Watson Weakly: Jargon and Resource Allocations
March 9, 2016
In case you missed the news, IBM seems to be trimming its workforce. Does anyone remember Robert X. Cringely’s “IBM Is So Screwed?” I do. I would wager that Mr. Cringely remembers IBM’s suggestion that Mr. Cringely was off base with his analysis.
Perhaps Mr. Cringely is vindicated. I read “IBM Job Cuts: US Tech Giant Begins Mass Firing One Third of Workforce.” Hmmm. One third of a workforce having an opportunity to find its future elsewhere? That sounds like a swell way to greet spring 2016. March in like a lion and march out like a lamb. Is the lamb heading to the local meat packers?
Against this cheerful seasonal background, I want to mention “Moving from Enterprise Search to Cognitive Exploration.” This is a recycling of an earlier white paper for which one must register in order to read or download the document. Please, note that you will have to jump through some hoops to get this March 2016 publication. Do not complain to me about the link, the involvement of a middleman, and the need to provide details about your interest in enterprise search. Take it up with IBM; that is, if someone will take your call or answer your email. Hey, good luck with that.
What’s notable about this white paper is this word pair: Cognitive Exploration. Original? Nah. The phrase turns up in the title of a collection of essays called Cognitive Exploratioin of Language and Linguistics in 1999. The phrase is some of the jingoism from the super reliable psychology linguistics disciplines. IBM has dallied with the phrase for a number of years but in the RA world, the phrase is getting a jump start. An example of IBM’s arguement is that no one no longer runs a search across a customer service database. Nope, one cognitively explores that customer database.
Cognitive Exploration. It flows trippingly on the tongue does it not. IBM does not fire people; IBM RA’s them. (RA. Resource allocation or termination or reduction in force.)
What is Cognitive Exploration? Well, it is Lucene search plus some home brew code and a dollop of acquired technology. IBM’s original commercial enterprise search system (STAIRS) is just not up to the task of cognitively exploring one’s information assets it seems.
The white paper is a tribute to the search buzzwords that have been used by marketers in the past. I just love Cognitive Exploration.
What is it? For the full answer, you will need to read the 13 pages of explanation. Here’s a sampling of the facts in the write up:
Analysts expect the total data created and copied to reach 44 ZB by the year 2020 (Analyst firm IDC). After all, there are more than 204,000,000 emails launched every minute every day (Mashable.com). How do you manage, search, and process that data and turn it into usable information?
Yep, that’s a lot of information. How is an organization going to deal with “all” those zeros and ones? I suppose I would begin by using a system designed to manipulate large data flows. How about Palantir, BAE Systems, Leidos for starters. What no IBM? Bummer.
The IBM argument advances:
To meet today’s expectations, a search system must be able to access all of your important data sources and filter results based on a user’s access permissions within the organization.
I love the “all”. IBM obviously has nailed video, audio, binaries of various types, disparate file types, and dynamic content flows from intercepts, social media, and interesting sources from the Dark Web. I love “all” type solutions. Too bad these are science fiction based on my experience.
The fix is Cognitive Exploration. Thank you, IBM. A new buzzword to explain what search and retrieval has flubbed for — what? — 50 years” IBM explains:
Cognitive exploration is the combination of search, content analytics, and cognitive computing. Not only can cognitive exploration accelerate the rate at which users can find and navigate information; by leveraging advanced technologies such as content analytics, machine learning, and reasoning it has the potential to augment human expertise.
I don’t want to be a party pooper, but this is perilously close to Palantir’s “augmented intelligence” jargon. Attivio, BA Insight, and even the French folks at Sinequa use similar lingo. Me-too’ism at its finest? Nah, this is IBM, the outfit taking Groupon (a discount coupong business) to court for allegedly infringing on Prodigy patents. Prodigy? Remember that online service?
After snoozing through the white paper’s three pillars of Cognitive Exploration, I raced to the the finish line.
Cognitive Exploration involves the i2 type of relationship analysis, some good old fashioned cuddling between search and cognitive computing (think Watson, gentle reader), and a unified view or what a popular novelist calls “God’s eye” view. Please note that IBM offers some examples, but get the numbering wrong. Where is number one? Watson, Watson, can you assist me? Guess not. IBM’s cognitive exploration essay begins counting with number 2. I am okay with zero. I am okay with one. But I am not okay with an enumerated list beginning with the number two. Careless typo? Indifference? Rushing to the RA meeting? Don’t know. Cognitive Watson counts two, three, four, not one, two, three.
