Why Data Filtering Matters: Benefits and Best Practices

Businesses generate and process vast amounts of data from varied sources. However, not all of this data would be useful or relevant. With such huge volumes of raw data and without proper handling, the expected outcomes are inaccurate insights, inefficiencies, and poor decision-making.

The appropriate solution to this problem is refined good quality data. Whether you own a business or are a researcher or analyst, well-structured data is essential for informed decision-making. This can help you optimize operations and enhance customer experiences. To ensure you use only relevant and actionable information, you can utilize data filtering.

This blog will explain why is it important to filter data along with the benefits, challenges, and types of data filtration.

What is Data Filtering?

Data filtering is the process of selecting and showing specific parts of a larger dataset according to certain conditions or criteria. This helps simplify data analysis since it allows you to focus on the data that meets specific conditions while also removing unnecessary or irrelevant information. Data filtering makes your analysis more efficient and focused; you can quickly analyze relevant data without sifting through the entire dataset.

What is the Purpose of Data Filtering?

The constantly growing data volumes necessitate data filtering for effective data analysis and decision-making. Here are some essential purposes of data filtering:

• Dataset Evaluation: By filtering data, you can identify patterns, trends, or anomalies within a dataset, benefiting exploratory data analysis.‍
• Removal of Irrelevant Data: You can remove irrelevant data, such as certain fields or values, by filtering before restructuring using means such as pivoting, grouping, or aggregating. This exclusion of unnecessary data promotes more focused analytics.‍
• Processing Records: With data filtering, you can process records based on predefined criteria, streamlining workflows.‍
• Modify Values: You can use data filtering to modify or replace values with new ones. It’s also helpful if you want to filter data by the most recent modified date to delete older files or update older data.‍
• Create New Structures from Old Datasets: By filtering old datasets and applying logic and algorithms to change the structure, you can create new structures. This is particularly useful for cleaning data that you want to import into an application or creating subsets of a large dataset for analytical purposes.

Difference Between Data Filtering vs Data Sorting vs Data Sampling

Benefits of Using Data Filtering

Data filtering enables your organization to derive valuable insights from data. Here are some of the notable benefits that justify the question, ‘Why is it important to filter data’:

Enhanced Decision-Making

When you isolate relevant data, you can identify patterns, trends, or outliers that could be hidden in a larger, unfiltered dataset. By ignoring irrelevant data, you can create clear and insightful visualization. This facilitates more informed and accurate decision-making and is especially beneficial in dynamic environments that require quick decisions.

Improves Efficiency and Performance

By retrieving and processing only the necessary data subsets, the result is reduced computational load and faster operations. This is useful in scenarios requiring quick and responsive access to information and can lead to potential cost savings.

Improves Data Security

Data filtering helps create a safe environment for your IT systems. By using data filters, you can create the requirement for onboarding new users, entering credit limits, or user requirements that are essential for your organization. An example is if you need new users to register only after they meet certain qualifications, such as submitting documents providing their address and identity.

Reduces Redundancy and Unnecessary Data

With data filtering, you can eliminate unnecessary data. Consider an example where you want the total number of records in a dataset with two different field types: integers and strings. You can utilize data filtering to filter out the records consisting of either field type in them. When you narrow down to specific datasets, it improves data relevance, enabling you to eliminate irrelevant information.

Data Filtering Tools to Use

To perform data filtering, you can use manual scripting or no-code solutions. Let’s look into the two options:

• Scripting Using Programming Languages: You can write custom scripts in programming languages such as Python or R to perform manual data filtering. These languages have robust functions and libraries for manipulating data.‍
• No-Code Data Filtering Software: These tools offer a GUI, allowing you to filter data without having to write code. Designed to be user-friendly, such tools are also accessible to those with minimal programming experience. You can use regular expressions to write custom filter expressions.

Types of Data Filtering

There are many different data filtering techniques to help you quickly access the required data. Now that you know the answer to ‘Why is it important to filter data,’ let’s look into the common techniques to do this:

Basic Filtering Techniques

Simple techniques like range or set membership comprise basic filtering. A good example is a database of temperature recordings throughout a year; you can use a range filter to select all records for temperatures between 20^oC and 30^oC. On the other hand, a set membership filter allows you to select records for certain months, like June, July, and August.

Criteria-Based Filtering

Criteria-based filtering involves more advanced filtering on the basis of several criteria. An instance is an e-commerce company that might filter customer data to target a marketing campaign. Multiple criteria can be considered to do this. It could be customers in the 25-35 age range who’ve purchased over $100 in the last month and have a history of buying electronic products.

Time Range-Based Filtering

Temporal filters involve selecting data within a particular time frame. Time range filters can be used by financial analysts. It can help analyze stock market trends by filtering transaction data to include the ones that occurred in the last quarter. Such information can help focus on recent market behaviors and predict future trends.

Text Filtering

Techniques such as pattern matching that are used for filtering textual data comprise text filtering. Social media platforms may use text filtering. An example is to filter posts that include specific keywords or phrases to monitor content related to a specific topic or event. With pattern matching, it is possible to filter all posts with the hashtag #Entrepreneurship.

Numeric Filtering

The numeric filtering process involves techniques for filtering numeric data based on value thresholds. This can be beneficial to filter patients with high blood pressure within a healthcare database. The numeric filter can be set to include all records with systolic pressure greater than 140 mmHg and diastolic pressure greater than 90 mmHg.

