A Guide To Successfully Managing a Data Science Team

While technology continues to advance at a breakneck pace, we can only realize the full potential of digital transformation if we harness the power of the data it generates. Data, along with basic resources like land, labor, and capital, is now cited by 90% of business leaders as one of the key resources and a fundamental distinguishing factor for companies. 

Companies that want to get the most out of their business data must understand the value of data science and have a data scientist on staff to customize algorithms, make the most of data, and make data-driven decisions. Data scientists excel at leading organizations toward data-driven success because they have a deep understanding of data.

A business needs a data science team to

• Collect data
• Analyze social media 
• Segment customer base and 
• Optimize modeling

Having a data science team is extremely advantageous. It recognizes data as a tactical asset by analyzing predictive models and datasets on which it makes accurate predictions about people and processes. All of this leads to massive profits and unprecedented growth.

Steps For Leading Successful Data Science Teams 

Data science teams can add significant value to a company, but failing to provide adequate training is not a recipe for success.

Below mentioned steps will assist data science teams in realizing their full potential, which will benefit your company:

• Direct Data Science Teams To The Appropriate Problem

Business leaders should define the problem they want their data science teams to solve with extreme caution. Data scientists, especially new ones, are often eager to get started with data preparation and model building. They may not have the confidence to question a senior business executive, especially if that person is the project sponsor, at least at first. Leaders must ensure that the team is focused on the right problem. 

Examine what other companies in your industry, particularly early adopters of data science, are doing to increase your chances of identifying the right problem. Pay less attention to how they’re solving it. Any data science problem can be solved in various ways, and more attention to what they’re solving.

•  Decide On a Clear Evaluation Metric 

To solve a problem, data science teams typically create many models before choosing the best one. They need a metric to make this decision. They can use this metric to rank and select the best model out of multiple options.

Leaders must use their business judgment to decide what that metric should be, which is more complex than it appears. There is no perfect metric in any complicated business situation. Many relevant metrics are frequently in conflict with one another.

If you’re unsure, ask your data science team to educate you on the metrics that are commonly used in the industry to evaluate models for similar problems. You can choose one that reflects what’s important to the company or work with your data science team to create a custom metric if none of them fit.

•  Establish a Practical Baseline

You need to create a common-sense baseline, which is how your team would solve the problem if they didn’t know any data science after you’ve decided on a relevant, significant problem and defined a clear evaluation metric that reflects business priorities. 

Creating a common-sense baseline will force the team to get the end-to-end data and evaluation pipeline up and running, revealing any issues with data access, cleanliness, and timeliness.

•  Manage Data Science Projects More Like Research Than Like Engineering

Well-intentioned executives will likely ask data science teams to commit to a specific timeline and hold them accountable. After all, project managers do this all the time. But it’s a mistake in this case.

Most data science projects include a significant amount of research, which means a lot of time spent on dead ends with nothing to show. It’s difficult to say when a breakthrough will happen because of trial and error.

•  Check For Truth And Consequences

It’s critical to scrutinize the results to ensure that the benefits are real and that there are no unintended negative consequences. The most fundamental check is to ensure that the results are calculated using data that was not used to create the models.

Assuming the results are accurate, make sure there are no adverse side effects. When a model improves performance on one metric, it may sacrifice performance on other essential metrics. There will always be a need to judge the trade-offs between metrics, and business leaders should be involved in those decisions.

•  Keep Track Of Everything And Retrain As Needed

With certain types of input data, no amount of testing before launch can completely protect models from producing unexpected or incorrect predictions. However, investigating and fixing problems will be easier and faster if every input and output is logged in as much detail as possible. This is especially important for applications that interact with customers.

And the nature of the data fed to the model will begin to diverge from the data used to build the model over time. If nothing is done, the model’s efficacy will suffer, so data science teams should ensure that automated processes are in place to track model performance over time and retrain as needed.

Conclusion

Organizations rapidly adopt new technologies and replace traditional analytics methods with more advanced approaches, which rely heavily on historical data. This frequently necessitates specialized skills and forward-thinking data and analytics teams that can take advantage of a new class of analytics that relies on more diverse data.

The organization’s size, how centralized its analytics initiatives are, and its overall data strategy, objectives, and budget all influence how a data science team is structured.

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