Radiance Blog

Elusive data.

We love data.  Give us a bunch – or allow us to go gather it on our own – and we’ll roll around in it for hours.  Ok, maybe that sounds mildly creepy.

We talk a lot about the virtues of data, but there is one important caveat – you have to have good data.  Sounds easy, right?

While I’m not going to write a dissertation here on how to set up databases or program data collection forms, I do hope to provide some high level thoughts on the process of data collection (not just the tools):

  • Determine what data needs to be collected. Start with the end in mind – what do you hope to gain from the data?  Also consider what is possible – what is feasible to collect?
  • Create a system. This one of the biggest areas I have personally seen gone astray.  You can have all the databases, software, and forms, but if the data doesn’t get in there right, or consistently, the whole database breaks down.  Develop a simple process that everyone can follow.  Then enforce it.  Where possible, make it automated or at least integrate with other processes.
  • Share success. It can be hard for people collecting, entering, and sorting the data to understand how they are helping the big picture.  Keep them informed with how their efforts have helped and the benefits gained.
  • Review and update regularly. Something not working?  Need additional/different information?  Check your data and processes regularly to ensure you get the most out of your data.

What else has been helpful in getting the right data?



2 comments on “Elusive data.”

  1. David,

    In regards to your blog article on Elusive Data -I will quote (myself):

    ‘The goal of collecting data is to provide accurate and factual information on a particular subject to be utilized to assist in vital decision making processes or to help draw conclusions on presupposed premises or deductions where logical evidence, support or substantiation is invariably lacking but needed…’ (Elaine Medina, August 2010)

    The focus of data should not be so much on aquiring ‘good’ data. The focus should be on aquiring the ‘right’ data to achieve the desired objectives requested by the individual or organization that has recruited us to provide them service support.

    The way the data is received into the system (i.e., scrubbing, architecture) is important to data assimilation. Duplication, bad matching, inconsistent architecture, and incorrect algorithyms will invariably cause unfortunate consequences…i.e., inaccurate skewed reporting data results.

    But more importantly – collecting the ‘right’ data is necessary for the right outcome…i.e., the right outcome being the right information is given to those who are in need of accurate reports on the subject they are requesting information. If we load correctly the ‘wrong’ data- it won’t even matter if it is loaded correctly or can be extracted correctly. The results will still remain the same…’wrong’ data, wrong (inaccurate) reports.

    Finally shared success is achieved (while collecting data) primarily through the implementation of proper documentation on all processes related to the data that is easily available to both internal or external customers. Documention provides process flow, overview and directions concerning the data and provides the ability to update processes or configurations wherever needed.

    The goal in recruitment is to provide beneficial support to help the customer achieve their long term objectives by providing the the best possible reporting solutions to make that happen.

    Respectfully,

    Elaine Medina
    Data Quality Audit Specialist

  2. Thanks for commenting, Elaine.

    We agree that you need to collect the right data. Our intent here was simply to lend some thought on the overall process. Thanks for helping clarify the two and further explaining our first bullet.

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