Steps for Data Cleaning and why it matters?

by contenteCommerceblog

In the world of data processing there is one saying

   “Garbage in – Garbage out”

It means your results are only as good as the data you’re using to get them. Incorrect or inconsistent data leads to false conclusions and false conclusions have bad impact on your business. This is true if you are a researcher, small business owner or a large enterprise.

If you make your decisions based on incorrect or inconsistent data, you can be sure that the business results will not be good. You may lose clients, business opportunities, time and money.

Data cleansing is referred to as data cleaning or data scrubbing. Data cleaning are steps to clean data before using data for analysis. This is accomplished by removing or modifying data that is incomplete, incorrect, irrelevant, duplicated or inaccurate. This technique minimizes the risk of wrong or inaccurate conclusions or results.

Steps for cleansing data :

The techniques used for data cleaning may vary according to the types of data your company stores.

Following are the basic steps for cleaning data :

#1. Removing duplicate or irrelevant data :

Duplicate observations will happen most often during data collection. When you combine data sets from multiple places or receive data from clients or multiple departments, there are chances of creating duplicate data. Deduplication of data has to be considered in this process.

Irrelevant data are those observations that do not fit into the specific problem you are trying to analyze. For example if you are analyzing data regarding young customers, but your data set includes older generations, then in such case you have to remove those irrelevant observations. This can make analysis more efficient.

#2. Structural errors : 

There are different types of structural errors from typos to inconsistent capitalization. This can create problems when categorizing or grouping data, so they need cleansing. For example “gender” is a categorical variable, usually of two classes, male and female, but you may encounter more than two different categories of the variable such as : *m; *male; *F; *fem. Data cleansing helps to recognize such mislabeled or inconsistently capitalized classes. Also review you data collection and data transformation process to prevent data issues.

#3. Handling missing data :

‘Missing data’ is a tricky issue. Just be clear that you cannot simply ignore missing values in your data set. Deciding whether to drop, impute or flag missing data. Using/not using the missing data affects the accuracy of your analysis.

  • Imputing : It means working out the missing value based on the other data. The pattern will be re-created that the observations have already created.
  • Dropping : Dropping observations that have missing values when analyzing statistical data. Study shows dropping is better than imputing values.
  • Flagging : Flagging means telling your ML algorithm about any missing value. Flagging is done when the data is missing continuously, rather than randomly.

#4. Filtering outliers :

Another thing you have to remember during  the process of data cleansing are outliers. Outliers are values that are totally very different. For example, you are researching your app user’s age and find entries like 72 and 2. The former might be a senior citizen who is up to date with the technology. But the latter is mostly likely an error since toddlers don’t use apps. If an outlier proves to be irrelevant for analysis or proves to be a mistake, it should be removed, in doing so you can increase the performance of the dataset.

#5. Standardization of data :

Cleansing your data includes standardizing it, to have a uniform format for each value. For example, all values of height should be in the same unit, so you may need to convert from feet to meters or vice-versa, to achieve uniformity. 

Make sure that you use a standardized unit of measurement. These include weight, distance and temperature. As for dates, choose either the USA style or the European format.

#6. Validate the data :

In the conclusion of the data cleaning process, you should be able to answer these questions:

  • Does the data make sense?
  • Is the data is appropriate with regard to its field?
  • Does your data help to develop your next theory?

False results, as a result of incorrect data, may inform poor strategy and decision making. Conversely, data cleansing can help achieve a long list of benefits which may lead to maximize profits.

Pull up :

Monitoring errors and better reporting to see where errors are coming from, Making it easier to fix incorrect or corrupt data for future applications. Clean data helps in taking effective and efficient decisions, resulting in increased productivity and revenue. Using tools for cleansing will make for more efficient business practices and quicker decision-making. Therefore cleansing data from time to time is advisable, for a good result.

Top Three Reasons to Normalize Your Data

by contentblog

Data Normalization

Most businesses focus on data cleanliness. Having accurate data helps to segment customers and analyze the data in terms of marketing in order to engage the brand further. There are a number of reasons to normalize your Data. This facilitates the entire data cleaning process and keeps the customer data clean and organized. Without data normalization one may face several types of data errors.

Data normalization is the process of restructuring the data to ‘normal’ in terms of data integrity. It is a key part of data management that can improve data cleansing, lead routine, segmentation, and other data quality processes.

