Business Intelligence: What Is Hot? What Is Not?
July 16, 2018
I read “Where Business Intelligence is Delivering Value in 2018.” The write up summarizes principal findings from a study conducted by Dresner Advisory Services, an outfit with which I am not familiar. I suggest you scan the summary in Cloud Tweaks and then, if you find the data interesting, chase after the Dresner outfit. My hunch is that the sales professionals will respond to your query.
Several items warranted my uncapping my trusty pink marker and circling an item of information.
First, I noticed a chart called Technologies and Initiatives Strategic to Business Intelligence. The chart presents data about 36 “technologies.” I noticed that “enterprise search” did not make the list. I did note that cognitive business intelligence, artificial intelligence, t4ext analytics, and natural language analytics did. If I were generous to a fault, I would say, “These Dresner analysts are covering enterprise search, just taking the Tinker Toy approach by naming areas of technologies.” However, I am not feeling generous, and I find it difficult to believe that Dresner or any other knowledge worker can do “work” without being able to find a file, data, look up a factoid, or perform even the most rudimentary type of research without using search. The omission of this category is foundational, and I am not sure I have much confidence in the other data arrayed in the report.
Second, I don’t know what “data storytelling” is. I suppose (and I am making a wild and crazy guess here) that a person who has some understanding of the source data, the algorithmic methods used to produce output, and the time to think about the likely accuracy of the output creates a narrative. For example, I have been in a recent meeting with the president of a high technology company who said, “We have talked to our customers, and we know we have to create our own system.” Obviously the fellow knows his customers, essentially government agencies. The customers (apparently most of them) want an alternative, and realizes change is necessary. The actual story based on my knowledge of the company, the product and service he delivers, and the government agencies’ budget constraints. The “real story” boils down to: “Deliver a cheaper product or you will lose the contract.” Stories, like those from teenagers who lose their homework, often do not reflect reality. What’s astounding is that data story telling is number eight on the hit parade of initiatives strategic to business intelligence. I was indeed surprised. But governance made the list as did governance. What the heck is governance?
Journalists: Smart Software Is Learning How to Be a Real Journalist
July 15, 2018
I read “Why Bots Taking Over (Some) Journalism Could Be a Good Thing.” I love optimists who lack a good understanding of how numerical recipes work. The notion of “artificial intelligence” is just cool like something out of science fiction like “Ralph 124C 41+” except for the wrong headed predictions. In my 50 year work career, technologies are not revolutions. Technologies appear, die, reform, and then interact, often in surprising ways. Then one day, a clever person identifies a “paradigm shift” or “a big thing.”
The problem with smart software which seems obvious to me boils down to:
- The selection of numerical recipes to use
- The threshold settings or the Bayesian best guesses that inform the system
- The order in which the processes are implemented within the system.
There are other issues, but these provide a reasonable checklist. What does on under the kimono is quite important.
The write up states:
If robots can take over the grunt work, which in many cases they can, then that has the potential to lower media organizations’ costs and enable them to spend a greater proportion of their advertising income on more serious material. That’s terrible news for anybody whose current job is to trawl Twitter for slightly smutty tweets by reality TV show contestants, but great news for organizations funding the likes of Guardian journalist Carole Cadwalladr, who broke the Facebook / Cambridge Analytica scandal. Isn’t it?
Good question. I also learned:
Technology can help with a lot of basic reporting. For example, the UK Press Association’s Radar project (Reporters And Data And Robots) aims to automate a lot of local news reporting by pulling information from government agencies, local authorities and the police. It’ll still be overseen by “skilled human journalists”, at least for the foreseeable future, but the actual writing will be automated: it uses a technology called Natural Language Generation, or NLG for short. Think Siri, Alexa or the recent Google Duplex demos that mimic human speech, but dedicated to writing rather than speaking.
I recall reading this idea to steal:
In fact, human reporters will continue to play a vital role in the process, and Rogers doesn’t see this changing anytime soon. It’s humans that make the decision on which datasets to analyze. Humans also “define” the story templates – for example, by deciding that if a certain variable in one region is above a particular threshold, then that’s a strong indicator that the data will make a good news story.
