Sisu | The Decision Intelligence Engine for Automated Data Analysis

In today’s data-driven world, businesses are drowning in information but starving for wisdom. We collect terabytes of data on customer behavior, operational performance, and market trends, yet the process of extracting meaningful insights remains slow, manual, and often inconclusive. Traditional Business Intelligence (BI) dashboards can show you what is happening to your Key Performance Indicators (KPIs), but they rarely explain why. This is the critical gap where opportunities are missed and problems escalate. Enter Sisu, the Decision Intelligence Engine designed to bridge this gap. Sisu leverages the power of AI to automate the complex process of Data Analysis, allowing teams to move beyond simple reporting and understand the root causes behind their changing metrics. This article provides a comprehensive overview of Sisu, exploring its features, pricing model, and how it is pioneering the field of Decision Intelligence to help you make faster, smarter business decisions. We’ll delve into how its unique approach to Automated Analytics sets it apart from conventional tools and empowers your entire organization to become data-informed.

Core Features: How Sisu Delivers Automated Analytics

Sisu is not just another dashboarding tool; it’s an analytical engine that works tirelessly behind the scenes. Its core mission is to automate the manual, time-consuming work of hypothesis testing that data analysts typically perform. By connecting directly to your cloud data warehouse, Sisu can comprehensively test every possible factor in your data to deliver prioritized, statistically significant reasons for metric changes in seconds.

Key Feature Breakdown:

• Automated KPI Analysis: At its heart, Sisu specializes in KPI Analysis. You define a key metric you want to monitor—such as user conversion rate, average order value, or customer churn. Sisu then continuously tracks this metric. When a significant change occurs (either positive or negative), Sisu automatically kicks off an analysis. It sifts through millions or even billions of data combinations to identify the top drivers contributing to that change. For instance, it might discover that a drop in conversion rate was primarily driven by users on a specific mobile OS version, from a particular marketing campaign, in a single geographic region. This level of detail is nearly impossible to find manually in a timely manner.
• General Purpose Anomaly Detection: Beyond pre-defined KPIs, Sisu can proactively monitor your entire dataset for unexpected trends and anomalies. This feature acts as an early warning system, flagging unusual patterns before they become major issues. This proactive approach to Data Analysis helps businesses stay ahead of the curve, addressing problems and capitalizing on opportunities as they emerge, rather than after they’ve already impacted the bottom line.
• Direct Cloud Data Warehouse Integration: Sisu is built for the modern data stack. It integrates seamlessly with leading cloud data platforms like Snowflake, Google BigQuery, Amazon Redshift, and Databricks. This means there’s no need to move or duplicate your data. Sisu queries your data directly where it lives, ensuring security, governance, and access to the most up-to-date information. This architecture allows Sisu to leverage the full power of your data warehouse for its high-speed computations.

Understanding Sisu’s Pricing Model

For enterprise-grade platforms like Sisu, pricing is rarely a one-size-fits-all package. Instead of offering rigid, tiered plans, Sisu provides a custom pricing model tailored to the specific needs and scale of your organization. This approach ensures that you only pay for the value you receive and that the platform can scale with your business’s growth. While you won’t find a public pricing page, the model is typically based on a combination of key factors. Understanding these can help you prepare for a conversation with their sales team.

Key factors that generally influence Sisu’s pricing include:

1. Data Volume and Complexity: The sheer volume of data to be analyzed is a primary consideration. A company analyzing billions of rows per day will have different computational needs than one analyzing millions. The complexity and width of your datasets (i.e., the number of columns or potential factors) also play a role in the analytical workload.
2. Number of Users and Analyses: The number of team members who will be using the platform to create and view analyses impacts the cost. This includes data analysts who set up the analyses and business stakeholders who consume the insights.
3. Feature Set and Use Cases: Sisu offers a range of capabilities, from core KPI driver analysis to proactive anomaly detection and trend analysis. The specific use cases you intend to deploy (e.g., monitoring marketing funnel conversions, diagnosing supply chain disruptions, analyzing product feature adoption) will help determine the required feature set and associated cost.

To get a precise quote, the recommended path is to request a personalized demo. This allows the Sisu team to understand your data infrastructure, your most critical KPIs, and your strategic goals. It’s a consultative process designed to build a solution that directly addresses your biggest challenges in Data Analysis.

Sisu vs. Traditional BI: The Decision Intelligence Advantage

The true power of Sisu becomes clear when you compare it to traditional BI tools and manual analysis methods. While tools like Tableau, Power BI, and Looker are excellent for data visualization and reporting, they are fundamentally reactive. They require a human to form a hypothesis, then manually slice and dice the data to test it. Sisu automates this entire discovery process.

Here is a comparison highlighting the key differences:

This table illustrates that Sisu is not a replacement for BI tools but a powerful accelerator that complements them. You might use a Power BI dashboard to spot a drop in sales (Business Intelligence), then immediately pivot to Sisu to get a prioritized list of reasons why it dropped (Decision Intelligence). This synergy transforms the workflow from slow, manual investigation to a rapid, automated diagnosis, allowing teams to spend less time exploring and more time acting.

A Quick User Guide: From Data to Decision

Getting started with Sisu is a streamlined process designed to deliver value quickly. The workflow empowers data teams to serve business stakeholders with unprecedented speed and depth.

Step 1: Connect to Your Data and Define a Metric The first step is to connect Sisu to your cloud data warehouse. Once connected, a data analyst defines the primary metric for analysis. This is often done with a simple SQL query that specifies the metric, the timestamp, and the relevant dimensions you want Sisu to investigate.

-- Example: Defining a metric for Daily Active Users (DAU) analysis
SELECT
  user_id,
  event_timestamp,
  country,
  device_type,
  app_version,
  marketing_source,
  user_acquisition_cohort
FROM
  production.user_activity_logs

This query tells Sisu what data to use for its Automated Analytics.

Step 2: Run the Analysis With the metric defined, you simply ask Sisu to compare two time periods (e.g., “this week vs. last week”) or two groups (e.g., “converted users vs. non-converted users”). Sisu takes over from here. Its engine rapidly scans all the dimensions provided (country, device_type, etc.) and their countless combinations to see which factors best explain the difference in the metric.

Step 3: Interpret the Fact Board Sisu presents its findings in a clear, prioritized “Fact Board.” It doesn’t just give you a chart; it tells you a story. For example, it might show:

• “The number of Daily Active Users decreased by 15%.”
• “The subgroup of users on app_version 2.1 in country Germany contributed to 45% of this decline.”
• “This subgroup was 3x more likely to churn than the average user.”

Each finding is backed by statistical details, giving you confidence in the results of the AI Analytics.

Step 4: Share and Take Action These insights can be easily shared with business leaders, product managers, or marketers. Instead of saying “DAU is down,” the team can now say, “DAU is down because of a bug affecting German users on our latest app version.” This specific, actionable insight allows for immediate, targeted intervention.

Conclusion: Embrace the Future with Sisu

In an era of increasing data complexity, the old ways of analysis are no longer sufficient. Relying solely on manual exploration and traditional BI dashboards means leaving speed, accuracy, and critical insights on the table. Sisu represents the next evolutionary step in how businesses interact with their data, moving from passive reporting to active Decision Intelligence. By automating the exhaustive work of finding the “why,” Sisu empowers teams to solve problems faster, seize opportunities sooner, and build a true data-driven culture. If your organization is ready to move beyond what-if scenarios and get definitive answers from your data, it’s time to explore the power of Automated Analytics.

To see how Sisu can transform your Data Analysis workflow, visit www.sisu.ai to request a demo and start your journey toward faster, more confident decision-making.