The Importance of Understanding Your Data

Have you ever heard the saying “the journey is more important than the destination”? Data Analytics projects aren’t exempt to this – it’s easy to let final deliverables take precedent over the process. Whether your end goal is to receive key business insights from a machine learning model, develop data visualizations, or incorporate your data into a user-friendly application, developing fluency in the data you own at the beginning of the project will bolster your success. Document the data you own, and you will save time, money, and frustration throughout your project duration.

Understanding Your Data Upfront

You should know your data like you do your groceries. Raw data is like raw produce. You’ll want to know its source, freshness, and contents before you consume or use it.

Optimize your time and resources by making sure you and your team members can answer with confidence to understanding the source (collection and storage methods, architecture, data owner), freshness (update frequency, changes, errors), and contents (metrics/responses captured) of your data before you consider building processes around it. It is much more efficient to spend the time answering these questions upfront, than when you are halfway into designing your solution.

Questions to ask before you begin analyzing your data:

  • Origin: If you had to make decisions based on your data today, would you know where to find the right data fields to answer your questions?
  • Freshness: Do you trust your data source(s) to be up-to-date and accurate, ready to include in your analysis?
  • Contents: Are you able to describe the purpose, calculations involved, and contents of each data field you are using?

Are You Fully Capturing What You Need to Succeed?

Mark Clerkin, VP of Data Science at High Alpha, presented a simple yet crucial point when presenting at Indy’s Powderkeg series, which was that often data science success revolves around the accessibility of clean, complete data. Before you begin running with your analyses, define the questions that you want to answer with your project. Then, work backward to reflect upon what data truly needs to be captured to best attack the questions identified. You may need to modernize what you currently collect or clean the data structure you have to isolate valuable data points. Exploring your data at the early onset of a project will help you communicate this need to data scientists and your team alike, thus easily narrowing the project’s focus and streamlining the path toward results.

Early Exploration Leads to Long-Lasting Benefits

This last point delves deeper into the importance of documenting the contents of your “grocery list” of raw data. Avoid misinterpretation and confusion on what data is available and used in your solutions by auditing your sources for sustainability.

Tips and Tricks for Early Exploration
  • Document data source(s), the owner(s), and structure(s)
  • Read over any metadata (the technical term for data about your data) you have and ask your data source owner for metadata if you do not have it.

If you do not know of a data source owner who can provide you information about your data, that issue needs to become a top priority in your project. Be adamant about building metadata around any solution you own. It is critical that data be validated before a data scientist puts it into any form of the deliverable model. Documenting your data structure will also help you to track and quickly react to future changes.

Documenting and tracking changes in your data is data governance – a process that will save your data from breaking your business if an error in your workflow erupts.

TLDR;
  • Understand your data upfront – projects will run smoother when you have all of the needed information.
  • Compare what you’re capturing with your goals – make sure you have the data you need to answer your most immediate questions
  • Govern your data early on – you’ll thank yourself in the future if you need to go back to a project due to changes or errors

Organizing your data domain can seem like an intimidating task, but it will lead to confidence and reliability in your solutions.

Looking for a consulting partner for your next data analytics project? Reach out to a consultant today for a free consultation.