Now back to the points in the checklist. In the mad rush to reduce costs, provide more and better news, and create opportunities to cover certain stories more effectively, who is questioning the prioritization of content from an available stream, the selection of items from the stream, and the evaluation of the data pulled from the stream for automatic story generation?
My thought is that it will be the developers who are deciding what to do in one of those whiteboard meetings lubricated with latte and fizzy water.
The business models which once sustained “real” journalism focused on media battles, yellow journalism, interesting advertising deals, and the localized monopolies. (I once worked for such an outfit.)
With technology concentration a natural consequence of online information services, I would not get too excited about the NLG and NLP (natural language generation and natural language processing services). These capabilities for smart software will arrive. But I think the functionality will arrive in dribs and drabs. One day an MBA or electrical engineer turned business school professor will explain what happened.
What’s lost? Typing, hanging out in the newspaper lunch room, gossip, and hitting the bar a block from the office. Judgment? Did I leave out judgment. Probably not important. What’s important that I almost forgot? Getting rid of staff, health coverage, pensions, vacations, and sick leave. Software doesn’t get sick even though it may arrive in a questionable condition.
Stephen E Arnold, July 15, 2018
An Amazon Statistic and the Word All
July 14, 2018
I read “Amazon’s Share of the US E Commerce Market Is Now 49% or 5% of All Retail Spend.” The idea of “all” is reassuring. One does not have to worry about precision. All means all, right. Cantor struggled with his view of all. That worry does not trouble the expert writing this article.
Setting aside word choice, the factoid is semi interesting. Amazon has, according to this source, about half of the e commerce market in the US. Now the sample has to ask people who own a computing device, are able to get online, and who have some method for making a digital payment. If one considers what percentage of the US population checks these boxes, the “all” becomes a subset of the US population. The reason check cashing services exist pivots on the individuals who do not have a banking relationship either directly or via the mostly available prepaid credit cards. Using these prepaid credit cards can be interesting.
Let’s assume that Amazon does have a big chunk of the US e commerce market. The write up suggests that Amazon is heading toward a tipping point. The idea, I think, is that the “all” really will mean every nickel and dime spent online for “retail products.” The idea that Amazon’s growth is surprising strikes me as interesting. The key metric is the rate of change between each major financial milestone. At one time, Google was smoking along. Has Amazon’s growth been chugging along with a nifty slope between time and financial data (remember those?)
An outfit called eMarketer provides data which illustrates how Amazon is making revenue in clothing, beauty items, and groceries.
The only problem I have is that Amazon’s online success is old news. Not far from our log cabin in rural Kentucky, Wal-Mart closed three of its retail outlets. I think that Amazon’s success in e commerce was a contributing factor. In some demographic segments, Amazon’s share of the US retail market is nosing toward 80 percent. Even in rural Kentucky, our rescue French bulldog can be run over by one of the six or seven Amazon deliveries each day unless we are on our toes.
So what?
- Amazon’s tipping point was reached a couple of years ago? Amazon is now just running plays from its 20 year old playbook. We’re into déjà vu territory.
- Amazon’s e commerce system is part of a slightly more sophisticated online store. I think it may be helpful for some whiz kid analysts to think about Oracle’s data marketplace and ask, “What does that have in common with Amazon’s retail business?”
- The notion of “all” is not a helpful way to explain what Amazon has achieved. eMarketer like many other professionals think about consumer products. Big market indeed. There are other ways to look at Amazon’s platform.
Why are these questions important? If Amazon is going to generate enough revenue to double or triple its revenue, it will have to do more than sell T shirts, avocados, and vinyl records.
Wal-Mart’s “Amazon disease” is now spreading to other markets. The “all” misleads when used without a more informed context.
Stephen E Arnold, July 14, 2018
AI: Useful to Major Major Major of In and Out Fame
July 13, 2018
While the capacity for work and accuracy of artificial intelligence is pretty hard to argue with, the expense of starting a machine learning program from the ground up is pretty easy to argue. In some cases, it is more expensive to teach a machine to act like a human than to actually hire a human, we discovered after reading a recent Guardian story, “The Rise of Pseudo-AI: How Tech Firms Quietly Use Humans to Do Bots’ Work.”
