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Spot Buying 101: Big Benefits from Small Transactions

Rob Mihalko

It’s summertime, and you’re hosting a big barbecue on the coming weekend. Your gas grill breaks unexpectedly, so on short notice, you head down to your local hardware store and buy a new one. Guess what you just did? You just made a spot buy.

Almost half of companies’ indirect spend

Companies make spot buys, too—all the time. They account for a whopping 42% of a company’s total indirect spend overseen by procurement, and half of the sourcing activities. In spot buyingcorporate procurement parlance, spot buys usually include one or more of these characteristics: one-time buys/emergency buys, low-dollar-value/low-complexity buys, unmanaged category buys, unique buys, or buys in a new commodity area that can’t be fulfilled by incumbent suppliers. At most companies, spot buying is definitely an under-managed, under-served purchasing type, with gaps in policy and process.

For sellers, most opportunities to participate in spot buys are unexpected—many times resulting in improperly routed requests for a quote (i.e. a phone call to the main receptionist).  At best, this can be distracting for sellers; worst case, business can be lost because the lead does not get to the right part of the organization for quoting.

How spot buying works

That’s where an online business network comes in. Buyers simply describe their needs in an online posting and get matched to sellers who are automatically notified of appropriate opportunities. Sellers have a single interface to evaluate and bid on spot buy opportunities quickly and efficiently. A streamlined response mechanism allows sellers to quickly submit bids, and gives buyers an easy framework for comparing them.

Clearly, spot buying is a lot different than the RFP process typically used by companies when making bigger-ticket, more considered purchases. The RFP process is longer, more formal and, at most larger companies, is well established and technology-enabled.

So if you’re a seller participating in an online business network, the good news is that you’re exactly in the right place to win more spot buy business.

Benefits for buyers…

Facilitated and accelerated by an online business network, buyers can find what they need in a way that’s economical, fast, and easy. They can procure the right product or services from a trusted community, on a platform that ensures they get it at a competitive price, and integrates spot buying with other key buying functions. Better matches between buyers and sellers mean faster turnaround, which in turn delivers significant savings in supplier identification cycles and resource costs.

… and sellers

Sellers have the opportunity to win business that is immediate, real, and needs to happen quickly. These sales are a great way for sellers to show off their capabilities. A spot buy win could be the beginning of a new customer relationship, or an opportunity to get added business with existing customers.

So, whether you’re starting a new relationship or building an existing one, spot buying presents a sure way to tap into 42% of a typical company’s business.

If you are a buyer, how are you managing your spot buys?

If you are a seller, how much of your business comes from quick-turn spot buys? If you’re already winning spot buy bids on an online network, how is it helping?

Let me know by commenting below.

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Will The Collaborative Economy Completely Reimagine Tomorrow's Big Business?

Daniel Newman

Today, the largest car rental and hospitality companies are Uber and Airbnb, respectively. What do they have in common? Let’s see — neither of them own physical possessions associated with their service, and both have turned a non-performing asset into an incredible revenue source.

Don’t be surprised, because this is the new model for doing business. People want to rent instead of own, and at the same time, they want to monetize whatever they have in excess. This is the core of the sharing economy. The concept of earning money by sharing may have existed before, but not at such a large scale. From renting rooms to rides to clothes to parking spaces to just about anything else you can imagine, the sharing economy is rethinking how businesses are growing.

What’s driving the collaborative economy?

The sharing economy, or the collaborative economy, as it’s also called, is “an economic model where technologies enable people to get what they need from each other—rather than from centralized institutions,” explains Jeremiah Owyang, business analyst and founder of Crowd Companies, a collaborative economy platform. This means you could rent someone’s living room for a day or two, ride someone else’s bike for a couple of hours, or even take someone’s pet out for a walk—all for a rental fee.

