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Cleanse Data

Improve your data quality: Cleanse your data

Data cleansing is the first step to data management. It is a process of cleaning old, invalid, duplicate and useless data from the existing database.

Incorrect, inappropriate, outdated, redundant and missing data are key examples of dirty data. If this data is used for decision making then it may lead to a critical error. Predictive, predefined model will become erratic and unreliable.

Why there are erroneous, incorrect data?

The inconsistencies detected or removed may have been originally caused by user entry errors, by corruption in transmission or storage, or by different data dictionary classifications of similar entities in different data stores.

The one and only solution: Accuracy

Once these dirty data are detected, they should be rectified as soon as possible. The entire process deals with removing and detecting errors and inconsistencies from the data. As a result the quality of the data is improved. Data quality problems reside in single source data collection such as files and databases. Some data have problems when these were created in some other source for some other purposes.

Some wrong data are produced because of typo/misprint error or might have come from multiple sources. Data, sometimes produces more error while integration. When multiple data source gets integrated, it enhances the probability of getting redundant resulting in erroneous data.

Incorrect or inconsistent data can lead to erroneous calculation, false conclusions or misdirected investment. Dedicated resources in a company, work on maintaining the data quality.

How do I start a data cleansing process? Do we start with our operational system or in the warehouse?

We do not necessarily �cleanse� all the data; the data which is actually low in quality. Most of the time, cleansing the data records is carried out based on propositions where analytics require strict comparisons.

Poor quality customer data leads to significant cost, such as:

  • customer turnover
  • excessive expenses on customer communication process like mail-outs and missed sales prospects

Dirty data has a significant impact on most of the strategic business initiatives, not only in sales and marketing. Compliance and transparency of data are now at the top of the list for any existing company.

There are several steps required to transform raw incoming data into the clean, cohesive and enhanced data a business needs for effective customer information management.

These are semi-structuring (also known as parsing) standardization, local as well as global consistency checks and documentation. These factors can reduce a significant amount of unclean data in your organization.

Dirty data is obsolete, non-normalized, entered and maintained incorrectly. They are often misspelled, lacking lead bases, loaded with invalid and incomplete details. For sales and marketing organization it can make the entire process of conversion a mess. If it gets into the map the undesirable consequences it can infuse the entire organization.

You can enhance the quality of the data management by cleaning the erroneous data and paying attention to accuracy.

At Span Global Services, our services are designed for those companies that want to venture into new territories, or are looking to revamp their professional image. Learn more about our services and how we can create a climate of change for your business and achieve revenue targets. Switch to data cleansing with Span Global Services.

Call our experts at 877-837-4884 or email us at to know more.


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