Summary: Our brief began with the premise that a Fortune 500 company’s data warehouse was underutilized because too few employees knew SQL. After an in-depth technology audit and user assessment, we determined that the crux of the problem was that people needed access to human knowledge and context more than raw data.
Our deliverable: an enterprise-wide portal that provides access to knowledge, information, and data to all employees.
Detail: A Fortune 500 client approached us with a problem: their major investments in data systems were being questions because employees could find data across their two dozen data systems. Employees couldn't figure out where anything was, they didn't know what they could trust, and they didn't know how to learn what they needed to learn in order to make good decisions with the data.
Today's data landscape is fragmented and human-de-centered. Data tools are designed for expert users and passive consumers. The tools do not encourage collaboration which is essential for establishing and sharing the necessary data context to use in stories.
The world is awash in data that is unused because people can't make use of it. Abundant dashboards don't provide insight. Thin presentations don't translate. People ditch data without the insight and context required for sense-making. Our aim in this project was to help them figure out: What's going on? What does it mean? And, what are we going to do about it?
Employees stumble around with data tools, blindly hunting for the people who know and trying to shove data into non-data oriented presentation designs. The data platforms actively ignore interoperability and collaboration needs. Data science platforms are code-focused, not analysis focused, missing the people in the business whose goal is storytelling, not writing code.
What we designed: a makerspace for knowledge. People use the platform to discover, understand, trust, share, track, and tell stories with data. As we dug deep in our research, we discovered the true problem was that people didn't just need to find data, they needed to find the context surrounding the data. Finding context required finding people. Without context, they couldn't trust the data. We also discovered that metadata-oriented search didn't help people find relevant data--because human context isn't captured in metadata (seeing the theme here?). Finally, we discovered that people used a piecemeal approach to sharing data and telling stories, using a jumble of tools that left no history or lineage to trace and learn from. Our solution: capture context through collaboration and story-building and use that context to inform a knowledge graph to recommend data artifacts and allow storytelling that exposes the richness of data and knowledge.
Check out screenshots of our design below.
Get in touch if you have ideas for an AI-based application and would like to talk about how we can help.