at the speed necessary to drive Live Business. On the other hand, carefully crafted algorithms can process terabytes or even petabytes of data, analyze patterns and detect outliers, arrive at a decision in seconds or less—and even learn from their mistakes (see How to Train Your Algorithm).

How to Train Your Algorithm 

The data that feeds process digitization can’t just simmer.
It needs constant stirring.

Successfully digitizing a business process requires you to build a model of the business process based on existing data. For example, a bank creates a customer record that includes not just the customer’s name, address, and date of birth but also the amount and date of the first deposit, the type of account, and so forth. Over time, as the customer develops a history with the bank and the bank introduces new products and services, customer records expand to include more data. Predictive analytics can then extrapolate from these records to reach conclusions about new customers, such as calculating the likelihood that someone who just opened a money market account with a large balance will apply for a mortgage in the next year.

Germany --- Germany, Lower Bavaria, Man training English Springer Spaniel in grass field --- Image by © Roman M‰rzinger/Westend61/CorbisTo keep data models accurate, you have to have enough data to ensure that your models are complete—that is, that they account for every possible predictable outcome. The model also has to push outlying data and exceptions, which create unpredictable outcomes, to human beings who can address their special circumstances. For example, an algorithm may be able to determine that a delivery will fail to show up as scheduled and can point to the most likely reasons why, but it can only do that based on the data it can access. It may take a human to start the process of locating the misdirected shipment, expediting a replacement, and establishing what went wrong by using business knowledge not yet included in the data model.

Indeed, data models need to be monitored for relevance. Whenever the results of a predictive model start to drift significantly from expectations, it’s time to examine the model to determine whether you need to dump old data that no longer reflects your customer base, add a new product or subtract a defunct one, or include a new variable, such as marital status or length of customer relationship that further refines your results.

It’s also important to remember that data doesn’t need to be perfect—and, in fact, probably shouldn’t be, no matter what you might have heard about the difficulty of starting predictive analytics with lower-quality data. To train an optical character recognition system to recognize and read handwriting in real time, for example, your samples of block printing and cursive writing data stores also have to include a few sloppy scrawls so the system can learn to decode them.

On the other hand, in a fast-changing marketplace, all the products and services in your database need consistent and unchanging references, even though outside the database, names, SKUs, and other identifiers for a single item may vary from one month or one order to the next. Without consistency, your business process model won’t be accurate, nor will the results.

Finally, when you’re using algorithms to generate recommendations to drive your business process, the process needs to include opportunities to test new messages and products against existing successful ones as well as against random offerings, Saravana says. Otherwise, instead of responding to your customers’ needs, your automated system will actually control their choices by presenting them with only a limited group of options drawn from those that have already received the most
positive results.

Any process is only as good as it’s been designed to be. Digitizing business processes doesn’t eliminate the possibility of mistakes and problems; but it does ensure that the mistakes and problems that arise are easy to spot and fix.

From Waste to Gold

Organizations moving to digitize and streamline core processes are even discovering new business opportunities and building new digitized models around them. That’s what happened at Hopper, an airfare prediction app firm in Cambridge, Massachusetts, which discovered in 2013 that it could mine its archives of billions of itineraries to spot historical trends in airfare pricing—data that was previously considered “waste product,” according to Hopper’s chief data scientist, Patrick Surry.

Hopper developed AI algorithms to correlate those past trends with current fares and to predict whether and when the price of any given flight was likely to rise or fall. The results were so accurate that Hopper jettisoned its previous business model. “We check up to 3 billion itineraries live, in real time, each day, then compare them to the last three to four years of historical airfare data,” Surry says. “When consumers ask our smartphone app whether they should buy now or wait, we can tell them, ‘yes, that’s a good deal, buy it now,’ or ‘no, we think that fare is too expensive, we predict it will drop, and we’ll alert you when it does.’ And we can give them that answer in less than one second.”

When consumers ask our smartphone app whether they should buy now or wait, we can tell them, ‘yes, that’s a good deal, buy it now’.

— Patrick Surry, chief data scientist, Hopper

While trying to predict airfare trends is nothing new, Hopper has told TechCrunch that it can not only save users up to 40% on airfares but it can also find them the lowest possible price 95% of the time. Surry says that’s all due to Hopper’s algorithms and data models.

The Hopper app launched on iOS in January 2015 and on Android eight months later. The company also switched in September 2015 from directing customers to external travel agencies to taking bookings directly through the app for a small fee. The Hopper app has already been downloaded to more than 2 million phones worldwide.

Surry predicts that we’ll soon see sophisticated chatbots that can start with vague requests from customers like “I want to go somewhere warm in February for less than $500,” proceed to ask questions that help users narrow their options, and finally book a trip that meets all their desired parameters. Eventually, he says, these chatbots will be able to handle millions of interactions simultaneously, allowing a wide variety of companies to reassign human call center agents to the handling of high-value transactions and exceptions to the rules built into the digitized booking process.

