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Trends and Outliers

TIBCO Spotfire's Business Intelligence Blog

Monthly Archives: April 2013

04/30
2013

In-Memory Analytics: Taming the Big Data Storage Beast

These days companies are seeing greater volumes of customer, ERP, and other types of data streaming into their organizations. And this is placing an immense burden on their storage systems.

a 610x408 In Memory Analytics: Taming the Big Data Storage Beast For its part, IDC forecasts that global digital data growth is expected to undergo 50-fold growth from 2010 to 2020. Meanwhile, the volume of business data is growing at an average rate of 36% per year, according to research by Aberdeen Group.

The three key challenges often associated with big data are the “3 Vs”: volume, velocity, and variety, as Aberdeen and other industry experts note.

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04/29
2013

CPG Firms: Finding the New Analytics Normal

Consumer packaged goods companies and grocers are focused on growth more than ever, but they must move beyond traditional localized use of big data and analytics to gain the insight required to drive top- and bottom-line growth.

shopping11 CPG Firms: Finding the New Analytics NormalThat’s according to a new report by the Grocery Manufacturers Association and Deloitte Consulting.

“The research will assist CPG companies in understanding the broad set of capabilities and competencies required to improve their analytical IQ, with big data making that need even more urgent,” says Marcus Shingles, principal, Deloitte Consulting LLP.

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04/25
2013

Are You Ready for Some Analytics 3.0?

Despite all the recent hoopla surrounding big data, analytics is not a new concept. Analytics tools have been used by businesses since the mid-1950s. So what’s new now?

ready Are You Ready for Some Analytics 3.0?The massive amount of unstructured data – from the web, social media, mobile, forums and other sources – deluging corporate networks is driving a new era of analytics, Analytics 3.0.

That’s the assertion of Thomas Davenport, visiting professor at Harvard Business School and a senior adviser to Deloitte Analytics.

Davenport describes the differences between the first and second generations of analytics as well as the vast potential the third generation of analytics offers organizations in the Wall Street Journal.

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04/24
2013

Who’s the Most Influential in Big Data? A Twitter Study

Big Data Republic (BDR), a big data community, recently ran a contest for its readers to nominate their favorite Twitter influencers on our favorite topic #BigData.

twitterinfluence Who’s the Most Influential in Big Data? A Twitter Study  Using PeerIndex and a panel of real, live, human judges (which we consider a key ingredient to the legitimacy of big data analytics), BDR has compiled a list of the top 100 influencers on Twitter – the result of the month-long contest. BDR threw out any “inbound marketing spambots” and accounts not keenly focused on big data to further legitimize the list.

Tom H. C. Anderson (@tomhcanderson) came in first and we’re honored that our @TibcoSpotfire tweets are in the top 40 – ringing in at No. 34.

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04/23
2013

Predictive Analytics to Head Off Retail Disaster

Had ousted J.C. Penney CEO, Ron Johnson, figured out a way to give customers what they wanted – a stellar in-store experience – he might still be at the helm of the brick-and-mortar retail giant.

stores Predictive Analytics to Head Off Retail DisasterBut Johnson didn’t give customers what they wanted, he gave them what he thought they wanted, causing them to abandon the sinking retail ship in droves, and costing him his job.

The lesson to be learned from the J.C. Penney debacle is that to survive, brick-and-mortar retailers must create in-store customer experiences that set them apart from their online competitors – or face extinction, according to an article in Forbes.

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04/22
2013

The Geico Gecko: Born of Big Data Analytics

Unless you live in a cave, you’re well aware of the Geico insurance company’s Gecko spokes-lizard. You probably know that he’s about six inches tall; his scaly skin is a calming shade of green; he has a winning personality – although some might say annoying – not to mention a “loverly” Cockney accent.

petridish The Geico Gecko: Born of Big Data AnalyticsHis claim to fame is that he loves people, and he especially loves helping them save money on their car insurance.

But there are a couple things about the Geico Gecko (@TheGEICOGecko) that I bet you don’t know.

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04/18
2013

Analytics to ID, Troubleshoot Telco Network Performance Snags

Wireless carriers are spending billions of dollars to build out 4G networks that are purported to be able to deliver data, including video, 10 times faster than 3G networks. 4G network expansion is occurring on a global scale.

telcogrid Analytics to ID, Troubleshoot Telco Network Performance SnagsMore than 150 carriers across 60 countries are committed to 4G deployments and trials, according to Deloitte. For its part, China Mobile is planning to spend $6.7 billion this year alone on its 4G networks, according to this article in AppleInsider.

With access, speed, and reliability top of mind for mobile consumers, combined with the amount of investment that’s being poured into network upgrades, it’s imperative for telecom carriers to be able to quickly identify and act on network performance disruptions as they arise.

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04/17
2013

Procter & Gamble’s Data Analysis Success Drives Faster Decisions

Companies that can establish common visual languages for data can leverage that data to drive successful decision making.

That’s what Thomas Davenport, visiting professor at Harvard Business School and a senior adviser to Deloitte Analytics, says in a recent Harvard Business Review post.

PG Procter & Gambles Data Analysis Success Drives Faster DecisionsDavenport cites Procter & Gamble, which has “institutionalized data visualization as a primary tool of management,” as one of the best supporting examples of his position on effective data analysis.

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04/16
2013

5 Ways Predictive Analytics Help Insurance Agents, Carriers Work Together

What if you, Mr. or Ms. Insurance Agent, could predict the future so you could keep your best customers? And what if you, Big Insurance Carrier, could work with your agents to more accurately price risks?

Unknown 5 Ways Predictive Analytics Help Insurance Agents, Carriers Work TogetherWell, you can.

All you both have to do is focus on sharing data and using up-to-date predictive analytics to target risks in order to retain customers, according to this article in PropertyCasualty360.

Sharing data can help you and your carriers operate more efficiently around “pricing and underwriting; customer-retention strategies; finding good prospects; customer segmentation; and cross-selling among various lines of business,” according to the article.

Here are five ways agents and carriers can use predictive analytics to improve business:

1. Out With the Old and in With the New. In the past, predictive analytics solutions were so costly and so slow that carriers just didn’t want to “take on the challenge of working with agents individually to target business,” according to Wade Bontrager, the author of the article.

But the days of expensive, old-school analytics tools are long gone. Now, using modern predictive analytics, carriers can analyze more data and get the answers they need more quickly. And cloud-based predictive analytics solutions make it even easier and less expensive for carriers to share that information with brokers and agents.

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04/15
2013

For Retail Banks, Data Analysis Critical to Battle New Rivals

bank For Retail Banks, Data Analysis Critical to Battle New Rivals

The recent Spotfire on-demand webcast, “Data Science 2.0: Guided and In-line Analytics with Spotfire,” covers how Spotfire and data science are impacting global business across every market and industry.

 

For example, if you work in the retail banking industry this may mean using analytics to successfully combine data from all available sources to develop a better understanding of customer needs so you can serve them more efficiently.

 

 

Here’s a sample clip from the retail banking segment of the webcast followed by a post on how retail banks can benefit from data analysis.

 For Retail Banks, Data Analysis Critical to Battle New Rivals

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