January 28, 2015
The article titled How to Force Giants to “Stop & Listen”- The Legal Tech Entrepeneur Prising Open “Closed Systems” on The Legal Review examines the rewards available for those firms and entrepreneurs willing to take risks. The story of Solcara, the “federated search” technology company that started up just after the .com bubble burst in 2001. The article explains,
“Using Solcara, firms would be able to search legal content buried deep within the likes of “Lexis Nexis, Westlaw UK and Practical Law from Thomson Reuters…using a single search interface. Unsurprisingly, legal publishers who were used to a “closed system”, where they could print and sell entire libraries of bound books to clients, were initially uncomfortable with Solcara’s cherry-picking innovation. ..”The only way Solcara was able to successfully achieve [federated search] was working directly with the law firms… such as Norton Rose.”
Eventually Thomson Reuters acquired Solcara as well as Practical Law, leading Solcara’s co-founder Rob Martin to suggest that something similar needs to happen soon in law firms or clients will force a change on their own. Martin firmly believes that taking risks on innovation and being prepared to change direction is the only way to thrive in a market that fluctuates so easily.
Chelsea Kerwin, January 28, 2014
January 27, 2015
If you need help finding file analysis solutions, Nieuwsbank published this press release that might help you with your research, “Gartner ZyLAB in ‘The File Analysis Market Guide, 2014.’” File analysis refers to file storage and users who have access to them. It is used to manage intellectual property, keep personal information private, and protect data. File analysis solutions have many benefits for companies, including reducing businesses risks, reducing costs, and increasing operational efficiencies.
The guide provides valuable insights:
“In the Market Guide Gartner writes: ZyLAB enter this market from the perspective of and with a legacy in eDiscovery. The company has a strong presence in the legal community and is widely used by governments and organizations in the field of law enforcement. ZyLAB emphasis on activities such as IP protection and detection, fraud investigations, eDiscovery and responsibly removal of redundant data. ZyLAB supports storage types 200 and 700 file formats in 450 languages. This makes it a good choice for international companies. ‘”
ZyLAB is a respected eDiscovery and information risk management solutions company and this guide is a compilation of their insights. The articles point out that companies might have their own file analysis manuals, but few actually enforce its policies or monitor violations. Gartner is a leading market research and their endorsement should be all you need to use this guide.
January 26, 2015
Numbers are supposed to be cold, hard facts that prove the truth without a doubt. While numbers can be fudged, they can also be interpreted in different ways. Neowin tells us that two respected market research companies are arguing over a specific number sets. Read the article, “Gartner Reports PC Shipments Went Up In Q4, But IDC Says Otherwise.”
PC’s have faced steep competition in the past few years with people switching over to the Apple camp, tablets becoming more prevalent, and a small percentage building their own machines. PC’s still remain a huge staple in the computer market, but sales are declining.
Gartner says 83.7 million PC units were sold, while IDC says 80.8 million units were sold. IDC does agree with Gartner that the fourth quarter saw a rise in sales, but there was a 2.4% decrease in sales compared in 2013 numbers.
The reason for the difference in numbers relates to how the market research firms gather their information:
“The different results reported by IDC are easy to explain since the two research firms have different ways to define what constitutes a PC. IDC for example doesn’t count Windows tablet/hybrids such as the Surface Pro 3 but includes Chromebooks, while Gartner excludes any portables other than Windows tablets.”
It is not a surprise that number experts cannot agree on the numbers, especially when they are pulling data from different sources. This means both are right and both are wrong at the same time. The numbers do not lie, but the humans interpreting the data can read it wrong.
January 26, 2015
Last year the IT industry raved about the benefits of big data and the advancements that had been made in that field. Big data is kind of old news and other than the occasional breakthrough, what else do we need to know about how it can help companies? While big data will continue to play a big role in 2015, GCN highlights other trends predicted to take off this year: “5 Trends That Will Drive IT Management in 2015.”
This article, of course, mentions big data, but rather than focusing on the technology, it describes its practical applications. Most big data advancements talk about the technology that drives it and how much data can be processed. The article explains that it will be used for more than combing through old data, but it will be even more important as video is utilized more especially for police officers.
Data collection will become even more complex as ownership debates will be tied to the locations of data stores. Cyber investigations will need to be ramped up, especially in infrastructure. Many tools exist to help investigators track attack patterns and trace packet traffic, but these are expensive toys. Organizations will have to start investing more in their front end.
Predictive analytics is something we have heard about, but has taken a back seat to big data. It might take off this year:
“Predictive analytics can be tied to many activities. Security experts are building applications that monitor external penetration efforts while predicting the next steps that hackers will take. At the same time behavioral profiling – collecting, analyzing and interpreting information to identify risks and predict threats – is on the rise.”
