Clean Your Data with Data Quality Platforms: A Step-by-Step Guide

Learn how to cleanse your data using data quality platforms and ensure high-quality data for your business with this step-by-step guide. Improve your data management process today.

Introduction

In today’s digital age, businesses generate and collect large volumes of data on a daily basis. However, this data is only useful if it is accurate, complete, and up-to-date. Dirty data can lead to poor decision-making, increased costs, and lost revenue. Therefore, it’s essential to ensure that your data is of high quality. In this article, we will discuss how data quality platforms can help you cleanse your data and provide practical tips on how to improve your data management process.

What are data quality platforms?

Data quality platforms are software tools designed to help businesses improve the quality of their data. These platforms perform various functions such as data profiling, cleansing, standardization, and enrichment. They can identify errors, duplicates, inconsistencies, and missing data, and provide suggestions on how to correct them. Data quality platforms can also help you comply with data privacy regulations and ensure that your data is accurate, complete, and up-to-date.

Data quality platforms can help you integrate, validate, enrich, and master your data from various sources and formats, such as databases, files, APIs, cloud applications, or streaming data. They can also help you leverage advanced technologies such as artificial intelligence (AI) and machine learning (ML) to automate and enhance your data quality processes.

Why use data quality platforms?

Using data quality platforms has many benefits. Firstly, it can help you save time and money by automating the data cleansing process. This means that you can focus on other important aspects of your business while the platform takes care of the data cleaning. Secondly, it can improve the accuracy of your data, which can lead to better decision-making and increased revenue. Thirdly, it can help you comply with data privacy regulations, which is essential in today’s digital age.

How to choose a data quality platform?

When choosing a data quality platform, there are several factors to consider. Firstly, you should consider the features and capabilities of the platform. Look for a platform that can perform the functions you require such as data profiling, cleansing, standardization, and enrichment. Secondly, consider the scalability of the platform. Make sure that the platform can handle large volumes of data and can grow with your business. Thirdly, consider the ease of use and user interface of the platform. Look for a platform that is user-friendly and intuitive.

How to use data quality platforms?

Data is an integral part of any business, and it is crucial to have accurate and reliable data to make informed decisions. However, data can become inaccurate, outdated, or irrelevant over time. This can lead to poor decision-making, lost revenue, and reduced efficiency. Therefore, it is essential to ensure that your data is clean and accurate to achieve optimal results. So how do you use a data quality platform to clean your data? In this article, we will provide a step-by-step guide to help you clean your data using data quality platforms.

Step 1: Define your data quality goals, requirements and metrics

Before you start cleaning your data, you need to have a clear idea of what you want to achieve and how you will measure it, you need to have a clear understanding of what you want to achieve with your data quality efforts. For example:

• Do you want to improve customer satisfaction, increase sales conversions, reduce operational costs, or comply with regulations?
• What are the key performance indicators (KPIs) that reflect these goals?
• How will you quantify and track them?
• What are the business objectives and use cases that depend on your data?
• What are the expected outcomes and benefits of improving your data quality?
• What are the specific criteria and standards that define high-quality data for your organization?

You can use these questions to guide your data quality strategy and prioritize your actions.

You need to define your data quality goals and criteria based on your business needs and objectives. For example, you may want to:

• Improve customer satisfaction by ensuring that your contact information is up-to-date and accurate.
• Increase sales by segmenting your customers based on their preferences and behavior.
• Reduce costs by eliminating duplicate records and unnecessary data storage.
  Comply with regulations by protecting sensitive data and maintaining audit trails.

To define your data quality goals and criteria, you can use a framework such as the DAMA International’s six primary dimensions of data quality:

• Accuracy: The degree to which the data correctly reflects the real-world objects or events it represents.
• Completeness: The degree to which the data contains all the required values and attributes for its intended use.
• Consistency: The degree to which the data is coherent and compatible across different sources and formats.
• Timeliness: The degree to which the data is available and up-to-date for its intended use.
• Uniqueness: The degree to which the data does not contain any duplicate or redundant records or values.
• Validity: The degree to which the data conforms to the rules and constraints of its domain.

You can also use metrics such as error rate, completeness rate, duplication rate, validity rate, etc. to quantify your data quality goals and criteria.