One example they gave was:
“In the case of the San Jose-based company Edison Software, artificial intelligence engineers went through the personal email messages of hundreds of users – with their identities redacted – to improve a “smart replies” feature. The company did not mention that humans would view users’ emails in its privacy policy.”
How do you get around this modern day Catch-22? Some think the answer lies in blocks. By using Blockchain technology, development costs for AI could drastically be reduced, some experts think. This is because one of the great costs of AI is data management and sorting. If that process is simplified by Blockchain, the reasoning is that the cost of the program would go down. Finally, we can relieve those poor humans from doing a machine’s job.
When it’s in, it’s out. When it’s out, it’s in. Poetic, no?
Patrick Roland, July 13, 2018
Facebook: A Fan of Infowars
July 13, 2018
I don’t know much about Infowars. I do know that the host has an interesting verbal style. The stories, however, don’t make much sense to me. I just ignore the host and the program.
However, if the information in “Facebook Proves It Isn’t Ready To Handle Fake News” is accurate, Facebook is okay with the host and the Infowars’ approach to information.
The write up reports a Facebook news expert as saying:
“I guess just for being false that doesn’t violate the community standards.” I think part of the fundamental thing here is that we created Facebook to be a place where different people can have a voice. And different publishers have very different points of view.
The Buzzfeed story makes this statement:
Despite investing considerable money into national ad campaigns and expensive mini documentaries, Facebook is not yet up to the challenge of vanquishing misinformation from its platform. As its videos and reporter Q&As take pains to note, Facebook knows the truth is messy and hard, but it’s still not clear if the company is ready to make the difficult choices to protect it.
Hey, it’s difficult for some people to deal with responsibility. Ease off. Facebook is trying hard to be better. Every day. Better.
Stephen E Arnold, July 13, 2018
The Future of Enterprise Search: The View of a WeWork World
July 13, 2018
I read “The Future of Enterprise Search: Visual, Voice & Vertical.” My reaction was, “This approach to enterprise search describes a WeWork world.” My view of enterprise search is much, much different. Let me point out that I am okay with voice interfaces, but I am struggling to come to grips with the idea of visual search in an enterprise as one of the three pillars of enterprise search as i understand the function. The “vertical” angle is another way of saying, “Enterprise search does not work as a one size fits all solution. Therefore, let’s embrace a search engine for the legal unit, one for the computational chemists, one for marketers, and so on.
The write up points out that organizations, needs, and marketing are in flux. Uncertainty is the name of the game. That’s why there are employees who aren’t really full time equivalents working in Starbuck’s and WeWork offices. Who has a desk, assistants, and a regular nine to five job? Darned few people today. If we recognize the medieval set up of most organizations, the traditional definition of a job has more in common with the world of the Willy Loman (low man on totem pole, get it?). Life today has kings, court staff, and peasants. The difference is that the staff and peasants have mobile phones; otherwise, we’re back in the 7th century CE.
Skipping over the copy and paste of an Economist chart, the guts of the expository essay explains visual, voice, and vertical. The Vs reminded me of IBM’s alleged insight about Big Data’s volume, velocity, and variety. A mnemonic with alliteration. Okay, just not enterprise search as I have defined it in a number of my books; for example, The New Landscape of Search, published by Panda, years ago.
Enterprise search makes it possible for an employee to obtain the information needed to complete a business task. I pointed out that an employee cannot perform some work without locating digital information and data needed to answer a question. My examples included locating the most recent version of a CEO’s PowerPoint presentation, a list of the suppliers for a particular component in a product, information about an alleged personnel matter which violated the terms of an agreement with a customer, and the lab notes relative to a new compound developed by a chemical engineer with a structure diagram.
Now it would be wonderful if I could speak to a mobile device and have the data for any one of these enterprise search tasks delivered to me. But there are a couple of problems; namely, the screen size and capabilities of most mobile devices. For these information tasks, I personally prefer a multi monitor set up, a printer, plus old fashioned paper and pencil for notes.
The visual search angle is useful when looking for engineering drawings or chemical structures. But the visual component is only a part of the information I needed. That lab notebook is important, particularly if the product is going to be commercialized or patented or used as a bargaining chip in a deal with a potential partner or acquisition.