This year will be about more than big data, though if you want to be nit picky all these predictions tie back to big data’s overall purpose.
January 22, 2015
The article titled Boardroom Priorities in 2015: Can IT Deliver on ZDNet discusses a recent survey of 200 CXOs on boardroom concerns for 2015 from Constellation Research. Digital transformation was at the top of many lists, and the article posits that there is a fear among many companies that a “Digital Darwinism” will take down corporations that have not invested in digital strategies. The article states,
“Constellation notes that digital personas represent the brand, but also expand on the brick and mortar experience…[it] spans the entire customer experience…The companies that have invested early in their digital presence have a lead, but it’s unclear what happens to everyone else. Not all of these investments will work and execution is likely to get spotty. Tech vendors are promising magic bullets, but if every company is on the same bandwagon it’s going to be tough for anyone to pull away.”
The article suggests weariness in all areas of investment is wise, especially big data. Other priorities the survey listed include consistent customer experience, preparedness for inorganic growth, mass automation and the reduction of costs for regulatory and security compliance. There is a missing point in this article, which is money. Companies still have to deliver sustainable revenue and continue making new sales.
Chelsea Kerwin, January 22, 2014
January 20, 2015
Is the cloud giving new life to tape? The Register reports on “The Year When Google Made TAPE Cool Again….” With both Google and Amazon archiving data onto tape, the old medium suddenly seems relevant again. Our question—does today’s tape guarantee 100% restores? We want to see test results. One cannot find information that is no longer there, after all.
The article hastens to point out that the tape-manufacturing sector is not out of the irrelevance woods yet. Reporter Chris Mellor writes about industry attempts to survive:
“Overall the tape backup market is still in decline, with active vendors pursuing defensive strategies. Struggling tape system vendors Overland Storage and Tandberg Data, both pushing forwards in disk-based product sales, are merging in an attempt to gain critical and stable business mass for profitable revenues.
“Quantum still has a large tape business and is managing its decline and hoping a profitable business will eventually emerge. SpectraLogic has emerged as an archive tape champion and one of the tape technology area’s leaders, certainly the most visionary with the Black Pearl technology.
“Oracle launched its higher-capacity T10000D format and IBM is pushing tape drive and media capacity forwards, heading past a 100TB capacity tape.”
The write-up concludes with ambivalence about the future of tape. Mellor does not see the medium disappearing any time soon, but is less confident about its long-term relevance. After all, who knows what storage-medium breakthrough is around the corner?
Cynthia Murrell, January 20, 2015
January 18, 2015
I am not sure what the folks in Massachusetts are thinking. I read “Google Glass: Down but Not Out.” Reading the story was an interesting exercise in filling a small tumbler with not-so-hot lemonade.
Here’s the passage I noted:
True, Glass has struggled to find its place in the mainstream.
Now that’s a statement that puts Brin and X Labs in some context.
Then I highlighted in yellow:
But make no mistake: Glass isn’t going away—not without more of a fight. While it’s struggled to find support among consumers, some businesses have been highly receptive to the electronic eyewear, and the next iterations of Glass might suit them even better.
Chuck full of quotes, the write up points out some of the issues; for example, backlash, the economics, and wonderful phrase “glasshole.”
But what was not said may be more important. The big thinker on this project was the multi-named Babak Amir Parviz. Then there was the alleged interaction between one of Google’s founders and an employee. There was some discord, which is a pretty nice way of summing up some fast dancing in the interpersonal relationships department. Finally there has been the two step in reorganizing the Glass house.
What we have is a write up that ignores the impact of business and personal decisions on a product that warranted a business school type of analysis. That means looking at the company, the professionals involved, the market reaction, and the social implications of hard-to-detect Glass functions.
Like much of IDC’s work, including Dave Schubmehl’s sale of my research under his name on Amazon, the issue of covering a topic is almost more interesting than the high school lab experiments themselves. But who cares? In an age of content marketing and zoom-zoom analysis, there just isn’t time between Oscar antics, analysis, and making sales, is there?
A photo of a Glass fashion show featuring Mr. Brin and Ms. Rosenberg would have helped put the write up in a fashion context. Remarkable that Vanity Fair would have more substance than an IDC publication. Fascinating.
Stephen E Arnold, January 18, 2015
January 15, 2015
If you are building your personal knowledgebase about smart software, I suggest you read “A Brief overview of Deep Learning.” The write up is accessible, which is not something I usually associate with outputs from Cal Tech wonks.
I highlighted this passage with my light blue marker:
In the old days, people believed that neural networks could “solve everything”. Why couldn’t they do it in the past? There are several reasons.
- Computers were slow. So the neural networks of past were tiny. And tiny neural networks cannot achieve very high performance on anything. In other words, small neural networks are not powerful.
