Text mining simply shines through business science: a data cleansing use case
Are you tired of manually extracting, transforming and loading (ETL) raw data in SAP systems? There is a solution to this time-consuming and resource-intensive process. Text mining is the process of using various techniques to extract and analyze unstructured data. It involves to breaking down raw text into individual words and analyzing their meanings and context using Natural language processing (NLP) and machine learning algorithms.
There are several applications for text mining, including identifying and correcting errors in data, such as typos and incorrect values, as well as to identify and remove duplicates and handle missing values. In general, text mining is an effective tool for cleansing data, helping to improve the accuracy and reliability of the data and making it easier to analyze and use for business decision-making. According to feedback from the main users, our text mining solution has been able to save 60% of the time spent on cleansing data!
Text mining allows you to see “the whole elephant in the room”
When working with customers, we often encounter a lot of unstructured and chaotic data including data with no headers, empty fields, and duplicated records. Unstructured data is data that does not fit in a relational database management system (Relational data is stored in SQL, Not relational data is not stored in SQRl, also referred to as NoSQL). This type of data is often difficult to process and analyze using traditional methods. Examples of unstructured data include emails, invoice records, geo-spatial data, sensor data, surveillance data, addresses, and ticker data.
Applying an automated text mining process to cleanse data allows businesses to keep their data current and identify gaps in their operations. It also helps to remove analytical and user biases. Sometimes we may only have a partial understanding of a situation, leading to incorrect assumptions due to biases. By cleaning the data, we can reduce the influence of these biases on our analysis. And we must do that to avoid the “Elephant” example, see figure below.
Figure: How bias can impact the data analysis and therefore your interpretation (Wu, Zhu, Wu, & Ding, 2014)
McCoy has developed a method using text mining to automatically check names and addresses from a costumer database against Google Maps API data, and clean any discrepancies found.. This process involves converting raw text into a machine-readable format using text mining, in which each word is carefully analyzed and converted into statistical data. This allows for easy search, comparison and editing of millions of inputs.
Benefit from automated data cleansing
Text mining is a powerful tool for revolutionizing your business analytics! With its ability to extract and analyze unstructured text data, text mining is a game-changer for data cleansing. Text mining means saying goodbye to manually sifting through vast amounts of raw data. It can quickly and accurately convert the data into a structured format, making it easy to identify and fix errors, remove duplicates, and handle missing values. The result is clean, reliable data that will take your business to the next level.
Don't miss out on this opportunity to transform your data management. Try text mining today. To learn more watch the McCoy TV episode about data cleansing or read the step-by-step guide to automate data cleansing using text mining. For more information contact Dovile Kliusovaite or Ana Xavier.