How Data Cleansing Helps Your Business

Excess of everything is harmful, and it stands true for data as well. While organizations are finding meaningful ways to leverage mountains of data; bad or poor-quality data is creating equal and hindrances. Staying organized or keeping your data healthy and running is critical to business. Duplicate, outdated, inaccurate, incomplete or inconsistent data can have grave impact on your efforts to grow profitably.

• 69% companies claim that their bad data hinders them providing an enriched customer experience.
• About 29% organizations believe that their data in accurate and the biggest culprit in this case is manual errors.
• 89% of businesses struggle with managing data quality.

Why your company should cleanse dirty data?

Data is the new oil, but we are living in an era of data explosion or data disaggregation. And data accuracy is the life line for your business. For example, Ola or Uber are required to optimize their fleet program by charting out routes for drivers. Their routing algorithm should be accurate with customer details. But if their customer details are not proper, drivers won’t be able to reach the location in stipulated times and it will result in late pickup and deliveries; along with unnecessary efforts, costs and time waste. Quality data always answers the question “Where are your customers”?

Garbage data in, garbage insights out has become a reality for businesses of your stature. How well you clean your data has a higher impact on the quality of the results your business achieves. The discipline of data cleansing, also known as data cleansing best practices, ensures that your customer information is ‘fit for businesses’; in the context of streamlining business operations, analytics and keep pace with evolving market scenarios.

How Data Cleansing Helps Your Business?

Every business depends largely on trustworthy data. Quality or cleansed data is the lifeblood of your business that helps to deliver an engaging customer experience, gain competitive advantage and grow. Let’s see how if happens:

• Good data leads to better experiences: If you wish to send birthday greetings to your customers- it must be on the right day, at the right time and to the right person- with the right message. Good data helps your teams to align this entire cycle — precisely. When your efforts are aligned with your buyer’s journey, your customers feel valued and happy.
• Good data leads to greater efficiencies: With great data comes greater data cleansing responsibilities! Clean data helps to make workflows more efficient, make processes effective and workforce more productive — which spends less time on fixing data errors and concentrates more on revenue-building activities.
• Good data leads to improved bottom line: Cleansed or quality data leads to increased profits. With accurate data, you’re employees do not waste time on sending multiple contact points to your customers. Ultimately; you save costs involved in the activity as duplicate contacts were identified well in advance.
• Good data leads to lasting trust: Quality data helps to cultivate trust of existing customers, consumers and your employees. No one wants to be spelled wrong, receive misleading emails or communication at wrong addresses. Plus, data can destroy internal trust of employees’ one wrong record at a time.

What are data cleansing best practices to gain valuable business insights?

First step is usually the most important and hard to take. To meet your expectations on how quality data can help, the first decision you need to make is to adhere to data cleansing best practices.

If you are convinced to clean up your data and keep it clean- here are the trusted ways to get started:

• Monitor errors: Understand and keep a record of how most errors occur or creep in your database. If you are integrating other solutions with your customer database, such errors shouldn’t clog the functioning of other departments.
• Standardize processes: Avoid the risk of duplication, inconsistency and ensure a valid point of data entry data with standardized processes.
• Maintain data accuracy: Validating, standardizing and maintaining accuracy of your data once the existing database has been cleansed is vital. There are third party experts and automated data cleansing tools that use RPA — robotic process automation backed with Artificial Intelligence — AI and Machine Learning- ML algorithms for better accuracy of your business data.
• Scrub duplicate data: Duplicate data prevents you from having essential insights, duplicate data leads to delivering unsettling customer experience. Eliminate duplicates by verifying and scrubbing your business data regularly. It will save you costs in data maintenance of your data sets.
• Analyze results: Once your data is standardized, validated, and duplicates are removed, reliable third-party data cleansing experts append it with latest information from reliable sources. With clean and compiled data, you get a better picture for business intelligence and analytics.
• Communicate clearly: Keeping clean data- clean is equally important. Communicating clearly with the team will help you to establish and standardize your data cleaning workflow. Eventually, it is all about developing stronger customer segmentation and your team needs to have the same approach.

Which are the key steps involved in data cleansing?

Proper data cleaning has the capability to make or break your project. While it is no rocket-science, a systematic approach to data cleaning plays a significant role to get desired results. Steps involved in cleansing business data or customer information typically are based on the problem and type of data. While each data cleansing method has its own trade-off, the core idea is to remove, correct or impute incorrect data.

• Remove unwanted observations: The first step includes removing duplicate or irrelevant observations. These anomalies mostly arise during data collection and data entry stages.
• Fix structural errors: Errors that arise during transfer, measurement or any other similar situation are structural errors. It may include typos, mislabels, inconsistent naming convention, or same attribute with different names.
• Manage unwanted outliers: Outliers are tricky and most like are the cause of problems with certain type of models. Removing it might or might not enhance performance. Generally, it is a trend to not remove outliers until there is a legitimate reason to do so.
• Handle missing data: Missing data is like missing pieces in a jigsaw puzzle. Missing data points denote various things and must be handled carefully as it can be an indication of something important. Once these data cleaning steps have been properly completed, you can count on a robust dataset that avoids many of the most common pitfalls and gives better results.

Automate for quality business data

Manual data cleansing can be overwhelming. It is both- time-intensive and prone to errors, so many companies are moving to automation and standardized processes. Using trusted data cleansing services is the easiest and simplest way to improve the effectiveness and consistency of your company’s strategy to ensure that all of your data is clean, no matter where it comes from.

Conclusion

Today, data cleansing is becoming a strategic driver involving various teams and a robust data cleansing program is one subset of the larger effort. With clear data comes clear direction — all your good and bad decisions rely on the quality of data which backs such decisions. Errors will not only cost money but tinge reputation if not corrected. Data cleansing is emerging to be a reliable way to make sure you can trust the data and make decisions with accuracy, precision and far-sightedness.

Originally published at https://datafloq.com.