Finding value within mountains of unstructured data is both the challenge and the opportunity. Organizations have amassed millions, and in some instances billions, of files over the years. They pay heavily to store them as they believe that hidden in that mass of files are some of value. For sure there is value hidden within the mass, but there is also a high likelihood that there are files there that could cause organizational damage.
When you think about it, finding the right starting point for information governance is often the biggest stumbling block for any governance initiative. As we like to say back in England,“It can be like fighting with fog.” So much to do, so much legacy, so many points of contact, so many people that it is hard to know where to start. In reality many don’t do anything at all. That became our focus – where and how to start. You can check out the webinar here.
Once a year Information Management professionals travel from around the world to attend the AIIM Conference. It is the premier event for networking, education and industry gossip. As such it is particularly important for Deep Analysis, as we get to talk with dozens of end user organizations and find out what they are thinking, planning and doing in the world of Information Management. It’s a chance for us to truly check the industry pulse, make new contacts and reconnect with technology buyers from far and wide.
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.