The Power of Clean Data: How to Optimize Your No-code Database App

Data and analytics are playing an ever-increasing role in business decision-making. The power of those data-driven insights, however, depends on real-time access to clean data – ensuring that data is accurate, complete, consistent, and properly organized and formatted. The importance of data cleaning can’t be underestimated. And with large volumes of data coming from a variety of sources, effective data management and maintaining data quality takes focused effort and consistency in data cleaning processes.

Gartner reports that poor data quality costs organizations an average of $15 million each year. And it’s not just the immediate financial hit, a lack of clean data has longer-term effects. It compromises key digital initiatives, can undermine customer trust and the lack of meaningful data-driven insights leads to competitive vulnerabilities.

What is Data Cleaning?

In most organizations, data collection is done through a combination of paper-based forms, spreadsheets, and, in some cases, a direct connection that transfers data from departmental systems like payroll, purchasing, and order information. 

The variety of data types and input sources, especially manual data entry from paper forms or spreadsheets, leaves data vulnerable to errors because these tools are static and lack automation and validation capabilities. They rely on inconsistent workflows involving email circulation (or manual hand-off), which creates version control problems and out-of-date or incomplete datasets. Paper and PDF forms can also easily be lost or misfiled, and they increase the likelihood of introducing errors when the data is manually input.

Data cleaning, which is also referred to as data cleansing or data scrubbing, is the process of identifying and fixing incorrect data, incomplete data, typos and other structural errors and inconsistencies, missing values, and corrupted data within a dataset. This can include removing duplicate data records, correcting errors in values, completing missing values, converting data into a standardized format, and removing irrelevant data.

Standardizing an ongoing data cleansing process to remove bad data and inaccurate data is a critical part of data management and delivers important benefits that support strong, healthy business growth.

Why is it Important to Clean Your Data?

Errors in your data compound over time as your database grows. When dirty data is allowed to live in your data, it skews data analysis and reporting. Poor data quality leads to missed opportunities because you don’t get the accurate and meaningful business intelligence insights needed to close more business deals and drive immediate revenue, improve your customer experience, or streamline operational workflows to gain efficiencies.

By having clean data, businesses can ensure that their algorithms and machine learning models are using accurate, up-to-date information to gain big data insights that can be used to make better decisions. 

Specific benefits of data cleansing include:

Greater Data Accuracy: Cleaning your data ensures the information stored in your database app is accurate and up to date, giving real-time insights for data-driven decision-making.

Improved Data Consistency: Data cleansing keeps data consistent and formatted in a standardized way, making it easier to work with and analyze.

Better Data Integration: Your database app likely incorporates a variety of data types and sources used for insight and analysis. Clean data helps ensure that different data streams can be unified based on standardized formats that are easier to map and correlate to create a single source of truth.

Increased Data Quality: Cleaning data improves the overall quality of the data in your database app so when you depend on reporting and analytics to inform your business operations and decision-making, you’re working with accurate, real-time information.

Faster Database Performance: Dirty or inconsistent data can slow database performance and response to queries. Cleaning the data makes it faster and more efficient.

Enables Data Privacy and Data Security: Cleaning data can also help to protect sensitive information and ensure that the data is stored securely. This is particularly important in the context of privacy regulations like GDPR, HIPAA, credit card, or other personal information that must meet specific compliance standards in its handling and management.

How to Cleanse Data

Scrubbing data is an essential step in database maintenance. Here are some step-by-step guidelines for data cleaning:

1. Identify the data source: Determining where the data is coming from and what format it should be in, will help identify any issues that exist and spot the areas where you need to take corrective action.
2. Audit the data quality: Check the quality of data against the source and format guidelines you’ve established. Identify any inaccuracies, inconsistencies, or missing data values.
3. Perform data correction: Remove any duplicate records, correct format inconsistencies, and correct or complete missing values.
4. Establish data validation rules: Enlist the capabilities of your database app to define standardized formats for data types like date, time, phone numbers and other information and then set up validation rules to ensure that future data entries comply with the rules, automatically improving data integrity moving forward.
5. Document the cleaning process: Data cleansing is an ongoing need. Documenting the cleaning process, and the changes made during the cleaning, ensures you’ve got a clear record of your audit and actions and establishes the workflow for future maintenance.

In today’s rapidly-changing business landscape, leveraging the power of data-driven insights is key to long-term success – and maintaining clean data is a necessity. It’s important to periodically review data quality and data cleansing practices to keep your database accurate and relevant to your business operations and data analysis needs.

The review should also include an assessment of tools and workflows to optimize for success from the start. As data management apps and processes are developed, insist on capabilities that will eliminate the dirty data problem from the start:

1. A single-source-of-truth repository: users need one app that provides anytime, anywhere access where they can add, manage and report on the data. 
2. Automated data rules: a database built with data validation rules to ensure only the right type and format of data can be entered in specific fields and records.
3. Audit logs: a historical trail of changes made to the data so changes can be tracked and reversed, if necessary.
4. Data normalization: a robust database structure that can create connections and relationships between datasets, remove redundancies and standardize formats for consistencies.
5. Data privacy: ensure secure, role-based access for users, whether under strict compliance regulations like HIPAA and GDPR, storing general personally identifiable information (PII) or curating views to improve the user experience.

Identifying and eliminating dirty data makes it easier to see the data analytics trends, patterns, and relationships that may otherwise stay hidden. This more informed decision-making contributes to growth, customer satisfaction, and competitive advantage.

Learn more about how no-code database apps can help you manage and maintain clean data.