- Datasets were small. So even if it was somehow magically possible to train LDNNs, there were no large datasets that had enough information to constrain their numerous parameters. So failure was inevitable.
- Nobody knew how to train deep nets. Deep networks are important. The current best object recognition networks have between 20 and 25 successive layers of convolutions. A 2 layer neural network cannot do anything good on object recognition. Yet back in the day everyone was very sure that deep nets cannot be trained with SGD, since that would’ve been too good to be true!
It’s funny how science progresses, and how easy it is to train deep neural networks, especially in retrospect.
Stephen E Arnold, January 15, 2015
January 14, 2015
I enjoy the IBM marketing hoo hah about Watson. Perhaps it lags behind the silliness of some other open source search repackagers, it is among my top five most enjoyable emissions about information access.
I read “IBM Debuts New Mainframe in a $1 Billion Bet on Mobile.” I love IBM mainframes, particularly the older MVS TSO variety for which we developed the Bellcore MARS billing system. Ah, those were the days. Using Information Dimensions BASIS and its wonder little exit and run this routine, we did some nifty things.
Furthermore, the mainframe is still a good business. Just think of the banks running IBM mainframes. Those puppies need TLC and most of the new whiz kids are amazed at keyboards with lots and lots of function keys. Fiddle with a running process and make an error. Let me tell you that produces billable hours for the unsnarlers.
IBM has “new” mainframe. Please, no oxymoron emails. Dubbed the z13—you, know alpha and omega, so with omega taken—z is the ultimate. Los primeros required hard wiring and caution when walking amidst the DASDs. Not today. These puppies are pretty much like tame mainframes with a maintenance dependency. z13s are not iPads.
The blue bomber has spent $1 billion on this new model. Watson received big buck love too, but mainframes are evergreen revenue. Watson is sort of open sourcey. The z13 is not open sourcey. That’s important because proprietary means recurring revenue.
Companies with ageing mainframes are not going to shift to a stack of Mac Minis bought on eBay. Companies with ageing mainframes are going to lease—wait for it—more mainframes. Try to find a recent comp sci grad and tell him to port the inter bank transfer system to a Mac Mini. How eager will that lass be?
Now to the write up. Here’s the passage I highlighted in pink this morning:
The mainframe is one of IBM’s signature hardware products that will help sell related software and services, and it’s debuting at a critical time for the Armonk, New York-based company. Chief Executive Officer Ginni Rometty is trying to find new sources of revenue growth from mobile offerings, cloud computing and data analytics as demand for its legacy hardware wanes.
There you go. The mainframe does mobile. The new version also does in line, real time fraud detection. The idea is that z13 prevents money from leaving one account for another account if there is a hint, a mere sniff, of fraud.
My view is that it will be some time before Amazon, Facebook, and Google port their mobile systems to the z13, but for banks? This is possible a good thing.
Will the z13 allow me to view transaction data on a simulated green screen? Will their be a Hummingbird widget to convert this stuff to a 1980 interface?
I am delighted I don’t have to come up with ideas to generate hundreds of millions in new revenue for IBM. This is a very big task, only marginally more difficult than converting Yahoo into the next Whatsapp.
No word on pricing for a z13 running Watson.
Stephen E Arnold, January 14, 2015
January 14, 2015
IBM’s Watson has some open-source competition. As EE Times reports in “DARPA Offers Free Watson-Like Artificial Intelligence,” DARPA’s DeepDive is now a freely available alternative to the famous machine-learning AI. Both systems have their roots in the same DARPA-funded project. According to DeepDive’s primary programmer, Christopher Re, while Watson is built to answer questions, DeepDive’s focus is on extracting a wealth of structured data from unstructured sources. Writer R. Colin Johnson informs us:
DeepDive incorporates probability-based learning algorithms as well as open-source tools such as MADlib, Impala (from Oracle), and low-level techniques, such as Hogwild, some of which have also been included in Microsoft’s Adam. To build DeepDive into your application, you should be familiar with SQL and Python.
“Underneath the covers, DeepDive is based on a probability model; this is a very principled, academic approach to build these systems, but the question for use was, ‘Could it actually scale in practice?’ Our biggest innovations in Deep Dive have to do with giving it this ability to scale,” Re told us.
For the future, DeepDive aims to be proven in other domains. “We hope to have similar results in those domains soon, but it’s too early to be very specific about our plans here,” Re told us. “We use a RISC processor right now, we’re trying to make a compiler, and we think machine learning will let us make it much easier to program in the next generation of DeepDive. We also plan to get more data types into DeepDive.”
It sounds like the developers are just getting started. Click here to download DeepDive and for installation instructions.
Cynthia Murrell, January 14, 2015