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HP Idol and Hadoop: Search, Analytics, and Big Data for You

I was clicking through links related to Autonomy IDOL. One of the links which I noted was to a YouTube video labeled “HP IDOL for for Hadoop: Create a Smarter Data Lake.” Hadoop has become a simile for making sense of Big Data. I am not sure what Big Data are, but I assume I will know when my eight gigabyte USB key cannot accept another file. Big Data? Doesn’t it depend on one’s point of view?

What is fascinating about the HP Idol video is that it carries a posting date of October 2014, which is in the period when HP was ramping up its anti-Autonomy legal activities. The video, I assumed before watching, would break from the Autonomy marketing assertions and move in a bold, new direction.

The video contained some remarkable assertions. Please, watch the video yourself because I may have missed some howlers as I was chuckling and writing on my old school notepad with a decidedly old fashioned pencil. Hey, these tools work, which is more than I can say for some of the software we examined last week.

Here’s what I noted with the accompanying screenshot so you can locate the frame in the YouTube video to double check my observation with the reality of the video.

First, there is the statement that in an organization 88 percent of its information is “unanalyzed.” The source is a 2012 study from Forrsights Strategy Spotlight: Business Intelligence and Big Data. Forrester, another mid tier consulting firm, produces these reports for its customers. Okay, a couple of years old research. Maybe it is valid? Maybe not? My thought was that HP may be a company which did not examine the data to which it had access about Autonomy before it wrote a check for billions of dollars. I assume HP has rectified any glitch along this line. HP’s litigation with Autonomy and the billions in write down for the deal underscore the problem with unanalyzed data. Alas, no reference was made to this case example in the HP video.

Second, Hadoop, a variant of Google’s MapReduce technology, is presented as a way to reap the benefits of cost efficiency and scalability. These are generally desirable attributes of Hadoop and other data management systems. The hitch, in my opinion, is that it is a collection of projects. These have been developed via the open source / commercial model. Hadoop works well for certain types of problems. Extract, transform, and load works reasonably well once the Hadoop installation is set up, properly resourced, and the Java code debugged so it works. Hadoop requires some degree of technical sophistication; otherwise, the system can be slow, stuffed with duplicates, and a bit like a Rube Goldberg machine. But the Hadoop references in the video are not a demonstration. I noted this “explanation.”

Third, HP jumps from the Hadoop segment to “what if” questions. I liked the “democratize Big Data” because “Big Data Changes everything.” Okay, but the solution is Idol for Hadoop. The HP approach is to create a “smarter data lake.” Hmmm. Hadoop to Idol to data lake for the purpose of advanced analytics, machine learning functions, and enterprise level security. That sounds quite a bit like Autonomy’s value proposition before it was purchased from Dr. Lynch and company. In fact, Autonomy’s connectors permitted the system to ingest disparate types of data as I recall.

Fourth, the next logical discontinuity is the shift from Hadoop to something called “contextual search.” A Gartner report is presented which states with Douglas McArthur-like confidence:

HP Idol. A leader in the 2014 Garnter Magic Quadrant for Contextual Search.

What the heck is contextual search in a Hadoop system accessed by Autonomy Idol? The answer is SEARCH. Yep, a concept that has been difficult to implement for 20, maybe 30 years. Search is so difficult to sell that Dr. Lynch generated revenues by acquiring companies and applying his neuro-linguistic methods to these firms’ software. I learned:

The sophistication and extensibility of HP Autonomy’s Intelligent Data Operating Layer (Idol) offering enable it to tackle the most demanding use cases, such as fraud detection and search within large video libraries and feeds.

Yo, video. I thought Autonomy acquired video centric companies and the video content resided within specialized storage systems using quite specific indexing and information access features. Has HP cracked the problem of storing video in Hadoop so that a licensee can perform fraud detection and search within video libraries. My experience with large video libraries is that certain video like surveillance footage is pretty tough to process with accuracy. Humans, even academic trainees, can be placed in front of a video monitor and told, “Watch this stream. Note anomalies.” Not exciting but necessary because processing large volumes of video remains what I would describe as “a bit of a challenge, grasshopper.” Why is Google adding wild and crazy banners, overlays, and required metadata inputs? Maybe because automated processing and magical deep linking are out of reach? HP appears to have improved or overhauled Autonomy’s video analysis functions, and the Gartner analyst is reporting a major technical leap forward. Identifying a muzzle flash is different from recognizing a face in a flow of subway patrons captured on a surveillance camera, is it not?

I have heard some pre HP Autonomy sales pitches, but I can’t recall hearing that Idol can crunch flows of video content unless one uses the quite specialized system Autonomy acquired. Well, I have been wrong before, and I am certainly not qualified to be an analyst like the ones Gartner relies upon. I learned that HP Idol has a comprehensive list of data connectors. I think I would use the word “library,” but why niggle?

Fifth, the video jumps to a presentation of a “content hub.” The idea is that HP idol provides visual programming tools. I assume an HP Idol customer will point and click to create queries. The  queries will deliver outputs from the Hadoop data management system and the content which embodies the data lake. The user can also run a query and see a list of documents. but the video jumps from what strikes me as exactly what many users no longer want to do to locate information. One can search effectively when one knows what one is looking for and that the needed information is actually in the index. The use case appears to be health care and the video concludes with a reminder that one can perform advanced analytics. There is a different point of view available in this ParAccel  white paper.

I understand the strengths and weaknesses of videos. I have been doing some home brew videos since I retired. But HP is presenting assertions about Autonomy’s technology which seem to be out of step with my understanding of what Idol, the digital reasoning engine, Autonomy’s acquired video technology.

The point is that HP seems to be out marketing Autonomy’s marketing. The assert6ions and logical leaps in the HP Idol Hadoop video stretch the boundaries of my credulity. I find this interesting because HP is alleging that Autonomy used similar verbal polishing to convince HP to write a billion dollar check for a search vendor which had grown via acquisitions over a period of 15 years.

Stephen E Arnold, May 16, 2015

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