Book description
To satisfy end users who want easily accessible answers, many software vendors are looking to add analytics and reporting capabilities to their applications. Embedding analytics into applications can lead to wider adoption and product use, improved user experience, and differentiated products, but embedding analytics can also come with challenges and complexities.
In this report, author Courtney Webster reviews several approaches and methods for embedding analytics capabilities into your applications. Should you implement a separate reporting portal, an in-application reporting tab, or go all in with a fully embedded in-page analytics solution? And do you build your own or buy a solution out of the box?
To help you choose the right embedded analytics tool, Webster examines seven challenges—from customization, usability, and capabilities to scalability, performance, and data structure support—and presents best practice solutions for each.
Product information
- Title: Embedding Analytics in Modern Applications
- Author(s):
- Release date: July 2016
- Publisher(s): O'Reilly Media, Inc.
- ISBN: 9781491959886
You might also like
book
Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow, 2nd Edition
Through a series of recent breakthroughs, deep learning has boosted the entire field of machine learning. …
book
The AI Ladder
AI may be the greatest opportunity of our time, with the potential to add nearly $16 …
book
Building Machine Learning Powered Applications
Learn the skills necessary to design, build, and deploy applications powered by machine learning (ML). Through …
book
Architecting Modern Data Platforms
There’s a lot of information about big data technologies, but splicing these technologies into an end-to-end …