Once again, I have to spend time with Law Enforcement and Lawyers; fortunately, this time around, it’s at the EnFuse OpenText conference in Las Vegas. It’s the security conference that isn’t – in the sense that it’s more about compliance, legal and forensics than an RSA conference would be. Though we don’t specialize in security …
OpenText to bridge the governance vs security divide? Read More »
This past week OpenText announced a partnership with Google Cloud (GCP), that announcement lifted OpenText’s share price to his highest ever level. At first blush, the excitement seemed odd, as frankly, everyone has a partnership with Google, Amazon, and Microsoft. Working to unpack the real relevance of this deal for both OpenText & Google reveals an intriguing partnership strategy.
I find the world of OpenText observers fall into two well defined camps. The first camp believes that OpenText’s business is in serious decline and dependent almost entirely on maintenance fees from legacy products. The other camp sees OpenText as steady, slow, profitable but dangerously reliant on maintenance fees from legacy products. Though there are threads of accuracy in both camps, the reality is somewhat different. As of 2019 OpenText is a major player undertaking a key, and to date, pretty successful, pragmatic pivot.
To be fair, Artificial Intelligence and Machine Learning have been used for a long time in enterprise applications but their usage has really been for really complicated scenarios such as enterprise search (e.g., for for proximity, sounds etc) or sentiment analysis of social media content. But it has never been easy to use machine learning for relatively simpler use cases. Additionally, no vendor provided any SDKs or APIs using which you could use machine learning on your own for your specific use cases.
