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Trends and Outliers

TIBCO Spotfire's Business Intelligence Blog

Category Archives: Unstructured Data

08/14
2013

Lumbering Giants Welcome and 4 Other Big Data Myths

There is a lot of fanfare around big data. But there’s also a surplus of misinformation floating around about the potential for leveraging the data deluge from mobile, social networks and other unstructured data sources.

giantturtle1 Lumbering Giants Welcome and 4 Other Big Data MythsAuthor Phil Simon has assembled the four biggest myths of big data. We’ve added a fifth from a recent Businessweek article noting that slow and steady doesn’t win the race when it comes to effectively leveraging big data.

Simon’s myth roundup is as follows:

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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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03/21
2013

Data Analysis to Tame the Unstructured Data Beast

One of the most daunting challenges for companies seeking to bolster business decisions by tapping the power of big data with analytics is managing the huge swell of unstructured data including comments on social networks, video files, images and critical documents like call center customer comments.

beast2 Data Analysis to Tame the Unstructured Data BeastThis unstructured data – data that can’t be analyzed in traditional databases – is surging into company networks full of actionable insights for the organizations that can effectively use data analysis to mine it.

For example, 80% of the highest performing companies note that they use a significant amount of unstructured data, compared with 46% of all other companies, according to a recent report from research firm Aberdeen Group.

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12/12
2012

Predictive Analytics to Listen for Customer ‘Signals’

In today’s competitive retail environment, companies must learn to “listen” and interpret the signals that their customers are giving them about their products and services or face being bested by a competitor who is monitoring customer feedback.

buying signals 300x225 Predictive Analytics to Listen for Customer SignalsThat’s according to a Harvard Business Review blog post that suggests that companies add more “signals” to their predictive analytics and data analysis efforts to better predict what products and services will resonate best with customers.

For example, signals may be how a customer pays for a transaction, the time of day she makes her purchase or if she buys more than one item.

“These are known as signals, because they may help predict some future target variable,” according to the HBR article. “It might be, for example, what else you might want to purchase, given a current purchase.”

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11/19
2012

Empowering Business Users with Data Visualization

A growing number of business users are taking ownership of business intelligence, relying less on IT and creating more of their own reports and dashboards in a self-service approach, according to a recent report by the Aberdeen Group.

Investing opprtunities 150x150 Empowering Business Users with Data VisualizationCertainly one of the drivers for the DIY approach is that the data that’s presented in traditional BI reports and dashboards often invites more questions and deeper analysis, notes Aberdeen Group analyst Michael Lock.

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10/24
2012

Unstructured Data Analysis: 5 Steps to Avoid Drowning in Data

On average, companies are reporting more than a 40% annual growth in the data they use for analysis, according to a recent research report from Aberdeen Group.

help 150x150 Unstructured Data Analysis: 5 Steps to Avoid Drowning in DataMuch of this data explosion represents unstructured data that can be difficult to format and evaluate via data analysis.

This includes unstructured data such as social media posts, recorded call center interactions between customers and agents, health records, and the bodies of email messages.

However, there are steps that businesses can take to improve how they go about gathering data, integrating data from multiple sources, and using data analysis techniques to manage the data explosion sensibly, as Glenda Nevill notes in a recent blog post.

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10/10
2012

Data Visualization and the Art of Discovery: Beyond Number Crunching

Information technology advances – especially the immense processing power of supercomputers – have helped power the mapping of the human genome. This insight into the human body undoubtedly is among the most major advances in scientific research and medicine.

datavisualization 300x229 Data Visualization and the Art of Discovery: Beyond Number CrunchingBut while the advantages of being able to crunch large data sets faster are well known, unstructured data like text has been notoriously more difficult to analyze than structured data that can be easily housed and manipulated in a database.

However, technological innovation is enabling researchers to quickly cull through the text of scientific research reports and articles to identify relationships between previously isolated work through data visualization.

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07/30
2012

Big Data Mining Shift: From Mining Transactions to Reverse-Engineering the Brain

reverse engineer brain 150x150 Big Data Mining Shift: From Mining Transactions to Reverse Engineering the BrainThe paradigm shift from businesses struggling to pinpoint what data to store to what they can do with the infusion of big data is in full swing.

Transactional data is still the foundation for many businesses trying to mine data for insights, but big data has opened an entirely new realm of data mining prospects for a multitude of industries.

Instead of simply modeling data, big data provides the opportunity to model human intent, notes Mok Oh, chief scientist of PayPal.

“Ultimately, what we’re trying to model is every person’s brain – at least the part of the brain that decides how to shop, when to shop, and what you want,” Oh says. “We’re trying to reverse-engineer transactional data to figure out what people are going to buy next.”

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07/19
2012

Embrace Big Data or Face Getting ‘Left in the Dust’

bigdatapic 150x150 Embrace Big Data or Face Getting Left in the DustUntil big data rolled onto the scene, banks have traditionally had easy access to all the transactional data they need from credit card activity to customize products for their customers.

But now, they are about to be “left in the dust” unless they start using big data techniques like business analytics to combine their traditional forte of analyzing structured transactional data with the multitude of valuable information contained in unstructured data, notes Ovum Research Director Denise Montgomery, speaking at the Bank Tech 2012 conference.

She adds that the latest generation of data, such as geolocation data, is much more powerful than banks’ credit card and transactional information when used in combination with other data. And combining data sources is something organizations like Google and Amazon already have a lot of experience doing.

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07/10
2012

Taming the Social Media Beast

taming social media beast 150x150 Taming the Social Media BeastMany companies have struggled for years to analyze and generate actionable insight from structured data – data from rewards cards, transactional systems and personal information shoppers provide online.

Now, add the big data behemoth – mostly unstructured data streaming in from “likes” on Facebook, Tweets, data from smartphone apps – and the task of culling through customer data can seem downright daunting.

Companies need to build robust systems to analyze the huge amounts of data flowing in from social media and then determine how they link to all the other ways consumers interact with their brands, argues Marita Scarfi, CEO of Organic, a digital ad agency with clients like Kimberly-Clark, American Express and Chrysler. And the key to effectively corralling social media and other big data is hiring and training people who can make sense of the social media data, Scarfi notes.trans Taming the Social Media Beast

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