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Data Mapping Best Practices

How to prepare your data for use and what to keep in mind when setting up your data.

Data mapping is one of the first stages of data management.  It involves establishing connections between different objects in an integration flow.  In data mapping, specific procedures are used to sort data into sets, and you need to follow certain standards in order to effectively link data.  Data that is not efficiently mapped may lead to skewed analysis results and questionable findings.  Ideally, you need a single source, with access and editing rights granted to a select few.

Data mapping best practices entail following a few set standards, to ensure that you get the most out of your data.  Linking data should be precise and depend on the domain values of the data being handled.  A few good practices include identifying and mapping data, making the process automatic, ensuring data security, keeping up with maintenance, and keeping records of data sharing.

Data collection and analysis is a vital part of any business in the consolidation of information.  Information gathered through data collection gives insight into your existing and potential customers.  It allows businesses to control expenditure and allows you to hone your marketing efforts.  Data that is used in a non-uniform way can hamper the information you are attempting to collect.  Keeping a few good data mapping practices in mind will go a long way in keeping your data meaningful.

Identifying and Mapping Data

When working with a large amount of intricate data, you may wish to use a data mapping tool.  This will assist you with a drag-and-drop feature, where one object is dropped into the corresponding field of another.  When selecting your mapping tool, be sure that it complies with the General Data Protection Regulation guidelines.

Ensuring Data Security

Your integration projects need to be secure.  This can be achieved by allowing access to only certain users, based on their roles in the project.  If you are making use of a data mapping tool, as mentioned in the previous point, the tool will most likely also have a risk analysis feature that you can carry out on your data.

Automating the Process

One of the biggest mistakes businesses make with data mapping is not using uniform names, terms, and spelling conventions.  Using the words “Johannesburg” and “Joburg”, for example, will lead to two separate streams of data.  If you use a mapping tool, you are able to automate terminology, spelling conventions and words, by creating a synonym file.  This is a dictionary that includes current and alternative names for words you and your clients are likely to use.  The tool will then match the slightly different words and extract data from them all.  Tools like this are especially useful when working with large datasets, where the probability for error is high.

Doing Maintenance 

You may need to update or change your data flow from time to time.  This will allow you to address specific challenges the business is experiencing, debug complex mappings or simply modify the flow.  For maintenance to cause the least amount of disruption, it is good practice to have a maintenance schedule in place.  Be sure to check the accuracy of your mappings before you begin the maintenance procedure, as it can disrupt your data flow.

Record Keeping

By sharing data with a limited group, you will be able to track personal information.  This is especially helpful when the time comes for deletion.  Keep track of use-cases for each mapping, classify applications and record the source-to-target convention and how it is used in your business.

Do Data Keys Form Part of Data Mapping?

Data keys are used for the encryption and decryption of data.  They convert data into unreadable ciphers for security purposes.  To encrypt or decrypt data, you need a data key.  By implementing data keys in your business, you limit the number of people that have access to your data, ensuring its safety.  Having a limited number of people working on data at any given time is considered good data mapping.  So, yes.  Data keys form part of data mapping.

What are Data Mapping Techniques?

Data mapping can include elements such as:

  • Ensuring names are spelled correctly
  • Uniformity in written terminology
  • Ensuring all users are collecting the correct data
  • Adding a unique data key
  • Creating one source of truth that all users work on
  • Limiting changes
  • Limiting access
  • Making use of filtering 
  • Mapping to or from specific instances
  • Linking independent sources to order structures
  • Splitting a data element

Wrapping it up

Data mapping is a necessary part of ensuring the smooth running of your business while gathering valuable information about your clients, both current and future.  Consider it part of the housekeeping and a solid way to maintain the integrity of your data.  Make use of a data mapping tool, to help you get the most out of your data.

How can we help?

Opennetworks Data Science Team helps clients accelerate time to insights with Google Cloud, realizing the benefits of integrated analytics services without limitations on scale, performance, and cost-efficiency.

Book your FREE Data Analytics Exploration Workshop with our Cloud Team today!

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