These Important Data Cleansing Practices To Empower Your Business Decision
Data has the very potential to transform the outlook and stats of almost any type of business. For better or for worse! The year 2020 is very likely to become an infuriating subject for the data scientists of our near future. The reason- COVID 19 Pandemic.
This global health concern has been around for a little over three months now. Skewed data resulting from this massive anomaly has invaded almost every domain, including ecommerce, travel, healthcare, and finance. By now, several analytics teams around the globe have faced failed forecasting models and are expecting delayed quarterly targets.
This biased data will show up in every forecasting model that investigates even a few months’ worth of data for 2020, forever.
The Corona pandemic has created the most significant and recent example of the potential influence of bad data on businesses worldwide. That is why understanding and following the best data cleansing practices and services are more critical now than they ever were.
Enhance Your Business Decisions with These important Data Cleansing Practices
Monitor Errors at the Point of Entry
It is vital to understand how and why datasets get corrupted. One of the prominent reasons that happen is because the data entering your records isn’t accurate or verified in the first place.
Therefore, it is critical to monitor data at the point of entry.
Result-yielding strategies need high-quality data to build upon. So, the first tip for a better cleansing process is to verify your sources. Use tools, if necessary, to validate the hygiene of your data. Create a standard operating procedure for integrity checks at the time of entry in datasets. That will help curb the chances of duplication and inconsistency in the dataset.
Additionally, keep updating your sources and checking them for precision. Carefully examine the data-entry services and processes you use, if you plan to integrate the dataset with other systems.
Validation is one of the core data cleansing services that deal with issues in an existing dataset, like unclean, incorrect, and duplicate records.
Data validation ensures a few crucial outcomes for your dataset by keeping it-
- Valid- Accurate formats for every record
- Complete- No blank, missing, or empty values in the set
- Consistent- Fair and congruous value ranges
- Unique- Deduplication of all ambiguous entries
- Accurate- Data that represent real values
- Relevant – Values in accordance with the applicable time-period
It’s important to note that scrubbing databases can take too much time and effort, depending on how vast they are. While validation is essential to achieve efficient data cleansing, businesses are recommended to explore the right tools for the task.
You may choose to create the scripting code by yourself. Otherwise, there are different open-source as well as premium enterprise tools that can be used for data validation. Or, businesses may also hire database cleansing services for the job. It depends on the volume of your dataset as well as the extent of data cleansing services you require.
Create a Data Quality Plan for Your Business
Accurate data fuels more informed and appropriate decision making-we’ve already been over this. That realization should ideally be followed by a data plan that can maintain high-quality values in your databases.
Hence, the needs for a data quality plan!
Here’s what a data quality plan should ensure for businesses-
- Accurate, understandable, and clear data
- Timely distribution of relevant and correct data to the responsible parties (managers etc.)
- Guidelines for error-free interpretation of data in the proper context
- Simplified transfer of data and integration with other systems
- Reliable sub-systems for data reporting and collection
- Minimal budget wastage
- Minimal compliance problems
- Single entity view and appropriate segmentation
In simple words, a data quality plan is required to secure clarity throughout all the phases of data management, collection, validation, categorization, and implementation. So, businesses should focus on coming up with a quality plan before they can move on to data analytics.