Data management is one of the most important practices undertaken by any organization. It helps companies in planning ahead and recording the internal as well as external business activities. Data scientists collect and analyze data to find patterns and trends. Their findings provide opportunities as well as solutions to the organization.
Data in the real-world is inconsistent, noisy, has outliers, and needs refining. So even before starting with data cleaning, a data scientist has to spend time in understanding all the attributes available in the data during the data discovery phase. He/she also needs to evaluate if the existing data is adequate to build a solution for the business problem. And then finally try to find the missing values based on the availability of the data either by eliminating or by creating them using certain techniques.
However, writing a code to do so can be time-consuming and costly. Fortunately, there are a number of data quality methods that will clean your data for you.
1. Invest in a good data quality software
The easiest way to clean 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. Focus on 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 helps in easy identification of 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, disparate data sources may list XYZ Infotech as XYZ, XyzInfotech, or XYZ Inc., but standardization software will ensure that all entries conform to an agreed-upon standard (for example, XYZ Infotech).
3. Maintain a uniform platform
Uniformity is the basis of data cleaning and is the most important hack a data scientist can use. For example, rather than selecting the country from a drop down menu, letting your leads or customers write the name of their country of origin is bound to lead to inconsistent results. For example, if a person belongs to the US, he/she can write usa, U.S.A or the 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. Extract information through 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 while 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. Then it cleans enough data to create ‘reasonably accurate’ models.
Data cleaning is the first step to a successful data optimization process. Every data science professional needs to have a strong foundation in data cleaning. Properly cleaned data leads to easier analysis and ultimately to better insights.
You can choose from several data science courses. However, only a few boast of a curriculum that lays a good foundation. For instance, the Manipal Global Academy of Data Science’s 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 other data cleaning hacks have you come across? Tell us in the comments section!
Manipal Global Academy of Data Science offers cutting-edge learning solutions in the field of data science. MGADS faculty comprises of academicians, data science experts, and IT professionals to guide you in today’s competitive environment. If you are keen on becoming a data scientist, MGADS will equip you to do the same.