Data is an essential aspect for modern businesses to grow and stay ahead of their competitors. Data is generated by companies at a staggering rate and needs to be well managed for further use for generating insights and making better business decisions. Managing data is a quite cumbersome but essential need for companies to keep their operations running efficiently.
Data Enrichment vs Data Cleansing are the two terms used together when it comes to managing data for better business functioning. Both of these play an important role from loading data to generating insights because it helps in improving the quality of data, accessing more data, simplifying hidden patterns, etc. Data Enrichment vs Data Cleansing is a different process to keep data quality and accessibility in check.
Data Enrichment vs Data Cleansing helps in keeping high-quality data as well as up-to-date for faster and more accurate Data Analysis. In this article, you will go through the critical differences between Data Enrichment vs Data Cleansing and why they are essential for smoother functioning of business operations.
Table of Contents
What is Data Enrichment?
Data Enrichment is the process of extracting data from a third party or external data sources and loading it into the existing Database. It is used to improve the overall Marketing and Sales. Data Enrichment aims to deliver additional insights useful to make data-driven decisions to enhance growth and change approach.
If you have access to more data then you can understand the data more easily and identify patterns and extract information. Data Enrichment transforms the raw data into valuable insights for better decision-making. The raw that the company already have is used to append with other existing data after transformation which allows joining the pieces of data and provides a better understanding.
Key Features of Data Enrichment
Some of the main features of Data Enrichment are listed below:
- Reduce Churn: Data Enrichment process adds data and removes obstacles in the user journey data reducing the churn rate.
- Data Appending Service: With the help of the Data Enrichment tool, you can easily add data that fit business requirements.
- Maximizes Customer Nurturing: Data Enrichment improves customer nurturing by identifying segments of customers to be nurtured.
What is Data Cleansing?
Data Cleansing is the process of making your Database valid, clean, and accurate. Raw and inaccurate data can lead to false outputs that tend to make wrong business decisions. Also, without Data Cleansing, it wastes time dealing with the data that is irrelevant to your business. For example, it can risk your E-Mail Marketing campaigns by sending too many incorrect E-Mails.
Data Cleansing is an essential step for making accurate and better decisions. It allows you to fix incorrect, misplaced data and identify gaps. It makes the data consistent and predictable with accurate information. Also, Data Cleansing helps you to have a better understanding of data before cleansing it.
Key Features of Data Cleansing
Some of the features of Data Cleansing are listed below:
- Minimize Risks: Data security and permissions are essential aspects of Database management. Opting for regular Data Cleansing helps businesses to keep track of customer contact permissions.
- Enhance Productivity: Data Cleansing provide access to clean and filtered data that led to faster and easy Data Analysis.
- Improve Decision Making: Clean data generate accurate results that help businesses to make better decisions build a greater understanding of their audiences.
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Key Differences Between Data Enrichment vs Data Cleansing
Now that you have a brief understanding of Data Enrichment and Data Cleansing. In this section, you will read about a few critical differences between Data Enrichment vs Data Cleansing that will make you better understand both. The following differences between Data Enrichment vs Data Cleansing are listed below.
Data Enrichment vs Data Cleansing: Generic Differences
Data Enrichment is the process of supplementing the data from other business data sources to one Database for the availability of more fresh data. Whereas, Data Cleansing involves fixing inconsistent and unreliable data and keeping it up-to-date.
First, you need to enrich data by connecting your Database or storage with external data sources to add more data using Data Pipelines. Then, once you have access to the data, you need to perform Data Cleansing on it by analyzing the data errors, inconsistencies, and gaps in data. Then, you can understand more about the data and start fixing it.
|Data Cleansing||Data Enrichment|
|Processes your existing data.||Adds information to your existing data.|
|Fixing existing columns and fields.||Fills in the gaps in the Database.|
|Makes your data consistent and predictable.||Makes your data rich and detailed.|
|Focuses on quality of data.||Focuses on complete data.|
Data Enrichment vs Data Cleansing: Process
Data Enrichment is an ongoing process that needs to be monitored regularly because customer data is just a snapshot in time that becomes old and needs to be updated. For example, an employee’s salary may change over time, their marital status, phone number, designation, etc which requires an update in the Database. Companies should conduct Data Enrichment regularly using Data Preparation tools and other third-party software used to manage and sync data using Data Pipelines.
