Erste Schritte

Building an Inclusive AI Strategy for Data Democratization

Dataiku believes that the more people are involved in AI processes, the better the outcome. It is the only platform that brings collaboration at every stage, from ETL to model management.

Practically within a business, inclusive AI means not restricting the use of data or AI systems to specific teams or roles, but rather equipping and empowering everyone at the company to make day-to-day decisions, as well as larger process changes, with data at the core.

Exclusive and Inclusive AI: Pros & Cons

Ultimately, crafting an Enterprise AI strategy that is inclusive will allow for the democratization of data use through all teams, lines of business, and profiles – technical or not – at the business. This is the key to unlocking scalable AI.

Whu Data Science is a Team Sport Schau jetzt

3 Components of Inclusive AI

  1. Collaboration on AI projects between different people with different profiles, strengths, and educational backgrounds; by nature, this also usually means different departments working together to achieve a common goal.

“[The Self-Service Data program] at GE Aviation was born out of a conversation in a conference room. The idea was that you would never be able to hire enough data professionals to meet the data demands of the business, so instead, why not turn the business into data professionals. Taking that premise we started to define what self-service meant for us and how it would work.”

– Jonathan Tudor, Senior Manager of Self-Service Data Engineering and Analytics at GE Aviation, from GE Aviation: From Data Silos to Self-Service

  1. But more broadly than simple collaboration for one particular project, it also translates into the wider infusion of AI processes throughout an organization — a complete transformation in the way of working.
  2. Lastly, but perhaps most importantly today, inclusive AI also has started to take on a slightly different meaning that is outside of the way businesses and companies work internally. It deals with issues like bias, responsibility, interpretability, and fairness — areas where we are also arguably in a wild west situation.
Schau jetzt

Dataiku: The Platform for Inclusive AI

Dataiku is the platform democratizing access to data and enabling enterprises to build their own path to AI. It is the only platform that provides one simple UI for data wrangling, mining, visualization, machine learning, and deployment.

For companies building an inclusive AI strategy, Dataiku offers:

  • A common ground for data experts and explorers, with robust coding and code-free features for everyone in the organization.
  • AutoML and augmented analytics that expand the capabilities of the entire company to design and implement machine learning models.
  • Integrated documentation and knowledge sharing for increased communication around bias, responsibility, interpretability, and model fairness.

Responsible AI for a Sustainable Data Future

Dataiku is the basis of a responsible AI strategy, ensuring that models are accountable, architecture and infrastructure is sustainable, and data processes are governable.

Read more

Go Further

Data Democratization Through Self-Service Analytics

Data-powered organizations give everyone (whether technical or not) the ability to make decisions based on data via a self-service analytics program.

Mehr Erfahren

Effectively Managing Enterprise-Wide Risk

The age of AI presents additional risks across the enterprise that require a tighter — yet more flexible — governance structure.

Mehr Erfahren

Making Enterprise AI an Organizational Asset

How can your company become an AI enterprise? Dataiku enables organizations across all industries to embed machine learning methodology into the very core of their business to bring real value.

Mehr Erfahren

Full Elasticity as the Future of AI

There is no question that elasticity, including on-demand compute resource management, is the future of Enterprise AI.

Mehr Erfahren