How clean data is beneficial for
productivity and decision-making
Author
Christoffer Hansen
ch@hanei.se
DATA CLEANSING
How clean data is beneficial for
productivity and decision-making
Author
Christoffer Hansen
ch@hanei.se
Data cleansing is all about correcting or removing incorrect, duplicate, or incomplete data that will be used for analysis. Data cleansing is not only about deleting information to make space for new data but maximizing the data accuracy that exists. With clean data, the analysis will come out correctly and provide accurate information. Data cleansing include fixing spelling errors, standardizing data sets, empty fields, missing code, and identifying duplicate data.
Data consist of everything from the company name, customer names, e-mail, phone numbers, addresses, and everything that qualifies someone as a sales lead and how to get in touch with them.
Data is the component that propels business success – but only if the data is of good, clean quality. Using insufficient data to guide you in business initiatives is like mending a leaky pipe with duct tape; it will not last, and the decision will damage the business in the long run.
Data cleansing aims to create data sets that are the same for everyone in the organization so that everyone has access to the same, correct information that can be used for marketing and sales.
BENEFITS OF CLEAN DATA
The benefits of having clean data are several; it will increase productivity and provide quality information in decision making:
- Removes errors and inconsistencies created when several sources of data are used in one data set
- Fewer inaccuracies make for happier customers and less frustrated employees
- Ability to map the separate functions and your data’s purpose
- Utilizing tools for the data cleansing process produces efficient business practices and faster decision making
THE PROCESS
- Errors
Make sure that deviating values are accurate. Many algorithms do not accept missing values so input the missing data. Keep a record of where errors are coming from to make it easier to identify incorrect data. - Remove duplicates
Duplication happens during data collection. When data is combined from different sources, there is a chance that some data will be duplicates. A data cleansing tool can be used to help automate the process of avoiding duplicates. - Standardize
Reduce duplicates of data; it is good to have a standardized point of entry. - Validate
Faulty conclusion from insufficient data make for bad business strategy and decision making. At the end of the cleaning process, these questions should be answered to counteract faulty conclusions:• Does the data make sense?
• Does the data follow the rules for its field?
• Does it prove your theory? Or provide new insights?
• Can you find tendencies in the data to help form new theories? - Analyze
When the data is standardized, validated, and cleaned from duplicates, use a data cleansing tool that automates the data compilation and analytics process to streamline the business.
When the data is clean, it is vital to communicate how cleansing and data entry is done to ensure that the data stays clean. Clean data will help the organization strengthen and develop customer segmentation and send targeted information to the right people.
QUALITY DATA
Determine the quality of data by looking at its’ characteristics:
- Validity – Does the data match up to business rules
- Accuracy – The correctness of the data values
- Completeness – How much of the necessary values are known
- Consistency – Is the data the same across multiple data sets?
- Uniformity – Is the data specified in the same measuring unit?
IMPORTANCE OF QUALITY DATA
A UK-based Account Manager at a global manufacturing company was hired to manage and grow sales in England’s southern part. When he logged into the CRM system and searched for his largest account, with more than 100 sites, he discovered that there were no address or contact details on any of the many sites. Surprisingly, he found that there have been logged activities with each site by his predecessor. If the data on the account level is not accurate, imagine how much time the salespeople need to spend on admin time that generates zero value. Insufficient data often lead to bad decisions and more workload.
SO WHAT?
Clean data helps increase the organization’s productivity since everyone is provided with the right information that can be used to make the right decisions in the daily job. With the correct data, everyone knows where to go and whom to communicate with at the company.
Quality data leads to better decision making. Suppose the data input and analysis are incorrect. In that case, your decisions will be inaccurate as well, and it could hurt the business, which is why data cleansing is of utmost importance.
If you want to learn more about Data Cleansing and how it helps increase productivity and efficiency, feel free to reach out to us at Hanei Consulting Group.
If you want to learn more about Data Cleansing and how it helps increase productivity and efficiency, feel free to reach out to us at Hanei Consulting Group.
About us
We are a team of independent professionals from diverse backgrounds who want to make consulting simple. We have extensive experience from strategy- and technology consulting firms, start-ups, and corporations, where we have solved problems in 50+ countries – from start-ups to Fortune 500 companies. We are the architects of your growth.
