Management Observations about Yahoo from a Real Newspaper
December 16, 2015
I am fascinated when publishers offer management advice and opinions. The newspaper publishing sector has done a bang up job with digital in the last 30 years.
I read the UK newspaper written by “real” journalists online and spotted this article: “Don’t Blame Marissa Mayer: nobody Was Going to Save Yahoo.”
That’s a great headline from a newspaper. I think it also emphasizes the value a Xoogler has despite the somewhat tarnished performance of the company the Xoogler is “turning around.”
I highlighted a couple of passages as particularly interesting observations.
For example, I highlighted in Yahoo purple:
All of it [Yahoo’s management actions], sadly, has been pretty irrelevant.
I like the “all”. There is nothing quite like a categorical affirmative to add heft to an argument.
I noted:
It would be easy to blame Mayer for this [revenue malaise]; in several ways she has done herself few favors – hiring and firing a chief operating officer who earned $58m in 15 months, cancelling working from home while bringing her newborn son and a full-time nanny to the office, and overseeing an exodus of top executives.
Well, I am not sure that the assertion “it’s not clear that anybody could have saved Yahoo.”
Again a categorical, embracing lots of folks” does not provide much insight into the Yahoo we know and love.
Too bad for those who rely on generalizations to navigate the tough business climate for information, whether in print or online.
I wonder how newspapers are doing. I assume super peachy. These outfits, including the Telegraph, are paragons of management excellence, organic revenue growth, hefty profits, and keen thinking.
Thank goodness for “real” journalists. These outfits and their professionals will make bang up consultants.
Stephen E Arnold, December 16, 2015
Mid Tier Consultant Sees Ripples of Opportunity in Data Lakes
December 16, 2015
I love predictions from mid tier consultants. One can spot what these folks will be pitching to their customers. One can also see the buzzwords likely to replace plain talk in their reports.
A good example of this type of forecasting—which if it worked would be used to pick horse race winners, not technologies—appears in “Big Data’s Future According to Ovum.” To spare extra wear and tear on your rapidly beating heart, SQL data management will remain popular but nothing will capture the excitement of Hadoop on Spark or is it Spark in Hadoop? Oh, well.
Here’s the passage I found as chilling as a dip in the lake near my shack in rural Kentucky:
Ovum’s other big prediction for 2016 is for data lake adoption to become a “front-burner issue” for mature Hadoop adopters that have already successfully put analytics into production serving multiple lines of business and stakeholder groups across the organization. The result will be a new demand for tools to govern the data lake and make it more transparent. Ovum expects significant growth in tooling that builds on emerging data lineage capabilities to catalogue, protect, govern access, tier storage, and manage the lifecycle of data stored in data lakes.
The word for 2016 will involve govern as in “governance.” The idea is that once folks dump stuff in the lake, a digital and procedural mechanism will be needed to figure out exactly what’s in the lake.
Wow, mid tier consulting pitching the need for management. I wonder if the mid tier consulting firms are able to sell their clients management consulting services?
I think this means that these predictions and the attendant reports are essentially content marketing exercises. That’s okay, but writing about a problem is exactly the same as solving a problem. Right?
How did that work out when search was the topic of the moment?
Stephen E Arnold, December 16, 2015
Distribution Ready Reference
December 16, 2015
Distributions are nifty. Some are easy, like the bell curve. Nice and symmetrical. Others are less regular. If you want to see what type of distribution your data generates, navigate to “Common Probability Distributions: The Data Scientist’s Crib Sheet.” Is it necessary to understand the mathematics underpinning each curve? If you are an MBA, the answer is, “No.” If you are more catholic in your approach, you can use these curves to poke into the underbelly of the numerical recipes. Nice write up. It does not include the Tracy Widom distribution, but the beta distribution may be close enough for MBA horse shoes.