At the end of this remarkable description of Cognitive Exploration I learned:
The cognitive capabilities that can be leveraged by Watson Explorer are provided by the IBM Watson platform.
Isn’t this a recycling of some of the early 1990s marketing material from i2 Group Limited, which IBM bought. Isn’t this lingo influenced by Palantir’s explanations of its Gotham platform?
Omitted from the “all” I assume is the seamless interchange of Gotham files with i2 Analyst Notebook and i2 Analyst Notebook with Gotham. The users and customers have to learn that “all,” like Mr. Clinton’s “is” may not be exactly congruent with one’s understanding of “federation” and “unified.”
Enough already. Go for the close:
IBM Watson Explorer unlocks the value within your data, utilizing that information to help employees make well-informed decisions, provide better support, and identify more customers and business opportunities. By reaching across multiple silos of information within your enterprise, search results will include information never previously integrated into single solutions. Users will benefit from search results from all the data in your company, structured and unstructured, and include data from outside as well. Rather than trying to make good decisions with limited insight, cognitive exploration users can now extract and understand all of the valuable information at their fingertips.
With such a wonderful tool at IBM’s disposal, why is IBM’s management unable to generate revenues? Perhaps the silliness of the marketing explanation of Cognitive Exploration does not deliver the results that obviously someone at IBM believes.
I am stuck on that error in numbering, the recycling of Palantir’s marketing lingo, and the somewhat silly phrase “Cognitive Exploration.”
I won’t sail my Nina, Pinta, and Santa Maria to that digital shore. I will use Google Earth and tools which I know sort of work.
Stephen E Arnold, March 9, 2016
Enterprise Search Revisionism: Can One Change What Happened
March 9, 2016
I read “The Search Continues: A History of Search’s Unsatisfactory Progress.” I noted some points which, in my opinion, underscore why enterprise search has been problematic and why the menagerie of experts and marketers have put search and retrieval on the path to enterprise irrelevance. The word that came to mind when I read the article was “revisionism” for the millennials among us.
The write up ignores the fact that enterprise search dates back to the early 1970s. One can argue that IBM’s Storage and Information Retrieval System (STAIRS) was the first significant enterprise search system. The point is that enterprise search as a productized service has a history of over promising and under delivering of more than 40 years.
Enterprise search with a touch of Stalinist revisionism.
Customers said they wanted to “find” information. What those individuals meant was have access to information that provided the relevant facts, documents, and data needed to deal with a problem.
Because providing on point information was and remains a very, very difficult problem, the vendors interpreted “find” to mean a list of indexed documents that contained the users’ search terms. But there was a problem. Users were not skilled in crafting queries which were essentially computer instructions between words the index actually contained.
After STAIRS came other systems, many other systems which have been documented reasonably well in Bourne and Bellardo-Hahn’s A History of Online information Services 1963-1976. (The period prior to 1970 describes for-fee research centric online systems. STAIRS was among the most well known early enterprise information retrieval system.) I provided some history in the first three editions of the Enterprise Search Report, published from 2003 to 2007. I have continued to document enterprise search in the Xenky profiles and in this blog.
The history makes painful reading for those who invested in many search and retrieval companies and for the executives who experienced the crushing of their dreams and sometimes career under the buzz saw of reality.
In a nutshell, enterprise search vendors heard what prospects, workers overwhelmed with digital and print information, and unhappy users of those early systems were saying.
The disconnect was that enterprise search vendors parroted back marketing pitches that assured enterprise procurement teams of these functions:
- Easy to use
- “All” information instantly available
- Answers to business questions
- Faster decision making
- Access to the organization’s knowledge.
The result was a steady stream of enterprise search product launches. Some of these were funded by US government money like Verity. Sure, the company struggled with the cost of infrastructure the Verity system required. The work arounds were okay as long as the infrastructure could keep pace with the new and changed word-centric documents. Toss in other types of digital information, make the system perform ever faster indexing, and keep the Verity system responding quickly was another kettle of fish.
Research oriented information retrieval experts looked at the Verity type system and concluded, “We can do more. We can use better algorithms. We can use smart software to eliminate some of the costs and indexing delays. We can [ fill in the blank ].
The cycle of describing what an enterprise search system could actually deliver was disconnected from the promises the vendors made. As one moves through the decades from 1973 to the present, the failures of search vendors made it clear that:
- Companies and government agencies would buy a system, discover it did not do the job users needed, and buy another system.
