October 21, 2014
A happy quack to the reader who alerted us to “”What Is Big Data?” The write up consists of 43 definitions provided by luminaries in a variety of fields. If you are in search of enlightenment with regard to Big Data, navigate to the story and dig in.
I found a couple of definitions interesting. Let me highlight Daniel Gillick’s and Hal Varian’s. Both are hooked up with Google, one of the big time big data outfits.
Mr. Gillick says:
Historically, most decisions — political, military, business, and personal — have been made by brains [that] have unpredictable logic and operate on subjective experiential evidence. “Big data” represents a cultural shift in which more and more decisions are made by algorithms with transparent logic, operating on documented immutable evidence. I think “big” refers more to the pervasive nature of this change than to any particular amount of data.
Mr. Varian says:
Big data means data that cannot fit easily into a standard relational database.
There you have it: A cultural shift and anything that won’t fit in a Codd-style data management system. Are the other 41 definitions superfluous?
Stephen E Arnold, October 21, 2014
October 21, 2014
Here’s another prediction on the future of Big Data. WhaTech calls our attention to a recent report from ReportsnReports in, “Explore Global Big Data Market that Will Grow at a CAGR of 34.17% by 2018.” Those on the hook to venture firms looking for Big Data payoffs hope the estimate is on the low side. Keep in mind, though, that this figure comes from a wild and crazy consulting firm report. The press release tells us:
“Global Big Data Market 2014-2018, has been prepared based on an in-depth market analysis with inputs from industry experts. The report covers the Americas, and the EMEA and APAC regions; it also covers the Global Big Data market landscape and its growth prospects in the coming years. The report also includes a discussion of the key vendors operating in this market.”
See the write-up for a list of vendors mentioned in the report; that we can get for free. The post goes on to list the “key questions” addressed by the $2500 report:
“What will the market size be in 2018 and what will the growth rate be?
What are the key market trends?
What is driving this market?
What are the challenges to market growth?
Who are the key vendors in this market space?
What are the market opportunities and threats faced by the key vendors?
What are the strengths and weaknesses of the key vendors?”
Good questions, all.
Cynthia Murrell, October 21, 2014
October 14, 2014
The article titled The Truth About Big Data on Datamation was posted September 26, 2014 and debunks some of the myths surrounding big data. Gartner, tech research firm, has collected data on the plans of organizations for big data. The most hopeful information may have been for businesses who have yet to hop on the big data bandwagon. This may sound like old news, but Gartner’s analysis of its findings leads to their claim that big data solution’s market “is in its infancy.” The article states,
“Seventy-three percent of organizations surveyed by the research group said that they are investing or plan to invest in big data technologies. Yet, only 13 percent said that they had deployed related solutions. Big data projects are stalling out in the planning stage, Gartner discovered. “The biggest challenges that organizations face are to determine how to obtain value from big data, and how to decide where to start,” said the firm in a statement… Gartner recommends that organizations sweat the small stuff.”
This means that the idea that individual flaws in data will have less impact on big data is wrongheaded. More data means more flaws, so keeping a close eye on data quality remains important. Companies need to clear away these misconceptions and others mentioned in the article in order to get the most bang for their big data bucks.
Chelsea Kerwin, October 14, 2014
October 3, 2014
Big Data. Biggish Data. Now dark data. The idea plays on the silliness of the dark Web; that is, it is information that is “there”, but you don’t know about it. Well, get with it, pilgrim. Datameer used this term in “Shine Light on Dark Data.”
Here’s the definition:
At every organization neglected data sits overlooked in log files and archives accumulating digital dust and incurring costs. But as more organizations look for ways to become better, stronger and faster, they’re digging into this “dark” data and uncovering a gold mine of business intelligence.
Now how do you shine light on dark data? Great question. I will not probe the logical aspects of this concept. There are, according to the article, five steps to take. These are—unsurprisingly—the same steps a prudent and informed manager takes to figure out just plain old data.
Words to marketers make all the difference. I am not sure data has an opinion.
Stephen E Arnold, October 3, 2014
September 30, 2014
Connotate has been going through many changes through 2014. According to Virtual Strategy they can count adding a new leader to the list: “Connotate Appoints Rich Kennelly As Chief Executive.” Connotate sells big data technology, specializing in enterprise grade Web data harvesting services. The newest leader for the company is Richard J. Kennelly. Kennelly has worked in the IT sector for over twenty years. Most of his experience has been helping developing businesses harness Internet and data. He has worked at Ipswitch and Akami Technologies, holding leadership roles at both companies.
