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Most businesses today want to be data-driven, but data quality is an underlying issue that prevents them from achieving this goal. Companies that want to be data-driven require data cleaning solutions to guarantee that their transformation efforts are not hampered by raw, unclean, or poor data.

The health of your company's data is referred to as data quality. Do you have data that is prone to errors like:

• Inaccurate information
• Invalid and incomplete information
• Typos, character errors, punctuation issues
• Duplicate data that affects data quality
• Incorrect formatting and messy data (upper/lower case, inconsistencies etc)

If you answered yes to all of them, you have a data quality problem.

And it is for this reason that Data Cleaning must be implemented.

In this detailed guide, we’ll cover:

• What is Data Cleaning
• How Does Data Cleaning Help Businesses
• Characteristics of High-Quality Data
• Available Solutions & Best Practices

Let’s get started!

What is Data Cleaning?

Data cleansing, often known as data scrubbing, is a method of preparing data for use. It assists with data transformation as well as "cleaning" duplicate data. Broadly referred to as data cleaning, the process involves:

• Deduplicating data and removing redundancies
• Fixing incomplete or invalid data
• Formatting and standardizing data
• Transforming messy data into usable data

Your data sources will be readied for their intended usage – free of harmful errors and untidy blunders – with efficient and frequent data cleaning.

How Does Data Cleaning Help Businesses?

Cleaning data isn't simply an IT issue. Departments gather data from a variety of linked apps and activity logs throughout the business. Data is required by each of these divisions for analysis, statistical report generation, and strategic business choices.

Here’s how data cleaning can help different departments of your organization:

Data Compliance:

Organizations must ensure that they follow data laws and are data compliant in an era when governments across the globe are regulating data gathering.

Bringing Diverse Data Sources Together:

Multiple data sources may exist in an organization, each gathering and storing various types of information on an object. These data sources have a high likelihood of storing duplicate data in various forms and styles.

Customer Service:

Incorrect, partial, or incorrect address data prevents customer support from responding. A misdirected email. An email with the customer's name misspelled. Bad data may impact customer service in many ways. Clean data ensures you have the most up-to-date contact information.

Operational Efficiency:

When businesses can detect mistakes in their data and clean it of duplicates, typos, and messy errors, they may build procedures that improve operational efficiency and boost ROI.

Marketing:

Marketing is the only department in a company charged with managing high-quality data. Consumer data is at the center of email marketing, social media campaigns, advertising, and more. Incorrect data may have catastrophic effects. It's fairly unusual for businesses to mail to the incorrect demographic.

Sales:

Customer data is vital for both marketing and sales. In reality, sales data is critical for determining ROI, revenue, and profitability. Most sales departments use data cleaning technologies to deduplicate sales records. Duplicate sales records may distort ROI statistics and negatively impact the company.

These are only a few instances of poor data implications. Bad data is firmly embedded in business processes, and managers and leaders must work hard to overcome it.

An company may avoid all of these issues and get the advantages of clean data by prioritizing data cleansing.

What Makes High-Quality or Clean Data?

While clean data is essential, how can we determine data quality? A few “standards” are commonly used in the business to assess data quality. The goal of data cleansing is to meet these criteria, which can be defined as any data that is:

Valid:

Sources of data are subject to specific rules. For example, all addresses must have ZIP codes, and all phone numbers must have country + city codes. Invalid data fields are those that do not meet these standards. For example, incomplete ZIP codes are invalid. Validity rules are defined by business rules or constraints for example:

• Important columns such as Last Name, Email Addresses must not be empty
• Data input must follow defined formats
• A field or fields must be unique in a dataset

A major component of data cleaning is making sure that any incorrect data is identified and corrected before it's used again.

Accurate:

The grade of accuracy is affected by the typos, spelling errors, character mistakes etc. A Matt name is not regarded to be accurate data, instead of Matthew.

Complete:

This is characterized by the accuracy of a data set instead of being left blank. Is all phone number fields full, for instance? Are all single fields of identification complete?

Consistent:

Analyzing data requires consistency. In phone numbers, some country codes are expressed with +, others with 00. Data consistency implies using the same technique for all records.

Timely:

How frequently do you wipe your data? In most cases, businesses just ignore their data after it has been acquired or utilized. Most merely sanitize data for a report or analysis and put it on hold as fresh data piles up. If old data isn't sorted or updated frequently, it becomes a bottleneck and creates duplicates.

