The importance of data quality across diverse industries is increasing day by day. Most enterprises rely on data for their business growth, and there arises the need to keep that data error-free. Achieving maximum efficiency depends upon reducing data errors and inconsistencies. Data quality is of utmost importance if your business aims to optimize the working and increase profits by using data. Inaccurate and old data impacts your business outcomes. Want to create a culture around quality data decision-making? You should take a vital data cleaning step, also referred to as data cleansing or data scrubbing.

What is Data Cleansing or Data Cleaning?

Determining and removing inaccurate, incomplete, corrupted, or unreasonable information within a dataset is known as data Cleaning. It can be elaborated as eliminating and perceiving the mistakes available in data to expand its worth. Better data helps in beating fancier algorithms.

Combining multiple sources can give rise to duplicate or mislabeled information. Incorrect data is unreliable. Proper data cleaning can make or break your project. The process of data cleaning may vary from dataset to dataset. However, it is crucial to have a template established to understand whether you are doing it the right way.

Why is data cleaning so important these days?

Data cleaning, also known as data scrubbing, identifies issues related to your data within your dataset. Insufficient information needs to be cleaned thoroughly to wipe out the inconsistencies businesses face. The primary reason behind increasing data inconsistency is the new norm of multichannel data. Before starting your analysis, make sure you complete this data cleansing process. It can offer incorrect or misleading insights which might harm your business decisions.

Research shows inaccurate data can be costly for your businesses. It may cost you millions, and it will be a massive wastage of time and energy for your business. Hope you have heard of a phrase: “Garbage in, garbage out.”

A phrase that is suitable in the case of insufficient data, when kept harmful or unclean, will give bad results and vice-versa.

What are the characteristics of quality data?

  1. Accurate
  2. Complete
  3. Consistent
  4. Uniform

Different types of data issues

Different types of issues can occur when businesses receive data from other clients, scrape from the web or combine datasets from multiple places. Some example issues are

  1. Duplicate data
  2. Conflicting Data
  3. Incomplete Data
  4. Invalid Data

Important data cleaning techniques

  1. You will have to follow strict guidelines to improve data quality, including mandatory field filling. A proper information input mechanism needs to be followed.
  2. A unique reference number or URN and data consistency also play a vital role in accurately managing information. URN helps in tracking changes and helps significantly in data cleaning.
  3. Cleaning is the best practice if done in small volumes. Ample downtime will result in breakage and heavy loss of information.
  4. Capturing all the core fields should be done separately.
  5. Careful selection of data cleaning tools and techniques is mandatory.
  6. There are different new and better tools to manage big data and all the complexities accompanied.

Businesses can also outsource their data cleaning tasks to a reliable data cleaning company considering the security factor.

What are the steps involved in the Data Cleaning Process?

Data cleansing is a tedious task, but when done in a proper way can help your businesses grow to a greater extent. Here are a few data cleaning steps that can help you move forward with the process.

  • Step 1# Remove irrelevant or duplicate observations.

    Data collection usually gives rise to duplicate observations. Unwanted observations, including duplicate and irrelevant comments, should be removed. Statements that do not fit into the specific problem when analyzed are irrelevant. Make your analysis more efficient to minimize distraction from your primary target and create a performing dataset. Deduplication is one of the significant areas to be considered in the data cleaning process.

  • Step 2# Care for your missing information

    Many of the algorithms do not accept missing values. It would help if you dealt with missing data properly. Dropping observations with missing values will result in losing information. Better input missing values based on other comments. But there is a chance of losing data integrity because you are operating on assumptions. So decide how to resolve this missing data issue and move forward.

    An appropriate way to handle your missing data for categorical features is to label them as “missing.” In case of missing numeric data, flag and fill the values.

  • Step 3# Fixing structural errors

    There are chances of getting strange naming conventions, incorrect capitalization, and massive typos when transferring or measuring data. These are the inconsistencies that can cause mislabeled categories or classes. These are the structural issues that need to be fixed in the data cleaning process.

  • Step 4# Unwanted outliers need to be filtered.

    There might be one-off observations that do not appear to fit within the data. Removing an outlier will help enhance the performance of the statistics you are working with. If an outlier proves to be irrelevant for analysis, consider removing it to improve your model’s performance.

  • Step 5# QA

    After the completion of the data cleaning process, you should be able to answer the following questions

    1. Does your data make sense now?
    2. Does the information follow the appropriate rules for its field?
    3. Does it prove or disprove your working theory?
    4. Were you able to find trends in the data to form your next idea?

    What are the best practices in data cleaning?

    1. Rather than thinking about who will be doing the analysis, start thinking about who will be using the derived results from the study.
    2. Ensure control of your database input.
    3. Avoid software solutions that cannot highlight and resolve faulty data issues.

    Do not forget to limit your sample size to large datasets, as it can help you in performance acceleration.

What are the benefits of the data cleaning process for business enterprises?

  1. Develops Customer Acquisition activities

    Accurate information can significantly increase customer acquisition efforts. Clean, precise, and up-to-date data can aid in a smooth marketing process. It can also ensure better returns on different email/ marketing campaigns. With data cleaning, multichannel customer data can be seamlessly handled.

  2. Better Decision Making

    Making the right decisions for your business depends upon the accurate customer information available. Unfortunately, when data doubles up, errors also creep in. Data cleaning can be effectively used to get rid of these issues. Up-to-date data can help businesses to benefit from business intelligence and smooth analytics. Proper data cleaning can bring massive success to your business enterprise.

  3. Helps in growing your business revenue

    Accurate information available within your dataset can help bring drastic improvements in revenue growth. In addition, the data cleaning process will help you keep your records updated, which will reduce your emails’ bounce rate. As a result, you can reach out to the maximum of your customers quickly during promotions of your product and services.

  4. Streamline your business effectively

    Eradication of duplicate data from your database can help reduce costs and streamline your business practices effectively. Up-to-date data related to your sales activities can help accelerate your product’s performance in the market. The process of data cleaning with accurate analytics can help you decide the right time to launch your new development in the market.

  5. Increased productivity

    Best utilization of your employees’ work can also be based on the clean data available within your dataset. They can save their time by contacting the right customer at the right time if the data record is up-to-date. Data cleaning is one of the perfect processes helping in minimizing the risk of fraud.

Why is outsourcing the best option?

Data cleanup is a time-consuming, labor-intensive process that needs the perfect use of tools and technology. The expertise required to maintain the integrity of your data is best available with data cleansing companies. Outsourcing your data cleaning requirements to a third-party service provider, your business can enjoy more benefits from the process.

Outsourcing will help you save time on data management activities like finding, organizing, and cleaning data. Outsource data cleansing services to clean up your data mess all the time, giving you opportunities to grow your business exponentially.

Businesses will direct their energy towards entries with better outcomes and utilize their resources better to pursue quality leads. In addition, enjoy the scalability & flexibility to keep up with seasonal fluctuations and market dynamics.

Outsourcing helps you in getting ahead of your competitors. Increase the ratio of getting the right party contact during sales and marketing activities by arming your team with up-to-date data sets, and ultimately lead your business to increased sales and revenues. At PGBS, our experts can help companies to remove their data duplication, do their data audits, and help them in enriching it with a high level of accuracy and organization techniques.

Conclusion

There is an alarming increase in digitization, and data plays a crucial role in businesses. A unique aspect of this digital era is the easy accessibility of the data online through social media platforms, websites, search engines, etc. However, the irrelevancy of the available data is the most critical challenge businesses face these days. Hence to leverage this readily available data, we have to take time and clean it accordingly. Therefore, data cleansing is one of the vital steps for every business to achieve its success objective from the data analysis process.