Pioneering Efficiency: A Leader’s Guide to Data Science Efficiency
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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:
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:
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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 by +40% in 2 months. Netflix experienced this first-hand when...