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The Best Tips To Help
Clean Your Dirty Data
Better
In today’s business world,
quality data means gold. No matter what
kind of business you do, having quality data
in hand goes a long way in performing to
the best of your organization’s capability.
But sometimes maintaining data sets
becomes a hassle. As per various marketing
researches, contact data of corporates
decays on a rate of 25% annually. This
means that about a quarter of the current
databases will be invalid & won’t be useful
in just a year. Sad…but true. Dirty data
originates due to multiple factors & reasons
*Incomplete data *Inaccurate data
*Duplicate data *Business rule violations
*Incorrect data *Inconsistent data
Unfortunately our technology is not advanced enough to
identify the errors from the start & to nip them out from
the bud. But there are certain tips that you can follow to
clean up your dirty data.
Dirty data originates due to multiple factors & reasons.
Sometimes, the error can be caused by something as
simple as a data mistake. Let’s see the six most common
types of dirty data.
 Dedicate resources for
maintaining data integrity
 Implant analytics
 Standardize and automate data
entry
 Impart visibility into the history &
origin of the data
 Attain help of experts
 Dedicate resources for
maintaining data integrity
It’s not possible for just one person to address
all the quality issues with data. Yea, it’s
necessary to have employees with statistical
skills but more important to have a data
champion with the knowledge for driving
successful projects. Still, good decisions require
inputs from across the industries than just one
person. Having a shared understanding among
employees regarding the uses & value of data,
itself can be a remedy to data errors. Analytics
tools, can play a major role in this part, as it’s
driving the growth of collaborative analysis
between the IT staff and business users.
 Implant analytics
Organizations should move beyond
creating analytical models to
implanting analytics into their business
operations for improving performance
& ensuring data accuracy. Implanting
analytic solutions is one of the best
ways for identifying errors & warning
conditions, enabling businesses in
eliminating dirty data at the source and
reacting faster to situation changes &
process anomalies.
 Standardize and automate data entry
Standardizing formats for data entry & requirements
will ensure that critical fields are complete & the
formats are consistent. Empower your data
champions to apply these requirements without fail
& automate data entry points where ever possible
while introducing data into CRM & marketing
automation platforms.
Sometimes it’s a hard task to convince your
colleagues that dirty data exists & is badly
impacting the quality of your decisions. With the
aid of a visual data analytics tool, you can achieve
this by showing the origin of the data along with
it’s track record & the steps that are taken for
arriving at any given result.
 Impart visibility into the history & origin of the
data
 Attain help of experts
In general there are four main aspects of data
management which are: Data Appending, Data
Verification, Data Validation & Data Cleansing.
There are vendors in the market who have more
experience than you in these aspects of data
management. Attaining the help of these data
providers like DataCaptive can aid in curating the
database as per your business needs in the best
way possible.
Currently it’s impossible to
stop the data rot. It’s a
problem that all corporates
will have to face. What
marketers should focus is on
having a proactive approach
to replace the “afterthought”
crisis management approach
to dirty data. With genuine
business collaborations &
flexible analytics solutions,
organizations will be able to
successfully clean dirty data
& maintain a vigilant
approach to ensure data
integrity.
Thanks!
Any questions?
Call us for a quote at 1-800-523-1387
Or
contact us at connect@datacaptive.com

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The best tips to help clean your dirty data better

  • 1. The Best Tips To Help Clean Your Dirty Data Better
  • 2. In today’s business world, quality data means gold. No matter what kind of business you do, having quality data in hand goes a long way in performing to the best of your organization’s capability. But sometimes maintaining data sets becomes a hassle. As per various marketing researches, contact data of corporates decays on a rate of 25% annually. This means that about a quarter of the current databases will be invalid & won’t be useful in just a year. Sad…but true. Dirty data originates due to multiple factors & reasons
  • 3. *Incomplete data *Inaccurate data *Duplicate data *Business rule violations *Incorrect data *Inconsistent data Unfortunately our technology is not advanced enough to identify the errors from the start & to nip them out from the bud. But there are certain tips that you can follow to clean up your dirty data. Dirty data originates due to multiple factors & reasons. Sometimes, the error can be caused by something as simple as a data mistake. Let’s see the six most common types of dirty data.
  • 4.
  • 5.  Dedicate resources for maintaining data integrity  Implant analytics  Standardize and automate data entry  Impart visibility into the history & origin of the data  Attain help of experts
  • 6.  Dedicate resources for maintaining data integrity It’s not possible for just one person to address all the quality issues with data. Yea, it’s necessary to have employees with statistical skills but more important to have a data champion with the knowledge for driving successful projects. Still, good decisions require inputs from across the industries than just one person. Having a shared understanding among employees regarding the uses & value of data, itself can be a remedy to data errors. Analytics tools, can play a major role in this part, as it’s driving the growth of collaborative analysis between the IT staff and business users.
  • 7.  Implant analytics Organizations should move beyond creating analytical models to implanting analytics into their business operations for improving performance & ensuring data accuracy. Implanting analytic solutions is one of the best ways for identifying errors & warning conditions, enabling businesses in eliminating dirty data at the source and reacting faster to situation changes & process anomalies.
  • 8.  Standardize and automate data entry Standardizing formats for data entry & requirements will ensure that critical fields are complete & the formats are consistent. Empower your data champions to apply these requirements without fail & automate data entry points where ever possible while introducing data into CRM & marketing automation platforms.
  • 9. Sometimes it’s a hard task to convince your colleagues that dirty data exists & is badly impacting the quality of your decisions. With the aid of a visual data analytics tool, you can achieve this by showing the origin of the data along with it’s track record & the steps that are taken for arriving at any given result.  Impart visibility into the history & origin of the data
  • 10.  Attain help of experts In general there are four main aspects of data management which are: Data Appending, Data Verification, Data Validation & Data Cleansing. There are vendors in the market who have more experience than you in these aspects of data management. Attaining the help of these data providers like DataCaptive can aid in curating the database as per your business needs in the best way possible.
  • 11. Currently it’s impossible to stop the data rot. It’s a problem that all corporates will have to face. What marketers should focus is on having a proactive approach to replace the “afterthought” crisis management approach to dirty data. With genuine business collaborations & flexible analytics solutions, organizations will be able to successfully clean dirty data & maintain a vigilant approach to ensure data integrity.
  • 12. Thanks! Any questions? Call us for a quote at 1-800-523-1387 Or contact us at connect@datacaptive.com