- New search vendors picked up the methods taught at Cornell, Stanford, and other search-centric research centers and wrap on additional functions like semantics. The core of most modern enterprise search systems is unchanged from what STAIRS implemented.
- Search vendors came like Convera, failed, and went away. Some hit revenue ceilings and sold to larger companies looking for a search utility. The acquisitions hit a high water mark with the sale of Autonomy (a 1990s system) to HP for $11 billion.
What about Oracle, as a representative outfit. Oracle database has included search as a core system function since the day Larry Ellison envisioned becoming a big dog in enterprise software. The search language was Oracle’s version of the structured query language. But people found that difficult to use. Oracle purchased Artificial Linguistics in order to make finding information more intuitive. Oracle continued to try to crack the find information problem through the acquisitions of Triple Hop, its in-house Secure Enterprise Search, and some other odds and ends until it bought in rapid succession InQuira (a company formed from the failure of two search vendors), RightNow (technology from a Dutch outfit RightNow acquired), and Endeca. Where is search at Oracle today? Essentially search is a utility and it is available in Oracle applications: customer support, ecommerce, and business intelligence. In short, search has shifted from the “solution” to a component used to get started with an application that allows the user to find the answer to business questions.
I mention the Oracle story because it illustrates the consistent pattern of companies which are actually trying to deliver information that the u9ser of a search system needs to answer a business or technical question.
I don’t want to highlight the inaccuracies of “The Search Continues.” Instead I want to point out the problem buzzwords create when trying to understand why search has consistently been a problem and why today’s most promising solutions may relegate search to a permanent role of necessary evil.
In the write up, the notion of answering questions, analytics, federation (that is, running a single query across multiple collections of content and file types), the cloud, and system performance are the conclusion of the write up.
Wrong.
The use of open source search systems means that good enough is the foundation of many modern systems. Palantir-type outfits, essential an enterprise search vendors describing themselves as “intelligence” providing systems,, uses open source technology in order to reduce costs, shift bug chasing to a community, The good enough core is wrapped with subsystems that deal with the pesky problems of video, audio, data streams from sensors or similar sources. Attivio, formed by professionals who worked at the infamous Fast Search & Transfer company, delivers active intelligence but uses open source to handle the STAIRS-type functions. These companies have figured out that open source search is a good foundation. Available resources can be invested in visualizations, generating reports instead of results lists, and graphical interfaces which involve the user in performing tasks smart software at this time cannot perform.
For a low cost enterprise search system, one can download Lucene, Solr, SphinxSearch, or any one of a number of open source systems. There are low cost (keep in mind that costs of search can be tricky to nail down) appliances from vendors like Maxxcat and Thunderstone. One can make do with the craziness of the search included with Microsoft SharePoint.
For a serious application, enterprises have many choices. Some of these are highly specialized like BAE NetReveal and Palantir Metropolitan. Others are more generic like the Elastic offering. Some are free like the Effective File Search system.
The point is that enterprise search is not what users wanted in the 1970s when IBM pitched the mainframe centric STAIRS system, in the 1980s when Verity pitched its system, in the 1990s when Excalibur (later Convera) sold its system, in the 2000s when Fast Search shifted from Web search to enterprise search and put the company on the road to improper financial behavior, and in the efflorescence of search sell offs (Dassault bought Exalead, IBM bought iPhrase and other search vendors), and Lexmark bought Brainware and ISYS Search Software.
Where are we today?
Users still want on point information. The solutions on offer today are application and use case centric, not the silly one-size-fits-all approach of the period from 2001 to 2011 when Autonomy sold to HP.
Open source search has helped create an opportunity for vendors to deliver information access in interesting ways. There are cloud solutions. There are open source solutions. There are small company solutions. There are more ways to find information than at any other time in the history of search as I know it.
Unfortunately, the same problems remain. These are:
- As the volume of digital information goes up, so does the cost of indexing and accessing the sources in the corpus
- Multimedia remains a significant challenge for which there is no particularly good solution
- Federation of content requires considerable investment in data grooming and normalizing
- Multi-lingual corpuses require humans to deal with certain synonyms and entity names
- Graphical interfaces still are stupid and need more intelligence behind the icons and links
- Visualizations have to be “accurate” because a bad decision can have significant real world consequences
- Intelligent systems are creeping forward but crazy Watson-like marketing raises expectations and exacerbates the credibility of enterprise search’s capabilities.
I am okay with history. I am not okay with analyses that ignore some very real and painful lessons. I sure would like some of the experts today to know a bit more about the facts behind the implosions of Convera, Delphis, Entopia, and many other companies.
