Poor data quality is a rising concern for many business leaders as they fail to meet their targeted goals. The team of data analysts – that is supposed to produce reliable data insights – spend 80% of their time cleaning and preparing data, and only 20% of the time is left to do the actual analysis. This has a huge impact on the team’s productivity as they have to manually validate the data quality of multiple datasets.
84% of CEOs are concerned about the quality of the data they’re basing their decisions on.
After facing such issues, organizations look for an automated, simpler, and more accurate way of cleaning and standardizing data. In this blog, we will look at some of the basic activities involved in data cleansing, and how you can implement them.
What Is Data Cleansing?
Data cleansing is a broad term that refers to the process of making data usable for any intended purpose. It is a data quality fixing process that eliminates incorrect and invalid information from datasets and standardized values to achieve a consistent view across all disparate sources. The process usually includes the following activities:
- Remove and replace – Fields in a dataset often contain leading or tracing characters or punctuations that are of no use and need to be replaced or removed for better analysis (such as spaces, zeros, slashes, etc.).
- Parse and merge – Sometimes fields contain aggregated data elements, for example, the Address field contains Street Number, Street Name, City, State, etc. In such cases, aggregated fields must be parsed into separate columns, while some columns must be merged together to get a better view of data – or something that works for your use case.
- Transform data types – This involves changing the data type of a field, such as a transforming Phone Number field that was previously String to Number. This ensures all values in the field are accurate and valid.
- Validate patterns – Some fields are supposed to follow a valid pattern or format. For that, the process of data cleansing recognizes current patterns and transforms them to ensure accuracy. For example, the US Phone Number following the pattern: AAA-BBB-CCCC
- Remove noise – Data fields often contain words that don’t add much value and hence, introduce noise. For example, consider these company names ‘XYZ Inc.’, ‘XYZ Incorporated’, ‘XYZ LLC’. All company names are the same but your analysis processes can consider them to be unique, and removing words like Inc., LLC, and Incorporated can improve the accuracy of your analysis.
- Match data to detect duplicates – Datasets usually contain multiple records for the same entity. Slight variations in customer names can lead your team to make multiple entries in your customer database. A clean and standardized dataset should contain unique records – one record per entity.
Structured versus Unstructured Data
One modern aspect of digital data is that it’s not consistent in fitting into a numeric field or textual value. Structured data is what companies are typically working with – quantitative data stored in specific formats like spreadsheets or tables to work with easier. However, businesses are working with unstructured data more and more as well… this is qualitative data.
An example of unstructured data is natural language from text, audio, and video sources. One common one in marketing is gleaning brand sentiment from online reviews. The star option is structured (eg. score of 1 through 5 stars), but the comment is unstructured and the qualitative data must be processed through natural language processing (NLP) algorithms to form a quantitative value of sentiment.
How To Ensure Clean Data?
The most effective means of ensuring clean data is to audit every entry point into your platforms and programmatically update them to ensure data is properly entered. This can be accomplished in a number of ways:
- Requiring fields – ensuring a form or integration must pass specific fields.
- Utilizing field data types – providing limited lists for selection, regular expressions to format data, and storing data in the proper data types to constrain data to the proper format and type stored.
- Third-party service integration – integrating third-party tools to ensure data is properly stored, like an address field that validates the address, can provide consistent, quality data.
- Validation – having your customers validate their phone number or email address can ensure that accurate data is stored.
An entry point need not just be a form, it should be the connector between every system that passes data from one system to another. Companies often utilize platforms to extract, transform, and load (ETL) data between systems to ensure clean data is stored. Companies are encouraged to perform data discovery audits to document all entry points, processing, and utilization points for the data within their control. This is critical for ensuring compliance with security standards and privacy regulations as well.
How To Clean Your Data?
While having clean data would be optimal, legacy systems and lax discipline for importing and capturing data often exist. This makes data cleansing a part of most marketing teams’ activities. We looked into the processes that data cleansing processes involve. Here are the optional ways your organization can implement data cleansing:
Option 1: Using A Code-Based Approach
Python and R are two commonly used programming languages for coding solutions to manipulate data. Writing scripts to clean data can seem beneficial since you get to tune the algorithms according to the nature of your data, still, it can be difficult to maintain these scripts over time. Moreover, the biggest challenge with this approach is to code a generalized solution that works well with various datasets, rather than hard-coding specific scenarios.
Option 2: Using Platform Integration Tools
Many platforms offer programmatic or codeless connectors to move data between systems in the proper format. Built-in automation platforms are gaining popularity so that platforms can integrate easier between their company’s toolsets. These tools often incorporate triggered or scheduled processes that can be run on importing, querying, or writing data from one system to another. Some platforms, like Robotic Process Automation (RPA) platforms, can even enter data in screens when data integrations are not available.
Option 3: Using Artificial Intelligence
Real-world datasets are very diverse and implementing direct constraints on the fields can give inaccurate results. This is where artificial intelligence (AI) can be very helpful. Training models on correct, valid, and accurate data and then using the trained models on incoming records can help flag anomalies, identify cleansing opportunities, etc.
Some of the processes that can be enhanced with AI during data cleansing are mentioned below:
- Detecting anomalies in a column.
- Identifying incorrect relational dependencies.
- Finding duplicate records through clustering.
- Selecting master records based on the computed likelihood.
Option 4: Using Self-Service Data Quality Tools
Certain vendors offer various data quality functions packaged as tools, such as data cleansing software. They use industry-leading as well as proprietary algorithms for profiling, cleansing, standardizing, matching, and merging data across disparate sources. Such tools can act as plug-and-play and require the least amount of onboarding time as compared to other approaches.
The results of a data analysis process are as good as the quality of the input data. For this reason, understanding the challenges of data quality and implementing an end-to-end solution for rectifying these errors can help to keep your data clean, standardized, and usable for any intended purpose.
Data Ladder offers a feature-rich toolkit that helps you to eliminate inconsistent and invalid values, create and validate patterns, and achieve a standardized view across all data sources, ensuring high data quality, accuracy, and usability.