Big Data Technology, Semantics and Analytics
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Big Data Technology, Semantics and Analytics
Trends, success and applications for big data including the use of semantic technology
Curated by Tony Agresta
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Healthcare IT: The 4 Pillars Of Technical Innovation - CRN

Healthcare IT: The 4 Pillars Of Technical Innovation - CRN | Big Data Technology, Semantics and Analytics | Scoop.it
Healthcare IT: The 4 Pillars Of Technical Innovation CRN Four major technology trends, which are becoming more intertwined every day, will dominate the healthcare IT landscape in 2013, according to IDC Health Insights' top researcher, Scott...

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Mason Powers's curator insight, December 20, 2012 4:10 PM

The 4 trends for Healthcare IT in 2013:

 

1. Cloud

2. Big Data & Analytics

3. Social Media

4. Mobility

 

Here is how the industry's leading big data platform provider, MarkLogic, supports these 4 trends, with the agility and security Healthcare organizations require:

 

1. Cloud: http://www.marklogic.com/solutions/data-virtualization/  

2. Big Data & Analytics:  http://www.marklogic.com/what-is-marklogic/analytics-business-intelligence/

3. Social Media:  http://www.marklogic.com/solutions/social-media-analysis/

4. Mobility:  http://www.marklogic.com/solutions/content-delivery/

 

 

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Database Revolution; Future of Information Publishing

Tony Agresta's insight:

Here’s a brilliant presentation from Mike Bowers, Principal Engineer at the Church of Jesus Christ of Latter Day Saints.  It accomplishes two major objectives:


  • Mike reviews the strengths and weaknesses of the five major classes of databases today (relational, dimensional, object, graph and document). 
  • He then dissects the major NoSQL databases on the market including MarkLogic, Mongo, Riak, Cloudant/Couch DB and Cassandra.  How do they stack up? Are they enterprise ready? 


If developer productivity, application performance and enterprise readiness are concerns that your company has, this video is a “must see”.  Here are some sound bites I took away from the presentation.  Please note these comments only begin to scratch the surface of Mike’s message.

 

Over 80 % of the data being created today is unstructured and organizations need to store, search and analyze hundreds of different data formats at light speed.   The ability to handle data variability, data variety and data relevance has jumped to the top of the agenda for both business and IT. But how can organizations discern meaning from this data?   How do they create context around unstructured data with so many formats in play?  How do they make it discoverable?  

 

Relational Models are not well suited to handle the problem since they were designed to organize your data in rows, columns and tables.  The variety and complexity of unstructured data coupled with the overriding need to scale out on commodity hardware prevent them from leveraging over 80% of the data today.

 

Mike shows a great example of how the document database (NoSQL database) takes unstructured data in the form of a story, identifies the data elements in the story (topic, location, author), semantically links these elements to show relationships between the elements and then identifies the hierarchy within the story (title, subtitle, body, etc…).   Armed with all of this, the unstructured data lives with context.  The original document persists but now all of the elements are discoverable in a variety of ways.  

 

Given the reality that unstructured data is growing so rapidly and needs to be integrated and analyzed alongside structured data to complete the picture, what does an application need from a NoSQL database?  Basically what every database needs - five core capabilities:  1) inserts, updates and deletes 2) the ability to query the data 3) the ability to search the data 4) the ability to bulk process the data and 5) the ability to do all of this consistently.  With extraordinary data volumes, this has to be done at scale in an affordable way.  

 

The only enterprise NoSQL database that handles all of this today is MarkLogic.   Mike evaluates search relevance, advanced search using facets, geospatial search, entity enrichment, data consistency, developer productivity using JAVA, the ability to retrieve multiple documents, integration with the BI stack using SQL, real time data ingestion, indexing and much more.   Imagine if you had to ask your programmers to develop an application to handle data locks, threading bugs, serialization, dead locks and rare conditions?     Imagine if you had to write the code to ensure all parts of your data transactions succeeded?  How would you ensure all of the data has been committed consistently? Do the commits meet all of your data rules?    How do you ensure your data survives system failures and can be recovered after an inadvertent deletion?   

 

The vast majority of NoSQL databases lack these capabilities but MarkLogic has all of them.  If you are evaluating database technology today, I would highly recommend watching this video – at least twice.  