I also would like investors in search start ups to know a bit more about the risks associated with search and content processing.
In short, for a history of search, one needs more than 900 words mixing up what happened with what is.
Stephen E Arnold, March 9, 2016
Alphabet Google to Advise the US Department of Defense
March 8, 2016
There is a delicious irony in “Former Google CEO Schmidt to Head New Pentagon Innovation Board.” Alphabet Google’s core business is based on the experiences of some search predecessors. I understand the shoulders of giants thing. But in Google’s case, there are some specific folks to thank for the efficacy of the pre-2006 Google search system. Say what, Ghemawat? How mean, Mr. Dean? And for the revenue model, there is always the outfit one can “GoTo.” But no more Clever.
I recall that Mr. Schmidt had a seat on the Apple board. I wonder how some of the members of the Apple board liked their Android powered Samsung phones?
The point is that Google does some innovative things, but these are often built on top of other ideas, concepts, and implementations. Did you forget the pre IPO settlement of Yahoo’s issue with the Google ad system? Harvard did and lots of others folks ignore that hiccup as well.
The write up reports:
Eric Schmidt, the former chief executive officer of Google, will head a new Pentagon advisory board aimed at bringing Silicon Valley innovation and best practices to the U.S. military… Modeled on the Defense Business Board, which provides advice on best business practices from the private sector, the new panel is intended to help the Pentagon become more innovative and adaptive in developing technology and doing business.
One imagines that Palantir’s executives will be eager to join the new group.
As both Alphabet Google and Palantir turn to buying companies to acquire innovative people and technology, the Department of Defense may rekindle its love for In-Q-Tel-type deals. Make no mistake. Any outfit with a seat on the board has some Tesla-like spark.
Stephen E Arnold, March 8, 2016
Italian Firm Adds to the Buzzword Blizzard in an Expert Way
March 7, 2016
I don’t pay too much attention to lists of functions an information intelligence system must have. The needs are many because federation, normalization of disparate data, and real time content processing are not ready for prime time. Don’t believe me? Ask the US Army which is struggling with the challenges of DCGS-A, Palantir, and other vendors’ next generation systems in actual use in a battle zone. (See this presentation for one example.)
I read “No Time to Waste! 5 Essential Features for Your Information Intelligence Solution.” I like the idea of a company (Expert System) which was founded a quarter century ago, urging speedy action.
You can work through the well worn checklist of entity extraction, links and relationships, classification, and sticking info in a “knowledge base.” I want to focus on one point which introduces a nifty bit of jargon which I had not seen in use since I was in college decades ago.
The word is anaphora.
There you go. An anaphora, as I recall, is repetition or word substitution. Not clear? Here are a couple of examples:
Rhetorical:
For want of revenue the investors were lost.
For want of a product credibility was lost.
For want of an application the market was lost.
Grammatical:
The marketing cacophony increased and that drove off the potential customers.
Now you can work these points into your presentation when the users want actionable information which fuses available information into a meaningful output.
Because modern systems are essentially works in progress, buzzwords like anaphora take the place of dealing with real world information problems.
But marketing by thought leaders is so much more fun. That may trouble some. Parse that, gentle reader. What can one make in the midst of a blizzard of buzzwords? One hopes revenue which keeps the stock out of penny territory.
Expert System SpA, if Google Finance is accurate, about $2 a share. Roger, anaphora that.
Stephen E Arnold, March 7, 2016
Attivio: Dines on Data Dexterity
March 7, 2016
Attivio was founded by some former Fast Search & Transfer executives. Attivio also had a brush with a board member who found himself in a sticky wicket. Quite a pedigree.
I read “Enterprise Search Takes Its Place at the Big Data Table.” The write up is built upon an interview with the chief executive officer of Attivio. Nice looking fellow who had a degree in music and marketing and an MBA from Wharton, the institution which helped educate Donald Trump.
What caught my attention were these points in the write up. My observations are in italics:
- Enterprise search has been around for two decades. [Nah, enterprise search is closing in on 50 years of fun and delight.]
- Enterprise search “finds unstructured content housed in file shares like SharePoint and other content management systems, in email archives, and in the content repositories of applications like customer relationship management. [Yep, and that is part of the problem with enterprise search. The bulk of the systems I have examined do not handle video, audio, binaries, and odd ball file types like those in ANB format very well or not at all. Plus users expect comprehensive results updated in near real time presented in a form which allows instant use.]