Kennelly is excited about his new position:
“ ‘This is the perfect time to join Connotate,’ said Kennelly. ‘The Web is the largest data source ever created. The biggest brands are moving quickly to leverage that data to drive competitive advantage and create new revenue streams. Connotate’s patented technology, scalability, and deep technical expertise make us the natural choice for these forward thinking companies.’”
The rest of the quote includes a small, but impressive client list, more praise for Kennelly, and how Connotate is a leading big data company.
If Connotate did not have good products and services, then they would not keep their clients. Despite the big names, they are still going through financial woes. Is choosing Kennelly a sign that they are trying to raise harvest more funding?
September 15, 2014
Say, here’s a thought: After spending billions for big-data software, federal managers are being advised to do their research before investing in solutions. We learn about this nugget of wisdom from Executive Gov in their piece, “Report: Fed Managers Should Ask Data Questions, Determine Quality/Impact Before Investing in Tech.” Writer Abba Forrester sums up the Federal Times report:
“Rutrell Yasin writes that the above managers should follow three steps as they seek to compress the high volume of data their agencies encounter in daily tasks and to derive value from them. According to Shawn Kingsberry, chief information officer for the Recovery Accountability and Transparency Board, federal managers should first determine the questions they need to ask of data then create a profile for the customer or target audience.
“Finally, they should consider the potential impact of the data, the insights and resulting technology investments on the agency.”
For any managers new to data management, the article notes they should choose a platform that includes data analysis tools and compiles data from multiple sources into one repository. It also advises agencies to employ a dedicated chief data officer and data scientists/ architects. Good suggestions, all. Apparently, agencies need to be told that a cursory or haphazard approach to data is almost certain to require more time, effort, and expense down the line.
Cynthia Murrell, September 15, 2014
September 4, 2014
Autonomy, Recommind, and dozens of other search and content processing firms rely on statistical procedures. Anyone who has survived Statistics 101 believe in the power of numbers. Textbook examples are—well—pat. The numbers work out even for B and C students.
The real world, on the other hand, is different. What was formulaic in the textbook exercises is more difficult with most data sets. The data are incomplete, inconsistent, generated by systems whose integrity is unknown, and often wrong. Human carelessness, the lack of time, a lack of expertise, and plain vanilla cluelessness makes those nifty data sets squishier than a memory foam pillow.
If you have some questions about statistical evidence in today’s go go world, check out “I Disagree with Alan Turing and Daniel Kahneman Regarding the Strength of Statistical Evidence.”
I noted this passage:
It’s good to have an open mind. When a striking result appears in the dataset, it’s possible that this result does not represent an enduring truth or even a pattern in the general population but rather is just an artifact of a particular small and noisy dataset. One frustration I’ve had in recent discussions regarding controversial research is the seeming unwillingness of researchers to entertain the possibility that their published findings are just noise.
An open mind is important. Just looking at the outputs of zippy systems that do prediction for various entities can be instructive. In the last couple of months, I learned that predictive systems:
- Failed to size the Ebola outbreak by orders of magnitude
- Did not provide reliable outputs for analysts trying to figure out where a crashed airplane was
- Came up short regarding resources available to ISIS.
The Big Data revolution is one of those hoped for events. The idea is that Big Data will allow content processing vendors to sell big buck solutions. Another is that massive flows of unstructured content can only be tapped in a meaningful way with expensive information retrieval solutions.
Dreams, hopes, wishes—yep, all valid for children waiting for the tooth fairy. The real world has slightly more bumps and sharp places.
Stephen E Arnold, September, 2014
September 4, 2014
Here is an article that makes you question the past two years, from the Federal Times comes “Steps To Make Big Data Relevant” from August 2014. For the past two years, big data has been the go-to term for technology and information professionals. IT companies have sold software meant to harness big data’s potential and generate revenue. So why is there an article explaining how to make it relevant now? It is using the federal government as an example and any bureaucrat can tell you government implementation is slow.
If, however, you do not even know what big data is and you want to get started, this article explains it in basic terms. It has three steps people need to think about to develop a big data plan:
- Determine what questions need to be asked of the data.
- Determine where all of the data you want is located and ask the data owners’ to understand the data’s quality.
- Decide what it means to answer these questions and use technology to help answer them.
Then the last suggestion is to have a dedicated team to manage big data:
“To address that challenge, federal agencies need a chief data officer and data architects or scientists. The chief data officer would keep the chief information officer and chief information security officer better informed about the value of their information and how to interact with that information to make it useful. Chief data architects/scientists are needed to design the data infrastructure and quantify the value of the data at its lowest common elements.”