The vertical part is, as I have said, the reason that the typical organization has dozens of information access systems. A decade ago, according to our research, a Fortune 500 company licensed most of the available enterprise search systems, one or more legal search systems, and the specialist tools for those working with engineering drawings, specifications, vendor profiles, etc.
I don’t want to suggest that the discussion of visual, voice, and vertical search in the Search Engine Journal is with value. The information is simply not on point for today’s organization information access requirements.
For those in the top tier of workers — that is, those with senior positions and staff — the tools needed are more diverse and must be more robust. For those laboring away in WeWork offices, voice and visual search may be the go to ways to get information. The vertical search systems are useful, but for many workers, the expertise required to make a chemical structure search system deliver useful outputs is outside those workers’ skill set without some work and midnight oil.
To sum up, enterprise search is a difficult concept. Simplifying it to the three Vs understates the challenge. Explaining enterprise search in terms of semantic technology, natural language processing, and the other difficult to define jargon sprints to the far end of the complexity spectrum.
That’s why enterprise search is problematic. The vendors hope for a buyer and then head for the beach or a new job. The customers end up like Robinson Caruso, stranded and alone with tools that usually fall into disrepair quickly. Enterprise search itself is jargon, but it is jargon which has been marginalized by systems which over promised and under delivered.
That’s a mnemonic and acronym for you: OPUD.
Stephen E Arnold, July 13, 2018
Some Happy, Some Sad in Seattle Over Cloud Deal Review
July 12, 2018
I know little about the procurement skirmishes fought over multi billion dollar deals for cloud services. The pragmatic part of my experience suggests that the last thing most statement of work and contract processes produce is efficient, cost effective contracts. Quite a few COTRs, lawyers, super grades, and mere SETAs depend on three things:
- Complex, lengthy processes; that is, work producing tasks
- Multiple vendors; for example, how many databases does one agency need? Answer: Many, many databases. Believe me, there are many great reasons ranging from the way things work in Washington to legacy systems which will never be improved in my lifetime.
- Politics. Ah, yes, lobbyists, special interests, friends of friends, and sometimes the fact that a senior official knows that a person once worked at a specific outfit.
When I read, “Deasy Pauses on JEDI Cloud Acquisition,” I immediately thought about the giant incumbent database champions like IBM Federal Systems and Oracle’s government operations unit.
Department of Defense CIO Dana Deasy wants a “full top down, bottom up review” of the JEDI infrastructure acquisition.
But there was a moment of reflection, when I realized that this procurement tussle will have significant impact on the Seattle area. You know, Seattle, the city which has delivered Microsoft Bob and the Amazon mobile phone.
Microsoft and Amazon are in the cloud business. Microsoft is the newcomer, but it is the outfit which has the desktops of many government agencies. Everyone loves SharePoint. The Department of Defense could not hold a briefing without PowerPoint.
Let’s not forget Amazon. That is the platform used by most government workers, their families, and possibly their friends if that Amazon account slips into the wild. Who could exist in Tyson’s Corner or Gaithersburg without Amazon delivering essential foods such as probiotic supplements for the dog.
Microsoft is probably thrilled that the JEDI procurement continues to be a work in progress. Amazon, on the other hand, is likely to be concerned that its slam dunk for a government cloud game home run has been halted due to procedural thunderstorms.
Thus, part of Seattle is really happy. Another part of Seattle is not so happy.
Since I don’t’ have a dog in this fight, my hunch is that little in Washington, DC changes from administrative change to administrative change.
But this Seattle dust up will be interesting to watch. I think it will have a significant impact on Amazon and Microsoft. IBM Federal Systems and Oracle will be largely unscathed.
Exciting procurement activity is underway. Defense Department CIO Deasy Deasy’s promise of a “full top down, bottom up review” sounds like the words to a song I have heard many times.
With $10 billion in play, how long will that review take? My hunch is that it will introduce the new CIO to a new concept, “government time.”
Stephen E Arnold, July 12, 2018
The Western Electric Model: Has It Resurfaced?
July 12, 2018
I read “Magic Leap Signs AT&T as sole U.S. Wireless Vendor and Gets Investment.” The story asserts that AT&T has become the exclusive distributor of the Magic Leap virtual realty device. The story makes no reference to Western Electric. Who remembers Western Electric, how its equipment deals worked, or how it meshed with Bell/AT&T. Perhaps the Western Electric model has surfaced again?