Step 2: Choose a suitable data quality platform

There are many data quality platforms available in the market today, each with its own features and capabilities. You need to choose one that meets your specific needs and budget. Some of the factors that you should consider when choosing a data quality platform are:

• The types and sources of data that it supports
• The range and depth of data quality functions that it offers
• The ease of use and customization that it provides
• The scalability and performance that it delivers
• The integration and compatibility that it enables
• The security and reliability that it ensures

There are many factors to consider when choosing a data quality platform, such as:

• Functionality: The features and capabilities of the platform that match your data cleaning needs.
• Usability: The ease of use and user-friendliness of the platform that suit your skill level and preferences.
• Scalability: The ability of the platform to handle large volumes and varieties of data without compromising performance or quality.
• Integration: The compatibility of the platform with your existing data sources, systems, tools, and processes.
• Security: The protection of the platform against unauthorized access, modification, or loss of your data.Cost: The price of the platform that fits your budget and provides a good return on investment.

You can compare different data quality platforms based on these factors and choose the one that best meets your requirements.

Step 3: Connect your data sources to the platform

Once you have chosen a data quality platform, you need to connect the data quality platform to the data sources that you want to clean. Depending on the platform and the type of data source, this may involve uploading files, configuring APIs, setting up connectors, or using wizards. You should also define the metadata and schema of your data sources to enable the platform to understand and process them correctly. These can be databases, files, applications, cloud services, or any other type of data source that your platform supports. You can use various methods to connect to your data sources, such as JDBC drivers, ODBC drivers, web services, APIs, or connectors.

To connect to a data source in Informatica Data Quality, you need to create a connection object in the Developer tool. You can specify the connection details, such as the type of data source, the host name, the port number, the user name, and the password. You can also test the connection to verify that it works properly.

Step 4: Understanding Data Quality Platforms

Data quality platforms are software solutions that help businesses identify and correct errors and inconsistencies in their data. These platforms work by analyzing your data and identifying any errors or inconsistencies that may exist. Once identified, the platform will then provide you with tools and solutions to help you correct these errors and ensure that your data is accurate and reliable. Some of the key features of data quality platforms include data profiling, data cleansing, data enrichment, and data matching.

To ensure that you are making the most of your data quality platform, it is important to first understand the features and capabilities of the platform. For example, data profiling is the process of analyzing your data to identify any errors or inconsistencies that may exist. Data cleansing, on the other hand, involves the process of correcting these errors and inconsistencies to ensure that your data is accurate and reliable. By understanding the features and capabilities of your data quality platform, you can ensure that you are effectively using the platform to improve the quality of your data.

Step 5: Identifying Data Quality Issues

Before you can clean your data, you need to identify the quality issues. Common data quality issues include duplicates, missing data, incorrect formatting, and inconsistent values. Data profiling is a useful technique to identify data quality issues. It involves analyzing the data to understand its characteristics, such as data types, length, and patterns. Data profiling can help you to identify data quality issues, such as missing values, outliers, or inconsistencies.

Step 6: Perform data discovery and profiling

After connecting your data sources to the platform, you need to perform data discovery and profiling to understand its characteristics and identify any quality issues. You can use various tools and techniques provided by the platform to analyze your data sources and identify their characteristics and issues. For example, you can use:

• Data profiling tools to generate statistics and summaries of your data sources
• Data visualization tools to create charts and graphs of your data sources
• Data lineage tools to trace the origin and flow of your data sources
• Data quality assessment tools to evaluate the quality of your data sources based on predefined or custom metrics

Data profiling can help you answer questions such as:

• What are the types and formats of your data fields?
• How many records and columns do you have in your data sets?
• What are the ranges and distributions of your data values?
• How many nulls, blanks, duplicates, or outliers do you have in your data?
• How consistent and complete is your data across different sources or tables?
• How does your data comply with business rules or standards?

To profile your data in Informatica Data Quality, you need to create a project and a folder in the Developer tool. Then you can drag and drop your connection objects into the folder and select the tables or files that you want to profile. You can also create custom queries to filter or join your data sources. You can then run the profiling task and view the results in the Analyst tool. You can see various statistics and charts that show the quality of your data fields and records.