Port of Hamburg Lets the Machines Untangle Complexity

In early 2015, AI experts told Wired magazine that at least another 10 years would pass before a computer could best the top human players at Go, an ancient game that’s exponentially harder than chess. Yet before the end of that same year, Wired also reported that machine learning techniques drove Google’s AlphaGo AI to win four games out of five against one of the world’s top Go players. This feat proves just how good algorithms have become at managing extremely complex situations with multiple interdependent choices, Saravana points out.

The Port of Hamburg, which has digitized traffic management for an estimated 40,000 trucks a day, is a good example. In the past, truck drivers had to show up at the port to check traffic and parking message boards. If they arrived before their ships docked, they had to drive around or park in the neighboring residential area, contributing to congestion and air pollution while they waited to load or unload. Today, the HPA’s smartPORT mobile app tracks individual trucks using telematics. It customizes the information that drivers receive based on location and optimizes truck routes and parking in real time so drivers can make more stops a day with less wasted time and fuel.

The platform that drives the smartPORT app also uses sensor data in other ways: it tracks wind speed and direction and transmits the data to ship pilots so they can navigate in and out of the port more safely. It monitors emissions and their impact on air quality in various locations in order to adjust operations in real time for better control over environmental impact. It automatically activates streetlights for vehicle and pedestrian traffic, then switches them off again to save energy when the road is empty. This ability to coordinate and optimize multiple business functions on the fly makes the Port of Hamburg a textbook example of a Live Business.

Digitization Is Not Bounded by Industry

Other retail and B2B businesses of all types will inevitably join the Port of Hamburg in further digitizing processes, both in predictable ways and in those we can only begin to imagine.

sap_Q216_digital_double_feature1_images4Customer service, for example, is likely to be in the vanguard. Automated systems already feed information about customers to online and phone-based service representatives in real time, generate cross-selling and upselling opportunities based on past transactions, and answer customers’ frequently asked questions. Saravana foresees these systems becoming even more sophisticated, powered by AI algorithms that are virtually indistinguishable from human customer service agents in their ability to handle complex live interactions in real time.

In manufacturing and IT, Sven Bauszus, global vice president and general manager for predictive analytics at SAP, forecasts that sensors and predictive analysis will further automate the process of scheduling and performing maintenance, such as monitoring equipment for signs of failure in real time, predicting when parts or entire machines will need replacement, and even ordering replacements preemptively. Similarly, combining AI, sensors, data mining, and other technologies will enable factories to optimize workforce assignments in real time based on past trends, current orders, and changing market conditions.

Public health will be able to go live with technology that spots outbreaks of infectious disease, determines where medical professionals and support personnel are needed most and how many to send, and helps ensure that they arrive quickly with the right medication and equipment to treat patients and eradicate the root cause. It will also make it easier to track communicable illnesses, find people who are symptomatic, and recommend approaches to controlling the spread of the illness, Bauszus says.

He also predicts that the insurance industry, which has already begun to digitize its claims-handling processes, will refine its ability to sort through more claims in less time with greater accuracy and higher customer satisfaction. Algorithms will be better and faster at flagging claims that have a high probability of being fraudulent and then pushing them to claims inspectors for investigation. Simultaneously, the same technology will be able to identify and resolve valid claims in real time, possibly even cutting a check or depositing money directly into the insured person’s bank account within minutes.

Financial services firms will be able to apply machine learning, data mining, and AI to accelerate the process of rating borrowers’ credit and detecting fraud. Instead of filling out a detailed application, consumers might be able to get on-the-spot approval for a credit card or loan after inputting only enough information to be identified. Similarly, banks will be able to alert customers to suspicious transactions by text message or phone call—not within a day or an hour, as is common now, but in a minute or less.

Pitfalls and Possibilities

As intelligent as business processes can be programmed to be, there will always be a point beyond which they have to be supervised. Indeed, Saravana forecasts increasing regulation around when business processes can and can’t be digitized. Especially in areas involving data security, physical security, and health and safety, it’s one thing to allow machines to parse data and arrive at decisions to drive a critical business process, but it’s another thing entirely to allow them to act on those decisions without human oversight.

Automated, impersonal decision making is fine for supply chain automation, demand forecasting, inventory management, and other processes that need faster-than-human response times. In human-facing interactions, though, Saravana insists that it’s still best to digitize the part of the process that generates decisions, but leave it to a human to finalize the decision and decide how to put it into action.

“Any time the interaction is machine-to-machine, you don’t need a human to slow the process down,” he says. “But when the interaction involves a person, it’s much more tricky, because people have preferences, tastes, the ability to try something different, the ability to get fatigued—people are only statistically predictable.”

