The reason is a less-than-ideal data science workflow where data scientists are working across many disconnected tools while grappling with an excess of data management tasks, a lack of engineering support, and an inability to operationalize their output. The solution is a platform that puts the tools needed to create, socialize, and deploy data science projects all in one place. Here’s why.
Your data scientists aren’t collaborating effectively
Reproducibility is a tenet of modern data science, and a critical component to delivering value to your organization. If your data scientists are solving similar problems over and over again in different ways, you are not alone. This is a common occurrence among data science teams that work independently across standalone tools or in different departments. The solution is twofold: management must establish company-wide guidelines for coding and collaboration, as well as provide a flexible platform with a modern set of tools that every data scientist that can work within.
Why both? Well, management strategies and best practices have their limits. If your team is using a disparate set of tools, collaboration and reproducibility will remain elusive. A platform that unifies the tools and libraries that data scientists prefer–and that removes the guesswork from sharing and deploying projects–will drive your organization toward data science excellence.
Imagine a world where all the contributions of your data scientists — code libraries, visualizations, data models — are all in one shared, easily accessible location. Data scientists will be able to share best practices, reuse tried-and-true code and leverage versions of existing data models, making data science at your organization scalable, repeatable, and less resource intensive.
Scalability is critical: Poor practices hide in plain site on small teams. Collaboration and reproducibility break down when teams reach six or more data scientists. Companies like Salesforce now employ upwards of 175 data scientists, a number that was essentially unheard of just a year ago. In order to handle that increasing weight, teams need to build a foundation of collaboration and reproducibility. That’s why data science platforms will become the industry standard.
Engineering is holding up your data science efforts
Not on purpose, of course. Data scientists are expert statisticians and have deep industry knowledge, but they often aren’t qualified to deploy data models into production and ensure the scalability and high latency required by modern applications. It takes a village to support a data scientist, and if your company doesn’t have engineering and infrastructure resources devoted to doing just that, the output of both teams is at risk.
For example, if a data scientist builds a recommendation engine, a software engineer must then test, refine, and integrate that model before users can start seeing product recommendations based on their behavior. Frequently, models are designed, built, and tested in languages such as Python, R, or Scala. Meanwhile, the applications those models are being integrated into are written in Java. This means software engineers — not data scientists — are responsible for the models that are driving your business. At best, this is an inefficient. At worst, it means the algorithms you are trusting to run your business are faulty.
A platform that has the capability to make data models available behind an API will solve many pain points between engineering and data science. Once the model is built, data scientists can simply send the API to the engineering team to be embedded directly into its intended application.
You’re not leveraging the work of your data science team effectively
With companies that effectively leverage data, analytics, and software on track to generate $1.2 trillion in revenue by 2020, it’s no wonder that data science platforms are becoming a must-have. Companies willing to invest in technology that keeps them on the cutting edge will be using these powerful tools to give their data science teams a leg up in the race to deliver value. Companies that don’t make this investment — but continue to conduct data science across standalone tools, with or without appropriate management and best practices — will inevitably fall behind.