Passbook is one of the newest features of the iPhone. What many people were offput by standards today is that we rely too much on disposable tickets, and the like, such as when you go to the airport for a boarding pass, or a movie ticket. Apple strove to solve that with a new application that is bundled with the newest update: Passbook.

Passbook offers everything you need, especially when a ticket, coupon, or gift card is needed. When you first open the application you are greeted by a sampling of applications that support Passbook, and its features. To begin, install the particular application you want to use. For example, we will be going with the Starbucks application to satiate our coffee needs every morning, as we have a gift card to spend.

Open up the freshly downloaded application, and get ready to be blown away. Like most applications, especially when it has to do with airlines, or coffee, you are almost always required to sign up for a respective account. Don’t worry, it is easy. All you will be asked is to fill out your email, a password, and other miscellaneous information to identify you when you lose your password or others.

Once you have finished signing up, you’re ready to roll. It will prompt you to enter a gift card that you already own, or begin a digital gift card. If you don’t have a gift card, you can start a digital gift card (or, as many may call it: a prepaid card), and pay for your coffee in a fast, and efficient way. Just tap on “Tap to Pay” when you are ready to pay, and tap again after you have paid. It’s really that easy.

Over time, companies are migrating from COBOL to the latest standard of C# solutions due to reasons such as cumbersome deployment processes, scarcity of trained developers, platform dependencies, increasing maintenance fees. Whether a company wants to migrate to reporting applications, operational infrastructure, or management support systems, shifting from COBOL to C# solutions can be time-consuming and highly risky, expensive, and complicated. However, the following four techniques can help companies reduce the complexity and risk around their modernization efforts. 

All COBOL to C# Solutions are Equal 

It can be daunting for a company to sift through a set of sophisticated services and tools on the market to boost their modernization efforts. Manual modernization solutions often turn into an endless nightmare while the automated ones are saturated with solutions that generate codes that are impossible to maintain and extend once the migration is over. However, your IT department can still work with tools and services and create code that is easier to manage if it wants to capitalize on technologies such as DevOps. 

Narrow the Focus 

Most legacy systems are incompatible with newer systems. For years now, companies have passed legacy systems to one another without considering functional relationships and proper documentation features. However, a detailed analysis of databases and legacy systems can be useful in decision-making and risk mitigation in any modernization effort. It is fairly common for companies to uncover a lot of unused and dead code when they analyze their legacy inventory carefully. Those discoveries, however can help reduce the cost involved in project implementation and the scope of COBOL to C# modernization. Research has revealed that legacy inventory analysis can result in a 40% reduction of modernization risk. Besides making the modernization effort less complex, trimming unused and dead codes and cost reduction, companies can gain a lot more from analyzing these systems. 

Understand Thyself 

For most companies, the legacy system entails an entanglement of intertwined code developed by former employees who long ago left the organization. The developers could apply any standards and left behind little documentation, and this made it extremely risky for a company to migrate from a COBOL to C# solution. In 2013, CIOs teamed up with other IT stakeholders in the insurance industry in the U.S to conduct a study that found that only 18% of COBOL to C# modernization projects complete within the scheduled period. Further research revealed that poor legacy application understanding was the primary reason projects could not end as expected. 

Furthermore, using the accuracy of the legacy system for planning and poor understanding of the breadth of the influence of the company rules and policies within the legacy system are some of the risks associated with migrating from COBOL to C# solutions. The way an organization understands the source environment could also impact the ability to plan and implement a modernization project successfully. However, accurate, in-depth knowledge about the source environment can help reduce the chances of cost overrun since workers understand the internal operations in the migration project. That way, companies can understand how time and scope impact the efforts required to implement a plan successfully. 

Use of Sequential Files 

Companies often use sequential files as an intermediary when migrating from COBOL to C# solution to save data. Alternatively, sequential files can be used for report generation or communication with other programs. However, software mining doesn’t migrate these files to SQL tables; instead, it maintains them on file systems. Companies can use data generated on the COBOL system to continue to communicate with the rest of the system at no risk. Sequential files also facilitate a secure migration path to advanced standards such as MS Excel. 

Modern systems offer companies a range of portfolio analysis that allows for narrowing down their scope of legacy application migration. Organizations may also capitalize on it to shed light on migration rules hidden in the ancient legacy environment. COBOL to C# modernization solution uses an extensible and fully maintainable code base to develop functional equivalent target application. Migration from COBOL solution to C# applications involves language translation, analysis of all artifacts required for modernization, system acceptance testing, and database and data transfer. While it’s optional, companies could need improvements such as coding improvements, SOA integration, clean up, screen redesign, and cloud deployment.

The name placard in your cube might not say anything about sales, but the truth is that everyone, employed as such or not, is a salesperson at some point every single day. In the traditional sense, this could mean something like pitching your company’s solutions to a client. In the less-traditional sense, it could mean convincing your child to eat their vegetables. Yet for those two drastically different examples and everything in between, there is a constant for successful sellers: unveiling the “Why.”

