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Think About Growing Room When Buying an AI Platform

Scaling AI Lynn Heidmann

When shopping for kids’ clothes, you wouldn’t dream of buying anything that fits just right, or you risk finding yourself back at the store looking for the same item mere months later. You always think about a little growing room — sure, it has to be reasonably comfortable now, but it also needs to last. Buying an AI platform isn’t much different when it comes to that space for growth, and here’s why.

→ Get the Full Ebook: Why Enterprises Need AI Platforms

AI Maturity Is Exponential

If you’re just getting started on your AI journey, it might feel like things are slow going. There are seemingly endless challenges at the beginning (including — but certainly not limited to — data access, data quality, hiring data talent, and more), so it can often feel like progress will always be glacial. 

If you’re sticking with this mindset, choosing a tool that’s right for today’s needs might seem logical. After all, today and probably this year, what the majority of people need is facilitated access to data for business intelligence (BI) projects, but certainly no extensive machine learning capabilities, integrated governance systems, or ability to incorporate data projects seamlessly into a production environment.

This thinking is short sighted — and a mistake.

That’s because AI maturity is exponential, not linear. Once teams solve for a few of the big, initial challenges and start to have the right technology and processes in place, reusing and capitalizing on work already completed for early use cases can speed up progress — fast (this is the principle behind the economics of AI). 

Dataiku AI Maturity Curve

That means your organization or team needs to choose tools and technology where they are comfortable today for their needs and use cases, but also in two, five, or 10 years from now as AI maturity soars. Thinking about future-proofing the investment is a critical part of building a modern AI platform strategy. Otherwise, you risk finding yourself back at the store (so to speak) a few months later, re-evaluating vendors, but this time with even more challenges, including:

  • Potentially figuring out how to transfer work done on early use cases to a new tool that allows for a wider range or maturity of AI use cases.
  • Having to retrain people who finally got used to the initial tool selected (no easy feat in the first place) to leverage yet another technology.

Data Projects Themselves Progress

As AI maturity grows, organizations can generally take on increasingly complex use cases. However, it’s also true that some data projects that start out small lead to brand new ideas or initiatives. For example, if you know certain customers have certain risks, that project might start out as a dashboard, but it can quickly grow into a more valuable data project that uses predictive analytics and machine learning to go one step further when it comes to customer behavior. 

Having data tools (and more specifically an AI platform, like Dataiku) that allows for BI-level insights as well as advanced analytics work all in one place allows for this type of growth at the project level. One Dataiku customer, Rabobank, cites this as one of the biggest advantages and differentiators of Dataiku. 


“If you start with a BI tool, then you have to do all kinds of work to set up a new environment once the project progresses. Dataiku allows us to start out with relatively simple insights questions and grow toward a more specific predictive question, developing a model all in the same tool.”

 — Roel Dirks, Product Manager Big Data Lab at Rabobank

→ Dive Into Rabobank’s Data Journey: Get the Full Ebook

Bottom line: while your most advanced data projects today might not involve machine learning, teams will inevitably need these capabilities in the near future (and probably sooner than you think!) to keep up with the pace of evolution in the AI space. You’ll want to buy data tools and AI platforms that allow for that growth.

Dataiku: For Today and Tomorrow

Silos of data, tools, and teams create roadblocks for data and AI initiatives. Analysis and models built in individual environments aren't visible and transferable, are rarely deployed for widespread use, and as a result, don't create impact and value. How do you break down the silos and ensure acceleration of AI impact for years to come? 

The answer is a central, unified platform where business, data, and IT teams can create and collaborate on data and AI projects; a place where all these users contribute their unique skills to create valuable insights.

Dataiku provides teams and organizations with room to grow because it:

  • Isn’t built for one specific use case, but rather is built to be leveraged for everything from simple to the most complex projects and pipelines.
  • Upskills business teams and empowers IT to manage production to free data scientists to pursue significant initiatives.
    for the automation of both design and production to reduce repetitive work around data projects in the long run to maximize quality and reliability.
  • Allows for scale with managed, elastic infrastructure to scale up and scale down as needed to manage costs.
  • Provides the infrastructure for organizations to govern AI projects at scale, including production lifecycle management (monitoring, retraining and testing).
  • Manages risk long-term including operational best practices to ensure legal and regulatory compliance.

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