Unlocking the Power of Better Data Science Workflows

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March 14, 2020
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It doesn’t matter what the project or desired outcome is, better data science workflows produce superior results. But if you’re still working with outdated methods, you need to look for ways to fully optimize your approach as you move forward.

5 Tips for Better Data Science Workflows

Data science is a complex field that requires experience, skill, patience, and systematic decision-making in order to be successful. If you want to thrive and add value to those around you, it’s imperative that you develop superior data science workflows. Here are a few helpful suggestions:

1. Demarcate Each Project Into Phases

It’s overwhelming to look at a data science project from the top down. Doing so will make you feel overwhelmed. If nothing else, it’ll compromise your ability to take tangible strides. The better strategy is to demarcate each data science project into four distinct phases:

  • Phase 1: Preliminary Analysis. This is the preparation step where data is gathered, goals are set, and objectives are clarified. A lot of data scientists gloss over this phase, but it’s an important one if you want the rest of the workflow to be efficient and productive.
  • Phase 2: Exploratory Data. During this phase, data is cleaned, analyzed, and assessed. This is also the period where specific questions are asked and confusion is cleared up.
  • Phase 3: Data Visualization. With the data analyzed and stored in spreadsheets, it’s time to visualize the data so that it can be presented in an effective and persuasive manner.
  • Phase 4: Knowledge Discovery. Finally, models are developed to explain the data. Algorithms can also be tested to come up with ideal outcomes and possibilities.

This four-stage workflow is just one framework – but it’s a good one. It should give you an idea of the importance of dividing work up into systematic phases that simplify the complex and bring clarity into the details.

2. Use the Proper Mix of Hardware and Software

When it comes to data science workflows, speed and efficiency are of the utmost importance. If you’re lacking in either of these areas, the entire project can become compromised. One way to ensure optimal speed and efficiency is to leverage the correct mix of hardware and software.

Take a 3D rendering project, for example. In order for an architect and data scientist to achieve fast rendering and improved workflow efficiency, there must be balance and alignment between the computer and the rending software. When these two elements are in harmony, there are fewer delays and less risk of data corruption.

3. Make the Workflow Obvious and Apparent to Others

Regardless of whether you’re working on a small, isolated project, or you’re involved in a much larger assignment that involves an array of people and groups, you need to make sure your workflow is clear, obvious, and apparent to anyone who encounters it.

Sterling Osborne, a data scientist and Ph.D. Researcher, likes to create notebooks for writing code. And any time he creates a notebook, he’s intentional about making it readable to all.

“My aim with any notebook is to enable someone to pick it up without any prior knowledge of the project and fully understand the analysis, decisions made and what the final output means,” Osborne explains.

Whether you’re writing code or analyzing data, this is a good rule of thumb to follow. Make your work so obvious that anyone can pick it up and quickly catch up with what’s happening.

4. Involve the Right Number of People

Be mindful of project involvement and try to keep your team small. This limits the outside noise and ensures you don’t become paralyzed by excessive opinions and diverse strategies. You want enough people to avoid tunnel vision, but not so many that you lose focus.

5. Select the Appropriate KPIs

One of the biggest challenges with any data science project is communicating what success looks like. And no matter how articulate your goals and objectives are on the front end, you need the appropriate key performance indicators (KPIs) on the back end to ensure results are analyzed in an objective fashion.

“After KPIs are established, you then must operationalize them,” Trenton Huey writes for Oracle. “Data-savvy people will pick them up quickly, but KPIs are for the entire team. Teams have higher performance when everyone understands the primary objective.”

The sooner you establish KPIs and start analyzing your results, the more effective your workflow will become.

Adding it All Up

When it’s all said and done, better data science workflows are more efficient, less expensive, and higher returning than the average approach. By implementing some of the aforementioned tips and suggestions, you can revolutionize your approach from the inside out.

Hopefully, this article spoke to you and provided both encouragement and insights. Regardless of whether you’ve been in the industry for decades, or you’re just now starting out, improving your workflow is a surefire way to grow your career.