This year we spent time researching and talking to experts in Asia, Europe, Latin America, and the US. The result is this new report Oil & Gas: Mitigate Future Disruptions with an Intelligent Supply Chain. The report is free to access, and we hope you take the time to read it and provide us any feedback you may have.
Large File Transfers (LFT’s), in other words, files over 40MB have long been a challenge. Most systems max out below that level and either reject the move or default to some kind of folder link. Specialist vendors such as WeTransfer and FileMail have provided systems to meet the need. FTP (File Transfer Protocol) also a …
Microsoft recently acquired the Alberta, Canada based file migration company Mover. It’s an interesting acquisition that though unsurprising, illustrates the challenges that firms like Microsoft, Box & Dropbox all face in getting people to move legacy systems to newer platforms. In short, enterprise files are stored all over the place, are often inaccessible and seldom …
Pretty much all enterprise content should now be stored and managed in the Cloud, for that was the bold analyst prediction circa 2008. The reality today is nothing close to those predictions. Most ECM content remains stubbornly on-premises, and much of the content that has technically migrated to the Cloud is on-premises in so-called ‘private clouds’… It’s …
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.
Here is our research agenda for the coming year – it may change and will certainly be updated but hopefully this provides you some insight into what we are working on. As always if you have any suggestions or feedback reach out as we want to be sure our work is relevant, timely and of value!
This week I had a chance to catch up with the folks at Dropbox, it’s been a busy time there with the acquisition of HelloSign and the launch of a radical redesign of the Dropbox for Business UI. I have been following the firm (and in full disclosure was a past advisor to them) for quite a few years and watched their growth and transformation with interest. However, what has most interested me about the company is their corporate culture and their approach to design and product development.
Over time, OneDrive has improved, and its consumer version is now equal to both of its significant rivals. But OneDrive for Business, the commercial version, has struggled to keep pace for several fundamental reasons. The main reason is that it didn’t do an excellent job of syncing files and that it relied on aging SharePoint legacy infrastructure, which wasn’t designed for the cloud. In 2016 Microsoft went as far as to state that OneDrive wasn’t for sharing files at all: that job was for a SharePoint team site. Twelve years on from the first OneDrive release, Box and Dropbox have both had IPOs and now provide file-sharing services to 350,000 businesses between them. At Deep Analysis, our question is whether all that is about is to change. Is OneDrive for Business finally ready for prime time?
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.