Even a few years ago, this sort of a thing was unthinkable. When Airbnb launched in 2008, many people were skeptical, as the whole idea seemed not only irrational, but totally stupid. I mean, why would anyone want to spend the night in a stranger’s room and sleep on an air bed, right? Well, turns out many people did! Airbnb moved from spare rooms to luxury condos, villas, and even castles and private islands in more than 30,000 cities across 190 countries, and rentals reached a staggering 15 million plus last year.

What is driving this trend? Millennials definitely play a role. Their love for everything on-demand, plus their frugal mindset, makes them ideal for the sharing economy. But the sharing economy is attractive to consumers across a wide demographic, as it only makes sense.

How collaborative economy is reshaping the future of businesses

Until recently, collaborative-economy startups like Uber and Airbnb were looked upon as threats. Disuptors to any marketplace are usually threatening, so this isn’t surprising. Established businesses that were accustomed to the way things had always been did (and still do) rail against companies like Uber or AirBnB, yet consumers seem to love them. And that’s what matters. Uber has faced many harsh criticisms, yet it continues to provide more than a million rides a month.

We are living in an era of consumer-driven enterprise, where consumers are at the helm. Perhaps this is the biggest reason why the collaborative economy is here to stay. No matter what industry, companies are trying to bring customers to the fore. A collaborative business model allows customers to call the shots. A great example is the cloud, which relies on resource sharing and allows users to scale up or down according to their needs.

Today, traditional businesses are participating in a collaborative economy in different ways. Some are acquiring startups. General Motors, for example, invested $3 million to acquire RelayRides, a peer-to-peer car sharing service. Others are entering into partnerships like Marriott, which partnered with LiquidSpace, an online platform to book flexible workspaces. Other brands, like GE, BMW, Walgreens, and Pepsi are also stepping into the collaborative-economy space and holding the hands of startups instead of competing with them.

Changes in the workplace

Remote work and telecommuting has taken off as companies become more comfortable with the idea of people working outside their offices, and cloud technology is enabling that. Now, let’s look at the scenario from the lens of the sharing economy. With companies looking to find temporary resources that can meet the fast-changing demands of the business, freelancers could replace a large chunk of full-time professionals in future. Why? Because at the heart of this disruptive practice lies the concept of sharing human resources.

As companies set out to temporarily use the services of people to meet short- and medium-term goals, it’s going to completely change the way we build companies. Also, as we have seen through the growth of companies like Airbnb and Uber, it’s going to change the deliverables that companies provide. With demand changing and technology proliferating at breakneck speed, it’s not just important that businesses start to see and adopt this change; it’s imperative because companies that over-commit to any one thing will find themselves obsolete.

When it comes to workplaces, so much is happening today that it’s impossible to predict where things are ultimately headed. But one thing is for sure: The collaborative economy is not going anywhere as long as our priorities are built around better, faster, more efficient and cost-effective.

Want more insight on today’s sharing economy? see Collaborative Economy: It’s Real And It’s Disrupting Enterprises.

This article was originally seen on Ricoh Blog.

The post Will the Collaborative Economy Completely Reimagine Tomorrows Big Business appeared first on Millennial CEO.

Photo Credit: Pedrolu33 via Compfight cc

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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

The Future Of Documents: Signing, Payment, And Automation

Mikita Mikado

Remember when closing an agreement meant that your team had to go through each page of a paper contract with a client, have them initial or sign by hand, then scan and email it back and forth? Thanks to the emergence of e-signature software, those days are gone.

E-signatures are already having a direct impact on the productivity of companies in a variety of ways. In fact, back in 2013, Ombud Research surveyed United Healthcare and found adopting a paperless e-signature process saved the company more than $1 million in administration costs. The provider-contract turnaround was also significantly reduced, going from an average of 32.5 days to only 2.

Others are seeing benefits, too. Salesforce declared in its annual 2014 report an average savings of $20 per document after implementing electronic signing.

