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Just like applications, databases and other IT constructs industry analysts tend to be siloed. The bigger the company the more this happens: you tend to have specialists with a narrow focus. In smaller companies such as ours, analysts have to be able to take a broader view. Nevertheless, we remain siloed, even if our remit is somewhat broader than it might be in other companies. Thus, as analysts, we all tend to have our own comfort zones.
What is the point of this rambling? I have started to notice a blurring of the lines. I am being pushed out of my comfort zones. For example, Dell Boomi recently briefed me on its new, in-cloud, API management capabilities. Very nice. And it’s obviously an important part of the integration infrastructure, not to mention it being a major issue, but it’s about programming and applications and not about data – which is my bailiwick.
To take another example consider S4/HANA. Is that a database story or an application story? And what about the various data products from vendors like Informatica, Mentis and Dataguise, which are essentially security products? Of course, SIEM (security information and event management) has always been a crossover point between data (analytics) and security.
More generally, IT stories are getting broader. Data warehousing isn’t just about the data warehouse any more, it’s about the whole analytic environment: how the data warehouse co-exists with stream processing and NoSQL. Customer engagement is about governance but it’s also about analytics. Similarly, cloud isn’t just about cloud, it’s about hybrid environments and how you migrate to them. To take another case, the Information Governance Initiative (www.iginitiative.com) defines information governance as “the activities and technologies that organisations employ to maximise the value of their information while minimising associated risks and costs.” If you look at some of the infographics on IGI’s website you’ll see that it sees information governance as having 19 different “facets” that span a very broad range of environments and technologies.
Another example is semantics. Here we are talking about a single technology that has broad implications across a variety of solution areas. We will shortly be publishing white papers and Market Updates (Bloor’s version of a Magic Quadrant) on both data preparation platforms (Paxata, Trifacta, ClearStory et al) and graph and RDF databases. Semantic technologies are deeply woven into both of these areas, as they are to cognitive computing and even application development (there is a 4GL called Aronto that generates applications from a semantic model).
Of course, this doesn’t mean that there is no place for looking in detail at individual products and product areas but they increasingly need to be evaluated within a wider context. We used to be able to treat software products in isolation but analysts increasingly have to recognise that no product is an island. That was always true but now more so than ever.