The Data Cleansing process differs based on the dataset and company requirements. The process follows mainly 4 steps that include analyzing data cleansing to reporting the data. The steps for the Data Cleansing process are given below:
- Profiling: In the initial stage, data is audited and inspected to understand the quality of data so that users can identify what issues are to be fixed in the data. It usually studies relationships between data elements, validates data quality, gathers statistics on datasets provided to detect errors.
- Cleaning: It is the main stage of the Data Cleansing process where data errors are fixed and other data inconsistencies, duplications, redundancy are corrected.
- Verification: After cleaning the data, companies verify and inspect the data to ensure the Data Cleansing process is successful, accurate, coordinated with internal data quality rules and standards.
- Reporting: The reports of cleaned data should then be reported to the IT teams, business executives to deal with data quality trends
Data Enrichment vs Data Cleansing: Benefits
A few benefits of Data Enrichment are listed below:
- Let You Collect Valuable Data: Data Enrichment allows companies to describe the relevancy of valuable data that meets the company’s requirements and solves problems. The enriched data helps users guide through the data to find new patterns and questions to ask their customers.
- Improve Accuracy of Data: The Data needs to be updated regularly because data decays continuously. If there is any change in data, it needs to be updated in the Database. With the help of Data Enrichment, improving the accuracy of data is achievable because the procedure verifies the information to ensure that data in the Database is up-to-date to deliver good results.
- Saves Time: Data Enrichment reduces time and effort as access to more data allows users to smartly automate the job. According to the requirements, Data Enrichment automation standardizes values, focusing on the most meaningful impact you can give to a target set of customers.
A few benefits of Data Cleansing are listed below:
- Improved Decision Making: Accurate and cleaned data allow users to produce better results using Data Analytics applications. This enables companies to make more promising data-driven decisions for business operations and strategies.
- Reduced Data Costs: Data Cleansing keeps data away from inconsistency and errors that can be a problem for Analytics applications. Avoiding unnecessary data processing saves time and cost because the teams don’t have to continue fixing the same errors in the datasets.
- Better Operational Performance: High-Quality data allow companies to stay up-to-date with the requirements for running different operations. It helps in avoiding any inventory shortage, delivery delay, and other business problems that result in higher costs, lower revenues, and damaged relationships with customers.
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- Fully Managed: Hevo requires no management and maintenance as it is a fully automated platform.
- Data Transformation: Hevo provides a simple interface to perfect, modify, and enrich the data you want to transfer.
- Faster Insight Generation: Hevo offers near real-time data replication so you have access to real-time insight generation and faster decision making.
- Schema Management: Hevo can automatically detect the schema of the incoming data and map it to the destination schema.
- Scalable Infrastructure: Hevo has in-built integrations for 100+ sources (with 40+ free sources) that can help you scale your data infrastructure as required.
- Live Support: Hevo team is available round the clock to extend exceptional support to its customers through chat, email, and support calls.
Applications of Data Enrichment
Some of the use cases of Data Enrichment are listed below:
- Using Customer Data for Marketing: Marketing is one of the first industries to embrace the use of Data Enrichment. It allows companies to have a deeper understanding of customer behavior. Various tools are used to gather data and analyze it.
- Property Data Insights for Insurance Risks: Data Enrichments allow access to much information that allows companies to have details about many factors that impact the insurance risks.
- Insights for Communication Service Providers: Data Enrichment provides a better understanding of the existing and new customers that can be beneficial for telecommunication companies by understanding demographic factors such as age, income level, and lifestyle preferences.
Applications of Data Cleansing
Some of the applications of Data Cleansing are listed below:
- Cleaning Data Lake: Data Lakes stores raw data from multiple sources and it is essential to perform Data Cleaning of that data to meet business requirements and provide high-quality data.
- CRM (Customer Relationship Management) systems contain Sales and Marketing data for salespeople. It allows companies to automatically clean data in the CRM systems and keep data ready for Analysis.
- Leveraging Machine Learning and Artificial Intelligence in the Data Cleansing process saves time and increases productivity.
Similarities Between Data Enrichment vs Data Cleansing
Data Enrichment vs Data Cleansing deals with managing data for improving the overall operations of the business activities. Both Data Enrichment vs Data Cleansing aims to simplify the workflow and aggregate data. The foremost step is Data Cleasing which makes sure that the data is accurate and Data Enrichment implies making the most out of the data.
In this article, you read about the key differences between Data Enrichment vs Data Cleansing. Also, you learnt why Data Enrichment and Data Cleasing are important procedures to follow for smoother functioning of business activities and making better and more accurate data-driven decisions.
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