Hadoop Marries Two Big Data Companies
October 22, 2013
How long has it been since we discussed a new partnership based on big data? It has been a while, so let us bring to your attention “The Zementis Partnership With Karmasphere” via the Zementis Blog. Zementis is the developer of a predictive analytics engine for Hadoop users who need to harness data across its various incarnates such as Hive, Datameer, and Karmasphere. Zementis and Karmasphere have teamed up to combine their powers.
How will the two companies improve big data for their customers? The partnership is described as making the big data process faster, easier, and more cost-efficient:
“Zementis makes PMML-based models available as standard Hive User Defined Functions (UDF’s) that can be readily consumed and managed by users of the Karmasphere Workspace for Big Data Analytics. Practically speaking, it is now easier than ever to move models from your favorite model-building environment for scoring in Hadoop. If you use R, for example, simply save your models in PMML-format and use Zementis and Karmasphere to deploy them natively on Hadoop, across all your data and dimensions.”
The entire goal is to make an out-of-the-box analytics solution that can be deployed with little hassle and allow users to get to work faster. In other words, a custom solution that fits everyone. Great idea, but will it work? No two types of data are the same and individually tailored solutions are what is hot now.
Whitney Grace, October 22, 2013
Sponsored by ArnoldIT.com, developer of Augmentext
Debunking Big Data Myths
October 10, 2013
Much has been written about big data, and apparently a bit of misinformation is now floating around. ITProPortal aims to set the record straight with, “5 Big Data Myths Businesses Need to Understand.”
The article’s first myth is that big data technology is only about unstructured data, when it is really the ability to work with multiple types of data at once that is important. This one is chalked up to “the imprecise use of the word ‘unstructured’.” Other myths include the idea that big data sets inherently contain data of poor quality (it depends on the company’s quality control); that adopting a big data system requires hiring a big data expert (it doesn’t); and that machine learning can eliminate human bias (certainly not). All important points—see the piece for writer Gil Allouche’s support for each of these.
I do take some issue with his elaboration on myth number two, “Only large companies produce such huge data volumes.” Allouche asserts:
“The inclusion of the word ‘big’ causes many small business owners to assume that big data is purely for large companies with huge amounts of information. Nothing could be further from the truth. Although volume is often discussed as a key attribute of big data, there is no set amount that qualifies. What matters is that everybody’s data is growing in size. In addition, enormous data sets are generally not analysed all at once anyway, so the amount of information you have really isn’t such an important factor when it comes to big data. The size of the organisation shouldn’t matter either, since every business should strive to run based off data-driven insights rather than gut feelings or intuition. In fact, big data may be the key that allows a small business to outpace their larger competitors.”
Maybe. I’m not so quick to dismiss “gut feelings or intuition,” especially in smaller organizations. The annoying truth is that the big data question is not so simple. Businesses must carefully examine their unique situations and decide whether “data-driven insights” will truly serve their needs. Jumping on the bandwagon without doing the research could prove costly in the end.
Cynthia Murrell, October 10, 2013
Sponsored by ArnoldIT.com, developer of Augmentext
How Big Data Yields Big Money
October 9, 2013
Remember the mailing houses that marketers used to contact in order to gather information about prospective consumers? Mailing houses have become obsolete, but their digital equivalents are far more effective than. KapitallWire takes a look at “Axciom: Big Data Equals Big Money” and explains how companies can make money from big data. Axciom is a marketing technology firm that processes more than a trillion transactions from over 700 million consumers worldwide. They have 1500 data points about individuals, including demographics and shopping habits. Unlike old-fashioned mailing houses, Axciom can deliver a more targeted approach to reach consumers.
Axcicom wants to change how consumers interact with their personal data:
“Yet, despite its prominence as a consumer data broker, Acxiom tends to fly relatively, and comfortably, under the radar. At least, that was the case until last Wednesday when the firm unveiled its new consumer portal AboutTheData.com. The beta site lets users log in and view, as well as edit, the personal data the firm has collected and maintains on file. “Make Data Work for You – know what data says about you and how it is used” the home page reads, reiterating the company’s claims of increasing transparency and empowering customers. The timing of the portal’s launch couldn’t have been better considering Acxiom is currently under investigation by the Federal Trade Commission.”
Why is the FTC investigating? It has to do with data collection firms possibly violating consumer privacy. The FTC wants these companies to make their actions more transparent, but thanks to the lack of laws these are more optional than mandatory. Despite the regulations, Axciom and other firms have made a $300 billion, but any regulations to rein in the data mining would hinder the earnings. Good money for now, but the big data boom may fall soon.
Whitney Grace, October 09, 2013
Sponsored by ArnoldIT.com, developer of Augmentext
Big Data for Automakers
October 8, 2013
It should come as no surprise that automakers are embracing analytics. The Indian site Livemint reports, “Auto Makers Bet on Big Data for Business Insights.” The article looks at ways several carmakers in India are leveraging big data. We learn:
“The nation’s largest car maker Maruti Suzuki has been using analytics since 2008, mostly deployed at its 1,300 sales and 3,000 service centres that serve as many as nine million customers, and uses the insights provided by the software to target the right customers. . . .
