Today’s most successful companies rely on data science to make them leaders in their industries. Still, only 22% of organizations report seeing significant value from their data science efforts. That’s because data science delivers maximum value when it’s scaled across your organization, meaning the results of your data scientists’ models and analyses are embedded into your decision-making processes.
There are many pitfalls to building predictive models at scale, so be sure to focus on these three areas as you develop your data science team:
Hire the right mix of talent
When data scientists are hired without the requisite supporting roles, they ultimately waste much of their time on tasks other than deriving valuable insights from data. That’s because the data science process is incredibly complex. Raw data are typically stored in a variety of disparate sources, so it requires the expertise of data engineers to extract this information and load it into a database that’s ready for your data scientists to work with. Engineers are also crucial at the final stages of data modeling, because the format of the model’s output is typically incompatible with the tools your decision makers use in their day-to-day work. Without a data science platform that deploys models behind an API, your engineers will need to translate the model from R or Python and move it into a production environment manually.
Initial data explorations can often be conducted in a manner that’s too subjectively driven to yield profitable results, so it’s important to include business intelligence analysts in your quantitative team. Domain specialists should help your data scientists hone in on specific questions they can answer with the output of a custom predictive data model that’s built with the needs of your decision makers in mind. Getting stakeholders involved early in the data modeling process is another way to ensure they’ll be able to make use of the final results of each model your data scientists deploy. The bottom line? Doing data science at scale means treating data science as a team sport so that stakeholders in your business get the information they need when they need it.
Take charge of your data science toolkit
Data scientists use a wide variety of tools to support each facet of the data modeling process. Essential technologies include programming languages, code editors and notebooks, model serialization and deployment tools, as well as open source libraries for data visualization, statistical analysis, and more. It’s unsurprising that tool sprawl — working across too many disjointed tools — is the top challenge that data-driven companies face. Locking down the tools that your team uses is an important element of scaling data science effectively.
A key challenge to address is how to integrate open source tools into your data science technology stack. The open source community continuously produces and improves upon packages and libraries that have become invaluable for quality data science work, even at the enterprise level. That’s because the contributions of the open source community make these tools more agile and less costly than common proprietary solutions. An end-to-end data science platform that supports integrations with open source tools provides an intuitive solution to issues with tool sprawl.
Manage your infrastructure closely
One especially costly component of data science work is spinning up and maintaining the computational resources that support it — especially when these resources are poorly managed. Not only is it expensive to keep large clusters running in the first place, IT can spend a lot of time spinning up these environments and tearing them down once a project is complete. That’s why companies are turning to on-demand computing, which reduces costs by spinning up resources on an as-needed basis. Putting this power into the hands of your data scientists means they don’t have to coordinate with IT every time a project begins, scales up, or terminates.
Repeatable development environments are another useful tool to effectively manage your data science infrastructure. Attempting to reproduce a development environment from scratch for every project is time consuming at best. At worst, data scientists attempting to collaborate on a project in fundamentally different environments can arrive at competing conclusions using the same data and modeling methods. Using containers allows you to set up repeatable, standardized environments for your data scientists’ favorite tools.
It’s impossible to maximize the value of your data science efforts without paying careful attention to the people, tools, and infrastructure that support the quantitative engine at your organization. An end-to-end data science platform can be an essential tool in streamlining data science workflows in your business and putting insights into production faster.
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