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Data Conversion: The Difference Between Good and Bad Data

Data conversion. What is data conversion? Is it the same as data migration? Not quite. Although, data migration does require data conversion in it’s process. Here are there definitions:

Data Conversion: the conversion of computer data from one format to another.

Data Migration: the process of transferring data from one database to another database.

Why do we need either of these? Well, when an association or regulatory body switches between new systems or new processes, sometimes data can’t be in a certain format or in the same database anymore. Therefore it needs to be converted.

Good Quality Data

The issue, however, isn’t the conversion or migration process, it’s making sure your newly stored data is quality data. What is quality data? You probably know what it is without realizing it. What words would you want to describe your dream data? Probably…

  • Accurate
  • Consistent
  • Complete
  • Valid
  • Timely
  • Accessible

These are all very important characteristics to use as standards for your data. What do they all mean though?

Accuracy: the data must be correct and precise.

Consistency: the data must be under the same units, same format, etc. Ex. All names listed as Last Name, First Name (Smith, Will).

Complete: the necessary data must all be present.

Validity all fields are entered correctly and within acceptable ranges. Ex. 13/30/2015 = Invalid date.

Timely: the data is up-to-date and available for immediate use.

Accessibility: the data must be understandable and easy to access.

Benefits of Good Quality Data

While the benefits of quality data are basically explained in the characteristic definitions above, there are many more benefits. These include:

  • Being confident in your database
  • The ability to produce accurate reports from your data
  • Spending less time fixing, organizing, and putting together the data
  • A happy staff that has to access the data
  • Reduction in costs through staff efficiency

Bad Quality Data

Bad quality data consists of data that is the opposite of accurate, consistent, complete, valid, timely, and accessible. It could have duplicates, misspelled names, wrong numbers, and outdated information.

Poor quality data can be a result of human error, multiple points of entry, staff turnover, or your technology might need to be updated to one that can detect errors and duplicates in entries.

Data and Regulatory Bodies

A regulatory body enforces members of various regulated industries, such as the medical industry, to make sure they are compliant with the federal and provincial regulations. This is to protect the safety of the public. Regulatory bodies use license management software in order to keep track of and ensure members are meeting the legislated requirements and professional standards to keep practicing. In the database, they store each members’ name, address, phone number, employment history, education, transcript, complaints against him or her(if any), and much more. This is what the data consists of, and needs to be recorded with consistency and accuracy, so that it is readable and understandable by any staff members that need to access it.

Alinity and Data Conversion

During implementation, Alinity staff help analyze a regulatory body’s data to understand it’s current quality and gives us the ability to provide the body an appropriate estimate of quality. If there were to be any missing data or gaps, we’d discuss it with the client. In the discussion we’d find out the importance or relevance of the missing data, whether we need it or not; and if we do, we find out if it can be derived from something else oif it can be supplied from another source.

Inconsistent Data

Alinity fixes inconsistencies in several ways. Inconsistencies, for example, can be how a name is entered in the data:

  • Jane Doe
  • Doe, Jane

Or how an address is entered:

  • 10374 170 St.
  • 10374 170 Street

We fix inconsistencies in several ways. Sometimes we ask clients to fix it themselves in the their current database, so that we can import it into ours cleanly. If needed, we’ll find a way to update our data conversion process to automatically get rid of bad data!

Our Process

Sometimes we do convert the data as it is and just clean it up in Alinity/Permitsy. Each scenario is judged on a case by case basis, we choose what makes the most sense and is most cost effective for the College.

When it comes to the conversion process, it is important to work in partnership with the client, because they understand their data. It takes at least weeks to months depending on the complexity of the data and how much we’ll convert.

The challenges of data conversion, besides bad data, include:

  • having to convert from poor systems
  • having to convert from systems that have redundant data in several tables where the data does not match
  • having to figure out a previous vendor’s database schema (framework or plan)

How We Guarantee Great Data

To make sure we get good quality data for our clients to report on, we do the following:

  • we create a system that’s easier to use for their staff
  • we provide consistent awareness of any members’ status (ex. if something is invalid or missing)
  • we create automated business rules that validate data consistency (if data claims they are employed, they should have an employment record)
  • wherever possible, we don’t let members free type, we provide drop down menus that they can select answers from to prevent inconsistencies
  • giving only certain staff access to certain data; people that are not trained in one department can’t edit or add data in another department, this also protects member’s privacy by having less people see less data