Stephen E Arnold, December 16, 2015
The Modern Law Firm and Data
December 16, 2015
We thought it was a problem if law enforcement officials did not know how the Internet and Dark Web worked as well as the capabilities of eDiscovery tools, but a law firm that does not know how to work with data-mining tools much less the importance of technology is losing credibility, profit, and evidence for cases. According to Information Week in “Data, Lawyers, And IT: How They’re Connected” the modern law firm needs to be aware of how eDiscovery tools, predictive coding, and data science work and see how they can benefit their cases.
It can be daunting trying to understand how new technology works, especially in a law firm. The article explains how the above tools and more work in four key segments: what role data plays before trial, how it is changing the courtroom, how new tools pave the way for unprecedented approaches to law practice, how data is improving how law firms operate.
Data in pretrial amounts to one word: evidence. People live their lives via their computers and create a digital trail without them realizing it. With a few eDiscovery tools lawyers can assemble all necessary information within hours. Data tools in the courtroom make practicing law seem like a scenario out of a fantasy or science fiction novel. Lawyers are able to immediately pull up information to use as evidence for cross-examination or to validate facts. New eDiscovery tools are also good to use, because it allows lawyers to prepare their arguments based on the judge and jury pool. More data is available on individual cases rather than just big name ones.
“The legal industry has historically been a technology laggard, but it is evolving rapidly to meet the requirements of a data-intensive world.
‘Years ago, document review was done by hand. Metadata didn’t exist. You didn’t know when a document was created, who authored it, or who changed it. eDiscovery and computers have made dealing with massive amounts of data easier,’ said Robb Helt, director of trial technology at Suann Ingle Associates.”
Legal eDiscovery is one of the main branches of big data that has skyrocketed in the past decade. While the examples discussed here are employed by respected law firms, keep in mind that eDiscovery technology is still new. Ambulance chasers and other law firms probably do not have a full IT squad on staff, so when learning about lawyers ask about their eDiscovery capabilities.
Whitney Grace, December 16, 2015
Sponsored by ArnoldIT.com, publisher of the CyberOSINT monograph
Google Timeline Knows Where You Have Been
December 16, 2015
We understand that to get the most out of the Internet, we sacrifice a bit of privacy; but do we all understand how far-reaching that sacrifice can be? The Intercept reveals “How Law Enforcement Can Use Google Timeline to Track Your Every Move.” For those who were not aware, Google helpfully stores all the places you (or your devices) have traveled, down to longitude and latitude, in Timeline. Now, with an expansion launched in July 2015, that information goes back years, instead of just six months. Android users must actively turn this feature off to avoid being tracked.
The article cites a report titled “Google Timelines: Location Investigations Involving Android Devices.” Written by a law-enforcement trainer, the report is a tool for investigators. To be fair, the document does give a brief nod to privacy concerns; at the same time, it calls it “unfortunate” that Google allows users to easily delete entries in their Timelines. Reporter Jana Winter writes:
“The 15-page document includes what information its author, an expert in mobile phone investigations, found being stored in his own Timeline: historic location data — extremely specific data — dating back to 2009, the first year he owned a phone with an Android operating system. Those six years of data, he writes, show the kind of information that law enforcement investigators can now obtain from Google….
“The ability of law enforcement to obtain data stored with privacy companies is similar — whether it’s in Dropbox or iCloud. What’s different about Google Timeline, however, is that it potentially allows law enforcement to access a treasure trove of data about someone’s individual movement over the course of years.”
For its part, Google admits they “respond to valid legal requests,” but insists the bar is high; a simple subpoena has never been enough, they insist. That is some comfort, I suppose.
Cynthia Murrell, December 16, 2015
Sponsored by ArnoldIT.com, publisher of the CyberOSINT monograph
HAL Flickers to Life
December 15, 2015
My Overflight intelligence system overflows with news about artificial intelligence. Talk about the bandwagon. How many more big time outfits can climb on the horse drawn wagon. I think I hear Meredith Willson’s music playing now.