Data normalization makes the data look clean, organized, easy to read and navigate through, and uniform across the entire customer database. Normalization includes standardization of specific fields in the customer database which brings uniformity.

In addition, here are the top three reasons to normalize your data.

1. Identifying Duplication of Data

Data duplication is a crucial problem that companies face and getting rid of duplicates is an important part of data management. Data duplication can hinder the overall customer experience. Customers may receive the same data more than once which is not very appealing. It not only impacts the sales and marketing aspects of the business, but also increases data storage cost. Normalization makes it easier to locate and eliminate the duplicated data.

2. Improving Lead Scoring

Lead scoring is defined as the process of assigning a value to specific leads in the CRM so that you can identify and grasp potential opportunities. Effective lead scoring is dependent on high-quality data and effective segmentation. For example, a B2B company will assign value to its specific leads based on the job titles as a variable. Moreover, proper segmentation is not possible without normalization. This will impact the values and business might lose out on the best opportunities. Data normalization enhances data quality and improves the process of lead scoring.

3. Reduce Response Times through Normalization

In B2C companies, customers expect faster response time for their queries. Having to feed in thousands of names along with their responses can often be time-consuming. In order to achieve an organized data, companies must have a perfect internal administration team and must use the data normalization tools. Data normalization ensures reduced response times and well-structured data.

There are specific tools that can identify standardization issues and assist in the data normalization process. And also these tools analyze the existing customer data to generate an assessment report. So, based on the report, multiple categories are assigned to help companies normalize and standardize their customer data. This is an ongoing process, which means that the business can track and fix the standardization issues as they arise. In addition, the number of data normalization errors can be limited, resulting in a high-quality customer database.

product data cleansing

Advantages of Product Data Cleansing for an eCommerce Store

by with No Comment eCommerceblog

The importance of offering clean and clear product data to the customers cannot be emphasized more. And if you own an eCommerce business, then this must stand out to be a golden rule for you. The quality of your product data is directly proportionate to your business sales. Inconsistent, incorrect, and poor-quality data can lead to loss of sales and business reputation.

Companies spend years to understand their customers and collect data. So, what exactly constitutes data?

Data may be customers’ chats, likes and dislikes, behavioral patterns, complaints, sales numbers, and also the future business goals. In other words, data provides the overall view of your products, services, and business processes.

An eCommerce store has to deal with a large amount of data on a daily basis. While managing such large chunks of data can be challenging; offering correct and updated product information to customers at all times is equally necessary. The ultimate motive of product data cleansing is to structure all the available data and make it useful for multiple users.

The eCommerce data cleansing is important as it impacts decision-making, marketing strategies, customer services, and also offers new updates in your products and services. Let us understand in detail why product cleansing is essential?

·  Product data cleansing allows the business to track customer behavior. Collecting accurate product data helps you to personalize the shopping experience based on customers’ purchasing habits, location, specific shopping requirements, and payment choices. These aspects directly impact your business revenues.

·  Data cleansing offers 100% accurate, correct, relevant, and uncorrupted information which can be used to gain valuable business insights. It also helps to reduce shipping errors, customer complaints, and any fraudulent practices.

·  Data cleansing also safeguards your mailing services. You can identify irrelevant recipients and spam emails. It helps you target your emails in the right manner leading to maximum leads and conversions.

·  Data cleansing minimizes errors caused unintentionally by customers; for example, errors caused while typing customer information, or wrong shopping details while browsing the site.

·  Clean data decreases the number of failed orders leading to reduced losses. Clean data enhances valid addresses and email contacts so that the material can reach the right customer.

All of the above-mentioned pointers explain the importance of product data cleansing. Here are some more advantages for an e-store to opt for data cleansing services.

·   Updated information is readily available to the customers at all times.

·   Accurate information reduces buying errors which lowers the rate of product returns. This is beneficial for the customers as well as the business.

·   Product data cleansing removes any duplicate inputs

·   Customers can locate their desired products easily

·   Product data presentation is consistent

Business data is sensitive to inaccuracies. It interferes with the productivity leading to loss of sales leads, revenue, and business reputation. Inaccurate analysis reports can be harmful for the business over the long run. Opting for product data cleansing services allows managers to use the accurate data efficiently and make the right business decisions. Investing in this service helps businesses to troubleshoot any problems caused due to data discrepancies. Ensure that your data is rectified and structured well so that your employees can focus on other crucial business functions.