It's a good idea to utilize these criteria as data quality assessment benchmarks when establishing a data cleaning strategy.

How Can Companies Achieve Data Quality?

When a failed project, a faulty report, or a huge marketing mistake becomes apparent, the data may not be accurate. When hype becomes a factor, short-term solutions are favored over long-term approaches. Leave this well-oiled data-cleaning machine in capable hands.

Best approaches to keep your data clean are:

1. Create a data quality management plan

   Before to obtaining executive buy-in, prior to purchasing a tool, devise a strategy. By identifying the source of your data's problems, you may fix the issue, thereby resolving the issue. In order to have a successful data quality strategy, you must decide whether there are new roles, new software solutions, or any new standards that must be adopted.

2. Search for the right data cleaning tools:

   A number of data cleaning solutions are available in the market, but they are costly and provide a full solution. You should ideally be able to match, dedupe, clean, and combine data using your data management solution.

3. Fix the source of data errors:

   Because raw data is inherently flawed, you must correct mistakes in your database. It may be a human or machine mistake, or a data collecting technique fault. Fix data at the source to avoid further stress. This is where a data quality tool may help you prevent bad data from entering the system.

In addition, here are some questions you may ask your team about your company's data while putting up the strategy.

• Is the data in good shape?
• What are the most frequent data-related issues?
• What are some of the most difficult issues that teams face when attempting to utilize data?
• What processes or checks are in place to deal with data quality issues?
• What sort of data cleaning or maintenance procedures are in place?
• Is this data trustworthy enough to provide accurate information?
• Is the data completing the job for which it was created?
• What are the best practices for implementing and maintaining data quality standards throughout an organization?
• Is data interfering with any of your key processes?
• What steps can the company take to establish a single source of truth?

If your responses to the above questions suggest that your data has a significant defect, you'll need to clean it up in order to improve operational efficiency.

Best Practices

In the world of data, the cliché, “prevention is better than cure,” applies. It's critical for businesses to have the proper parameters in place when they enter the realm of big data and data lakes.

Here are some recommended best practices:

1. Emphasis on Data Input:

   How clever of you to fill up the online form for a business email, instead than using a random Gmail account! This is an example of data input control on the front end. While it won't guarantee 100 percent accuracy (many individuals send phony emails), it will greatly assist you in separating important data from irrelevant info. To reduce the gathering of bad data, use such front-end, customer-facing measures.

2. Clean Data Before Reporting:

   Do not take out a report from a database only to appease your employer. Keep your data current or clean it up before using it for a campaign, report, or analysis. You don't want to have to redo a report because you didn't deal with duplicates.

3. Use Real-Time Data Cleaning Tools:

   Deploy data cleaning technologies that detect mistakes in the data intake process to prevent bad data from entering your database.

4. Try to Centralize Data Sources:

   Disparate data sources create the majority of data issues. There are so many different apps utilized by so many different departments, all of which dump their data into the database. This will not only assist you in maintaining data integrity, but it will also provide you with access to a single source of truth.

Clean data is essential in today's digital and data-driven world. If you really want to be data-driven, you must first ensure that your data is of sufficient quality to be utilized for insight. Data that is bad, filthy, and untidy will bring you down.

Using a Self-Service Data Cleansing Tool

Avoid a knee-jerk response now that you know you have poor data. Don't rush to recruit costly developers or drag your IT resources to create in-house applications. A data cleaning program that works quickly and satisfies quality data requirements takes years to develop.

In-house solutions may cost over $250K annually! Here's how an automated data cleaning solution can perform the job 10 times cheaper.

Despite its importance, data cleansing is a tedious job. Your specialists will waste hours of valuable time creating algorithms that will either work or not. Trials, exams, incorrect findings, and rising talent management expenses will be further issues. That's why it's preferable to utilize an automatic data cleaning solution that doesn't need human intervention.

A powerful data cleaning tool can help you to:

• Cleaning routines for all of your data sources may be automated.
• Remove typos, errors, case and character problems, and more from your data.
• Remove duplicates from your data lists by matching them up.
• Integrate several data sources to sanitize data in real time.
• Ensure data consistency and standardization across all data sources.
• Validate address and contact information.

Our approaches:

If you’d like to know more about in2in global automated data cleansing solution that can clean your data and make it usable for its intended purposes, please get in touch with us!