Another blanket article about the pros and cons of Direct to Consumer (D2C) isn’t needed, I know. By now, we all know the rules for how this model enters a market: its disruption fights any given sector’s established sales model, a fuzzy compromise is temporarily met, and the lean innovator always wins out in the end.

That’s exactly how it played out in the music industry when Apple and record companies created a digital storefront in iTunes to usher music sales into the online era. What now appears to have been a stopgap compromise, iTunes was the standard model for 5-6 years until consumers realized there was no point in purchasing and owning digital media when internet speeds increased and they could listen to it for free through a music streaming service.  In 2013, streaming models are the new music consumption standard. Netflix is nearly parallel in the film and TV world, though they’ve done a better job keeping it all under one roof. Apple mastered retail sales so well that the majority of Apple products, when bought in-person, are bought at an Apple store. That’s even more impressive when you consider how few Apple stores there are in the U.S. (253) compared to big box electronics stores that sell Apple products like Best Buy (1,100) Yet while some industries have implemented a D2C approach to great success, others haven’t even dipped a toe in the D2C pool, most notably the auto industry.

What got me thinking about this topic is the recent flurry of attention Tesla Motors has received for its D2C model. It all came to a head at the beginning of July when a petition on whitehouse.gov to allow Tesla to sell directly to consumers in all 50 states reached the 100,000 signatures required for administration comment. As you might imagine, many powerful car dealership owners armed with lobbyists have made a big stink about Elon Musk, Tesla’s CEO and Product Architect, choosing to sidestep the traditional supply chain and instead opting to sell directly to their customers through their website. These dealership owners say that they’re against the idea because they want to protect consumers, but the real motive is that they want to defend their right to exist (and who wouldn’t?). They essentially have a monopoly at their position in the sales process, and they want to keep it that way. More frightening for the dealerships is the possibility that once Tesla starts selling directly to consumers, so will the big three automakers, and they fear that would be the end of the road for their business. Interestingly enough, the big three flirted with the idea of D2C in the early 90’s before they were met with fierce backlash from dealerships. I’m sure the dealership community has no interest in mounting a fight like that again. 

To say that the laws preventing Tesla from selling online are peripherally relevant would be a compliment. By and large, the laws the dealerships point to fall under the umbrella of “Franchise Laws” that were put in place at the dawn of car sales to protect franchisees against manufacturers opening their own stores and undercutting the franchise that had invested so much to sell the manufacturer’s cars.  There’s certainly a need for those laws to exist, because no owner of a dealership selling Jeeps wants Chrysler to open their own dealership next door and sell them for substantially less. However, because Tesla is independently owned and isn’t currently selling their cars through any third party dealership, this law doesn’t really apply to them. Until their cars are sold through independent dealerships, they’re incapable of undercutting anyone by implementing D2C structure.

Millions of people experienced the frustration and failures of the Obamacare website when it first launched. Because the code for the back end is not open source, the exact technicalities of the initial failings are tricky to determine. Many curious programmers and web designers have had time to examine the open source coding on the front end, however, leading to reasonable conclusions about the nature of the overall difficulties.

Lack of End to End Collaboration
The website was developed with multiple contractors for the front-end and back-end functions. The site also needed to be integrated with insurance companies, IRS servers, Homeland Security servers, and the Department of Veterans Affairs, all of whom had their own legacy systems. The large number of parties involved and the complex nature of the various components naturally complicated the testing and integration of each portion of the project.

The errors displayed, and occasionally the lack thereof, indicated an absence of coordination between the parties developing the separate components. A failed sign up attempt, for instance, often resulted in a page that displayed the header but had no content or failure message. A look at end user requests revealed that the database was unavailable. Clearly, the coding for the front end did not include errors for failures on the back end.

Bloat and the Abundance of Minor Issues
Obviously, numerous bugs were also an issue. The system required users to create passwords that included numbers, for example, but failed to disclose that on the form and in subsequent failure messages, leaving users baffled. In another issue, one of the pages intended to ask users to please wait or call instead, but the message and the phone information were accidentally commented out in the code.

While the front-end design has been cleared of blame for the most serious failures, bloat in the code did contribute to the early difficulties users experienced. The site design was heavy with Javascript and CSS files, and it was peppered with small coding errors that became particularly troublesome when users faced bottlenecks in traffic. Frequent typos throughout the code proved to be an additional embarrassment and were another indication of a troubled development process.

NoSQL Database
The NoSQL database is intended to allow for scalability and flexibility in the architecture of projects that will use it. This made NoSQL a logical choice for the health insurance exchange website. The newness of the technology, however, means personnel with expertise can be elusive. Database-related missteps were more likely the result of a lack of experienced administrators than with the technology itself. The choice of the NoSQL database was thus another complication in the development, but did not itself cause the failures.

Another factor of consequence is that the website was built with both agile and waterfall methodology elements. With agile methods for the front end and the waterfall methodology for the back end, streamlining was naturally going to suffer further difficulties. The disparate contractors, varied methods of software development, and an unrealistically short project time line all contributed to the coding failures of the website.

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.

Although the index is an important itemized guide of what people are searching for on the internet, it’s arguable that certain languages getting recognition is a direct result of early adopters posting tutorials and filling up discussion boards on current trends. Additionally, popular tech blogs pick up on technological shifts and broadcast related versions of the same themes.

When does the popularity of a software language matter?

1. If you want marketable skills, knowing what employers are looking for is beneficial. As an example, languages such as Java and Objective C are highly coveted in the smart-phone apps businesses.
2. A consistently shrinking language in usage is an indicator not only that employers are apt to pass on those skills but fall in danger of being obsolete.
3. Focusing on languages that are compatible with other developers increases your chances to participate on projects that companies are working on.