Data science: Breaking down the silos
Activating the combined power of business, data and application professionals
Today’s data science and analytics teams are often composed of individuals with a variety of skill sets, educational backgrounds, levels of exposure to open source tools and professional needs. Here’s a typical breakdown:
- Business professionals need straightforward ways to first discover and then to experiment with data with algorithms and visualizations. They want a visual, drag-and-drop tool that doesn’t require them to code.
- Application developers integrate models to build apps and rely on a range of data services in order to drive business innovation. They require a toolset that ensures secure application development and deployment across any cloud.
- Data engineers want to become more data-driven and are looking for simplified data management and governance. They need the assurance they are equipped with the right tools that ensure they can work with everything they’ve built so far.
Finally, data scientists are increasingly challenged by the requirements for their in-demand skills from across the company. They need to produce value from relevant data sources. Like scientists in any field, they aspire to form and test hypotheses, experiment, test some more, learn from their key findings and iterate. But unlike scientists working in other disciplines, data scientists must perform these steps at the cadence of business.
Working alongside data engineers and data scientists are teams who initially trained on – and therefore prefer – using open source tools, since these tools can offer greater freedom and flexibility. The overall dynamic can create silos, with each person using a different tool or platform – potentially leading to stifled collaboration and knowledge-sharing across teams.
SPSS Modeler evolving in the IBM Watson Studio family
IBM has addressed these challenges by providing an enterprise data science offering. It’s led by IBM Watson Studio for building models, and IBM Watson Machine Learning for deploying them. Together, the offering serves each of the above constituents with a set of tools tailored to their needs in a unified environment. Watson Studio and Watson Machine Learning empower anyone – from business analysts to experienced data scientists – to build and deploy machine learning models quickly and easily. Together, they provide a governed, scalable solution engineered for hybrid, multicloud environments that demand mission-critical performance.
SPSS Modeler, our no-code data science tool that has pioneered visual modeling for 25 years, is embedded within Watson Studio. Alongside it are the most recent open source tools, including Jupyter Notebooks. Going a step further, IBM added AutoAI to Watson Studio, automating many time-consuming data science tasks that dramatically reduce the time needed to complete data science projects. These additions can help data scientists and analysts to get started without delay.
When you’re ready to push your model into production, Watson Machine Learning is flexible enough to deploy any model: created with SPSS Modeler, open source tools within Watson Studio, or an open source integrated development environment (IDE).
Multiple ways to get started with IBM Watson Studio
We’ve recently released two new deployment options within this portfolio: Watson Studio Desktop Perpetual, the perpetual version of our subscription service that’s been available since late 2018; and Watson Machine Learning Server, a lightweight, single-node option for model deployment.
Watson Studio Desktop Perpetual is currently available through SPSS Modeler Gold, and it connects to Watson Machine Learning Server.
Learn more about Watson Studio Desktop. Or check out our on-demand SPSS Modeler and Watson Studio webinar on building fraud detection models in three different ways to see how business, data and application professionals come together as a team to drive AI-powered business.
Finally, learn how to help your organization accelerate better AI outcomes with a complimentary IBM eBook: Six reasons to upgrade your data science.