How Does Data Mining Differ from Data Profiling?by Beverly McNally I love to read and write about technology such as
Data science is a new normal that the technology industry has provided to the corporate world. Entrepreneurs owe for being gifted with data mining and profiling. Analyzing business requirements, competencies, efficiency, customer support and decision making has never been as easier as it is today. It has the quickest and the most infallible response to counter challenges in the business world. All thanks to mining and profiling!
Let’s get started with what these are.
By this, we mean digging out underlying patterns from the pre-built databases. The topmost data mining outsourcing companies employ this technique to identify patterns, which are an end-product of the market or business research. Basically, these patterns are the values that underlie a massive heap of datasets in their warehouse. Upon extracting, capturing, loading for cleansing them, the miners research what could be the intelligence or breakthrough to turn around profitability of the customer.
In short, data mining ensures figuring out unique patterns, which have a potential to create opportunities through knowledge discovery & analysis of databases. The experts take it as an evaluation of the existing insights via large datasets to transform them into a new learning or business intelligence, which is useful and of a great value.
Simply put, you get the collection of patterns and knowledge from the availed data, filtering valid, novel and potentially useful databases so that the business can administer its challenges via decision/strategy making. Psychological analysis is its biggest example, which assists businesses to recommend and adapt their likelihood. Even, these can alter their mindset throwing strong strategic ideas in favour of what you want to market.
It also has some deep connections with the analysis of raw information drawn from existing databases in the form of informative summaries. This profiling is basically concerned with the data and the assessment of informative summaries, which are helpful in measuring up their consistency, uniqueness and logics so that they could be prepared for the next queuing cleansing, integration and analysis.
Data quality is what you should foremost look into for digging anomalies out of datasets. This is how you can pick up the wrong data in the beginning and correct at the right time. These corrections for profiling can be computed by finding mean, minimum, maximum, percentile, frequency, aggregates and many more methods.
There are some profiling tools to evaluate the actual content, structure and quality of the data. It happens by focusing on drawing relationships between values within and across datasets.
In short, data mining drives you to a flexible and actionable insight by relying on mathematical algorithms, whereas data profiling sticks around the quality and anomalies detection.
Best Techniques for Data Mining
There are many prominent techniques that are widely used in data science, especially for machine learning to draw artificial intelligence for automation, which include:
As this name suggests, this technique identifies relationships between items that form a particular pattern.
This technique ensures classifying variables into predefined groups or classes, using linear programming, statistics, decision trees & the most sophisticated artificial neural networks in data mining.
This technique enables the setting up meaningful object clusters, wherein the objects carry similar characteristics. However, you can put objects in classes that are defined by the cluster itself.
This technique helps to foresee the relationship between independent and dependent variables. Sometimes, you can spot such relationships in the independent variables alone.
• Sequential patterns
This is a technique to spot similar trends or patterns or fashion or events in databases, which take place over time.
Different Techniques of Data Profiling
Here are the most common ways to create data profiling:
• Structure Discovery/ Analysis
This technique ensures consistency and formatting in a right manner. The expert analyst observes it simply by applying basic statistics.
• Content Discovery
This technique gets you closer to some individual elements in the database, discovering null values or the incorrect ones.
• Relationship Discovery
It analyses the data that is useful for comprehending the connections between datasets in a better way. It starts off with metadata analysis, which subsequently shifts to overlapping data.
Created on Jul 23rd 2020 01:41. Viewed 322 times.