One of the biggest challenges faced by senior IT professionals in organizations is the choice of the right software vendor. In the highly competitive enterprise software industry, there are lot of vendors who claim to offer the best software for the problem and it can be really daunting to narrow down the best choice. Additionally, enterprise software costs can often run into millions of dollars thereby leaving very little margin of error. The real cost of choosing a wrong software can often result into losses much more than the cost of the software itself as highlighted by software disasters experienced by leading companies like HP, Nike etc. In such a scenario, senior IT professionals despite years of expertise can find it very difficult to choose the right business software vendor for their organization.

Here are some of the proven ways of short-listing and selecting the right business software vendor for your organization,

·         Understand and Define The Exact Need First: Before embarking on a journey to select the software vendor, it is critical to understand and define the exact problem you want the software to solve. The paramount question to be asked is what business objective does the software need to solve. Is the software required to “reduce costs” or is it to “improve productivity”? Extracting and defining this fundamental question is the bare minimum but necessary step to go searching for the right vendor. It will then form the basis of comparing multiple vendors on this very need that your organization has and will help drive the selection process going forward. The detailed approach involves creating a set of parameters that the software needs to meet in order to be considered. In fact, consider categorizing these parameters further in “must-haves”, “good to have” etc. which will help you assign relevant weights to these parameter and how the software’s fare on each of these parameters

·         Building The List of Vendors Who Meet The Need: Once you have defined your need and distilled that need into various parameters, it’s time to built the list of vendors who you think will meet the need. This is akin to a lead generation model wherein you want to identify a large enough pool and then filters your list down to the best ones. There are multiple ways of building a list of vendors and more often than not, you must use a combination of these methods to build a good enough list.

o   Use Industry Reports: We discussed the IT intelligence offered by leading industry firms Gartner and Forrester in How To Keep On Top Of Latest Trends In Information Technology. These firms based on their access to leading software vendors and CIO network publish vendor comparison research reports across specific verticals as well as specific technologies. Gartner’s Magic Quadrant and Forrester’s Wave are a very good starting point to get an insight into the best software vendors. For example, if you were looking for a CRM solution, you could look for Gartner’s Magic Quadrant for CRM and look at the vendors that make the list. These reports can be pricey but well worth the money if you are going to invest hundreds of thousands in the software. Having said that, you don’t have to trust these report blindly because how these firms define the best software may not match how you define the best software for your organization

o   Competitive Intelligence: If you are a smart professional, you are already keeping tabs of your competition. Chances are that if you are a big organization, you might see a Press Release either from your competitor or their vendor announcing the implementation of new software. Extrapolate that across 5-10 key competitors of yours and you might discover the vendors that your competitors are choosing. This gives you a good indicator that the vendors used by your competitors must be offering something right.

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.

Google is one of the most popular websites in the entire world that gets millions of views each day. Therefore, it should come as no surprise that it needs a strong and reliable programming language that it can rely on to run its searches and many of the apps that Google has created. Because of this, Google uses Python to ensure that every time a user uses one of their products, it will work smoothly and flawlessly. That being said, Google uses Python in a variety of different ways, outlined below.

Code.Google.Com
Since its creation, Google has always used Python as part of its core for programming language. This can still be seen today considering the strong relationship the two have with one another. Google supports and sponsors various Python events, and Python works to better itself so that Google remains on top of cutting edge material. One way that they do this is by working with code.google.com. This is the place where Google developers go to code, learn to code and test programs. And with it being built on Python, users can experience exactly what it is that they should expect once they start using the real site.

Google AdWords
Google AdWords is a great way for people to get their websites out there, through the use of advertising. Each time a person types in a certain string of keywords, or if they have history in their cookies, then they’ll come across these AdWords. The way that these AdWords are broadcasted to online web surfers is built on the foundation from Python. Python also helps clients access their AdWord accounts, so that they can tailor where they want their advertisements to go.

Beets
If you have loads of music, but some of it is uncategorized or sitting in a music player without a name or title, Beets is for you. This Google project uses Python and a music database to help arrange and organize music. The best part about Beets is that even if it doesn’t run exactly the way that you want, you can use a bit of Python knowledge to tailor it to be more specific to your desires.

Android-Scripting
Not only does Google run off Python, but Android also has its own value for the language. Whether you are someone who is just creating your own app for your phone or if you are someone who is looking to create the next app that gets downloaded multiple millions of times, you can use Python and Android-Scripting to create an app that does exactly what you want it to do.

YouTube
YouTube one just started as a video viewer on its own, but is now a billion-dollar company that is owned by Google. YouTube uses Python to let users view and upload video, share links, embed video and much more. Much like Google itself, YouTube relies heavily on Python to run seamlessly for the amount of traffic it gets daily.

Python is not your average coding language. Instead, it is a valuable and integral part of some of the biggest websites in the world, one of which is Google. And the resources listed here are just a fraction of what Google uses Python for in total.

Related:

What Are The 10 Most Famous Software Programs Written in Python?

The Future of Java and Python

Ranking Programming Languages: Which are Gaining Popularity?

Top 10 Software Skills for 2014 and Beyond

Working With Strings In Python

Working With Lists In Python

Conditional Programming In Python

Java still has its place in the world of software development, but is it quickly becoming obsolete by the more dynamically enabled Python programming language? The issue is hotly contested by both sides of the debate. Java experts point out that Java is still being developed with more programmer friendly updates. Python users swear that Java can take up to ten times longer to develop. Managers that need to make the best decision for a company need concrete information so that an informed and rational decision can be made.

First, Java is a static typed language while Python is dynamically typed. Static typed languages require that each variable name must be tied to both a type and an object. Dynamically typed languages only require that a variable name only gets bound to an object. Immediately, this puts Python ahead of the game in terms of productivity since a static typed language requires several elements and can make errors in coding more likely.

Python uses a concise language while Java uses verbose language. Concise language, as the name suggests, gets straight to the point without extra words. Removing additional syntax can greatly reduce the amount of time required to program.  A simple call in Java, such as the ever notorious "Hello, World" requires three several lines of coding while Python requires a single sentence. Java requires the use of checked exceptions. If the exceptions are not caught or thrown out then the code fails to compile. In terms of language, Python certainly has surpassed Java in terms of brevity.

Additionally, while Java's string handling capabilities have improved they haven't yet matched the sophistication of Python's. Web applications rely upon fast load times and extraneous code can increase user wait time. Python optimizes code in ways that Java doesn't, and this can make Python a more efficient language. However, Java does run faster than Python and this can be a significant advantage for programmers using Java. When you factor in the need for a compiler for Java applications the speed factor cancels itself out leaving Python and Java at an impasse.

While a programmer will continue to argue for the language that makes it easiest based on the programmer's current level of knowledge, new software compiled with Python takes less time and provides a simplified coding language that reduces the chance for errors. When things go right, Java works well and there are no problems. However, when errors get introduced into the code, it can become extremely time consuming to locate and correct those errors. Python generally uses less code to begin with and makes it easier and more efficient to work with.

Ultimately, both languages have their own strengths and weaknesses. For creating simple applications, Python provides a simpler and more effective application. Larger applications can benefit from Java and the verbosity of the code actually makes it more compatible with future versions. Python code has been known to break with new releases. Ultimately, Python works best as a type of connecting language to conduct quick and dirty work that would be too intensive when using Java alone. In this sense, Java is a low-level implementation language. While both languages are continuing to develop, it's unlikely that one language will surpass the other for all programming needs in the near future.