Sections

Breaking Down Barriers With Mobile Devices

Russ Novy

Mobile devices break down barriers between businesses and consumers. With mobile, a utility company can now notify customers in specific neighborhoods during power outages, updating them on the estimated time of a fix. That saves customers the aggravation of sitting on hold while trying to get through to an overwhelmed call center. Even better, what if the utility developed an app that not only gave users the ability to adjust the heat in their homes but notified them if the heat was on in the middle of the day?

Breaking Down Barriers With Mobile DevicesConsider the scenario of a CPG manufacturer seeking to develop a relationship with its end-customers. By placing a coupon code inside its packaging, it could entice customers to text the code by offering a discount.

It could also tie the offer to the downloading of an app, one that would request some basic demographics, track purchases, and reward points for activity. By understanding consumers’ behavior and demographics through the app, the CPG manufacturer could both up-sell and cross-sell (mouthwash with toothpaste purchases, toothpaste with toothbrush purchases). Immediately, marketing becomes more cost-effective because there is a more targeted and measurable universe.

But the opportunities go beyond cross-selling and up-selling. One of the biggest challenges of product development is gaining an understanding of what consumers want, even before they know they want it. With the establishment of a communications channel to actual customers, manufacturers have the ability to poll them or solicit insights. The mobile phone has the ability to be the 21st century version of the focus group — with really, really sharp focus.

But while the mobile phone gives manufacturers a built-in tracking mechanism, it also gives them a follow-up mechanism. They can link offers to redemption. If a customer uses a coupon to buy a new product, the manufacturer can follow up with a poll to see how they liked it. Conversely, if a customer doesn’t use a coupon within a month, the manufacturer can follow up with a poll asking if they’re dissatisfied.

Consider the case of banks seeking to expand into regions previously unbanked or underbanked. Mobile phones — with their ability to link to mobile wallets that can be used as payment mechanisms and refilled with currency electronically — are dramatically opening up the opportunity to attract new customers.

Mobile devices truly have the ability to be the 21st century version of the focus group — with really, really sharp focus.

At the same time, both banks and telecommunications companies can set up new generations of payment networks in conjunction with mobile operators that allow customers to use their phones for retail transac­tions, to transfer funds to friends and family, and to make payments also using SMS or mNFC (mobile near-field communication). The options are almost unlimited.

The upshot of using mobile devices to enhance the customer relationship and increase engagement is simple: enterprises can develop better insight into consumer behavior and preferences, thereby gaining a better understanding of what other products and services customers would be most likely to want. Even global manufacturers with extensive operations can use mobile devices apps to develop closer, more person­alized relationships. The mobile device creates a mechanism not only for establishing a virtuous circle of insight, but one that allows consumers to feel a greater intimacy with the business.

To learn more about mCommerce, SAP has developed the Mobile Commerce Playbook: The Path to Customer Engagement.

Check out the playbook, as well as two other helpful guides for your mobility journey here.

Comments

About Russ Novy

Russ Novy a Marketing Director at SAP responsible for developing media strategies for the Customer Engagement & Commerce integrated plan and the Platform integrated plan. As a digital media expert, I guide our marketing teams around the world to develop an effective mix of tactics while exploring the latest opportunities in advertising like programmatic audience buying.

Why New Technology Has An Adoption Problem

Danielle Beurteaux

When 3D printing became a practical reality, in the sense that the actual printers became more efficient, less expensive, and more accessible to the average consumer, there was an assumption that the consumer 3D printing market was going to take off. We’d all have printers at home printing…. what? Our clothes? Toys? Spare organs?

That has yet to happen. 3D printing company MakerBot just went through its second employee layoff this year, driven by a market that’s developing much slower than predicted.

That same thinking is in play with a somewhat more prosaic technology – digital wallets. Apple Pay was released this year, as was Samsung Pay. There’s also Google’s Android Pay. During an earnings call, Apple CEO Tim Cook said: “We are more confident than ever that 2015 will be the year of Apple Pay.” But that expectation has yet to be realized, at least vis-à-vis consumers.

Consumers aren’t using any of the digital wallets en masse. According to Bloomberg, payments made via mobile wallets – all of them – make up a mere 1% of retail purchases in the U.S. The reason is that consumers just don’t see a compelling reason to use them. There’s no real reward for them to change from SOP.

Both these instances highlight a problem with assumptions about mass adoption for new technology – just because it’s cool, interesting, and accessible doesn’t mean a market-worthy mass of people will use it.

Who is more likely to use mobile wallets? Emerging economies without a stable financial and banking systems. In those environments, digital payments present a more secure and quicker method for purchasing. These are the same areas where mobile adoption leapfrogged older technologies because there was a lack of telecommunications infrastructure, i.e. many never had a landline phone to begin with, and they went directly to mobile. The value-add already exists. (But there are also security issues, to which consumers are becoming more sensitive. A hack of Samsung’s U.S. subsidiary LoopPay network was uncovered five months post-hack. Although one was expert quoted as saying the hackers may not have been interested in selling consumer financial info but instead in tracking individuals.)

