Hex vs. BI tools

Learn why customers are using Hex to answer deeper questions, save on analytics spend, and build better reporting

The problem with BI

Business Intelligence products are tasked with informing the organization with data. But BI fails to deliver return on investment.

  • Business people find themselves waiting in long queues Self Service workflows are too hard

  • Data teams end up overwhelmed with requests

  • Business people export data to spreadsheets where it loses lineage, governance, and accuracy.

Modern data teams are adopting Hex to move beyond BI and provide more value to their organizations.

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Why do BI products fail?

figure - Other tools are needed

Other tools are needed

ETL, Data Science, Data Governance, Semantic Modeling

figure - Dashboards are too simplistic

Dashboards are too simplistic

Aggregate, backward looking data doesn’t answer user questions

figure - Follow-up questions are hard

Follow-up questions are hard

When you hit a wall, you have to reach out to an analyst, and wait, and wait, and wait

figure - Data is siloed

Data is siloed

Many business people are answering questions in spreadsheets, which are disconnected from BI

Hex offers a new approach to data and analytics

Everything is connected

Data users speak a variety of languages. Spreadsheet. SQL, Python. Visualization. Drag and Drop. AI prompt. In Hex, all of these ways of working snap together interchangeably. We call this the “Tao of Hex.” Chain together code with visualization, create calculations in a spreadsheet and incorporate them into a predictive model. There’s no limit to how deep you can go.

Self-serve that is actually self-serve

There’s a fast path to follow-up questions in Hex. Our data exploration experience has been reimagined with AI front-and-center, and drag and drop visualization and pivots for your 2nd and 3rd order questions.

More powerful data apps

Go beyond the limits of BI dashboards by incorporating predictive, ML, and other advanced models in your Hex apps. Now users can interact with analysis that offers more than simple aggregates. And when the user has a follow-up question that they need some help answering, it’s easy for the data team to jump in, because they don’t have to use a different tool.

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See it in action

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