“For instance, when Maruti launched its Eeco van in 2010, it was able to identify customers with large families for the product using direct mailers. ‘Usually direct mailer conversion into sales is less than 1% but with Eeco we saw a 9% conversion into sales,’ said Pareek.
The company has also been able to pick up trends in servicing from the data it collects. ‘We identified that working ladies found it difficult to drop their car for service, so we introduced a pick up and drop service for them, and soon extended it to all its customers,’ says [chief operating officer Mayank] Pareek.”
Those are good examples of smart ways to employ data analysis. The write-up also cites Hero MotoCorp, maker of two-wheelers, and its program that uses customer data to link its 4,000 dealers with 1,000 parts vendors and service centers. That company is also working with social media analytics. Meanwhile, Tata Motors is now beginning to analyze its procurement and spending data.
Writer Arundhati Ramanathan also looks outside her country, noting that global heavyweights like Ford, GM, and Volvo are exploring predictive analysis based on data from sensors in cars and on shop floors. It seems that automakers worldwide are gearing up to make the most of data analytics.
Cynthia Murrell, October 08, 2013
Sponsored by ArnoldIT.com, developer of Augmentext
Recommind Wants Big Data
October 5, 2013
The eDiscovery process is part of big data, so it does not come as a surprise when “Recommind Raises $15M From SAP Ventures To Transform Unstructured Big Data Analytics” says MarketWatch. Recommind already provides governance, unstructured data management, and analysis, but it is making a strategic move to enter the big data game. It raised $15 million in a Series C funding round from SAP Ventures. Recommind will use the monies to meet market demand for its CORE (R) technology platform and expansion into the big data market.
Unstructured data plays a big role in everyone’s jobs and lives and Recommind believes it can take solution to the next level of advancement:
” ‘Unstructured data is the largest, fastest-growing part of modern business, and right now it is managed ineffectively,’ said Recommind CEO Bob Tennant. ‘This impacts you whether you’re a CIO, an attorney, a contract analyst, an IT professional or someone who needs to find documents on an important project. Legacy solutions can’t keep up in an age of information overload. We need a new way to access, analyze and govern information — one that is both intelligent and highly automated. With our CORE platform, we have the opportunity to solve some of the most difficult and costly problems in the enterprise.’ “
Recommind already has a big data product even through it is marketed as an eDiscovery tool. The company’s predictive coding technology has revolutionized the legal field. The success will probably be duplicated in big data. Good move for the company.
Whitney Grace, October 05, 2013
Sponsored by ArnoldIT.com, developer of Augmentext
The Lemurs of Big Data
October 4, 2013
Even though this has been proven a false, the adage of lemurs jumping off a cliff together in a big group without thinking remains. The idea reminds me about what is going on with big data. Take a peek at ReadWrite’s article, “Gartner On Big Data: Everyone’s Doing It, No One Knows Why” if you do not believe me. The article reports that people who do not have any idea what big data means have projects, but they also do not know what to do with the data.
The article carries data from Big Data Adoption in 2013 Shows Substance Behind the Hype that says more people have adopted big data in order to improve customer experience, efficiency, and to launch new products or business models. While they have the projects and the data they have a problem:
“The problem for many of these same enterprises is that they struggle to understand what big data is all about, and how to make it work. When Gartner asked what the biggest big data challenges were, the responses suggest that for all these companies plans to move ahead with big data projects, they still don’t have a good idea as to what they’re doing, and why.”
The people running these projects are not stupid, but they got carried away with the hype and it has taken them over the cliff. Do not go blindly over the big data cliff, do some research, and invest in a current employee to manage the project. Also stay away from hypable lemurs.
Whitney Grace, October 04, 2013
Sponsored by ArnoldIT.com, developer of Augmentext
Big Data Remorse
October 3, 2013
With all the big talk about big data, one might think the technology is an automatic road to riches for every company. One would be mistaken; ReadWrite reports, “Big Data Investments Currently Earn 50 Cents for Every Dollar Invested.” Why the disparity between expectations and results? It seems that the hype has persuaded many businesses to leap into big data before figuring out just how to make use of it.
That 50 cents on the dollar is, of course, an average. Writer Matt Asay cites preliminary findings from Wikibon, which found that 46 percent of organizations using big data tech have met with only partial success, and two percent have had no success at all. He writes:
“As Wikibon points out, one of the biggest reasons for Big Data failure is that ‘enterprises invest in Big Data technology such as Hadoop without specific and measurable business applications tied to the projects.’ They hear it’s a big deal and throw money at it without really understanding what they’re hoping to achieve.”
