What Is A Data Science Platform And How Do I Choose One?
A data science platform is a big investment. Before you buy, find out what platform features can help your data scientists do their best work.
Data science platforms are the new must-have tools for companies that want to perform data science at scale. In fact, the new data science platform market is predicted to become a $385.2 billion global business in less than a decade, and Forrester named data science platforms a top emerging technology just last year.
But the newness of the data science platform concept also means there’s a lot of ambiguity surrounding its features and functionality. What is a data science platform, and how does it turn data scientists into invaluable, high-producing members of your team? And if your goal is to create data science work that is transparent, reproducible, and scalable, what platform features should you be looking for?
First, let’s talk about what a data science platform actually is.
What is a data science platform?
A data science platform is a software hub around which all data science work takes place. That work usually includes integrating and exploring data from various sources, coding and building models that leverage that data, deploying those models into production, and serving up results, whether that’s through model-powered applications or reports.
You’ll want to look for a platform that offers the flexibility of open-source tools and the scalability of elastic compute resources. A quality data science platform will also leverage best practices that have been refined through decades of software engineering, such as version control. On top of that, a good data science platform will orchestrate resources with containers and easily align with any type of data architecture. The combination of these features will allow your business to centralize data science work and compete in a data-driven economy.
A data science platform with these types of features usually requires a significant investment. Before you buy, you should ask a few questions about the platform you are considering:
Does the data science platform foster effective collaboration?
Only 22% of companies engaging in data science work today are seeing a sizable return on that investment. There are many reasons that some companies pull ahead — including bigger analytics budgets and an established data science roadmap — but one that is often overlooked is the reproducibility of the work being done. Are your data scientists reusing code? Are they sharing projects with one another? Are they building upon existing models that have already been battled tested?
In our experience, once a data science team reaches six members, reproducibility begins to suffer. But that isn’t the case for teams that are collaborating in a shared workspace with features that are designed to notify users of updates, track changes, and monitor the health of your projects. Plus, because there are a lot of moving parts to every data science project — the data itself, code, models, and outputs — a good platform will offer solutions for organizing these pieces intuitively.
Of course, you don’t always want every team member to have administrative powers in every project. Access-control features are a must and single sign-on is the industry standard.
Does the data science platform let you use the best tools for the job?
Proprietary solutions have long taken a backseat to open source software when it comes to data science, even in an enterprise capacity. In fact, 62% of analytics professionals prefer coding in R or Python over legacy solution SAS, according to Burtch Works. So shouldn’t the platform you choose let your data scientists work in the tools they already use and love (and that won’t cost your company thousands of dollars every year)?
There are a lot of reasons it should. Open-source solutions like Jupyter and RStudio are industry standards at this point, and their ongoing development means your company will always be on the cutting edge. (You can read more about how trends in open-source development are transforming enterprise data science in our latest white paper.)
Ultimately, a data science platform will better serve your needs if you can use the packages and languages you want. Closed platforms that rely on proprietary solutions will always be limited — by slow-moving updates, a lack of innovation, and finite integrations with other tools — plus, the data scientists you hire will need to learn new skill sets to work within them. You can remove these barriers instantly by choosing a platform that embraces open-source software.
Does the data science platform make sharing results with non-data scientists easy?
Data science can only really be successful if there’s buy-in from management. The majority of companies (64%) doing data science well have an executive team that knows the value data science can provide. Without that, much of the work your data science team does will end up undervalued and underutilized.
With the right features, a data science platform can optimize communication between data scientists and decision makers. A data model itself might not mean much to your CEO, but if it can be used to power an application, a dashboard, or another tool he or she uses, your data science results are being delivered instantly and directly. And it’s even better if executives or other non-technical users can generate their own reports by filling in a simple web form — without having to interact with the data model that powers it.
Our entire business was built on getting insights in front of decision makers. You can read more about the features of our platform that make sharing insights painless here.
Does the data science platform let your data scientists put work into production without hogging engineering resources?
It’s a common data science scenario: A data scientist has built a model that will power a product recommendation engine, and now the outputs of that model need to be served up to customers shopping on your website. But the model can’t be deployed into production until an engineer has rewritten the model into a production stack language and moved it into engineering production environment for testing. Only then will it be rolled out (or even later, in some cases, depending on your organization’s release cadence).
This doesn’t have to be the case. A good data science platform will put the entire modeling life cycle into the hands of data scientists by providing them with the capability to deploy a model behind a REST API. (We let your data scientists build that model in whatever language they prefer, whether that’s Python, R, or Spark, and deploy it instantly behind an API.) Your engineering team can then take that API and integrate it anywhere, without recoding.
Other features to look for: tools that help your data scientists monitor the health of their models (such as drift detection or scoring) and the ability to deploy multiple versions of the same model for testing.
Are you ready to start working in a data science platform?
A data science platform isn’t just another tool for your data science team; it can change the way you do business. To learn more about the features of DataScience’s enterprise data science platform, request a demo today.