Discovery that yields data-driven solutions

 

At Salt Data Labs, discovery is the foundation of successful projects. It sets the direction and scope of the project to ensure that the resources and efforts invested in the project are aligned with your organization’s goals.

We start by uncovering desired capabilities and outcomes across your organization, and make recommendations on what’s required to deliver those capabilities - including infrastructure, analyses and modeling, and even staffing.

 

Collaboration and communication based approach

We facilitate discussions and decision making by meeting stakeholders where they are. Whether that requires going deep into the technical details with your engineers or focusing more on strategy and use cases with your non-technical stakeholders.

Industry leading expertise and experience

We use our in-depth experience and knowledge to craft recommendations tailored to your organization’s needs. We are tool agnostic and will help you implement cost-effective, adaptable solutions.

Comprehensive Discovery report

We capture your needs and our recommendations in a report designed to be read by a diverse group of stakeholders. Our clients often share this with their engineers, leadership, investors, data scientists, and other stakeholders.

Seamless transition to implementation

By partnering with you during the discovery phase, everyone involved in the implementation will have the context needed to hit the ground running.

FAQ

  • How long does the Discovery process last?

The Discovery phase is typically 2 - 8 weeks depending on your organization and the scope of the discovery.

  • What is the output of the Discovery phase?

The final output of the Discovery phase is a report, typically Microsoft Word or Google Docs. We are always happy to create additional materials, such as a slide-based presentation or even a video if needed.

  • When is my organization ready for an engagement with Salt Data Labs?

Whenever you begin to feel the limitations of your current capabilities is the right time to start. Limitations might surface as stakeholder confusion about the right approach to solve a problem, data discrepancies, tedious or repetitive manual workflows, limits in reporting granularity, inaccurate or useless predictions from an ML model, or even something the inability to personalize user flows or effectively support automated ads bidding.