For example, technology has made it entirely possible to build a corporate security system that can gather information from cameras, sensors, voice recognition technology, and other IP-enabled devices. The system can then feed that information in a steady stream to an algorithm designed to identify potentially suspicious activity and act in real time to prevent or stop it while alerting the authorities. But what happens when an executive stays in the office unusually late to work on a presentation and the security system misidentifies her as an unauthorized intruder? What if the algorithm decides to lock the emergency exits, shut down the executive’s network access, or disable her with a Taser instead of simply sending an alert to the head of security asking what to do while waiting for the police to come?

sap_Q216_digital_double_feature1_images6The Risk Is Doing Nothing

The greater, if less dramatic, risk associated with digitizing business processes is simply failing to pursue it. It’s true that taking advantage of new digital technologies can be costly in the short term. There’s no question that companies have to invest in hardware, software, and qualified staff in order to prepare enormous data volumes for storage and analysis. They also have to implement new data sources such as sensors or Internet-connected devices, develop data models, and create and test algorithms to drive business processes that are currently analog. But as with any new technology, Saravana advises, it’s better to start small with a key use case, rack up a quick win with high ROI, and expand gradually than to drag your heels out of a failure to grasp the long-term potential.

The economy is digitizing rapidly, but not evenly. According to the McKinsey Global Institute’s December 2015 Digital America report, “The race to keep up with technology and put it to the most effective business use is producing digital ‘haves’ and ‘have-mores’—and the large, persistent gap between them is becoming a decisive factor in competition across the economy.” Companies that want to be among the have-mores need to commit to Live Business today. Failing to explore it now will put them on the wrong side of the gap and, in the long run, rack up a high price tag in unrealized efficiencies and missed opportunities. D!

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Erik Marcade

About Erik Marcade

Erik Marcade is vice president of Advanced Analytics Products at SAP.

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Strengthening Government Through Data Analytics

Dante Ricci

When it comes to analyzing data, you could say that there is a clash in culture due to disconnect within the government workforce. This is partly due to the fact that many organizations don’t have people in place with the right technical skill sets. But government can uncover hidden insights to drive better results and create more value for citizens.

The need has never been greater to empower knowledge workers with a comprehensive – yet simple – integrated platform that helps unlock the real value in data for smarter decision-making.

Governments move toward constituent-centered platforms

The fact is, leading government organizations have begun to transform by using consumer-grade solutions to garner better insights from data. The key lies in self-service and automated analytics that do not require technical skill sets. Such solutions enable government personnel at all levels to shift from asking IT for historical reports to a real-time and predictive view that considers multiple data points to deliver a personalized view.

Poised with the right technology and collaborative mindset, governments can uncover new insights to make life better, safer, and healthier, when:

  1. Technology is intuitive and easy to use.
  2. Personnel can make decisions based on a combination of historical and real-time data rather than decisions based on historical perspective alone.
  3. Collaborative technology can include constituent insight and ideas for better decision making.

Digital transformation of government removes that massive barrier between agencies and departments using a platform that shares data and removes the friction that slows down the entire process. The result is that agencies are able to do more, produce better results, and still save money. Digital by default is the key. The rewards are significant for those who successfully leverage analytics: stretching their competitive advantage, driving innovation, and improving lives.

Predictive solutions that appear before your eyes

Digitalized governments run frictionless with decisions based on real contextual insights. Analytics help leaders see problems before or as they occur. That real-time connection identifies potential problems and gives management time to correct them. As real-time data becomes available through input from sensors, transactions, constituents, and other information channels, decisions can be made at the moment of opportunity.

Putting it together

What happens when you need to make decisions, but your data is two years old? What if you need to rewrite a policy that focuses on performance and cost — but you have no information about costs?

Those sorts of problems occur every day. In the first scenario, your decision may be wrong because the data changed. In the second scenario, the policy update may be late. Both potential outcomes reflect negatively on performance and can negatively impact the safety and quality of citizens’ lives. These are both examples of the friction that occurs within governments. They are also the reasons why relevant and timely data is necessary.

The power and tools that a digital government wields are transformative. The rewards for government are many: lower costs, improved services, safer communities, and a better overall quality of life.  Services become seamless. Systems become fluid. Operational costs drop and better outcomes occur.

In short, you make better decisions when they are based on facts and context, not feelings. People who need help get help quickly. Operational issues become identified and fixed. People are happy. And isn’t that the way government should work?

Are you ready for change?

Read about more about SAP’s perspective on digital government here.

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About Dante Ricci

Dante Ricci is the Global Public Services Marketing & Communications lead at SAP. His specialties include enterprise software, business strategy, business development, cloud computing and solution selling.