Pioneering Efficiency: A Leader’s Guide to Data Science Efficiency

Like many other sectors, the tech industry has been heavily affected by the current state of the economy. Major tech companies, such as Microsoft, Meta, and Amazon, have experienced widespread layoffs. As data science teams deal with downsizing and budget constraints, data science leaders are tasked with making sure the quality of their teams’ output doesn’t falter. We take a look at the methods data science leaders can take to keep their teams more efficient than ever.

Clearly define requirements

While the data science field as a whole may be grappling with uncertainties, confusion should not be present at the project level. Data science leaders can provide clarity by ensuring that data scientists are equipped with all of the information needed to do their work from the very start. Initiating kickoff meetings with stakeholders allows data scientists to ask questions upfront and align on requirements, objectives and deliverables. This can save time in the long run, as they can serve as points of reference if data scientists have any uncertainties down the line.

Repurpose previous work when possible

In addition to kickoff meetings, past work also serves as great reference material for data scientists. Data science leaders should encourage their team members to document and share work that may be useful for future projects. Providing a shared, central place to store templatized versions of previous work has many benefits:

  • Data scientists will save time by not having to write new queries from scratch. For example, a piece of code used to calculate indexed foot traffic for McDonald’s can be repurposed to calculate indexed foot traffic for the retail category.
  • Past work can serve as study material for data scientists who want to brush up on their skills.
  • Using the same reference material ensures consistent work quality across all members of a team. For example, if all members of a team are in the habit of using the same naming conventions, it makes it easier for them to decipher the function of code written by someone else.

Maintain documentation

Documentation isn’t just useful to data science teams–it’s valuable to external stakeholders, as well. As such, it’s important to simplify information in a way that can be easily digested by people less familiar with more technical jargon. When external stakeholders can fully comprehend the work being produced by a data science team, it makes it easier for them to provide accurate, actionable feedback. Data science leaders should guide their teams to employ some of the following methods to increase comprehension of their work:

  • Simplified language – When sharing information with stakeholders, provide explanations in layman’s terms, as opposed to more technical jargon.
  • Visual aids – Charts and other graphics can do a better job of conveying information than just words.
  • Cheat sheets – An easily accessible glossary of definitions can provide stakeholders with a quick refresher of terms and metrics they are still learning.
  • Agendas – Agendas can help prevent stakeholders from getting lost when topics are discussed on a call. Providing one in advance also allows stakeholders time to research any topics they may be confused about, as well as time to formulate questions.

Leave time for QA

Sometimes when data scientists are juggling multiple projects, issues such as incorrect queries and bugs can go unnoticed. It is imperative that data science leaders build in time for QA when setting deadlines for projects. Catching issues early can prevent hours of corrective work later on, and highlight new issues to be mindful of moving forward.

Partition data

Another hindrance to data science efficiency is the massive size of datasets. Large databases can take forever for queries to parse, leading to much longer runtimes. Organizing data into multiple, smaller databases, as well as segmenting data in a way that makes it easier to extract needed attributes, can significantly quicken data pulls.

Reassess priorities

While every data science leader may want their team to be the pinnacle of efficiency, it’s important to recognize the team’s limits. Sometimes it’s not possible to improve results +40% in 2 months. Netflix experienced this first-hand when trying to improve its movie recommendation system. Sending out a global call to action for data science teams to drive a +10% improvement, incentivized by a $10MM grand prize, Netflix had to wait three years for that goal to be reached, even with 41,305 teams competing. Data science leaders should work with their teams to determine realistic deadlines and deliverables, to prevent burnout.

Take time to retrain

For a data science team to excel, they must put in the extra work to do so. New hires aren’t the only ones who can benefit from periodic training. Data science leaders should encourage all members of their teams to take part in workshops, conferences, and online courses to improve their skills. Teams should also explore new tools, such as business intelligence platforms, that may make their work easier. Setting aside a budget for learning stipends can help ensure that data scientists are staying on top of their game.

Investing in the right practices and tools today, can improve your data science team’s efficiency in the long run. Reach out to learn how Foursquare can help you achieve results.

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