How to analyze data
Data analysis is a process in the early stages of software development, when you examine a business activity and find the requirements to convert it into a software application. This is a formal definition, which may lead you to believe that data analysis is an action that you should better leave to the systems analysts, while you, the programmer, should focus on coding what somebody else has designed. If we follow strictly the software engineering paradigm, it may be correct. Experienced programmers become designers and the sharpest designers become business analysts, thus being entitled to think about all the data requirements and give you a well defined task to carry out. This is not entirely accurate, because data is the core value of every programming activity. Whatever you do in your programs, you are either moving around or modifying data. The business analyst is analysing the needs in a larger scale, and the software designer is further squeezing such scale so that, when the problem lands on your desk, it seems that all you need to do is to apply clever algorithms and start moving existing data.
Not so.
No matter at which stage you start looking at it, data is the main concern of a well designed application. If you look closely at how a business analyst gets the requirements out of the customer's requests, you'll realize that data plays a fundamental role. The analyst creates so called Data Flow Diagrams, where all data sources are identified and the flow of information is shaped. Having clearly defined which data should be part of the system, the designer will shape up the data sources, in terms of database relations, data exchange protocols, and file formats, so that the task is ready to be passed down to the programmer. However, the process is not over yet, because you (the programmer) even after this thorough process of data refinement, are required to analyze data to perform the task in the best possible way. The bottom line of your task is the core message of Niklaus Wirth, the father of several languages. "Algorithms + Data Structures = Programs." There is never an algorithm standing alone, doing something to itself. Every algorithm is supposed to do something to at least one piece of data.
Therefore, since algorithms don't spin their wheels in a vacuum, you need to analyze both the data that somebody else has identified for you and the data that is necessary to write down your code. A trivial example will make the matter clearer. You are implementing a search routine for a library. According to your specifications, the user can select books by a combination of genre, author, title, publisher, printing year, and number of pages. The ultimate goal of your routine is to produce a legal SQL statement to search the back-end database. Based on these requirements, you have several choices: check each control in turn, using a "switch" statement, or several "if" ones; make an array of data controls, checking each element to see if it is set; create (or use) an abstract control object from which to inherit all your specific controls, and connect them to an event-driven engine. If your requirements include also tuning up the query performance, by making sure that the items are checked in a specific order, you may consider using a tree of components to build your SQL statement. As you can see, the choice of the algorithm depends on the data you decide to use, or to create. Such decisions can make all the difference between an efficient algorithm and a disastrous one. However, efficiency is not the only concern. You may use a dozen named variables in your code and make it as efficient as it can ever be. But such a piece of code might not be easily maintainable. Perhaps choosing an appropriate container for your variables could keep the same speed and in addition allow your colleagues to understand the code better when they look at it next year. Furthermore, choosing a well defined data structure may allow them to extend the functionality of your code without rewriting it. In the long run, your choices of data determines how long your code will survive after you are finished with it. Let me give you another example, just some more food for thought. Let's suppose that your task is to find all the words in a dictionary with more than three anagrams, where an anagram must be another word in the same dictionary. If you think of it as a computational task, you will end up with an endless effort, trying to work out all the combinations of each word and then comparing it to the other words in the list. However, if you analyze the data at hand, you'll realize that each word may be represented by a record containing the word itself and a sorted array of its letters as ID. Armed with such knowledge, finding anagrams means just sorting the list on the additional field and picking up the ones that share the same ID. The brute force algorithm may take several days to run, while the smart one is just a matter of a few seconds. Remember this example the next time you are facing an intractable problem.
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