I read about the OpenAI initiative. in “Introducing OpenAI.” I view the artificial intelligence openness play as an automobile-lease lease deal for certain AI technology. As important is the cheerleading for smart software to learn just like a human. You can read about this breakthrough in smart software in “AI Software Learns a Simple Task Like a Human.” Yep, progress.
However, I interpreted these unrelated announcements against the background of the relentless cheerleading for IBM Watson and its cognitive computing campaign.
Is Watson a leader, able to claim that IBM was on the vanguard? On the other hand, is IBM now a follower, chasing a group of more dynamic companies and applications of the smart software?
The answer to the question is important. IBM, unlike the outfits involved in OpenAI and more innovative demonstrations of smart software, needs to generate revenue from smart software.
IBM will probably perceive itself as the Big Dog in smart software. It is possible to see IBM as a follower, and that is not good for stakeholders. IBM’s big play with Watson may turn out to be another missed opportunity to generate revenue from a leadership role.
Instead of HAL’s reminding Dave about his limits, the fresh AI activities may be saying something about the difference between a PR program and a leadership role.
Stephen E Arnold, December 15, 2015
UK Publisher Repositioning
December 15, 2015
I read “How Dennis Publishing Created a New Tech Media Brand.” I was looking forward to a how to, the nuts and bolts of converting a print and online operation into a zippy digital brand.
The write up explains what most folks involved in “real” journalism know: Publishing outfits are good at outputting content and maybe not so good at the organization of the overall operation.
I learned from the write up:
Dennis Publishing wants to be the top destination for technology-related content in the U.K. in the next two years, spurred by the quick success of its new digital brand, Alphr.
I remembered seeing Alphr on iTunes. A podcast about technology ran for a while and then disappeared in October 2015 with nary a peep. I noted the odd ball spelling, which I assume allows the company’s content to be located with a Google or Yandex search.
The write up said:
That decision seems to be paying off. Alphr attracted just under 600,000 unique visitors in the U.K. in November and 1.5 million globally, according to Google Analytics….Dennis claims that the latest data shows that it outstripped Wired U.K., Quartz and Tech Insider in November in terms of shares in U.K. visits across these categories.
So what was the “how”? The write up pointed out that the company:
- Centralized certain operations
- Implemented testing procedures for products
- Kept the same headcount
- Embraced the Ziff “network” ad sale model from the late 1980s.
In short, in 2015, Dennis took steps that other publishers have been forced to adopt for a number of years.
The one thing the new plan did not do was communicate that the podcast, one of those hippy dippy social media things, was not relevant to the firm.
Communication about podcasts, it seems, is not germane to the new digital brand.
Stephen E Arnold, December 15, 2015
Big Data and Enterprise Search: The Caution Lights Are Flashing
December 15, 2015
I read “How You Should Explain Big Data to Your CEO.” The write up included a link which triggered thoughts of how enterprise search dug itself a hole and climbed. Unable to extricate itself from a problem enterprise search vendors created, the entire sector has been marginalized. In some circles, enterprise search is essentially a joke. “Did you hear about the three enterprise search vendors who walked into a bar?” The bartender says, “What is this? Some kind of joke?”
The link pointed me to a Slideshare (owned by the email and job hunting champion LinkedIn). That presentation, “5 Signs Your Big Data Project is Doomed to Fail,” could have been borrowed from one of my talks about enterprise search in 2001. It was not, but the basic message was identical: Big Data has created a situation in which there are some challenges here and now.
The presentation was prepared by Qubole (maybe pronounced cue ball?). Qubole is a click to query outfit. This means that reports from Big Data are easy to generate.
Here are the problems Big Data faces:
- Failed implementations. Qubole asserts that 87 percent of the Big Data implementations are flops
- 73 percent of executive describe the Big Data project as flop
- 45 percent of Big Data projects are completed
These data are similar to the results of “satisfaction” with enterprise search solutions.
