IBM Tealeaf #10at20. Simpler, faster, more flexible. The world’s most powerful on-premises behavioral analytics solution is getting even better in June 2019.
Dear fellow Tealeaf users,
The long-awaited release of Tealeaf On-Premises v10.0 is fast approaching. The foundation remains the same – network capture data that provides insights you simply cannot get from any other solution. We’ve built on this world-class foundation with important new capabilities – many of which have come from you.
We introduced v10.0 in our last blog and you heard about it at IBM Think 2019, where we told you about a lot of the features and benefits to expect. Now, we invite you to take a closer look at just a few of the enhancements we have in store for you.
Usability and simplification examples
Save time, be more productive, and shorten learning curves with a new easier-to-use Tealeaf
- New User Experience: not just a coat of paint, we’ve revisited workflows throughout the product, modernizing and enhancing as we go.
- Enhanced native mobile replay: we’re still offering the option to do framework-based native mobile app replay, but we now are also offering snapshot-based, video-like replay, with an emphasis on user privacy for both options. You now have more flexibility than ever in terms of native mobile app replay quality, privacy, and storage requirements.
- Enhanced search: we’re delivering several browser-based replay enhancements to help you save time and to give you more replay flexibility than ever before, bringing your favorite features from the RealiTea Viewer to the browser. Here’s an example: cross-session subsearch. This feature allows you to perform an initial index-based search. Then, you can search across the resulting sessions using non-indexed criteria.
Furthermore, you will be able to readily locate events by searching across both event names and event descriptions. Previously, searches only applied to event names.
- Universal Behavior Exchange integration: syndicate events to IBM Universal Behavior Exchange without the need to engage IBM Services for assistance. Transmit event data to other systems for advanced use cases, like marketing analytics for enriching customer profiles with behavioral data. Or, create user segments based on behavior or struggle, and transmit to other solutions, such as marketing automation systems for behavior-based retargeting (such as re-engaging customers after abandonment).
- V 10.x: .0 is just the beginning of the work we have in store; we have significant plans for v10.1 and beyond following closely on the heels of v10.0. Make sure your voice is heard in the Watson Marketing Idea Portal, where you can share your feedback; engage quickly in the development process; vote and comment on other users’ ideas.
Evolve with Tealeaf
Consult with your IBM representative to plan your upgrade, and realize the benefits of v10 in 2019 - from those listed above to others planned for release. We thank our loyal users for their input as we prepare to release v10.0 and look forward to continuing to grow together.
Remember to check the blog going forward for updates on v10 and other Tealeaf-world happenings.
Please note: IBM’s statements regarding its plans, directions, and intent are subject to change or withdrawal without notice at IBM’s sole discretion.
Information regarding potential future products is intended to outline our general product direction and it should not be relied on in making a purchasing decision.
The information mentioned regarding potential future products is not a commitment, promise, or legal obligation to deliver any material, code or functionality. Information about potential future products may not be incorporated into any contract.
The development, release, and timing of any future features or functionality described for our products remains at our sole discretion.
Performance is based on measurements and projections using standard IBM benchmarks in a controlled environment. The actual throughput or performance that any user will experience will vary depending upon many factors, including considerations such as the amount of multiprogramming in the user’s job stream, the I/O configuration, the storage configuration, and the workload processed. Therefore, no assurance can be given that an individual user will achieve results similar to those stated here.