Domo
OCTOBER 22, 2021
Such an offering can also simplify and integrate data management on a massive scale—whether that data lives on premises or in cloud environments—and be used to develop an enterprise-wide data modeling process. The bottom line.
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.
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Sisense
JUNE 15, 2021
By this point, your head is probably filled with visions of how your teams can work smarter with data: coming up with more creative campaigns, streamlining decision-making, and cutting down time spent wrangling data to focus on what they’re best at. Ashley is passionate about data, analytics, AI, and machine learning.
Sisense
MAY 21, 2020
International retail brands like Coca-Cola, Nestlé, and Heineken turn to Trax’s computer vision platform to understand how their products look, perform, and compete on the shelf in real-time.
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
DECEMBER 8, 2023
Organizations seeking cloud migration must recalibrate processes, reconfigure data models, and adapt to a new interface and functionality. For finance teams, the shift to S/4HANA signifies a departure from familiar landscapes, often requiring a steep learning curve and a drop in productivity.
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