Four Ways to Perform Data Transformation

Apart from cryptocurrency, the bread and butter of the modern business lies in its ability to transform data.  Data transformation is an incredibly important part of any data stack.  Without it most other aspects of data reliability begin to crumble and give way to inaccurate data.

The bottom line is that sets of data almost never offer information that is entirely useful.  Having some information go to waste is the nature of the beast.  If your business always finds a use for each individual point on a graph, then that is great, but that is probably not going to happen.  Still, data transformation means working under the constraint that some of what you gather will go to waste.  Just because you do not want to throw food away does not mean you should refuse to open a restaurant.

Most simply, data transformation is the process by which to make data readable enough to advance your business, product, and service forward.  Sometimes, this involves merging information into one column.  It may involve filtering strings from where numbers ought to be or translating strings of written numbers into actual numerical symbols.  Finally, a necessary action might be as simple as barring duplicative data from entering a data set.  If you want to leverage data against future problems in the interest of future insights, then data transformation is very much a necessary step.

Data transformation is the most important step when it comes to the standardization of consistent and diverse data.  At its most complex, data transformation may involve new file formats and types, but this is not always necessary or even helpful.  The relevance of file type depends mostly on what your business’s needs are.  In any case, data transformation can move your data to target destinations in a way that guarantees the readability and usability of that data.

Especially for large businesses and companies, data transformation is the singular way in which to ensure that data is readable and useful because there is so much data without regard to industry.  If nothing else, data transformation simplifies the process of data management and analysis so that marketing and software teams can achieve more in much less time.  So, how can we implement data transformation?  Which method is going to be best for your company?  Here are some suggestions.

  1. Smoothing

Essentially, data smoothing is the process by which an algorithm detects trends in “noisy” data whose purpose is either irrelevant or not immediately useful.  When noisy data is present in a data set, future trends are much harder for analytical teams to discern, limiting the positive effects of research on further instantiations of a new product or service.  Smoothing is so useful as to function well across many industries, including economic analysis, journalism, and stock management.  Smoothing “smooths” whacky curves into simple parabolas with obvious trajectories.  It is what detects trends when data is too chaotic for trends to be discerned.  There are many mathematical techniques with which to smooth a data set, but all are effective in reaching the same goal.

  1. Manipulation

Manipulation is a type of data transformation wherein analytical teams change data or distill it into readable information that can be studied and employed in research.  Manipulation allows data to exhibit real patterns and suggest certain realities about the future of your product.  You can harness manipulated data to learn about your customers’ behavior and what their needs are.  Your customers’ immediate behavior may as well be at your fingertips.  It is important to recognize that data manipulation does not obfuscate data to make reality look better than it is, it is nothing beyond a way for humans to take a closer look at existing data sets and feel more confident in conclusions.

  1. Discretization

This type of data transformation is a fancy word for parsing.  Data discretization is the process of slicing up an interminable string of useless information into pieces from which human beings in analytics teams can discern genuine conclusions.  Each “slice” from the veritable pie has its own column; its own data type.  Discretization turns whopping chaotic data and reduces it to neat tidbits that fit nicely into data tables.  Once data is in a table, it can be queried.  From a computer science perspective, there are many ways in which your software development team can help you implement discretization.  As software development is a particularly challenging field, pay close attention to your teammates’ suggestions as to which discretization method to employ.

  1. Integration

A critical step in the data management process, data integration involves combining data from different sources into a more usable form.  It is the opposite of discretization.  Sometimes, pieces of information can be much more useful when they are together than when they are apart from each other