Stephen E Arnold, July 12, 2018
Sinequa Review: Questions Go Unanswered
July 12, 2018
I read “Sinequa Review” by an outfit called Finances Online. I think the idea is an interesting one. Navigate to a Web page and get a snapshot about a product. In this case, the vendor is Sinequa, and its product is described as “an integrated search platform that can extract information from your interconnected applications and environments. Aside from letting you draw data rapidly, it affords you actionable and intelligent insights that it gains through its deep content analyses.”
That suggests that Sinequa is more than a search engine. In my book CyberOSINT, I pointed out that next generation information access systems represent the path forward for making sense of digital information. I did not include Sinequa is that book’s profiles of vendors to watch.
Like many vendors of keyword search, Sinequa has been working hard to find a way to describe basic search in terms of higher value functions. The Finances Online write up about Sinequa illustrates the difficulty a company like Sinequa has in describing its various functions; for example, “extract information from your interconnected applications and environments.” I am not sure what that means.
The listing of benefits strikes me as different from what I identified in CyberOSINT. In that monograph, I focused on a system’s ability to identify high value or potentially high interest data automatically, interfaces which move beyond Google style lists of results which create more work for the analyst because relevance and a document’s inclusion of a specific item of data are impossible without directly reading a document, and analytic functions designed to present data in the context of the user.
Contrast CyberOSINT’s key features with Sinequa’s:
- In depth analytics
- Connectors
- Mobile strategy
We have congruence with analytics; however, misses on the other features.
The features of Sinequa are a listing of buzzwords. In CyberOSINT, the idea is that next generation information access systems emphasize outputs, not the mechanisms “under the hood.”
The cost of an enterprise search or NGIA system is a difficult issue. The big expenses for enterprise search or NGIA systems are planning, administration, training, set up, optimization, customization, content preparation, and remediation (most of these systems don’t work “out of the box.”)
Here’s how pricing of Sinequa is explained:
Sinequa is a platform that transforms your organization into an information driven one. If you are interested in powering your company or institution with the solution, you can request custom enterprise pricing from the sales team by phone, email, web form, or chat.
I think that inclusion of downsides about a product is important. Perhaps Finances Online will include:
-
- Pricing information from verified customers; for example, last year, the total cost of ownership was …
- Details about issues with the Sinequa system which may date from 2002
- A more informed listing of competitors
- Less jargon and fewer undefined buzzwords; for example, “deep content analytics” and “semantics”.
The trajectory of old school search vendors has been similar to the approach taken to extend the life and usefulness of DEC 10s and 20s. Wrappers of software can keep somethings youthful—at least at first glance. Don’t get me wrong. Quick looks can be useful.
Stephen E Arnold, July 12, 2018
Facebook: It Does Do Interesting Things
July 11, 2018
Look at Facebook from the vantage point of a grandmother looking at her grandchildren, and Facebook looks one way. Check out the company as a developer with special access to Facebook users, and Facebook looks another.
I read “Russian Company Had Access to Facebook User Data Through Apps.” Then I read “Facebook Introduced Secretive New Developer Terms to Prevent Another Cambridge Analytica.” The headline seems encouraging.
Put these stories against the background of Facebook’s US$600,000 fine for its unintentional, wow-we-are-sorry Cambridge Analytica misadventure.
What is Facebook?
In the US, Facebook is a success story, operating like any well oiled, money making machine. Outside the US, the UK and EU may see the firm differently. Companies do not get fined if it stays within the bright white lines of laws and regulations.
The question is, “What’s the correct way to pursue revenue via online information services?”
The answer, it seems is to do whatever is expedient. Do what can be done and apologize if those bold actions run over a baby deer or take out a flock of geese focused on their computing devices.
What’s the trajectory of these “act first, apologize later” tactics?
For one thing, we have increasing censorship. China, it appears, has cracked down on some aspects of the digital life. Other countries dabble with information control. Iran and Turkey have been innovative.
The epicenter of “act now, apologize later” seems to be centered in a relatively small number of companies. So what’s not to like about secret developer deals and disruption? Think of the opportunities.
Stephen E Arnold, July 11, 2018
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