Here’s some interesting data and a good point made: mobile payments are most popular in situations where the buyer already has his or her phone in hand and the transaction is made even quicker than swiping plastic. For example, purchases made for London Transit rides are responsible for a good portion of the U.K.’s mobile payments.

Mass technology adoption is no longer driven simply by the release of a new product. There are too many products released constantly now, the market is too diverse, and the products often lack a true raison d’être.

Learn more about how creative and innovative companies are finding their customers. Read Compelling Shopping Moments: 4 Creative Ways Stores Connect With Their Customers.

Comments

Mobile Marketing Continues To Explode

Daniel Newman

If your brand isn’t among those planning a significant spend on mobile marketing in 2016, you need to stop treating it like a fad and step up to meet your competition. Usage statistics show that today people live and work while on the move, and the astronomical rise of mobile ad spending proves it.

According to eMarketer, ad spending experienced triple-digit growth in 2013 and 2014. While it’s slowed in 2015, don’t let that fool you: Mobile ad spending was $19.2 billion in 2013, and eMarketer’s forecast for next year is $101.37 billion—51 percent of the digital market.

  1. Marketers follow consumer behavior, and consumers rely on their mobile devices. The latest findings from show that two-third of Americans are now smartphone owners. Around the world, there are two billion smartphone users and, particularly in developing regions, eMarketer notes “many consumers are accessing the internet mobile-first and mobile-only.”
  2. The number of mobile users has already surpassed the number of desktop users, as has the number of hours people spend on mobile Internet use, and business practices are changing as a result. Even Google has taken notice; earlier this year the search giant rolled out what many referred to as “Mobilegeddon”—an algorithm update that prioritizes mobile-optimized sites.

The implications are crystal clear: To ignore mobile is to ignore your customers. If your customers can’t connect with you via mobile—whether through an ad, social, or an optimized web experience—they’ll move to a competitor they can connect with.

Consumers prefer mobile — and so should you

Some people think mobile marketing has made things harder for marketers. In some ways, it has: It’s easy to make missteps in a constantly changing landscape.

At the same time, however, modern brands can now reach customers at any time of the day, wherever they are, as more than 90 percent of users now have a mobile device within arm’s reach 24/7. This has changed marketing, allowing brands to build better and more personalized connections with their fans.

  • With that extra nudge from Google, beating your competition and showing up in search by having a website optimized for devices of any size is essential.
  • Search engine optimization (SEO) helps people find you online; SEO integration for mobile is even more personalized, hyper local, and targeted to an individual searcher.
  • In-app advertisements put your brand in front of an engaged audience.
  • Push messages keep customers “in the know” about offers, discounts, opportunities for loyalty points, and so much more.

And don’t forget about the power of apps, whose usage takes up 85 percent of the total time consumers spend on their smartphones. Brands like Nike and Starbucks are excellent examples of how to leverage the power of being carried around in someone’s pocket.

Personal computers have never been able to offer such a targeted level of reach. We’ve come to a point where marketing without mobile isn’t really marketing at all.

Mobile marketing tools are on the upswing too

As more mobile-empowered consumers themselves from their desks to the street, the rapid rise of mobile shows no signs of slowing down. This is driving more investment into mobile marketing solutions and programs.

According to VentureBeat’s Mobile Success Landscape, mobile engagement—which includes mobile marketing automation—is second only to app analytics in terms of investment. Mobile marketing has become a universe unto itself, one that businesses are eager to measure more effectively.

Every day, mobile marketing is becoming ever more critical for businesses. Brands that fail to incorporate mobile into their ad, content, and social campaigns will be left wondering where their customers have gone.

 

For more content like this, follow Samsung Business on InsightsTwitterLinkedIn , YouTube and SlideShare

The post Mobile Marketing Continues to Explode appeared first on Millennial CEO.

photo credit: Samsung Galaxy S3 via photopin (license)

Comments

About Daniel Newman

Daniel Newman serves as the Co-Founder and CEO of EC3, a quickly growing hosted IT and Communication service provider. Prior to this role Daniel has held several prominent leadership roles including serving as CEO of United Visual. Parent company to United Visual Systems, United Visual Productions, and United GlobalComm; a family of companies focused on Visual Communications and Audio Visual Technologies. Daniel is also widely published and active in the Social Media Community. He is the Author of Amazon Best Selling Business Book "The Millennial CEO." Daniel also Co-Founded the Global online Community 12 Most and was recognized by the Huffington Post as one of the 100 Business and Leadership Accounts to Follow on Twitter. Newman is an Adjunct Professor of Management at North Central College. He attained his undergraduate degree in Marketing at Northern Illinois University and an Executive MBA from North Central College in Naperville, IL. Newman currently resides in Aurora, Illinois with his wife (Lisa) and his two daughters (Hailey 9, Avery 5). A Chicago native all of his life, Newman is an avid golfer, a fitness fan, and a classically trained pianist

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!

Comments

Erik Marcade

About Erik Marcade

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

Tags:

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.

Comments

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.