Data Modelling Embedded Analytics Events 
Sisense
MAY 11, 2021
You can’t talk about data analytics without talking about data modeling. These two functions are nearly inseparable as we move further into a world of analytics that blends sources of varying volume, variety, veracity, and velocity. Building the right data model is an important part of your data strategy.
Domo
AUGUST 4, 2023
SILICON SLOPES, Utah – Today Domo (Nasdaq: DOMO) announced it was named to the Q2 2023 Constellation ShortList for Multicloud Analytics and Business Intelligence Platforms (BI) for the eighth consecutive year. The company was also named to the first-ever Q2 2023 Embedded Analytics ShortList.
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Sisense
AUGUST 6, 2020
When building your data model, it’s vital to avoid both underfitting and overfitting. Although AI product managers may not be involved at the level of algorithm development, they can benefit from recognizing the symptoms of overfitting in the behavior of the model under development. The perfect fit.
Sisense
AUGUST 14, 2020
In the case of a stock trading AI, for example, product managers are now aware that the data required for the AI algorithm must include human emotion training data for sentiment analysis. It follows then that data scientists are suddenly integral to building embedded AI components.
Insight Software
MAY 1, 2023
Introduction Why should I read the definitive guide to embedded analytics? But many companies fail to achieve this goal because they struggle to provide the reporting and analytics users have come to expect. The Definitive Guide to Embedded Analytics is designed to answer any and all questions you have about the topic.
Insight Software
APRIL 12, 2024
By providing these tools, your users can transform their raw data into actionable intelligence, driving data-driven business decisions. This technology tackles the traditional data overload by integrating analytical tools directly within your users’ workflow. However, building this feature in-house wasn’t feasible.
Insight Software
SEPTEMBER 1, 2023
For example, in an e-commerce application, predictive analytics can help anticipate spikes in traffic during specific events or seasons, allowing the team to scale server capacity accordingly. Should You Build or Buy Your Predictive Analytics Solution?
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