Custom Filtering

Custom filtering involves user-defined filters for specialized needs. An example is a biologist studying a species’ population growth. The custom filter can include data points matching a complex set of conditions, such as habitat types, specific genetic markets, and observed behaviors. This can help with studies about the factors that influence population changes.

How Does Airbyte’s Data Filtration Method Save Time?

Having to manually filter voluminous data can be extremely time-consuming and require considerable resources. Airbyte, an effective data movement platform, can also help with data filtering.

With Airbyte, you can integrate data from multiple sources into a destination of your choice. It offers 550+ connectors that you can use to connect to a range of data sources and destinations. For custom transformations, you can create and run dbt transformations on your data immediately after syncs in Airbyte Cloud. By writing modular SQL code, you can modify and structure the loaded data based on your particular needs. This allows you to perform various data modifications like data cleaning, filtering, or aggregating.

Here are the different options Airbyte provides to facilitate data filtration:

• Connector Configuration: When you configure certain connectors, you will have the option to filter records based on specific conditions. These filters could be date ranges, particular column values, or other such conditions. This allows you to filter data before ingesting it into the data pipeline.‍
• Incremental Sync: If you have data sources that are continuously updating, you can use Airbyte’s incremental sync feature. It helps you fetch only the data that is modified after a particular point. This ensures that there is no data duplication and that only the necessary information is considered.‍
• PyAirbyte: Airbyte’s open-source library, PyAirbyte, allows you to use pre-built connectors in Python. With PyAirbyte, you can extract data from varied sources and load it to a variety of SQL caches. Then, you can use Python to perform transformations, including filtering, on your dataset.

Let’s look into the steps involved in the integration process with the details of the platform’s data filtration options.

Step 1: Data Ingestion

The following steps will help you perform data extraction

• Log in to your Airbyte account and select Sources from the left-side navigation pane.
• Click the + New source button and then search for the source connector of your choice.
• If the connector of your choice is not available, you can use Airbyte’s no-code Connector Builder or low-code or language-specific CDK. This is particularly useful if you want to perform data scraping from a unique source.
• When you are redirected to the connector configuration page, specify the necessary fields.
• Click the Set up source button.

Step 2: Data Loading

• To select a data destination, click Destinations on the left-side pane.
• Click the + New destination button and then search for the destination connector of your choice.
• Configure the connector by providing the necessary details and then click on Set up destination.

Step 3: Establishing a Connection

• Following the configuration of the source and destination connectors, click on the Connections option on the left-side pane.
• Click the + New connection button.
• You can select an existing source and destination.
• On the Schema tab of the connection, you can choose which streams you want to sync.

• You can stream each sync using a specific sync mode, such as Incremental | Append + Deduped or Incremental | Append.

• By default, Airbyte syncs all detected fields from the source. However, you can deselect a partial set of fields or columns from being synced to the destination.

• To select only a partial set of fields, toggle a stream to display the fields. Then, toggle individual fields to include or exclude them from the sync.
• Click Save changes > Save connection.

These steps allow you to transfer data streams from multiple sources to create a unified, filtered dataset in the destination for analysis.

Best Practices for Data Filtering

You can ensure effective and efficient data filtering by following these best practices:

• Defining Clear Objectives: It is essential to have clear goals for what you intend to achieve with data filtering. Ensure you’re certain about the insights you’re trying to obtain, data that is relevant to your analysis, and how you’ll use the filtered data.‍
• Understanding Data Structure and Format: You must thoroughly understand the data’s structure and format. This involves whether the data is structured, semi-structured, or unstructured, the data types of the columns, and relationships between data points that must be preserved. With this understanding, you can apply the most appropriate filters and prevent issues such as misinterpretation or data loss.‍
• Utilizing Multiple Filters for Complex Analysis: For complex analysis, use a combination of filters instead of a single filter. This involves using text filters with numeric filters for data segmentation and a range filter followed by a categorical filter to narrow your dataset. Multiple filters will give you a refined view of your data and reveal better insights.‍
• Validating Results and Adjusting Filters: You must regularly validate filtering results for accuracy. To do this, check if the filtered data is appropriate for your goals and that the results meet your objectives. Also, look out for unexpected results or anomalies needing investigation. For unsatisfactory results, you can adjust the filters and re-validate.

Challenges in Data Filtering

While there are numerous benefits of data filtering, there are certain challenges involved:

• Performance Issues Involving Large Datasets: Without proper optimization, filtering large datasets can cause performance issues and longer run times. To address these challenges, you can employ caching mechanisms, efficient indexing, and query optimization.‍
• Complex Filtering Requirements: Real-world data filtering is often complex and involves several conditions, logical operators, and nested criteria. Complex filters can easily malfunction, especially due to human error. To mitigate disruptions, intuitive tools and interfaces can help define and manage complex filters.‍
• Data Consistency and Integrity: When you filter inconsistent or outdated data, it can lead to inaccurate results that don’t reflect the dataset’s current state. To maintain data consistency, it’s essential to implement proper transaction management, concurrency control mechanisms, and isolation levels.

Conclusion

Data filtering is an essential process to help isolate certain information from your dataset based on some criteria. This facilitates efficient analysis and decision-making with a more relevant dataset.

With data filtering, you can remove irrelevant data, modify values, evaluate your dataset, and create new structures from old datasets. Some of the benefits of data filtering include enhanced decision-making, improved performance and efficiency, and reduced redundancy of data. To perform data filtering, you can either use custom scripts or no-code data filtering software.