When you read over the questions, you will see they are an implementation plan for any information technology software: what do you want to do, figure out how to do it, make a plan to implement it. Big data is complex, but the steps governing it are not.
Whitney Grace, September 04, 2014
September 2, 2014
Why does logic seem to fail in the face of fancy jargon? DataFusion’s Blog posted on the jargon fallacy in the post, “It All Begins With Data Quality.” The post explains how with new terms like big data, real-time analytics, and self-service business intelligence that the basic fundamentals that make this technology work are forgotten. Cleansing, data capture, and governance form the foundation for data quality. Without data quality, big data software is useless. According to a recent Aberdeen Group study, data quality was ranked as the most important data management function.
Data quality also leads to other benefits:
“When examining organizations that have invested in improving their data, Aberdeen’s research shows that data quality tools do in fact deliver quantifiable improvements. There is also an additional benefit: employees spend far less time searching for data and fixing errors. Data quality solutions provided an average improvement of 15% more records that were complete and 20% more records that were accurate and reliable. Furthermore, organizations without data quality tools reported twice the number of significant errors within their records; 22% of their records had these errors.”
Data quality saves man hours, discovers missing errors, and deleted duplicate records. The Aberdeen Group’s study also revealed that poor data quality is a top concern. Organizations should deploy a data quality tool, so they too can take advantage of its many benefits. It is a logical choice.
September 1, 2014
I suppose I am narrow minded. I don’t associate the Huffington Post with high technology analyses. My ignorance is understandable because I don’t read the Web site’s content.
However, a reader sent me a link to “Top Three Big Data Myths: Debunked”, authored by a search vendor’s employee at Recommind. Now Recommind is hardly a household word. I spoke with a Recommind PR person about my perception that Recommind is a variant of the technology embodied in Autonomy IDOL. Yep, that company making headlines because of the minor dust up with Hewlett Packard. Recommind provides a probabilistic search system to customers that were originally involved in the legal market. The company has positioned its technology to other markets and added a touch of predictive magic as well. At its core, Recommind indexes content and makes the indexes available to users and other services. The company in 2010 formed a partnership with the Solcara search folks. Solcara is now the go to search engine for Thomson Reuters. I have lost track of the other deals in which Recommind has engaged.
The write up reveals quite a bit about the need for search vendors to reach a broader market in order to gain visibility to make the cost of sales bearable. This write up is a good example of content marketing and the malleability of outfits like Huffington Post. The idea strikes me as something that looks interesting may get a shot at building the click traffic for Ms. Huffington’s properties.
So what does the article debunk? Fasten your seat belt and take your blood pressure medicine. The content of the write up may jolt you. Ready?
First, the article reveals that “all” data are not valuable. The way the write up expresses it takes this form, “Myth #1—All Data Is Valuable.” Set aside the subject verb agreement error. Data is the plural and datum is the singular. But in this remarkable content marketing essay, grammar is not my or the author’s concern. The notion of categorical propositions applied to data is interesting and raises many questions; for example, what data? So the first my is that if one if able to gather “all data”, it therefore follows that some is not germane. My goodness, I had a heart palpitation with this revelation.
Second, the next myth is that “with Big Data the more information the better.” I must admit this puzzles me. I am troubled by the statistical methods used to filter smaller, yet statistically valid, subsets of data. Obviously the predictive Bayesian methods of Recommind can address this issue. The challenges Autonomy like syst4ems face are well known to some Autonomy licensees and, I assume, to the experts at Hewlett Packard. The point is that if the training information is off base by a smidge and the flow of content does not conform to the training set, the outputs are often off point. Now with “more information” the sampling purists point to sampling theory and the value of carefully crafted training sets. No problem on my end, but aren’t we emphasizing that certain non Bayesian methods are just not a wonderful as Recommind’s methods? I think so.
The third myth that the write up “debunks” is “Big Data opportunities come with no costs.” I think this is a convoluted way of saying that get ready to spend a lot of money to embrace Big Data. When I flip this debunking on its head, and I get this hypothesis, “The Recommind method is less expensive than the Big Data methods that other hype artists are pitching as the best thing since sliced bread.
The fix is “information governance.” I musty admit that like knowledge management, I have zero idea what the phrase means. Invoking a trade association anchored in document scanning does not give me confidence that an explanation will illuminate the shadows.
Net net: The myths debunked just set up myths for systems based on aging technology. Does anyone notice? Doubt it.
Stephen E Arnold, September 1, 2014