Data holds the power to make or break your business at present. It drives major operations, from product designing and marketing to sales. Contrary to the growth it can give, corrupted data will lead to a waste of time and investment.
However, engaging data cleaning services is a smart way to protect your firm from this issue. Clean data will work as an efficient tool to compete in the current marketing system and support your company’s growth and strategy. Let’s discuss how you can create an ideal data cleaning process and how it operates.
Why Do You Need Clean Data?
Firstly, it is crucial for you to understand the importance of a clean database. Data Cleaning is the process of refining raw data, its structure, and the information it carries. It can be through removing duplicates, rectifying manual errors, and modifying or adding information to missing data.
It is important to cleanse data before it gets utilized for further procedures since any error will render the data useless. That is why it is also called data pre-processing. Data cleansing service professionals support its importance with the ‘1-10-100’ principle. According to them, if data enrichment services cost you 1$ to safeguard against corrupted data, $10 will be the capital spent on correcting bad data. If untidy data gets into your system, it will lead you to spend $100 to repair the damages.
Data provides crucial analytical support. It is the basis of machine learning algorithms that help automate various business processes. When untidy data is input into your system without scrutiny, errors will also affect the output. If you are using bad data to analyze each market to formulate your organization’s marketing strategy, it will lead to misleading interpretations.
Advanced businesses rely on data for product designing, marketing, sales, customer interaction, and many more operations. Clean data improves the results for these departments.
7 Steps To Data Cleaning
Depending on a business’s objectives and resources, the data cleaning process may vary from one another. However, there are a few basic steps that every organization needs to follow.
➔ Monitor Errors
Firstly, it is essential to keep a tab on the data sources that are responsible for feeding errant data into your database. You should also monitor the kind of errors that are commonly found in your data entry, along with their location. This step will help you identify and fix the issues easily and regularly. This task can also be outsourced to data entry services as their professional experience will assist you in better managing it.
Filter your data off the irrelevant information that is unnecessary for your daily operational requirements. For instance, if you require data on men in the 25 to 50 age group, data on women and other age groups can be omitted.
Common errors in this stage of data management can be spelling mistakes, duplicate entries, and missing contacts like email or incomplete addresses. This step in the data cleansing process makes the analysis more efficient and minimizes distraction from business goals.
➔ Rectify Data Structure
The next step will be setting the data set into the correct structural format. These errors include wrong pairing, like the contact information getting paired with occupation. It also includes spelling mistakes, wrong abbreviations, improper capitalization, incorrect word usage, etc.
Such errors result in mislabeled categories in the further data processing. The machine learning programs are incapable of understanding the similarity or differences in such cases, which leads to errors in analysis.
➔ Validate Data
Verifying each data value is another crucial step of data enrichment. It involves not only authenticating the stored information but also making sure that it is of standard quality, properly formatted, and consistent.
To validate data, you should analyze its quantity as per your requirement, check whether its format is aligned according to your tools, and also monitor that it supports the organization’s theoretical vision.
Businesses utilize artificial intelligence tools and machine learning models to scrutinize data accuracy. Also, there are techniques that can help automate the process for better efficiency.
➔ Remove Outliers
Outliers are data points that affect your average analysis by being far from normal data points. They affect the mean calculations during analysis and impart false results. Data cleansing services remove these outliers to achieve better results in operations through other data sets.
At the same time, dealing with outliers is tricky, as being just away from normal data points doesn’t provide a legitimate reason to remove them. Sometimes these values are equally important. Only experienced data enrichment services will know their requirement against the needed analysis for your business.
➔ Manage Missing Data
The management of missing data while data cleansing is another complex task. Missing values are sure to affect the algorithm if they are needed. Do not just jump on to omit the data set with missing values, it can have adverse effects as you may also lose useful information in the process.
Data cleansing services are experienced in such scenarios. They can assist you in analyzing whether the data set can be removed, or values can be added, or it can be managed by altering the data structure.
➔ Data Deduplication
It is often found that using data from multiple sources renders duplicate values. Scraping data also sometimes leads to duplicate values. These values not just waste the time in analysis, but also conclude to wrong results. They also waste your storage. Data cleansing services use AI tools to easily remove duplicates.
The machine learning models may give weightage to duplicate data due to their frequency in the set. They affect marketing operations too by repetitively including values, like contact information, resulting in annoying your client in business approach.
➔ Quality Analysis,
First of all, you need to have data quality standards in place for this analysis. Is your data meaningful, is it appropriate for operational usage, does it support your business operations, does it help in creating strategies, are some questions that will help you set up the standards.
Dirty data leads to incorrect conclusions and faulty operations. It can be a cause of embarrassment for your organization. So, it would be better to act before you have to face this issue, creating a culture of using quality data will reflect a confident management.
Features of Enriched Data
An efficient database has certain specific characteristics. Scaling your data on these basis will help you analyze data better:
- Validity : Data cleansing renders verified data that can be utilized in further processing.
- Accuracy : Enriched data has precise authentic information.
- Completeness : Data cleansing leads to filling-in the missing values that provide wholesome data sets.
- Consistency: A constant monitoring is important for data sets as they may lose validity or effectiveness with time. Data cleansing imparts this consistency.
- Uniformity : Structure is important for any data set to be integrated in an organizational operation. Data cleansing provides a uniformity of structure.
To Sum It Up
When conducting data cleaning for your organization, the process and consistency are two key drivers for efficient results. It is important that you set goal-oriented standards to successfully integrate data analytics into your operations. Data enrichment services can help you achieve optimum results in this management. Outsourcing data entry services will also help you in better scrutiny of the sources.
Jessica Campbell is working as a content strategist at Data-Entry-India.com, a leading data solutions & eCommerce management company. With an overall experience of 5+ years, she is fond of writing about various data transformative solutions like data standardization, data enrichment, data annotation, video annotation, etc.
Moreover, she has published more than 2000 articles & informative writeups about eCommerce & Amazon marketplace solutions covering Amazon listing optimization, Amazon PPC management services, Amazon SEO & marketing, Amazon store setup, and Amazon product data entry. Her well-researched and valuable writeups have helped thousands of businesses to uncover rich insights, strengthen their business processes, and stay afloat.