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Sisu

How to use Sisu for iterative analysis dimension curation

In the worlds of data analysis and data science, there’s a lot of emphasis on the tool, method, or algorithm. This often overlooks the essential ingredient: good, reliable data. And when people discuss data, it’s usually in the context of “big” data; the more robust the models, the better they fuel predictions.

OODA Loops: Winning faster with decision intelligence

In this post, I’ll share what decision intelligence and the OODA Loop (observe → orient → decide → act) have in common—helping people improve decision-making processes for competitive advantage. Imagine for a moment that it’s the early 1950s. One of the world’s earliest air-to-air combat, or “dogfight,” just took place between the F-86 Sabre and MiG-15.

Announcing Sisu's metric grain feature: Delivering faster, more accurate insights with ML

Organizations of every size have seen a massive increase in demand for faster, more comprehensive answers to vital business questions. Unfortunately, data’s increasing volume and dimensionality makes it difficult and time-consuming to analyze using business intelligence tools alone, often leaving rich and valuable data underutilized.

Announcing dbt + Sisu: Better, faster metric analysis

At Sisu, our goal is to help users leverage their data to understand what’s happening to their metrics, why these changes are occurring, and what they should do next. When dbt Labs announced first-party support for metrics in dbt, we were extremely excited: Sisu users could benefit from versioned, governed metrics as defined in dbt and go directly from defining metrics to running analyses.

Analyzing Star Wars datasets with Sisu

I love Star Wars, and I love data. And recently, when chatting with a friend about #MaytheFourth coming up and what characters, planets, and spaceships we liked best, I thought it would be interesting to explore and analyze public Star Wars datasets with Sisu. The hardest part was deciding which datasets I wanted to use, as there are a ton of great datasets and analyses out there for Star Wars fans!

It's time to decouple diagnostic from descriptive analytics

As a data practitioner, I don’t think I’ve ever been as excited to be part of the data industry as I am today. It’s truly amazing to see the pace of innovation and solutions companies are developing across the data stack. While all of this innovation has given data engineers and data analysts more choice and power than ever before (especially closer to the data infrastructure layer), we still have big challenges to solve as an industry.

How to use anomaly detection and machine learning together

Anomaly detection can be essential to identifying potential incidents using data—including fraud detection, intrusion and security alerts, manufacturing quality control, and medical diagnostics. Flexible and powerful, anomaly detection is an important part of the analyses you’ll need to track and optimize business operations.

How will data and analytics trends impact decision-making in 2022?

Advances in the modern data stack and rapid changes across industries—and every aspect of life—can make following the latest trends, challenges, and innovation feel like ‘drinking from the firehose.’ There’s always new information to learn and different perspectives on what’s most important to prioritize. Some of my favorite ways to stay up to date with the latest include listening to customers, following the data community, and reading industry analyst research.

Fundamentally better dashboards with the power of AI and ML

At Sisu, we think a lot about how people work, how decisions are made, and how we can use data to help where it matters most. One core way we do this is by enabling people to go beyond reporting a metric change to help them quickly understand why that metric changed, empowering data analysts to answer more questions, faster and helping decision makers to efficiently and confidently decide what actions to take next.