Experiencing low customer satisfaction or engagement with your data product, analytics service, or data science solution?

Are you a product leader or data strategist trying to increase customer engagement, stakeholder satisfaction, facilitate sales, or simplify the user experience?

Self-assessing your product or service through the lens of a data product designer can help you address these issues. However, designing for decision support doesn't start with data visualization.

It starts with people.

Gartner says 85%+ of big data projects will fail. Your company probably spent millions on a recent analytics/IOT/big data project too. The team wrote a lot of code and deployed software using “Agile,” cloud, and the latest tech stack. Somehow though, your data product, analytics solutions or digital transformation project isn’t really delivering the business value or user experience everyone hoped for. Whether you’re a CDO, head of analytics, product owner, or technology leader, better design can deliver better business outcomes and indispensable user experiences–and you don’t need to be a designer to get started. We’ll discuss 9 specific self-assessment techniques you can use today to get your project or service on the right track–and safely outside of Gartner’s 85% pool!

After working on many dashboards and data-driven analytics products from the IT domain to mobile and music, I've started to see common UX and UI design problems that can impact your product or solution's usability, and it's bottom line. Whether you're a SAAS company, or an enterprise trying to put analytics to work to save time or money, improve operations, or increase revenue, this guide is designed to help non-designers apply some of the strategic thinking that product designers use to create great user experiences.

Before you write more code, hire a designer, purchase a BI / charting tool, create more dashboards, or architect another "AI / big data solution," use this guide to assess your service's current state.

The guide is specifically intended to help business and technology stakeholders:

  • Assess the design and user experience quality of a data product or analytics service
  • Increase the service's usability, utility, and customer engagement
  • Understand why customers aren't engaging with your service

Download the free PDF below.

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