DataStax announced the general availability of a new Zero-Downtime Cloud Migration tool that enables organizations to seamlessly migrate live data from self-managed Apache Cassandra instances to the company’s fully managed serverless Cassandra offering, DataStax Astra with no downtime. The Apache Cassandra open source database is often used for workloads that need to deliver massive amounts of data to users around the world with high reliability. As such, many Cassandra production applications are business critical, always on, and downtime is not an option. With DataStax’s new migration tool, enterprises can easily migrate live production Cassandra or DataStax Enterprise workloads to the DataStax Astra database-as-a-service (DBaaS) to quickly take advantage of the cost savings and other benefits of fully-managed, serverless Cassandra. The DataStax Zero-Downtime Migration tool is available for zero cost, and it comes with every DataStax Astra subscription. For more information on the fastest way to get up and running on Astra without any downtime, see
ThoughtSpot, provider of search & AI-driven analytics, announced it has entered into a definitive agreement to acquire SeekWell. With SeekWell, customers will be able to operationalize their analytics and use SQL to push cloud data insights directly to business applications. As the companies integrate their offerings, the combination of ThoughtSpot and SeekWell will let users use natural language search to pull data from cloud data warehouses, modify it with productivity applications like Google Spreadsheets, then automatically and sync it back to business applications like Salesforce. With SeekWell and ThoughtSpot, customers can find insights easier, and close data loops by pushing insights directly back to applications and scaling data-driven decision making in the process.
SeekWell capabilities are available from ThoughtSpot starting today. As SeekWell becomes fully integrated into ThoughtSpot, this entire process will be powered by natural language search. No SQL will be required; instead, customers can use search to find data in the cloud, enable modification via productivity apps, and sync it with business apps. ThoughtSpot will also invest in building new business app integrations, expanding the number of end destinations for SeekWell.
Boostlingo, a provider of SAAS-based interpreting technology solutions for global language access, announced its addition to the Zoom App Marketplace. Zoom Meetings that requires foreign language support will be able to add professional interpreters directly into their Zoom Meetings experience. Professional remote interpreters accustomed to using the Boostlingo platform can now be added on-demand into the Zoom environment. The Boostlingo platform supports a network of 8000+ certified interpreters and 300 spoken word languages and multiple sign languages. Boostlingo will enable you to:
- Configure Zoom services for your Zoom accounts – Zoom‘s configuration policies allow you to create Zoom access for your clients.
- Allow your client accounts to register their Zoom instance with Boost – Your client’s Zoom administrators can authenticate their account with Boostlingo, and all of your client’s supported users will be automatically enabled.
- Boostlingo supports Zoom authentication – Boostlingo can support authentication at both the organizational level and at the individual user level.
- Add VRI interpreters On Demand – Once the configuration is done, interpreters receive interpretation requests like they normally do and when answered on the web, they will be automatically joined into a Zoom session. Use your own interpreter team members or enable the Boostlingo Professional Interpreter Network (BPIN) to take the call for you.
- Full back-end reporting – Boostlingo captures all the same important call data that a regular Boostlingo interpreting session collects.
SciBite, an Elsevier semantic technology company, announced the launch of SciBiteSearch, a scientific search and analytics platform that offers interrogation and analysis capabilities across unstructured and structured data, from public and proprietary sources. SciBiteSearch provides scientists with access to domain specific ontology and AI-powered search capabilities.
SciBiteSearch uses knowledge graphs to augment searches and deliver not only items relevant to the query but the structure and relationship between them. The addition of AI enables natural language understanding. SciBiteSearch can integrate data across a range of use cases including:
- Unify multiple data sources into a single solution, designed for departments wanting their own tailored search tool. For example, combining public biomedical literature, clinical trials, and grants with proprietary data.
- Incorporate full-text biomedical literature from publishers to better address researchers’ discovery needs. For example, users can load subscribed licensed data from partner publishers or content brokers.
- Enable users to get accurate search results without the need to understand the complexities of Named Entity Recognition (NER), its underlying data structures, or the functions required to surface.
SciBiteSearch creates sophisticated query and assertion indices created using SciBite’s tools and ontologies. A streaming load API, connectors, and parsers for different sources and content types let it load and process content to make it searchable.
Lucid, provider of visual collaboration software, announced its virtual whiteboard, Lucidspark, is now available as an iOS app for tablet devices. Lucidspark is a place for distributed teams to brainstorm and collaborate together in real time. The launch of the Lucidspark tablet app will provide users with greater flexibility and accessibility across devices, helping teams to seamlessly align and more quickly move into action. With the Lucidspark app, users can access all the features of the browser experience, including:
- Track individual contributions with assigned Collaborator Colors
- Facilitate large and small group sessions with an infinite canvas and Breakout Boards
- Automatically synthesize generated ideas into action plans with Gather and Sort
- Share feedback through comments, mentions, and in-product chat
- Brainstorm ideas in real time or asynchronously in a shared visual workspace
- Leverage integrations with Jira, Microsoft Teams, and Slack to align teams across existing workflows
Download the Lucidspark app on the App Store.
