Data Science Tools: What and Why?

organization| business| collaboration | | Lynn Heidmann

Most companies wouldn’t consider running their business without providing a customer relationship manager (CRM) for their sales and support teams, a recruiting tool for human resources teams, or a business intelligence tool for finance teams. Why, then, are tools for data science teams any different?

Some organizations today struggle to use their data to add real value to the business. Other organizations have seen tremendous value from their data and want to increase it, but they have difficulty scaling a data team and managing data projects accordingly.

Tools to the Rescue

Whether a data team is just starting out or is well established but not optimally productive, data science tools or platforms can provide the structure necessary to ensure productivity and efficiency.

tools.gifA data science tool or platform is the underlying framework that allows data teams to:

  1. Scale and be more productive.
  2. Have easy (but controlled) access to data necessary to complete complex data projects and initiatives.
  3. Keep all work centralized (and thus reproducible).
  4. Facilitate critical collaboration not only among similar profiles but between them (data scientist, business/data analyst, IT, etc.).

But What Exactly Is A Data Science Platform?

Those are things that data teams can do once they have a tool. But what exactly makes a tool a data science platform? In their most basic form, data science platforms have the following features:

  • Predictive analytics (or machine learning) solutions.
  • Transparency and reproducibility throughout the team and within a project.
  • Access to all data and collaborative features for working in a central location.
  • Ability to launch data projects seamlessly into production.

But data science platforms can also be much, much more. The more robust the platform, the more challenges (staffing, technological, and operational) the team will be able to overcome.

For example, a flexible platform that allows contributions both via code and through a visual interface and one that facilitates quicker data prep will increase teams contributions. And those that provide robust access controls, data-agnostic integration, and deployment into production will add another layer of ease and centralization that enterprises require. 

In a world where ad-hoc methodology is no longer a feasible option, the amount and diversity of data being collected is skyrocketing, and at the same time, demands for data governance are increasing, data science platforms are the answer.

Read more in our latest white paper about how to facilitate scalibility and efficiency on a data team by providing the right tools.


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