A very insightful article on importance of data cleaning and standardizing before working on it. Business Intelligence and Analytics operation results would be skewed if worked on unclean and not standardized data.

1. Business Intelligence & Analytics not without Clean Data and Standardized Data
When it comes to the matter of databases, a majority of enterprises are not aware of how valid
and updated their existing database is. Though I’ve seen many marketers take pride in how
extensive their database is, they are at sea, when queried about the duplication and freshness
of the data. The amount of replication and scaling that takes place in a database of an
appreciable number can be a daunting task as such without taking into account the cleaning
and standardization process, which needs to be done in a sustainable manner. Many
enterprises flounder in this aspect as they do not know how to clean data.
Probable Reasons for Data Issues
The unimaginable amount of data that get collected in an enterprise cannot be accounted for in
a majority of instances. As technology improves and brings forth newer and more sophisticated
methods of data storage, the need for organizing the data collected becomes more pertinent.
In order to get the right perspective on the customers, enterprises need to gather more
information.
Let us take the example of phone number records present. The issue with the data arises, when
you want to conducts an SMS marketing campaign. When the data has both landline and
mobile numbers, segregating them becomes a big issue. Such nuances need to be considered
and cleaned up, so a better and targeted marketing can be planned on.
Need for Quality Control in Data
In most businesses, the quality of data is affected because of reasons like:
 Improper methods of collection
 Insufficient input
 Integral difficulty inside the data itself
 Lack of proper structure in the data
In my opinion the issue in data arises mainly due to factors like the lack of time, efficiency, and
expediency. Though the cause is easy to understand, it is very difficult to clean up the data
issues and arrive at the right standardization.
Contextual Approach to Quality
Most often I’ve seen enterprises treat data quality on the very simple basis, which calls for a
single data model with only one integral data set. This therefore requires only a single

2. command for cleaning and standardizing data, before it is used. Approaching data in this way
can lead to the quality being affected which should be avoided at all cost.
Incorrect data management done to avoid an expensive cleaning and standardization process
can wreak havoc within the enterprise. Though the quality differs based on the type of data,
like for instance, an inventory data does not need much meticulous quality control, whereas
transaction data needs very high requirements, the clean and standardized format is a definite
requirement.
Refining Data
To enable businesses use the data collected in the optimal manner without loss of time,
efficiency or cost, sanitization of data is necessary. The quality of data determines the way it is
added, stored, and used. I know that in an organization it is impossible for a single person to
accomplish all these steps. Since different persons oversee the process, it ends up lacking
consistency. Though companies try to eliminate this by stipulating rules and validations that
help to homogenize data, implementation is not that easy. For proper cleansing of data an
enterprise needs:
 Creation of validations and rules for better consistency
 De-duplication of data – This has to be done from the time of recording data to the
management level. In addition to compiling data from several sites to a single base, it
also involves formulating strict rules regarding the permutations and combinations. This
practice helps you beat the chances of duplication thoroughly.
 Formatting data – This provides a consistent and uniform value, so analysis can be done
and better decisions made.
 Regular review – Frequent review is needed to maintain the quality and eliminate
anomalies.
 Software tools – By using data cleansing tools, the expensive process of manual
cleansing can be avoided, leading to a better and efficient data cleanup and
standardization.
In my opinion dirty and incorrect data is the main culprit when several of the business
intelligence projects hit the line of malfunction. As a result the data is curtailed, missing, or not
precise. In some cases the amount of incorrect data is so large that analysis is shunned, as the
results cannot be guaranteed as accurate. A cost effective, efficient, reliable, and scalable data
cleaning in BI & Analytics is therefore the need of the hour for enterprises. You can always visit
TBSS Page for more insight and favorable package.