Step 7: Cleansing Your Data

Once you have identified the data quality issues, you can start cleansing your data by applying various transformations and rules to correct or remove any errors or inconsistencies in your data. Data cleansing involves correcting, enriching, or removing the data to ensure its accuracy and consistency. Data cleansing can help you improve the accuracy, validity, and reliability of your data.  Data quality platforms can automate many data cleansing tasks, such as standardization, matching, and merging. For example, you can use data quality platforms to standardize the data format, remove duplicates, and enrich missing data.

Some common types of data cleansing are:

• Data parsing: Splitting or extracting parts of your data values into separate fields or columns.
• Data matching: Identifying and linking records that refer to the same entity across different sources or tables. Matching your data involves identifying any duplicate records in your data and merging them together to ensure that you have a single, accurate record for each piece of data.
• Data correction: This involves fixing spelling errors, typos, formatting errors, invalid values, or other mistakes in your data.
• Data standardization: This involves applying consistent rules and formats to your data values, such as dates, currencies, addresses, phone numbers, or names. Standardize the data involves converting the data into a consistent format, removing any special characters, and ensuring that the data is in the correct field. This step helps to ensure that the data is consistent and can be easily analyzed.
• Data enrichment: This involves adding missing or incomplete information to your data records from external sources or databases. Enriching your data involves adding external data sources to your existing data. This can include adding demographic data, geographic data, or other external sources. This step can help to improve the accuracy and completeness of your data.
• Data deduplication: This involves identifying and removing duplicate or redundant records from your data sources. Duplicates can cause confusion and can lead to inaccurate results. It is, therefore, essential to remove duplicates from your data. This can be done using a data quality platform, which can identify and remove duplicates.
• Data transformation: This involves changing the structure or format of your data to make it compatible with your target systems or applications.
• Data completion: Filling in missing values with default or derived ones.

Data cleansing tools can help you automate these tasks using predefined rules or functions or custom scripts or expressions. They can also help you apply these tasks in batch or real-time modes depending on your needs.

Step 8: Validating Your Data

After cleansing your data, it is crucial to validate it to ensure that it is accurate and reliable. Data validation involves verifying the data against predefined rules or criteria. Data quality platforms can automate data validation tasks, such as checking for data completeness, data consistency, and data accuracy. For example, you can use data quality platforms to validate customer addresses, email addresses, or phone numbers.

Step 9: Monitoring Maintaining Data Quality

After you have cleansed your data sources, you need to monitor and maintain their quality over time using data monitoring tools. Maintaining data quality is an ongoing process that requires continuous monitoring and improvement. You can use data quality platforms to set up rules and alerts to detect and correct data quality issues in real-time. Data quality platforms can also provide reports and dashboards to monitor data quality metrics, such as data completeness, data accuracy, and data consistency.

Data monitoring tools can help you track and measure the quality of your data using various metrics and indicators such as completeness, accuracy, consistency, timeliness, validity, or uniqueness. They can also help you visualize and report on your data quality status using dashboards and charts. Additionally, they can help you set up alerts and notifications to inform you of any changes or issues in your data quality.

Benefits of Using Data Quality Platforms

Using data quality platforms can provide several benefits for your business, including:

• Improved data quality: Data quality platforms can help to improve the accuracy, completeness, and consistency of your data.
• Increased efficiency: Data quality platforms can automate the process of cleansing and standardizing data, saving time and resources.
• Better decision-making: High-quality data can lead to better decision-making and can help businesses gain a competitive edge.
• Compliance: Data quality platforms can help businesses comply with regulatory requirements, such as GDPR and CCPA.

FAQs

Question: What is data cleansing?
Answer: Data cleansing is the process of identifying and correcting errors, inconsistencies, and duplicates in your data.

Conclusion

Data quality is a critical aspect of any business, and it is essential to ensure that your data is accurate, reliable, and consistent. Data quality platforms are an effective way to cleanse and validate your data to achieve optimal results. By following our step-by-step guide, you can effectively clean your data using data quality platforms and ensure high-quality data for your business needs. Remember to maintain data quality continuously to avoid potential issues and ensure optimal results.