- Enterprise search does analytics and accelerated data discovery. [Yep, if the customer licenses a system like BAE NetReveal, the Palantir platform, or another industrial-strength fusion vendor.]
What I found interesting was the phrase “reducing the time to insight.” There is a suggestion from Attivio and from other vendors that their systems process digital content in a super fast mode.
In our testing, we have found that throughput for new content can require considerable investment in engineering and processing capability. Furthermore, dealing with flows from intercepts or other high volume content sources, most enterprise search systems cannot handle:
- Processing large flows of content in a matter of minutes. Hours or days is a more suitable time unit
- Updating the index or indexes
- Integrating real time data into search results, reports, and visualizations in a dynamic manner.
That’s why outfits who are emulating Palantir-style information access use open source search and then invest hundreds of millions in specialized engineering, interfaces, and fusion technologies.
Enterprise search vendors chasing Palantir-type systems are delivering what marketers find quite easy to describe. Here’s an example:
Not only that, but many enterprises can only “see” 10 percent of their data. The other ninety percent remains hidden—dark data. Data is often locked in silos, and it’s just too time-consuming to get it out. And making connections across structured, semi-structured, and unstructured information to serve to a BI tool is a completely manual, slow process – although highly valuable for developing strategic insights. Organizations that can cross this chasm will be poised to transform productivity, mitigate risks, and seize market opportunities.
The only hitch in the git along is that systems which handle “dark data” are available now. There are outfits able to handle “dark” data today. True, these are not based on enterprise search concepts because the core of a utility function is not a solid foundation for next generation information access. There are platforms which deliver actionable outputs. Even more interesting is that the US government is funding research to develop next generation systems designed to leap frog Palantir, i2, DCGS-A, and many other solutions.
Why?
Marketing is one thing. Delivering a system which works reliably, exhibits consistency, and integrates with work flows is a work in progress.
The notion that a Fast-type system can deliver what a Palantir-type system does is something I believe is wordsmithing. Watson does wordsmithing; others deliver next generation information access. Has Attivio hit a home run with its new positioning? Is the Attivio solution a starter for the Hickory Crawdads? My hunch the folks investing $70 million in Attivio want to start for the Boston Red Sox this year. Play ball.
Stephen E Arnold, March 7, 2016
Alphabet Google Gets into the Corporate Storytelling Game
March 6, 2016
I read “Google’s New Site Lets Engineers Tell the Backstory of Some of Its Best Products.” Organizations crank out stories, but most of them are kept in the buildings or within the walled gardens of the minds eager to keep getting a paycheck.
According to the write up, Google has created barfoo where “engineers can tell their stories.” Okay, I expect the unvarnished truth, no editorial shaping. Well, that’s crazy. No secretive, paranoid outfit like Alphabet Google is going to do a Jerry Spring program for products and services that “emerge,” get bought and reinvented, or just me-too’ed.
The write up says:
The site currently covers four topics: collaboration in Docs, smart composing in Gmail, voice search recognition and how Google built a faster YouTube. The site also has a section for open jobs at Google, should you want to work there yourself. The topics are pretty in-depth, too. Not only does Google tell you the inspiration behind some of its products, it dives into the process of delivering them to users.
I am not sure the story of Google’s online advertising system will be revealed. There are some other interesting products and services which are likely to put on a lower priority track too.
But that GoTo/Overture/Yahoo “innovation” would be a story I would read. I would skim anything to do with the Glass, Parviz, and staff interaction activities as well. Yep, very low priority.
Palantir has its Tolkien and comic book “myths.” Google is going to do a reality show with post production I assume.
Stephen E Arnold, March 6, 2016
Quid Cheerleading: The Future of Search
March 4, 2016
I read “The Future of {Re}search.” (I love the curly braces.) The write up identifies the four big things in information access. Keep in mind that the write up is a rah rah for Quid, which is okay.
Here are the main points:
- Semantic search is the next big thing
- Visualization matters
- Humans are part of the search process
- Bots are the “Future of Search.” (The capitalization is from the source document.)
Quid is an interesting company. I thought that the firm was focused on analytics and nifty visualizations. Their catchphrase is “intelligence amplified,” which strikes me as similar to Palantir’s “augmented intelligence.”
If the write up is on the money, Quid is a search vendor in the same way Palantir Technologies is a search vendor.
The point about bots may catch the attention of the ever-alert Connotate folks. I think bots has been an important part of that firm’s services for many years.
So, “the next big thing”? Well, sort of.
