Data cleaning is a critical step in effective business data management. As organizations depend massively on data (information) to guide essential business choices, bad or poor data can cause inefficiencies, lost chances, or even financial losses. Thus, the business’s most prominent issue today is keeping a “healthy” database.
Here’s how you can do that through data cleaning and enrichment practices.
What Are The Data Cleaning Challenges Businesses Face?
Many complications arise when organizations gather information from the web or numerous other resources, combine data from different databases, obtain data directly from customers or other divisions, etc. These consist of:
- Redundant data: Several identical records
- Contrary Data: When various facts appear in the same database. As an illustration, consider a consumer whose postal address appears different in various documents.
- Missing Data: Information that lacks certain properties.
- Unsound/Baseless Data: Information that doesn’t match rules or standards.
The Risk Of Bad Data
Data has now become the core business asset for most enterprises worldwide. As a result, more individuals started depending on their databases (gathered & enriched information) to guide important decisions. However, it is important to know that only high-quality data can safeguard a company’s decision-making process.
Bad data costs businesses money and leads to analysts investing most of their time in maintaining and cleansing databases. The additional time will accumulate and slow the business’s practical, growth-oriented activities.
Furthermore, poor or bad business data might impact various other company functions. For example, a lack of information about customers’ requirements and choices may result in unsuccessful marketing initiatives, while shaky consumer data may impact sales.
Data Cleaning And Enrichment: Best Practices
Numerous methods and practices exist to maintain a healthy, clean, and rich database. These consist of the following:
- Make a plan to ensure data quality
- Decide on the data enrichment model and metrics and concentrate on them.
- Develop key performance indicators to monitor data state.
- Describe the preliminary data inspection procedure.
- Start sample checks to find complex datasets.
- Establish the particular tools, technology, and models required to clean a database
- Create checks to stop data from being “over-cleaned.”
- Start the data cleansing and error correction process.
- Create reports after validating the “clean data.”
- Send data to the database after carefully examining its quality.
- Assess the general data quality by conducting plausibility scanning and differentiating current data from earlier sets.
Ensure the input of accurate data
Cleaning data in the initial phase is all-important to ensure that all crucial attributes are without any faults and errors at entry, leading to a healthier database. Moving forward, this practice can help your internal resource pool save precious time and investments.
A systematic and standardized process for data input must be followed by every internal team member. As a result, you can rest assured that your system will only take in data of the highest quality.
Verify the integrity of your data
Verify the data to make sure it complies with all specifications. With not-so-extensive data, this can be done manually. However, the manual process is laborious, time-taking, and ineffective with extensive and complex databases since humans make more mistakes and create complications. Therefore, automatic data quality assurance software and applications are developed to help with this issue.
Manage data redundancy and duplication
Data duplicates are negative for any business. They cost business time and effort. They also hinder business operations, hurt business-client bonds, and complicate several organizational functions, including advertising, accounting sales, and client support.
Organizations should take all necessary measures to prevent data duplicates. And after removing all redundant information at the entry, it is necessary to take into account the following:
- Normalizing: Ensuring consistent data addition.
- Standardizing: creating a uniform format for data so it can be processed and analyzed.
- Merging: When data is dispersed over several datasets, merging brings together the pertinent pieces to create a single data record.
- Aggregating: Data filtering and integration
- Filtering: Limiting a dataset just to include the information that users are looking for.
- Scaling: The transformation of data to suit a predetermined range, for example, 1-10
- Removing: Duplicate and outlier data points removal to avoid incorrect regression analysis.
Add lacking details
Appending is filling in missing data, such as a contact number, email IDs, first and last name, house number, etc., to the needed records field. However, locating the lacking information may take time and effort. Employing a reputable data enrichment services supplier who can efficiently bridge the distance will help businesses accomplish this phase.
Examining the procedure
Additionally, it is crucial to keep track of your cleaning efforts so that you can quickly repeat and modify the process or eliminate any steps that are not required. To improve your team’s performance, you can use various technologies to analyze the operations and make it easier to keep records of them fast.
Modern businesses can gain a competitive edge by basing their decisions on high-quality data, which boosts revenues and improves customer support services. You don’t want to lose substantial sums of money every year due to insufficient data quality. Thus, start planning how to improve the quality of your data and define a business strategy for data cleaning.
If you wish to expedite the process of cleaning enterprise datasets while keeping your in-house teams, budget, and time involvement in check, you can outsource data entry services. However, look up the advantages of the same for your niche and your requirement before committing to a third-party data enrichment service provider.