AI-powered learning platform, Docebo Inc. announced the launch of a multi-product learning technology suite. Previously, Docebo has focused on solving the problem of how organizations deliver training with its learning management system (LMS). With the launch of Docebo Learning Suite, Docebo will go beyond content delivery and address challenges across the entire learning lifecycle, from content creation and management to measuring learning impact and key business drivers. The launch of Docebo Learning Suite coincides with the launch of Docebo Shape, a content creation product built on AI. Developed internally, Docebo Shape enables businesses to bring more internal experts into their elearning content strategy by leveraging AI to create engaging learning content in minutes. Including Docebo Shape, the core products that come together to transform the company’s offering into a cohesive learning suite include:
- Docebo Learn LMS, a Learning Management System that has been used by more than 2,000 customers;
- Docebo Shape, a content creation product that uses AI to create elearning content in minutes;
- Docebo Content, a library of thousands of off-the-shelf, mobile-ready learning courses, and;
- Docebo Learning Impact, a data-driven tool that allows users to measure the effectiveness of their learning programs on their people and improve ROI.
In this issue we have recommended reading on creating a modern data workspace, enterprise search & AI, enterprise AI trends, no-code / low-code companies, Medium’s latest pivot, Facebook’s new publishing platform plan, and Wikipedia getting the big guys to pay.
Our content technology news weekly will be out Wednesday as usual.
Decoding the no-code / low-code startup universe and its players
No-code and low-code companies are sprouting up everywhere and whatever your initial impression has been, it’s time to consider their increasing utility and role in even complex application development environments. No-code / low-code can democratize and speed development by bringing business analysts and software developers closer together. Pietro Invernizzi and Ben Tossell have put together a very helpful resource looking at 145 companies with lots of detail and access to an Airtable spreadsheet. (Click the image for a version you can read)
We failed to set up a data catalog 3x. Here’s why
Prukalpa Sankar generously and delightfully describes what she and her team learned from multiple attempts at creating a “modern data workspace”. Some of you will be familiar with the problems and lessons, but the approaches, tools used, and specific examples, will still be instructive.
When explainable AI meets enterprise search
Lack of transparency in AI is in general an unsolved problem even though there is obviously huge value in its application across domains. Martin White has some thoughts and advice on what this means for enterprise search.
Enterprise search presents a special challenge when it comes to AI transparency. Most other enterprise processes are close to linear in execution, so the impact of AI on performance can be relatively easily assessed and monitored. In the case of search, every query is a new workflow as it is dependent on the knowledge of the individual and the intent behind their search.
The mess at Medium
Lots of activity in the independent writing/publishing platform space recently: the Substack Pro controversy, Twitter‘s acquisition of Revue, Facebook’s announcement of a new platform for independent writers, and Medium’s latest pivot. None of these companies has figured out a sustainable business model, and none provide writers a safe long-term way to control their brand and content. They can in some cases be supplemental marketing and delivery channels if you own and control your content and publishing capability however. 🙂
Casey Newton reports on the changes at Medium…
Medium’s original journalism was meant to give shape and prestige to an essentially random collection of writing, gated behind a soft paywall that costs readers $5 a month or $50 a year. Eleven owned publications covered food, design, business, politics, and other subjects… But in the end, frustrated that Medium staff journalists’ stories weren’t converting more free readers to paid ones, Williams moved to wind down the experiment…
- Facebook’s new platform… Supporting independent voices, via Facebook
- Great free teaser content as usual… Enterprise AI trends to watch In 2021 via CBInsights
- Wikipedia Enterprise… Wikipedia Is finally asking big tech to pay up via Wired
- Long but worthy article by Tim O’Reilly on the bigger picture… The end of Silicon Valley as we know it? via O’Reilly Radar
The Gilbane Advisor is curated by Frank Gilbane for content technology, computing, and digital experience professionals. The focus is on strategic technologies. We publish more or less twice a month except for August and December. We also publish curated content technology news weekly We do not sell or share personal data.
Arthur, the machine learning model monitoring company, released a suite of new tools and features for monitoring natural language processing models. Natural language processing is one of the most widely adopted machine learning technologies in the enterprise. But organizations often struggle to find the right tools to monitor these models.
The Arthur platform now offers advanced performance monitoring for NLP models, including tracking data drift, bias detection, and prediction-level model explainability. Monitoring NLP models for data drift involves comparing the statistical similarity of new input documents to the documents used to train the model. The Arthur platform automatically alerts you when your input documents or output text starts drifting beyond pre-configured thresholds.
Arthur now also offers bias detection capabilities for NLP models, allowing data science teams to uncover differences in accuracy and other performance measures across different subgroups to identify and fix unfair model bias. The platform also offers performance-bias analysis for tabular models. The Arthur team has also released a new set of explainability tools for NLP models, providing token-level insights for language models. Organizations can now understand which specific words within a document contributed most to a given prediction, even for black-box models.