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While data mining is a trending topic in today’s world of machine learning, web scraping and artificial intelligence; data profiling is a relatively rare topic and a subject with a comparatively lesser presence on the web. Thinking about data profiling vs data mining?
Well, data mining refers to finding patterns in the data that you have collected or drawing a conclusion from certain data points. It is all about the data that has been collected–the rows and the columns in the CSV file. However, data profiling is about the metadata that can be extracted from a dataset and analyzing this metadata to find what use the dataset can be better put to.
Since both the topics mentioned today are heavyweights and involve numerous steps and procedures along with best practices, we will elaborate further on it.
While Data profiling is all about finding data or metadata from the dataset present at our hands, it can be further broken down into three different types of metadata:
The different types of metadata that we discussed give us a lot more information about the data at hand than the raw data itself. This information can be used to find where the data fits in your process and where would be the best place to use it. The percentage of data-cleanliness or missing data can also be identified from this metadata and changes can be made accordingly to make the data usable. Relationships found within the data-points and tables can also be used to set up redundancy checks and more.
While we have been discussing the data and the metadata and all that we can do with it, there are industry standards and best practices, i.e., pointers and references as to how to use the metadata and which metadata to look at. Deviating from the best practices and the common methodologies may lead you to findings that point you in the wrong direction. Some of the methodologies and best practices are as follows:
Data mining is an interdisciplinary topic that relies on statistics, web scraping, data extraction, machine learning as well as database systems. Due to this vast coverage, it is used by everyone starting from scientists working to identify cancerous cells in human bodies to sales teams trying to reach their monthly goals.
However, data-mining in itself consists of multiple steps such as data discovery, pre-processing, post-processing, visualization, and more, which we shall discuss. While there are many steps, the actual process of finding patterns in data is usually automatic or semi-automatic and mainly involves finding out which algorithm fits well for which data-set.
Again, an important point to be noted at this juncture is that data mining is very different from data analysis. While the former uses mostly machine-learning and statistical models to uncover hidden patterns, the latter is used to test models and hypotheses on datasets.
The usual steps involved in data-mining are as follows.
You might have noticed that certain steps such as data-cleaning and preparation of the data are similar in both the topics. Handling data always involves some universal “best practices” which need to be followed no matter what you are doing with the data. Data has become the input for most business-processes, where the output results in intelligent information. However, gathering the data is a herculean effort in itself. That is the reason why PromptCloud exists. Our data scraping team provides DaaS solutions that can fit companies ranging from small family businesses and startups to the frontrunners of the Fortune 500.
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