Data Cleaning Hacks Every Data Scientist Should Know
By Aditi Bhat
Data management is one of the most important practices undertaken by any organization as it helps the organization in planning ahead and recording the internal and external business activities. Data scientists are required to collect and analyze data to find patterns and trends to provide opportunities and solutions to the organization.
Data in the real-world is inconsistent, noisy, has outliers, and is dirty. Hence even before working on data cleaning, during the data discovery phase, a data scientist has to initially spend time in understanding all the attributes available in the data and evaluate if the existing data is adequate to build a solution for the business problem. And then try to handle missing values based on the availability of the data either by removing or by creating them using techniques.
But writing a code to do that can be time-consuming and costly. Fortunately, there are a number of data quality methods that will clean your data for you.
1. Data quality software: The easiest way to clean the data is to use a data quality software whose data correction tools reference a reliable secondary data source. These tools use an organization’s data against the data of an established data vendor for validation and correction. Vendors of these tools generally have a contractual arrangement with other established vendors to use their data for correction.
Data quality software cleans the data by:
•Modifying data values to meet domain restrictions, integrity constraints or other business rules that define sufficient data quality for the organization
•Identification, linking or merging related entries within or across sets of data
•Statistical analysis of data to capture statistics (metadata) that provides insight into the quality of the data and aid in the identification of data quality issues.
2. Data Standardization: Data standardization is the process of transforming data from disparate sources and systems into a consistent format. Standardizing data is a critical step in a data cleaning process because it makes it easier to identify errors, outliers, and other issues within your data sets. Data standardization typically employs algorithms based on match standards. Match standards are agreed-upon representations of data elements that can be assigned by standardization software.For example, whereas disparate data sources may list XYZ Infotech as XYZ, XyzInfotech, or XYZ Inc., standardization software will ensure that all entries conform to an agreed-upon standard (for example, XYZ Infotech).
3. Uniform Platform: Uniformity is the basis of data cleaning and is the most important hack a data scientist can use. For example, if rather than selecting the country from a drop down menu, you let the leads or the customers to write the name of the country to which they belong you are bound to have inconsistent results. For example, if a person belongs to USA, he can write usa, U.S.A or United states of America. This is why having uniformity in the platform will further ease your work as it will automatically clean similar data values.
4. Machine Learning Techniques: Big data is a big deal, but problems within the data can skew results and lead to problematic choices. Machine learning techniques include tools which analyze prediction models to determine which mistakes (e.g., typos, outliers, and missing values) to edit first, updating the models in the process. The tool uses machine learning to analyze a model’s structure to determine what errors are most likely to throw it off and then it cleans enough data to create ‘reasonably accurate’ models.
Data cleaning is the first step to a successful data optimisation process. Every data science professional needs to have a strong founding in it, because properly cleaned data leads to easier analyses and ultimately to better insights. There are a lot of data science courses out there but only few have a curriculum that lays a good foundation. For instance, Manipal ProLearn’s PG Diploma in Data Science course comprises a syllabus structured to provide students a good base in data science. Make the move to upgrade your career with smart choices.
What are other data cleaning hacks you’ve come across? Tell us in the comments!