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Data Profiling vs. Data Mining

September 25, 2019Category : Blog
Data Profiling vs. Data Mining

Last Updated on by Tarun

Ever thought about the difference between Data Profiling vs. Data Mining? 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. There are different definitions scattered around and often you might find that both seem to be the same thing. Well, they are not. And the difference is very simple. Data Mining refers to finding patterns in the data that you have collected or drawing a conclusion from certain data points, and more. 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 discuss them in detail to elaborate on them.

Data profiling

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:

  1. Relational information can be found from large datasets. Say you have a dataset with 10 tables. You may be able to find which tables are related and the data for which ones would change on changing the values in another table.
  2. Metadata can also be discovered from the content. This usually pertains to errors in the data, missing fields, and more. For example, if a particular field is empty in more than 50% of the data, we might have to forego that data-point when doing any analysis.
  3. Structural information can also be discovered from our data. This information can be of various types. It can be the statistical mean, median, max of your data sets. It can even be the percentage of data-points that were collected from rural households and the percentage collected from urban. In short, it would tell us a lot about how the data looks without the need for us to go inside the Excel sheet and check out every row.

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 these 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.

Best practices

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:

  1. Relations between data points – These need to be stored so that when using query languages like SQL, related data can easily be pulled out. Say you are parsing through the car manufacturers’ table, and you want to find the horsepower of every car that a particular manufacturer has sold till date. Such information can be easily derivable only if the relations between the manufacturer’s table, the car’s table, and the car-specifications table are well defined.
  2. Data-point checks – It is the identification of Null, blank and error-filled data points. It has to be stored along with the data-set so that anyone picking up the database is aware of these constraints right at the beginning.
  3. Statistical data points – This refers to statistical values which may be important in certain cases. It refers to values like mean, median, mode, max, min, frequency and more for every column of your database.
  4. Patterns – Different patterns exist in data. For example, on checking out a column, you may find that it consists only of yes or no- so it is a boolean column. For one, it may be male or female. So it is categorical data. Also, using regex matching, one can even identify whether certain columns are pin codes, addresses, names, age, email addresses or phone numbers. All such information must be captured separately so that anyone reading the database can get a better understanding of the data structure.

Data mining

Data mining is an interdisciplinary topic that relies on statistics, 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.

Steps involved

The usual steps involved in data-mining are the following:

  • Understanding the business problem.
  • Getting a clearer picture of the data.
  • Cleaning the data and preparing it for modeling.
  • Creating an ML or statistical model from the data.
  • Evaluating the model and reviewing its performance in a test environment.
  • Deploying the solution and reviewing its performance in a prod environment.
  • Often a simplified process is followed by most business, consisting of pre-processing, data-mining, and result-set validation.

Conclusion

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 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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