Old latin phrase “Mens sana in corpore sano” translated as “a healthy mind in a healthy body” can be easily applied to the bond between clean data and meaningful analysis. Clean data is a valuable asset for any business. It ensures lots of successful enterprise achievements. Data cleansing saves money and decreases operational costs by eliminating bogus data and well known manual human input errors, fixing tech processes glitches, filling up missed information, updating old irrelevant records to name a few.
Clean data not only increases return rates and the ROI but enriches post campaign analysis and major takeaways.
Here are some positive impacts of data cleaning for analytics.
Wiping of Erroneous data
No data collection can ensure complete accuracy. People often supply unexisting names, Email addresses, phone numbers, etc. just to access a website or make a purchase. When a prospective client is submitting personal data it is impossible to guarantee that this data is correct. Human input is also prone to multiple errors. Eliminating of fake and incorrect records during data cleansing routine allows to improve data quality and analysis accuracy.
Removing duplicate records
The same record can appear in your dataset multiple times. The same person can log to your website from various devices, provide different Email address, use abbreviations to their names. Working with data you can reveal that William Smith and Will Smith is clearly the same customer having the same Email address but the dataset does not recognize that. Without deduplication this record will be counted twice. Duplicate records are an unwanted reality that analysts have to deal with. No one wants to see double numbers in their analysis. The analysis based on duplicates can damage marketing campaign insights and cause unpredictable damage to future marketing strategies. Records deduplication, as a part of data cleanup procedure, makes any analysis more accurate and beneficial to every team in the company, from finance to marketing and HR .
Appending missing data
Pretty often, working with data we notice that some essential fields are empty. Incomplete dataset makes it impossible to tell a true story and submit a reliable report. Data cleansing procedure can be of a great help here. It will fill up gaps using a third party providers saving you time, money and most importantly ensure the completeness of your data.
Another function of a thorough data clean up is consistent records’ update. Imagine, you continue keeping outdated job titles, not valid addresses, unsubscribed Email addresses. Then your analytical team runs a segmentation analysis and CRM team uploads the data. As a result, the wrong customer is targeted, customer service team gets complaints, response rate goes down, Email bounce rate goes up, ROI is negative and future marketing initiatives are at stake. Smart companies don’t let it happen. The data is being updated through the data cleansing regular sessions that include data update.
Clean data is the cornerstone of any analysis. Ongoing data clean up provides cleaned data that any analysis should be based on. Only accurate numbers ensure right business insights, enhance customer loyalty, increase response rates, reduce Email bounce rate, increase the ROI, save money, improve marketing strategies, enrich creativity, help maintain company good name and prosperity. Accurate numbers are not coming from nowhere. Correct numbers are often the result of ongoing data hygiene meaning consistent data cleansing.
To clean your data prior to your data analysis, contact a data cleansing firm to save time and money.