Spending time and energy making prospects understand why you do what you do instead of exactly what it is you do or how you do it is not a new concept. But I’m a firm believer that proven concepts, no matter how old and frequently referenced they are, can’t be repeated enough. This idea has recently and fervently been popularized by marketer, author, and thinker extraordinaire Simon Sinek via his 2009 book, Start With Why. You can learn about him here on Wikipedia or here on his site. To begin, let me suggest that you watch Sinek’s TED talk on Starting With Why here on YouTube before reading any further. I’ll let him take care of the bulk of explaining the basics, and then will offer some ideas of my own to back this up in the real world and explore the best ways to start thinking this way and apply it to your business.

First, a little on me. After all, if I were to practice what Sinek preaches, it would follow that I explain why it is I’m writing this piece so that you, the reader, not only have a good reason to pay attention but also understand what drives me on a deeper level. So, who am I? I’m an entrepreneur in the music space. I do freelance work in the realms of copywriting, business development, and marketing for artists and industry / music-tech folks, but my main project is doing all of the above for a project I’ve been on the team for since day one called Presskit.to. In short, Presskit.to builds digital portfolios that artists of all kinds can use to represent themselves professionally when pitching their projects to gatekeepers like label reps, casting directors, managers, the press, etc. This core technology is also applicable to larger entertainment industry businesses and fine arts education institutions in enterprise formats, and solves a variety of the problems they’re facing.

Not interesting? I don’t blame you for thinking so, if you did. That’s because I just gave you a bland overview of what we do, instead of why we do it. What if, instead, I told you that myself and everyone I work with is an artist of some sort and believes that the most important thing you can do in life is create; that our technology exists to make creators’ careers more easily sustainable. Or, another approach, that we think the world is a better place when artists can make more art, and that because our technology was built to help artists win more business, we’re trying our best to do our part. Only you can be the judge, but I think that sort of pitch is more compelling. It touches on the emotions responsible for decision making that Sinek outlines in his Ted Talk, rather than the practical language-based reasons like pricing, technicalities, how everything works to accomplish given goals, etc. These things are on the outside of the golden circle Sinek shows us for a reason – they only really matter if you’ve aligned your beliefs with a client’s first. Otherwise these kind of tidbits are gobbledygook, and mind-numbingly boring gobbledygook at that.

Machine learning systems are equipped with artificial intelligence engines that provide these systems with the capability of learning by themselves without having to write programs to do so. They adjust and change programs as a result of being exposed to big data sets. The process of doing so is similar to the data mining concept where the data set is searched for patterns. The difference is in how those patterns are used. Data mining's purpose is to enhance human comprehension and understanding. Machine learning's algorithms purpose is to adjust some program's action without human supervision, learning from past searches and also continuously forward as it's exposed to new data.

The News Feed service in Facebook is an example, automatically personalizing a user's feed from his interaction with his or her friend's posts. The "machine" uses statistical and predictive analysis that identify interaction patterns (skipped, like, read, comment) and uses the results to adjust the News Feed output continuously without human intervention. 

Impact on Existing and Emerging Markets

The NBA is using machine analytics created by a California-based startup to create predictive models that allow coaches to better discern a player's ability. Fed with many seasons of data, the machine can make predictions of a player's abilities. Players can have good days and bad days, get sick or lose motivation, but over time a good player will be good and a bad player can be spotted. By examining big data sets of individual performance over many seasons, the machine develops predictive models that feed into the coach’s decision-making process when faced with certain teams or particular situations. 

General Electric, who has been around for 119 years is spending millions of dollars in artificial intelligence learning systems. Its many years of data from oil exploration and jet engine research is being fed to an IBM-developed system to reduce maintenance costs, optimize performance and anticipate breakdowns.

Over a dozen banks in Europe replaced their human-based statistical modeling processes with machines. The new engines create recommendations for low-profit customers such as retail clients, small and medium-sized companies. The lower-cost, faster results approach allows the bank to create micro-target models for forecasting service cancellations and loan defaults and then how to act under those potential situations. As a result of these new models and inputs into decision making some banks have experienced new product sales increases of 10 percent, lower capital expenses and increased collections by 20 percent. 

Emerging markets and industries

By now we have seen how cell phones and emerging and developing economies go together. This relationship has generated big data sets that hold information about behaviors and mobility patterns. Machine learning examines and analyzes the data to extract information in usage patterns for these new and little understood emergent economies. Both private and public policymakers can use this information to assess technology-based programs proposed by public officials and technology companies can use it to focus on developing personalized services and investment decisions.

Machine learning service providers targeting emerging economies in this example focus on evaluating demographic and socio-economic indicators and its impact on the way people use mobile technologies. The socioeconomic status of an individual or a population can be used to understand its access and expectations on education, housing, health and vital utilities such as water and electricity. Predictive models can then be created around customer's purchasing power and marketing campaigns created to offer new products. Instead of relying exclusively on phone interviews, focus groups or other kinds of person-to-person interactions, auto-learning algorithms can also be applied to the huge amounts of data collected by other entities such as Google and Facebook.

A warning

Traditional industries trying to profit from emerging markets will see a slowdown unless they adapt to new competitive forces unleashed in part by new technologies such as artificial intelligence that offer unprecedented capabilities at a lower entry and support cost than before. But small high-tech based companies are introducing new flexible, adaptable business models more suitable to new high-risk markets. Digital platforms rely on algorithms to host at a low cost and with quality services thousands of small and mid-size enterprises in countries such as China, India, Central America and Asia. These collaborations based on new technologies and tools gives the emerging market enterprises the reach and resources needed to challenge traditional business model companies.