Data Wrangling

There is no doubt about the fact that how important data is, in terms of healthcare. In the similar way it can be dangerous if organizations’ data is complex as this will hinder the process of reaching to correct and genuine healthcare providers that ensure best patient care. Data is growing each day that has to be organized well for convenient consumption. HC Data Solutions are the industry leaders in offer data wrangling services that are

  • Utilized with preferred tools
  • Fabricated for business users
  • Extraction is done from web pages and reports

Data Wrangling Purpose

  • Data collection from manifold sources leads to ‘deeper intelligence’ within one’s data
  • Time saving initiated in terms of amalgamating message data before utilization
  • Rendering actionable data to business analysts by stipulated time
  • Enabling data scientist to emphasize on data analysis

Key steps included

  • Data cleansing – Eradicating bad data & redesigning data into functional format
  • Data acquisition – It includes obtaining access to data that is within the sources
  • Joining data – The merging of edited data for further utilization

Processes of data wrangling

It includes six mainstay activities for preparing data for scrutiny as well as to get the best business value out of the data. It includes:
  • Discovering– enables you to understand data and it’s significance for analytical examination
  • Structuring– makes you capable of formatting data of all shapes & sizes for working with traditional applications
  • Cleaning– it allows you to fix & regulate the data which might disfigure your analysis
  • Enriching– enables you to take advantage of the already done wraggling
  • Validating– recognizes & surfaces quality of the data & uniformity issues
  • Publishing– give you the aptitude to plan & deliver data for the downstream exploration

Goals of data wrangling

  1. Collect data from various sources for revealing “deeper intelligence” in your data
  2. Make available exact data to the business analyst timely
  3. Lessen the time spent in gathering & systematizing disruptive data before utilizing it
  4. Facilitate data analysts & scientists for focusing on data analysis rather than wrangling
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