Although data profiling and data mining may seem similar, they serve different data management purposes. Two processes must work together to ensure data quality.
In theory, using the data should be easy. All data is easily accessible in an organized database with clearly labeled, consistent types, and ready for analysis.
In reality, it's not that simple. Organizations can store data across different departments in a variety of formats, often incompatible and sometimes without structure or organization at all. Understanding how data mining and profiling techniques can be used in conjunction can help organizations extract valuable insights from their data.
data profiling
Data profiling is the process of examining, analyzing, reviewing, and summarizing the quality of data sets. Identify where your data is, what it is, who can access it, and whether it is consistent, accurate, and complete.
“Data catalogs, metadata management, or BI and analytics platforms typically provide data profiling tools,” says Doug Henschen, vice president and principal analyst at Constellation Research.
”[Profiling is] “It's about understanding the data better in preparation for data refinement,” he said, “which will help us better understand which data to select and possibly combine into data mining analysis.” said.
“Enterprises use profiling when designing or coding data transformation and integration processes,” said Boris Evelson, vice president and principal analyst at Forrester Research.
thomas coughlinLife Fellow and IEEE President, President of Coughlin Associates
You can also use it to create data warehouse models and schemas, and to build semantic layers. A semantic layer simplifies the use of data by separating complex data lake or warehouse structures from business users.
Data profiling can improve the quality of data for any application, said Thomas Coughlin, lifetime fellow and president of the Institute of Electrical and Electronics Engineers (IEEE) and president of consulting firm Coughlin Associates. For example, it helps companies prepare data for training AI models. This is also an important first step in data mining.
“Data profiling improves the accuracy and utilization of data used for data mining, and generally improves the results of data mining,” Coughlin said. “Data mining can also be used on unprofiled data, but the results may not be as accurate.”
data mining
Data mining is the process of analyzing structured or unstructured data sets to identify patterns, relationships, and correlations. Analytical models use the results to generate insights that enable data-driven decision-making.
“Data mining is most often concerned with deriving insights from unmodeled data,” Ebelson said.
One of the most frequent use cases for data mining is text mining. Unstructured data types such as emails, contracts, and social media posts, in their raw form, are not ready to be analyzed by BI or other analytical tools.
“First, you need to mine the text to find structures such as entities, topics, sentiment, etc. Then you can analyze those extracted or derived structures,” Ebelson said.
Ani Chaudhuri, CEO of data security and governance company Dasera, said hospitals are using data mining to predict disease outbreaks and using profiled data to He said it may be possible to identify effective treatments.
Technique
Data profiling and mining each offer different techniques to get the job done. Organizations should choose the method that best suits their needs.
Data profiling techniques
Profiling techniques available to data teams include statistical analysis, data quality assessment, and schema discovery. Identifying data inconsistencies and anomalies results in higher quality data. Nick Kramer, vice president of applied solutions at SSA & Company, says improved quality will provide more reliable insights and help businesses comply with regulations.
Profiling large datasets can be time-consuming and resource-intensive. Analyzing sensitive data can raise privacy and security concerns. The analyst must also address quality issues discovered during his profiling of the data. If quality issues are left unchecked, they create further problems and waste time and money spent on the profiling process.
SSA & Company typically uses Python for profiling, Kramer said. He also uses tools that allow non-technical users to profile data, such as data quality and proprietary data wrangling tools.
Once profiling is complete, companies can begin extracting insights from the data.
data mining method
Data teams have a variety of data mining techniques at their disposal to identify relationships within data sets and organize data. Common techniques include anomaly detection, clustering, classification, regression, neural networks, decision trees, and K-nearest neighbors. This technology uncovers customer insights, improves marketing strategies, increases sales, optimizes business processes, and reduces costs.
“Identifying hidden patterns and relationships helps you make data-driven decisions and gain a competitive advantage,” said Kramer.
For example, segmenting customers based on their behavior and preferences can help businesses develop targeted marketing strategies.
Applications of data profiling and mining
Data profiling and mining work best when used in complement to each other. Different industries can apply both processes differently to achieve effective results.
- Science and technology. Scientific researchers collect large amounts of research notes and data points. Data profiling helps organize information, and data mining helps scientists draw conclusions, make predictions, and identify hypotheses for further research.
- Fraud Detection. Profiling helps companies identify data relevant to fraud detection, such as customer transaction records and employee expense reports. Mining can use that data to identify suspicious behavior through anomaly detection or pattern recognition.
- Market analysis. Data profiling and mining techniques are important processes in marketing. For example, profiling can identify and categorize the sources of relevant social media posts, and mining can uncover unmet customer needs to inform product development.
- customer retention. Data profiling allows you to identify key sources of customer data related to customer satisfaction, loyalty, and customer churn. Data mining can segment customers based on history and demographics, predict which customers are at risk of churn, and suggest intervention strategies.
Maria Korolov has been covering enterprise technology for nearly 20 years, currently focusing on artificial intelligence and cybersecurity.