Sisu Data was discontinued: what happened and the best alternatives in 2026

Sisu Data is no longer available as a standalone product. Snowflake acquired the decision-intelligence company in October 2023, and Sisu’s own announcement says the team would discontinue the standalone Sisu product as it joined Snowflake.

That leaves former Sisu customers and evaluators with a familiar question: where should you go when you need more than a dashboard? Sisu was designed to identify changes in a metric, surface the drivers behind them, and help a team investigate without manually slicing every dimension. This guide explains what changed and which alternatives now cover that job.

What was Sisu Data?

Sisu Data was an automated analytics and decision-intelligence platform. Rather than requiring someone to start with a hypothesis, it monitored metrics and looked for meaningful changes, segments, and contributing factors. Its pitch was closer to “explain why this changed” than “build me a chart.”

The core workflow included:

• Monitoring key metrics for unusual movement.
• Breaking a change down across dimensions such as segment, channel, plan, geography, or product.
• Highlighting drivers that may explain an increase or decline.
• Giving analysts and operators a starting point for a deeper investigation.

This made Sisu particularly relevant to teams with broad, fast-changing datasets and too many possible cuts to inspect manually.

What happened to Sisu Data?

Snowflake acquired Sisu in October 2023. On Sisu’s announcement page, the company said it had decided to join Snowflake and that it would discontinue the standalone Sisu product. The announcement positions the acquisition around Snowflake’s continued investment in machine-learning-powered functions.

That is different from a company quietly disappearing or a product suffering an unexplained outage. The standalone product ended as part of an acquisition, while the team and technology moved into a larger data-platform strategy. Still, customers who depended on the Sisu application needed a replacement for the application workflow itself.

What to look for in a Sisu alternative

Sisu was not just another dashboard builder, so do not select a replacement based on chart templates alone. Look for:

• Fast investigation from a real question. A tool should make it easy to ask why a KPI changed, compare periods, and drill into segments.
• Trustworthy data context. Definitions, relationships, and source visibility matter when several teams rely on the same metric.
• A clear path from insight to sharing. An investigation should become a dashboard, saved analysis, alert, or shared explanation instead of living in one person’s browser tab.
• Human control over conclusions. Automated signals can be useful, but people need to inspect the query, change assumptions, and rule out bad data.
• Fit with your stack. Confirm support for the databases, warehouses, and operational sources where your metrics live.

The best Sisu Data alternatives in 2026

1. Basedash: AI-native investigation with governed data

Basedash is a strong fit for teams that used Sisu to get from a metric movement to an explanation. Start with a question such as “why did expansion revenue fall last month?”, ask follow-ups to break it down, review the generated SQL, and save the useful result to a dashboard or alert.

Why it works as a Sisu replacement:

• Conversational investigation. Follow-up questions keep the analysis moving without forcing a fresh query for every cut.
• Visible SQL. Teams can inspect the query behind an answer, refine it, and validate the result rather than trusting an opaque narrative.
• Governed context. Business terms, table relationships, and column descriptions help analysis use the same definitions your team does.
• A complete delivery workflow. Turn an answer into a chart, dashboard, shared link, or recurring alert.
• Broad connectivity. Query databases and warehouses directly or bring in operational SaaS data through 750+ connectors.

Best for: teams that want AI-assisted root-cause exploration without separating investigation from their everyday BI workflow.

2. ThoughtSpot: enterprise search and automated insights

ThoughtSpot combines a search-oriented analytics interface with Spotter, its generative-AI assistant, and enterprise governance features. It is a credible option for large organizations that want a centrally managed semantic model and broad self-service access.

The trade-off is enterprise-oriented implementation and pricing. It generally makes more sense for organizations with mature data teams and a large internal audience.

Best for: large enterprises that need governed search-driven analytics at scale.

3. Looker with Gemini: governed exploration for Google Cloud teams

Looker’s semantic layer can make metric definitions durable across teams, and Gemini adds natural-language capabilities on top of modeled data. The combination is attractive when your warehouse is BigQuery and you already have the engineering capacity to maintain LookML.

Best for: Google Cloud organizations that value a deeply modeled, governed analytics layer.

4. Snowflake-native analytics: keep the workflow close to the warehouse

For organizations fully invested in Snowflake, native Snowflake capabilities can reduce data movement and keep access controls close to the warehouse. This is a logical route to evaluate after the Sisu acquisition, but it is not automatically a replacement for every feature of the former Sisu application. Verify the actual investigation, dashboard, alerting, and sharing workflows your users need.

Best for: Snowflake-centric teams that prefer warehouse-native tools and can assemble the surrounding BI workflow.

5. Metabase: approachable dashboards with a lower barrier

Metabase is a practical option when the real need is not automated diagnosis but easier dashboard exploration. Its visual query builder and SQL editor make it useful for general self-service reporting, though it does not center its product around AI-driven metric investigation in the same way as Basedash or ThoughtSpot.

Best for: teams that primarily need accessible dashboards and have a constrained budget.

Sisu Data alternatives compared

How to migrate from Sisu Data

Before choosing a tool, identify the decisions Sisu supported. A list of monitored metrics is not enough; capture the teams that consumed an alert, the dimensions they used to investigate it, the dashboards they consulted next, and the actions they took afterward.

Then rebuild the workflow in order:

1. Reconnect approved data sources using scoped permissions.
2. Define the KPI formulas, comparison windows, and business terms that matter.
3. Recreate the highest-value investigation paths: period comparisons, common segments, and follow-up questions.
4. Build the dashboards and alerts that route an insight to the right owner.
5. Test representative metric movements with business stakeholders before retiring the old process.

The goal is not to recreate every automated signal. It is to ensure that a meaningful change can be found, explained, verified, and acted on quickly.

The takeaway

Sisu’s standalone product ended because its team and technology joined Snowflake, not because decision intelligence stopped being useful. The need it addressed remains: teams still need a practical way to investigate changes in business metrics without manually interrogating every possible segment.

For teams that want to investigate in plain English, verify their SQL, and immediately share the answer, Basedash combines that workflow with governed dashboards and alerts.

FAQs

Is Sisu Data still available?

No. Sisu Data’s standalone product was discontinued after Snowflake acquired the company in October 2023. Sisu’s public announcement says the company joined Snowflake and would discontinue the standalone Sisu product. Any evaluation of Sisu today should focus on replacement workflows rather than a new Sisu subscription.

What did Sisu Data do?

Sisu Data was a decision-intelligence platform that monitored metrics and helped teams investigate what changed and which dimensions contributed to the change. It focused on surfacing possible drivers for metric movement, giving users a faster starting point than manually filtering dashboards or writing every exploratory query from scratch.

Did Snowflake replace Sisu with the same product?

Snowflake acquired the Sisu team and technology, but a customer should not assume that every former Sisu workflow is available as a one-for-one standalone replacement. Evaluate the concrete tasks you need—metric investigation, semantic context, dashboards, alerting, sharing, and access control—against the Snowflake features and surrounding tools you plan to use.

What is the best Sisu alternative for root-cause analysis?

The right choice depends on your stack and operating model. ThoughtSpot and Looker are viable enterprise options for governed analytics. Basedash is a strong option for teams that want to ask investigative questions in natural language, inspect the SQL, use governed context, and turn an answer into a shareable dashboard or alert.

How should I evaluate an AI analytics replacement?

Use your own schema and real historical questions. Choose a few known metric changes, ask the tool to investigate them, and verify the SQL and totals with an analyst. Also test whether you can encode metric definitions, control data access, share the result, and reproduce the analysis later. A benchmark demo is useful, but it cannot prove fit for your business logic.