Why? Qubole asserts:
- Inaccurate project scope
- Inadequate management support
- No business case
- Lack of talent (in search the talent may be present but overestimates its ability to deal with enterprise search technologies and processes)
- “Challenging tools.” I think this means that in the Big Data world there are lots of complexities.
What can one charged with either search or Big Data tasks do with this information?
My view is, “Ignore it.”
The “can do” spirit carries professionals forward. Hiring a consultant provides some job protection but does little to reverse the failure and disappointment rate.
My view is that the willingness of executives to grab at a magic solution presented by a showman marketer overrides failure date. The arrogance of those involved create a “that won’t happen to us” belief.
Who is to blame? The company for believing in baloney? The marketer for painting a picture and showing a Hollywood style demo? The developers who created the Big Data solution, knowing that chunks were not complete or just did not work before the ship date? The in house engineers who lacked self knowledge to understand their own limitations?
Everyone is in the hole with the enterprise software vendors. The hole is deep. Magic solutions are difficult to pin down. The future of Big Data is likely to parallel to some degree the dismal track record of enterprise search. Fascinating. I can hear the mid tier consultants and the handful of remaining enterprise search vendors asserting that Qubole’s points are not applicable to their specific situation.
Yep, and I believe in the tooth fairy and Santa.
Stephen E Arnold, December 15, 2015
Big Data Gets Emotional
December 15, 2015
Christmas is the biggest shopping time of the year and retailers spending months studying consumer data. They want to understand consumer buying habits, popular trends in clothing, toys, and other products, physical versus online retail, and especially what competition will be doing sale wise to entice more customers to buy more. Smart Data Collective recently wrote about the science of shopping in “Using Big Data To Track And Measure Emotion.”
Customer experience professionals study three things related to customer spending habits: ease, effectiveness, and emotion. Emotion is the biggest player and is the biggest factor to spur customer loyalty. If data specialists could figure out the perfect way to measure emotion, shopping and science would change as we know it.
“While it is impossible to ask customers how do they feel at every stage of their journey, there is a largely untapped source of data that can provide a hefty chunk of that information. Every day, enterprise servers store thousands of minutes of phone calls, during which customers are voicing their opinions, wishes and complaints about the brand, product or service, and sharing their feelings in their purest form.”
The article describes some methods emotional data is fathered: phone recordings, surveys, and with vocal layer speech layers being the biggest. Analytic platforms that measure vocal speech layers that measure relationships between words and phrases to understand the sentiment. The emotions are ranged on a five-point scale, ranging from positive to negative to discover patterns that trigger reactions.
Customer experience input is a data analyst’s dream as well as nightmare based on all of the data constantly coming.
Whitney Grace, December 15, 2015
Sponsored by ArnoldIT.com, publisher of the CyberOSINT monograph
Easy as 1,2,3 Common Mistakes Made with Data Lakes
December 15, 2015
The article titled Avoiding Three Common Pitfalls of Data Lakes on DataInformed explores several pitfalls that could negate the advantages of data lakes. The article begins with the perks, such as easier data access and of course, the cost-effectiveness of keeping data in a single hub. The first is sustainability (or the lack thereof), since the article emphasizes that data lakes actually require much more planning and management of data than conventional databases. The second pitfall raised is resource allocation,
“Another common pitfall of implementing data lakes arises when organizations need data scientists, who are notoriously scarce, to generate value from these hubs. Because data lakes store data in their native format, it is common for data scientists to spend as much as 80 percent of their time on basic data preparation. Consequently, many of the enterprise’s most valued resources are dedicated to mundane, time-consuming processes that considerably lengthen time to action on potentially time-sensitive big data.“
The third pitfall is technology contradictions or trying to use traditional approaches on a data lake that holds both big and unstructured data. Be not alarmed, however, the article goes into great detail about how to avoid these issues through data lake development with smart data technologies such as semantic tech.
Chelsea Kerwin, December 15, 2015
Sponsored by ArnoldIT.com, publisher of the CyberOSINT monograph
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