As more businesses realize the benefits of document automation technology, adoption rates will grow, furthering development. Business leaders who don’t adopt this technology soon will be left behind with an outdated process that impedes growth.

To keep up, here’s what’s ahead in document automation:

1. E-signatures will become fully commoditized.

Since the passing of the Electronic Signatures In Global And National Commerce (ESIGN) Act in 2000, signing all agreements on paper is no longer necessary. Electronic signatures for e-commerce agreements are legally binding and protected by the same rights as ink on paper. E-signatures are already increasing in popularity because of their convenience, and in a few years, they will be widely accepted as a transactional commodity.

As adoption grows, the demands for functionality in e-sign tools will grow, too. Signing will move beyond even some of today’s e-signature software features, like uploading a saved image of your personal signature or converting your typed name to script. Eventually, signing won’t require any typing. You’ll be able to sign with a voice command.

2. The use of enterprise automation platforms will expand.

Research from Raab Associates predicted revenue from B2B marketing automation would grow 60 percent last year, reaching $1.2 billion. The adoption of enterprise automation platforms will continue to increase as more companies experience the benefits: faster sales cycles and streamlined collaboration.

In fact, 58 percent of top-performing companies — or those where marketing contributes more than half of the sales pipeline — have already adopted marketing automation, according to a 2014 Forrester report. As marketing automation grows, businesses will be able to process more documents quickly, enabling growth.

B2B growth affects the document landscape, too. Sales is most innovative and efficient when it comes to adopting new technology. In fact, high-performing sales teams are the first to embrace new tech tools to streamline the sales process, with 44 percent using offer management tools, according to Salesforce’s 2015 State of Sales Report.

The rate at which sales grows will serve as a predictor of overall growth.

3. Document assembly will be entirely cloud-based.

Today, most sales documents are created and stored locally, either in PDFs or word processing programs. Creating and storing content in the cloud is a relatively new practice for many companies, but with the increased need to be always connected we’ll see a shift to cloud-based content, which can be accessed from any computer or mobile device.

Cloud-based office suites like Google Docs will be standard, almost entirely replacing word processing software. Compatibility will no longer be an issue, as it was with different versions of word processing documents, which will completely alter the day-to-day experience of people who work with documents. The ability to share and edit documents instantly will support tight deadlines and increase expectations for productivity.

4. Integrations will make projects seamless.

Bringing together data from separate systems that don’t otherwise talk to one another results in one complete view of the entire process. Several CRM integrations have already been developed among various document creation and storage platforms to import and keep track of customer data seamlessly.

Open API will continue to provide a vehicle for people to access and share data regardless of where or how it is stored. Extra steps of printing, signing, and scanning will be completely eliminated.

5. Processing and payment will be instant.

With the increased demand for integrations, there will be no need to upload documents into any system for approvals, payment processing, and storage; cloud-based app integrations will take care of that. Not only will it enable instant credit card transactions, but management approval will be simplified through automated requests managers can view and approve anywhere via mobile device.

Payments will be processed instantly within the document itself through integration with tools like Square and PayPal. Eventually, with the rise of virtual currencies like Bitcoin, smart documents will be able to accept payments, completely cutting out the middleman.

Once documents are processed, they’ll be automatically saved and uploaded right into the integrated cloud storage system of your choice. With a few keywords in the search bar, anyone from the team will be able to pull transaction and approval records immediately.

Even if you’re already using a document automation platform, think about areas of opportunity you could be missing. Many of the features that will be the norm in a couple of years are already available; they’re just not yet widely used. Look at how making some simple changes now might give your organization a head start on better sales efficiency.

What are some other changes you expect to see coming from document automation and e-signature software in the next few years?

For more insight on our increasingly connected world, see How To Direct Your Digital Future: 4 Questions.

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About Mikita Mikado

Mikita Mikado is the Co-Founder & CEO of PandaDoc, a platform helping sales teams create, deliver, and track intelligent sales content to close deals faster.

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.