Asay bolsters that conclusion with a recent Gartner report, which he covers in depth here. So, what is a company to do? The article advises:
“The best Big Data projects, according to Wikibon’s research, ‘are generally not initiated by IT but driven by line-of-business departments, often marketing, and focus on small but strategic use cases.’ They tap into in-house expertise and are realistic about how much they can do with the people they have. Such projects tend to start small, iterate and scale out based on early wins. . . .
“Given that all of the best Big Data technologies are open source, it’s easy to try before you buy, and get up-to-speed on the best technologies for your requirements.”
So, IT departments can consider the ball out of their court, at least until marketing brings it up. From they should proceed with caution to avoid big data remorse.
Cynthia Murrell, October 03, 2013
Sponsored by ArnoldIT.com, developer of Augmentext
Chasing Value in Big Data
October 2, 2013
That the term “big data” has become a huge buzzword is clearly an understatement. Yet, in all the hype, one crucial point may get overlooked—that collecting silos of data means nothing without good data analysis. Network Computing reminds us of this crucial fact in, “Text Analytics Key to Unlocking Big Data Value.”
Blogger David Hill was inspired to write this article after attending the Text and Social Media Analytics Summit in Cambridge, Massachusetts. He cites a talk by Harvard’s Gary King who, after citing examples, declares analysis to be more important than data itself. Hill mostly agrees, but notes:
“Although King makes a strong point, the answer is that both data and analytics are important. All the analytics in the world will be of no help if the data does not exist or you cannot access the data for use. Still, King’s thesis really speaks to the need for creativity in the use of analytics to take advantage of data.”
The post goes on to discuss the integration of structured and unstructured data. Hill also mentions some examples of text analytics’ practical uses. See the article for details. The piece concludes:
“We have been subject to an application-driven software intelligence perspective of IT. . . for most of our lives. So a data-driven software intelligence perspective such as big data, where value in IT is squeezed from the data itself, is not only unfamiliar and hard to comprehend but also a little uncomfortable. Yet the world of data-driven software intelligence is the world of text analytics and will transform our view of how to get value from the IT infrastructure.”
As we have mentioned before, big data (and the analysis thereof) are not necessarily important for every business. But for many, especially large corporations, it can be a useful tool indeed. Companies should take as much care with their analysis strategy as they do with their data collection, and they should start by identifying their business’ particular needs.
Cynthia Murrell, October 02, 2013
Sponsored by ArnoldIT.com, developer of Augmentext
Be Wise and Do Not Ignore Social Media
September 30, 2013
Half the time when I speak with a customer service representative they are either automated or come from a foreign country where their English speaking skills are less than adequate. My most recent experience involved Priceline.com and a bull-headed rep somewhere in Asia. Three hours later I won, he lost. You can be sure I wrote about my experience on Twitter and how poorly I was treated. Sameer Nori started working at Attivio this year and in his blog post, “360 Degree View Of The Customer-Broken Promise Or Technology Limitation?” he takes a look at how companies are constantly searching for a business intelligence solution that allows them to monitor all aspects of their information.
This directly relates to customer service, because not all companies are watching customer feedback on social networks. People flock to social media networks to complain, which is then instantly broadcasted to thousands if not millions. It is the quickest and easiest way to ruin a reputation. What do you do? Attivio says:
“Customers are talking about your products and their experiences with your brand and you need to be prepared with the right information about them at the right time. I’m very happy to report that Attivio is being used at some of the largest companies in the world to reduce customer churn and increase customer satisfaction by bringing together structured data, unstructured data and unstructured content. The promise of the 360 degree view of the customer can finally be realized through Attivio’s unified information access platform!”
A piece of advice quickly turns into a big data product pitch. Still, this is how big data can be used. It can monitor all instances of when a company is mentioned on social networks and analyze the data for companies to implement better customer service policies.
Whitney Grace, September 30, 2013
Sponsored by ArnoldIT.com, developer of Augmentext
Applied Relevance Enters Big Data Market
September 30, 2013
We have discussed multiple times how big data is not a new concept, yadda, yadda. Moving on with the trend, Applied Relevance is one of the few companies we have read about that admits to it. It is a good marketing gimmick, especially since the company has a new product called Epinomy that offers instant enterprise intelligence. We have never heard this name before and can only come to the conclusion that Applied Relevance wants neologism “Epinomy” to be a term synonymous with big data. Reading through the description of Epinomy it acknowledges all of the right information about big data, meaning that it contains every little ounce of data about an organization and harnessing it can be beneficial.
The biggest problem about big data is how to discover the information across all of the types, storage systems, and platforms. Epinomy points out that an even bigger challenge is the enterprise system. That is where Epinomy says it comes in and takes control of the situation:
“Epinomy is all about making enterprise information easy to find and enabling real-time decision making.”
It highlights the key features: tags, explore, discover, and find. Basic stuff, but apparently they are offered in a new way. The biggest concern we have is how to pronounce the word: epinion-mee, epin-o-me, e-pin-omy? Any hints?
Whitney Grace, September 30, 2013
Sponsored by ArnoldIT.com, developer of Augmentext
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