Stephen E Arnold, March 4, 2016
Hershey Chocolate: Semi Sweet Analytics?
March 4, 2016
I am wrapping up my profile of Palantir Technologies. I located a couple of references to Palantir’s activities in the non-government markets. One of the outfits allegedly swooned by the Hobbits was Hershey chocolate. A typical reference to the Hobbits and Kisses folks was “Hershey Turns Kisses and Hugs into Hard Data.”
When I read “The Hershey Company Partners with Infosys to Build Predictive Analytics Capability using Open Source Information Platform on Amazon Web Services,” I wondered why Palantir Technologies was not featured in the write up. Praescient Analytics, near Washington, DC, can plug industrial strength predictive analytics like Recorded Future’s into a Metropolitan installation without much hassle.
The write up makes clear that the chocolate outfit is going a new way. The path leads through Amazon Web Services to the Infosys Information Platform.
I find this quite a surprise. I have no doubt that Infosys has some competent folks on its team. But the questions flashing through my mind are:
- What’s up with the Palantir system?
- Why jump to Infosys when there are darned good outfits available in Boston and Washington, DC?
- What’s an outsourcing firm able to deliver that specialists with deep experience in making sense of data cannot?
I never understood Mars, and now I don’t understand the makers of the York Peppermint Patty.
Perhaps this is a “whopper” of a project?
Stephen E Arnold, March 4, 2016
Yahoo Has AI Advantage Maybe?
March 2, 2016
I read “Don’t Laugh: Yahoo’s Open Source AI Has a Secret Weapon.” Sorry, I did laugh. I find the Yahooligans’ periodic “we’re really good at technology” messages amusing. More interesting is the willingness of with it magazines to cover these breakthroughs.
I learned:
Yahoo published the source code to its CaffeOnSpark AI engine so that anyone from academic researchers to big corporations can use or modify it.
Good. Open source software is useful, very useful.
I noted this passage:
Yahoo, for example, uses it to improve search results on Flickr by determining the contents of different photos. Instead of relying on the descriptions and keywords entered by the people who upload photos to the site, Yahoo teaches its computers to recognize certain characteristics of a photo, such as specific colors or even objects and animals.
Interesting, but other outfits do image recognition reasonably well. Check out Yandex’s image search or look at the wonky similar images feature that makes it oh, so easy for me to lose my train of thought when looking for examples of Palantir’s interface via Google’s image search service.
I learned:
CaffeOnSpark, as the name suggests, combines two existing technologies: the popular deep learning framework Caffe and the up-and-coming data-crunching system Spark that can run on top of the even more popular big data platform Hadoop. What Yahoo did was simply create a way to run Caffee atop Spark clusters. It can be run either on Spark alone or atop Hadoop. Besides making it easy for AI developers to use familiar tools and avoid moving data around… CaffeOnSpark also makes it relatively easy to distribute deep learning processes across multiple servers, something that the open source version of Google’s TensorFlow can’t yet do.
The challenge for Yahoo is to deal with its here and now problems. The outfit is for sale and many of the researchers of yesteryear have ridden off into the sunrise to find companies able to generate revenue from innovations.
When you are for sale, publicity is a definite plus. By the way, companies with technology to distribute deep learning across multiple servers are chugging along and closing some deals based on their know how. When does open source become a source of revenue and when is it a PR play?
Stephen E Arnold, March 2, 2016
Analytics Reality: Do You Excel?
February 24, 2016
I read “What Is the Most used Feature in Any business Intelligence Solution? It’s the Export to Excel Button.” The write up asserts:
I was recently forwarded an article on the continued popularity of Excel in the BI community consisting of quotes from 27 experts saying how great and how relevant Excel remains. We do categorize BI as static and historical as opposed to forward looking predictive analytics but I bet it’s still true that Excel is a very widely used tool even by folks that categorize themselves as data scientists.
Let’s assume this is accurate. What does this suggest for complex analytics like my old pals SAS or IBM SPSS? What about high flying outfits like Palantir, and Centrifuge Systems?
I have some answers, but I think the questions are suggestive of a hurdle which high horsepower analytic systems must power around. There is a reason so few folks are adept at statistics whether the industrial strength variety or the weird approach taken in social science and economics classes.
Excel seems to be tough to master but compared to more supercharged methods, Excel sure looks like a push peddle tricycle. You can’t go too far or too fast. If you crash into something, there is F1 and semi automated procedures to kiss the boo boo and make it better.
Stephen E Arnold, February 24, 2016
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