Building upon a culture of AI development, vast deployment, management, and reuse means organizations must identify ways to access data insights from disparate data systems faster and diffuse valuable information across every team more effectively.
Organizations looking to dive into these areas in order to strategically scale AI initiatives should check out the insights and advice shared in this Everyday AI London conference session featuring Rachel Kurtz, Analytics Architect at Interworks. Interworks is a people-focused tech consultancy that guides clients to collaborative, customized solutions following the entire data lifecycle from data storage and data engineering to IT and visualization.
If you’re more of a reader than a watcher, but still want to learn about the ways that Dataiku can be leveraged to assemble valuable data assets and forge connections between data and domain experts, keep reading for a summarization of the principal points of the talk:
Overcome Silos and Enhance Collaboration With Full Stack Solutions
Analysts are in charge of visual data preparation; data scientists are busy coding; and engineers are monitoring away; but what is the thread that weaves the work of all of these different teams together? Interworks reveals that for many of their clients, this vital thread is Dataiku.
Think about looking at a use case where data engineers are in a silo in AWS and business analysts are working only over in Tableau. It is easy to see how this situation doesn’t turn out timely, data-driven business decisions. Dataiku mitigates this issue by providing a direct and seamless connection to the home of the database and the visualization tools that business teams use to retrieve their insights, yielding impactful results.
Rachel shares a specific example of a bank benefitting from this connection between data science teams and business stakeholders as they use predictive analytics to predict loan defaults in order to make granting decisions. Another less serious example she shares is using Dataiku with Snowflake to quickly determine who the “best” member of the Scooby-Doo crew is. This goes to show that no matter what the end goal of your project is, the way you get there makes a difference, and ultimately your entire data journey can be optimized with a systematic approach that places emphasis on platform and application symbiosis.
Prioritizing Data Access & Visibility
Knowing where data lives and being able to congregate those disparate data sources into a collaborative platform is key. Dataiku provides the opportunity to directly connect to visualization formats that are friendly for non-technical users without burdening the technical teams.
Ultimately, Dataiku lets you prepare data and build your flow all in one place — a place from which you can easily export and create an API for your model or dive into data reporting tools like Tableau and Microsoft Power BI. Whether you are spinning up your server on top of Snowflake or still pulling from spreadsheets, integrating data from disparate sources and turning that data into material that is digestible for people who don’t dig the minutiae of data science, is easy with the collaborative nature and supple capabilities of Dataiku.
Providing the Support to Scale
Tapping into a wider range of people to contribute to the development and use of AI within the enterprise, Dataiku accelerates the development of AI capabilities within your organization. At the individual level, daily steps go more quickly thanks to Dataiku’s highly intuitive, visual interface and flexible solutions. Finally, looking at it from a broader scope, collaboration goes more smoothly as team members work together in a single, shared environment.
By equipping users of diverse expertise and perspectives with the right resources and inviting them to join the data conversation early on, your organization can unlock untapped value while navigating decisions that drive major change across the organization as you continue to scale AI.