“Overhauling and modernizing legacy web and commerce systems, particularly those with multiple geographies, products and sites is very difficult indeed,” Pelz-Sharpe said, adding that the Adobe products aren’t the problem, but rather the free-form evolution of a company’s legacy web CMS that can’t just be quickly ripped and replaced. “Web content provides unique challenges as it is, not so much in the form of files; rather, it is made up of strings, links and items of data that have to be assembled dynamically.”
Recently we were contacted by Veritone to request an analyst briefing. At first I wasn’t sure that this was in the scope of our focus at Deep Analysis, but it turned out I was wrong. In short, Vertione is a publicly listed (NASDAQ – VERI) artificial intelligence vendor based in Southern California with roots in media and entertainment. Indeed it is still well known in that world, enabling media firms, studios, and sports organizations to analyze and monetize their digital assets.
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.
The first thing to note is Salesforce is no longer trying to compete with IBM Watson. Rather than leading with AI as a broad solution, Salesforce has baked Einstein into its core offerings very tactically. Instead of touting complexity and power, Salesforce is delivering its AI, with easy to use interfaces and rather than requiring a specialist data scientist to set it up, Einstein can be configured by Salesforce administrators. In the same vein, the outputs of the 35 different AI modules that Einstein runs today are equally simple to digest and leverage. At Deep Analysis we like the approach of small tactical modules baked into service offerings that are easy to use.
First, let’s put Cisco into context. Cisco is a giant with over 60,000 partners generating 85% of its revenue. Technology firms that sell through partners historically had a hands off approach to the customer experience (CX), as managing the customer is considered the partners job. Over the past few years this analyst has seen first-hand the enormous disconnect that such an approach to CX can generate. In a nutshell, the tech firm thinks the customer loves them (after all they spend money on their licenses and products), but in reality the customer loathes them, as nothing works as promised, and they are left to pick up the pieces and sort the mess out themselves.
Deep Analysis is truly delighted to announce that today, Connie Moore has joined our team. Connie is an industry analyst legend, having spent over 20 years leading research at Forrester, before moving to on to become Senior VP of Research at DCG in 2014. Connie’s research spans many facets of content and process management from CX …
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.