Smart Data Collective
AUGUST 11, 2022
While different metrics can be used to determine data quality, accuracy is the primary focus since it is easy to change for different data sets and concerns for decision-makers. Data quality is crucial in Artificial Intelligence and automated decision-making. Conclusion.
BA Coach
NOVEMBER 4, 2024
Rather than relying on abstract requirements, this principle encourages business analysts (BAs) to use real-world scenarios and examples to demonstrate how a solution will satisfy a need. This provides a clear, shared vision of the purpose and need, helping decision-makers make informed choices based on the latest evidence.
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Actian
OCTOBER 21, 2019
With this release, Actian Avalanche is now available on Microsoft Azure, AWS, and on-premises, delivering on our hybrid and multi-cloud vision. This is particularly appealing to those customers who have large amounts of data which is growing quickly but may not need compute to scale at the same pace.
Analysts Corner
NOVEMBER 28, 2024
We must be more than just number crunchers; we need to be visionaries who understand how to leverage data effectively within our organizations. The growing importance of data requires leaders to be poised to tackle new challenges. AI tools are transforming how we gather and interpret data.
Juice Analytics
MARCH 11, 2021
The right product manager We’ve helped launch data products in many industries including healthcare, education, insurance, advertising and market research. In our experience, the recipe for building a successful data product is dependent on a number of specialized roles. Our clients bring the product vision; we make it happen.
Bridging the Gap
SEPTEMBER 7, 2023
This specification might also be referred to as a business case or a vision document, or a business requirements document, although in practice, VRDs typically include many additional sections that would include functional requirements. There are a few common types of data requirements documentation.
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. The potential uses of app behavior and visitor activity data stores are bounded only by the ingenuity of the data engineer.
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