IT Outsourcing came to foray as a means for corporations to focus on critical business operations while having a specialized IT company take over the responsibility of managing the IT infrastructure and application development. For corporations especially in the developed countries, IT outsourcing provided two fold advantages, one was access to a highly talented pool of engineers and that too at a lower cost since countries like India were quickly growing their stature as an IT outsourcing hub.

IT Outsourcing is now as mainstream as ever and almost every leading organization outsources some or all parts of its IT infrastructure to a specialized company. It makes pure business sense and with tightening budget controls, IT outsourcing has become one of the strategic cost reduction driver for most organizations. Moreover, IT outsourcing is no more restricted to companies in USA & Europe outsourcing their IT projects to countries like India. Domestic companies within India itself actively use IT outsourcing including the Indian government like the India Post project given to TCS.

Is it then a no brainer that IT Outsourcing is critical to your business? Well, if it is cheaper and does not seem to have any inherent disadvantages why not!! Not really, IT Outsourcing despite proven benefits has its limitations and you should be cognizant be of the same before considering outsourcing your IT operations.

·         Limitations in estimating the actual cost of IT outsourcing:Let’s tackle the biggest driver of IT Outsourcing-Cost Savings. For anyone to estimate the cost savings from IT Outsourcing one needs to be able to predict the cost of outsourcing which then helps understand the cost savings from the same. Yes, at a higher level it is a matter of a simple $ per man-hour costs and IT outsourcing will appear to be cheaper in almost all cases. However, “hidden costs” are commonplace with IT outsourcing and it can be immensely difficult to accurately predict these hidden costs. For example, you need to be able to identify the costs of transitioning your in-house IT to an outsourcer, management overhead needed to manage the outsourcing relationship etc. In addition, IT outsourcing contracts are fixed at the start of the contract and as a result any additional requirement/change tends to be charged additionally. It is no surprise that IT requirements can change frequently and if your outsourcing contract doesn’t account for flexibility, you might be limited in the actual cost savings you might make. There is no surprise that there have been so many instances of IT outsourcing projects overshooting their budgets by a huge margin such as the one government shared services project going 500 million pounds over budget

Companies have been collecting and analyzing data forever, pretty much.” So what’s really new here? What’s driving the data-analytics revolution and what does it mean for those that choose to postpone or ignore the pivotal role big-data is currently having on productivity and competition globally?

General Electric chairman and CEO Jeff Immelt explains it best when stating that “industrial companies are now in the information business—whether they like it or not.”  Likewise, digital data is now everywhere, it’s in every industry, in every economy, in every organization and according to the McKinsey Global Institute (MGI), this topic might once have concerned only a few data geeks, but big data is now relevant for leaders across every sector as well as consumers of products and services.

In light of the new data-driven global landscape and rapid technological advances, the question for senior leaders in companies now is how to integrate new capabilities into their operations and strategies—and position themselves globally where analytics can influence entire industries. An interesting discussion with six of theses senior leaders is covered in MGI’s article, “How companies are using big data and analytics,” providing us with a glimpse into a real-time decision making processes.

The original article was posted by Michael Veksler on Quora

A very well known fact is that code is written once, but it is read many times. This means that a good developer, in any language, writes understandable code. Writing understandable code is not always easy, and takes practice. The difficult part, is that you read what you have just written and it makes perfect sense to you, but a year later you curse the idiot who wrote that code, without realizing it was you.

The best way to learn how to write readable code, is to collaborate with others. Other people will spot badly written code, faster than the author. There are plenty of open source projects, which you can start working on and learn from more experienced programmers.

Readability is a tricky thing, and involves several aspects:

1. Never surprise the reader of your code, even if it will be you a year from now. For example, don’t call a function max() when sometimes it returns the minimum().
2. Be consistent, and use the same conventions throughout your code. Not only the same naming conventions, and the same indentation, but also the same semantics. If, for example, most of your functions return a negative value for failure and a positive for success, then avoid writing functions that return false on failure.
3. Write short functions, so that they fit your screen. I hate strict rules, since there are always exceptions, but from my experience you can almost always write functions short enough to fit your screen. Throughout my carrier I had only a few cases when writing short function was either impossible, or resulted in much worse code.
4. Use descriptive names, unless this is one of those standard names, such as i or it in a loop. Don’t make the name too long, on one hand, but don’t make it cryptic on the other.
5. Define function names by what they do, not by what they are used for or how they are implemented. If you name functions by what they do, then code will be much more readable, and much more reusable.
6. Avoid global state as much as you can. Global variables, and sometimes attributes in an object, are difficult to reason about. It is difficult to understand why such global state changes, when it does, and requires a lot of debugging.
7. As Donald Knuth wrote in one of his papers: “Early optimization is the root of all evil”. Meaning, write for readability first, optimize later.
8. The opposite of the previous rule: if you have an alternative which has similar readability, but lower complexity, use it. Also, if you have a polynomial alternative to your exponential algorithm (when N > 10), you should use that.

Use standard library whenever it makes your code shorter; don’t implement everything yourself. External libraries are more problematic, and are both good and bad. With external libraries, such as boost, you can save a lot of work. You should really learn boost, with the added benefit that the c++ standard gets more and more form boost. The negative with boost is that it changes over time, and code that works today may break tomorrow. Also, if you try to combine a third-party library, which uses a specific version of boost, it may break with your current version of boost. This does not happen often, but it may.

Don’t blindly use C++ standard library without understanding what it does - learn it. You look at std::vector::push_back() documentation at it tells you that its complexity is O(1), amortized. What does that mean? How does it work? What are benefits and what are the costs? Same with std::map, and with std::unordered_map. Knowing the difference between these two maps, you’d know when to use each one of them.

Never call new or delete directly, use std::make_unique and [cost c++]std::make_shared[/code] instead. Try to implement usique_ptr, shared_ptr, weak_ptr yourself, in order to understand what they actually do. People do dumb things with these types, since they don’t understand what these pointers are.

Every time you look at a new class or function, in boost or in std, ask yourself “why is it done this way and not another?”. It will help you understand trade-offs in software development, and will help you use the right tool for your job. Don’t be afraid to peek into the source of boost and the std, and try to understand how it works. It will not be easy, at first, but you will learn a lot.

Know what complexity is, and how to calculate it. Avoid exponential and cubic complexity, unless you know your N is very low, and will always stay low.

Learn data-structures and algorithms, and know them. Many people think that it is simply a wasted time, since all data-structures are implemented in standard libraries, but this is not as simple as that. By understanding data-structures, you’d find it easier to pick the right library. Also, believe it or now, after 25 years since I learned data-structures, I still use this knowledge. Half a year ago I had to implemented a hash table, since I needed fast serialization capability which the available libraries did not provide. Now I am writing some sort of interval-btree, since using std::map, for the same purpose, turned up to be very very slow, and the performance bottleneck of my code.

Notice that you can’t just find interval-btree on Wikipedia, or stack-overflow. The closest thing you can find is Interval tree, but it has some performance drawbacks. So how can you implement an interval-btree, unless you know what a btree is and what an interval-tree is? I strongly suggest, again, that you learn and remember data-structures.

These are the most important things, which will make you a better programmer. The other things will follow.

Below are two pdf's which discuss and outline the Java Message Service, JMS, tutorial.

One Hour Java Messaging Service Tutorial
Advanced One Hour Java Messaging Service Tutorial