Strategies employed to clean up bad data

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Strategies employed to clean up bad data
Since, its already established that data is a
crucial aspect for most businesses to arrive at
insightful business decisions, identifying the
relevant and valid data from a huge volume of
data amassed for business purposes can be time
consuming and daunting. For data to be valid,
relevant and accurate, businesses will have to
collect, analyze and organize data by cleaning
it up and removing bad data for it to be useful.
For example, there have been instances of
companies claiming inefficiencies resulting from
bad data, thus triggering losses for them.
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• How will you identify if your business has bad
  data?
• Bad data, it is presumed can be a corrupted file
  or document, an inaccurate data field, duplicate
  values etc. Lets look at some categories of bad
  data, below
• Data that is not compliant It does not follow
  the companys standards
• Data that is incomplete Some of the information
  is missing or not filled in from the data fields
• Data that is considered irrelevant If any of the
  required information is not valid or entered
  into the wrong field
• Data that is not accurate If the data is not
  entered or updated with the required information
  properly. Make mistakes
• Data that is duplicate If the same information
  is available in various database records or if
  it comes multiple times in a particular database.
• In the meanwhile, it is also see how bad data can
  affect the efficiency and reputation of the
  business, also leading to missed opportunity, cut
  back on staff morale, inefficient customer
  service, inaccurate predictions, bad business
  decisions etc. This could also result in loss of
  revenue for businesses.
• Methods employed to clean up data
• From all this, you can see the significance of
  using valid, relevant and accurate data for your
  business requirements. Below, you can take a look
  at some steps to clean up data and assure its
  relevance and validity.
• Prevent The first step is to control and reduce
  the bad data, before entering the information
  into your system
• Remediation Second step is to monitor and clean
  up the data, after it is fed into the system, at
  the same time, focussing on complying with
  quality standards.
• It is also seen that most businesses opt to
  prevent bad data by looking up duplicate entries,
  before feeding the data into the system and
  cleanse it up. They would also fill in the
  complete required information.
• Thus, you can infer from the above points, how
  outsourcing the data processing services to a
  global professional in the field of data
  management is the best option for quality
  service, cleaning up the data and ensuring its
  validity, relevance and accuracy.

More clarifications send mail inquiry to
sales_at_outsourcedataworks.com
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