Your Path to Enterprise AI

Dataiku’s Mission

Dataiku’s mission is to democratize access to data and enable enterprises to build their own path to AI. By making AI accessible to a wider population within the enterprise, facilitating and accelerating the design of machine learning models, and providing a centralized, controlled, and governable environment, Dataiku allows businesses to massively scale AI efforts.

Data scientists, analysts, and business users across retail, e-commerce, health care, finance, transportation, the public sector, manufacturing, pharmaceuticals, and more use Dataiku DSS to power self-service analytics while also ensuring the operationalization of machine learning models in production.


What is Enterprise AI?

Enterprise AI is the ability to embed AI methodology into the very core of the organization and into the data governance strategy. This means augmenting the work of people across all teams and disciplines with AI for more innovative operations, processes, and products.

The fundamental challenge to achieving Enterprise AI is not software, but rather siloed people and processes. Any enterprise has individuals with vastly different skill sets and tool expertise. Only a very small percentage will be data scientists and engineers. A larger percentage prefer visual tools. An even larger group may not click or code, but instead need to consume the outcome of a data science process, perhaps through a dashboard, report, or application.

With these different skill sets, data projects can easily fall prey to a tower of Babel problem, where all parties speak (and use) different languages. The difficulty of bridging these differences in skill sets leads data projects to become siloed initiatives, failing to serve benefits across the entire enterprise. With this challenge in mind, Dataiku DSS is intended for both builders and beneficiaries of AI.


The Long March Across Technoslavia

Complicating the path to Enterprise AI is today’s highly fragmented ecosystem of data technologies. Any new data platform must successfully integrate at least thirteen dynamic technical areas, each requiring their own set of specialized tools and expertise. One could call this world Technoslavia.


The speed at which leading technologies change in this fragmented world heightens the risk of vendor lock-in, where organizations can soon become strategically dependent on an obsolete tech stack. In order to mitigate this risk, DSS allows for a unified experience of the best combination of technologies on the market that suit the particular needs and evolution of the business.

This technology-agnostic approach allows enterprises to:

  • Leverage the existing skills and secure sustained availability of resources within the workforce

  • Keep the current data infrastructure, but also be ready for tomorrow’s changes

  • Maximize usage of the most up-to-date technologies

  • Extend the adoption of new technologies based on current and future operating requirements

The benefit of this approach is especially apparent considering the move to cloud providers. With business value decoupled from data infrastructure, organizations can migrate between cloud providers or on-premise servers at a much lower cost.


An End-to-End Platform Solution

In addition to being agnostic to specific technologies, such as a single cloud provider, it is also important that Dataiku DSS is an end-to-end solution.

End-to-end in this context means that, with DSS, organizations can execute their own path to Enterprise AI with the following broad steps:

  • Establish connections to an enterprise’s data infrastructure

  • Manipulate flows of data through DSS’ own visual grammar and/or custom code

  • Build machine learning models through a visual interface and/or custom code

  • Deploy and monitor models in production with automated scenarios and checks

  • Maintain governance, version control, and security throughout this process


At least two benefits of being an end-to-end solution deserve highlighting. The first is perhaps obvious. A centralized process can accelerate the time to production.

The second is perhaps more subtle. An end-to-end solution creates the possibility for greater trust and transparency in the end results, a critical quality in an age where AI is poised to introduce radical transformation. When more personas across the organization use the same tool, the process is more inclusive and collaborative. This leads to better oversight and more trusted results.