Data analytics is the process of examining data sets in order to analyse and draw conclusions from historical outcomes, increasingly this is done through the aid of software such as dashboards.
There are five stages of data analytics which we will explore in this article.
Data sets exist across many different types of mediums, and data mining is the process of obtaining this information from a large amount of raw data, through different open data sets. The two most common ways to do this are web scraping and APIs.
An API (application programming interface) is a program that is written into a website by the web developer to collect information in a certain way or format, to then be able to feed it into another program in a way that program understands.
Web scraping is the use of ‘bots’ which crawl through websites extracting any useful data in its raw form from the code within that website. This is one of the messiest ways to pull raw data sets from, and often the data has to be reformatted at a later date to avoid copyright issues.
Ideally, data should exist in a uniform pattern, rows and columns with all data sets being in the same format, however, for most sources of historical data being pulled, this is not the case. Data cleaning is the process of data scientists either manually or programmatically reformatting this data into the most economical way for it to be able to be interpreted.
This is the initial analysis of the data, describing the main features of the numerical and categorical information in summaries or via graphs. While this step gives you preliminary information, without further investigation, the initial summary can paint a deceptive picture to the true value of the results.
Now that the data has been collected and formatted into a useable state, predictive analysis can take place. This is the process of using a form of advanced analytic techniques to forecast the likely next step.
With the results gained from the predictive analysis, marketers, along with the businesses they represent, are able to use the prescriptive analytics step to formulate a plan of attack when making business plans, backed by the assuredness of a comprehensive data examination.
The key to getting the most out of the data analytics process is by having a clear-cut understanding of the questions that are required to be answered. Without having an insight into the outcome you want to achieve, it can be difficult to extract the correct information during the initial steps.