 

Learn more at MarkLogic.com


To see related videos, visit http://developer.marklogic.com/

 

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Ian Sykes's comment, January 2, 2013 11:39 AM
Hi Dominic Happy New Year. Yes I was impressed by this and included it on my Blog. Certainly clarifies a lot for 2013.
Adrian Carr's curator insight, April 30, 2013 1:38 PM

this is a great presentation - full of great insight into the market

Edwin's curator insight, March 19, 2014 10:30 PM

Future of Database development

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5 Big Data Trends For 2013 - Tools Journal

5 Big Data Trends For 2013 - Tools Journal | Big Data Technology, Semantics and Analytics | Scoop.it
Last year this time we have been talking about big data not as a matured concept and very few number of big data software being trailed in beta versio...
Tony Agresta's insight:

It's also important to highlight some of the recent research from IDC focused on unified information access platforms.  These platforms will emerge in 2013 to knit together information silos across the enterprise, regardless of the form of content. Accounting for disparate security, archiving, and access features for each source repository will be important.   The big data stack will blend
the database, business intelligence, and search technologies with supporting functionality like alerting and should be capable of indexing and integrating large volumes of unstructured, semistructured, and structured information into a unified environment for information gathering, analysis, and decision support.

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AllAnalytics - Mark Pitts - Hadoop: Take Care Before You Whoop It Up

AllAnalytics - Mark Pitts - Hadoop: Take Care Before You Whoop It Up | Big Data Technology, Semantics and Analytics | Scoop.it
Despite all the hype, Hadoop addresses only a specific set of problems. Make sure they're the ones you need to solve.
Tony Agresta's insight:

This is why MarkLogic has integarted Hadoop into our Enterprise NoSQL approach.  You can read more about the advantages here:  http://www.dbta.com/Articles/Editorial/News-Flashes/MarkLogic-Enterprise-NoSQL-Database-Running-on-HDFS-Available-as-Technology-Preview-85861.aspx

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Predictive Analysis: 7 Reasons You Need It Today

Predictive Analysis: 7 Reasons You Need It Today | Big Data Technology, Semantics and Analytics | Scoop.it
With today’s enterprise software you no longer have to take a shot in the dark at decision making. Regardless of your organization’s size, industry, or the
Tony Agresta's insight:

I would expect predictive analytics technology to surge in growth, especially with the deluge of data arriving every day.   We are well beyond the early days when direct marketing pioneers applied predictive models to forecast response or performance (although that still happens more often than you think!).   Today, models can take advantage of more independent attributes than ever before – including structured and unstructured data    In turn, the predictive precision of the models increases.  There are more than a few ways to apply the results.   The obvious one is applying the scoring algorithms to new sets of data.   But don't lose sight of the fact that model scores can also be used as filters in queries to segment and report on your big data.   They can also be used as attributes in link analysis graphs designed to pinpoint fraud, cyber breaches, and networks of frequent buyers.  Imagine a network graph where links between people are scaled based on their predicted spend or the number of products they will buy during the holiday season.  It would easy to identify clusters of loyal customers which you could then study in more detail.  When coupled with other characteristics and contact info, targeting becomes precise and the meaning behind your big data becomes obvious.

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MarkLogic Customers | MarkLogic

MarkLogic Customers | MarkLogic | Big Data Technology, Semantics and Analytics | Scoop.it
Visit MarkLogic.com to find out who uses MarkLogic.
Tony Agresta's insight:

Here are a series of short videos describing applications of MarkLogic.   Learn how Press Association, ICA Informatics and Boeing have applied this technology to solve big data challenges.

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Big Data and the consumerization of healthcare | Stories | Data Science Series

Big Data and the consumerization of healthcare | Stories | Data Science Series | Big Data Technology, Semantics and Analytics | Scoop.it
{pt_meta_description) (How Big Data can mean the difference between life and death. http://t.co/JVohR9eg #datascienceseries #lifesciences)
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The Biggest Big-Data Opportunities: How to Choose the Right One - Forbes

The Biggest Big-Data Opportunities: How to Choose the Right One - Forbes | Big Data Technology, Semantics and Analytics | Scoop.it
(Kevin Krejci) Businesses evaluating how to get in on the Big Data boom need to decide which of three possible roles they want to play: information provider, information broker, or creator of networks through which all that Big Data-driven content...
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Tony Agresta's comment, December 12, 2012 9:40 AM
2) “The permutations of available data will explode, leading to sub-sub specialized streams that can tell you the number of left-handed Toyota drivers who drink four cups of coffee every day but are vegan and seek a car wash during their lunch break.” Here's an application of this idea using MarkLogic: http://www.youtube.com/watch?v=Z5dXKwlJG1Q&feature=player_embedded#!
Tony Agresta's comment, December 12, 2012 9:45 AM
3) Network Monetizers: “This means, first, ample opportunities for the arms dealers — the suppliers of the technologies that make all this gathering and exchange of data possible. It also suggests a role for new marketplaces that facilitate the spot trading of insight, and deal room services that allow for private information brokering.” http://www.marklogic.com/customers/cq-roll-call/
Tony Agresta's comment, December 12, 2012 9:47 AM
If you would like any additional information on these case studies, contact me at tony.agresta@marklogic.com
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HSBC to Pay $1.92 Billion to Settle Charges of Money Laundering

HSBC to Pay $1.92 Billion to Settle Charges of Money Laundering | Big Data Technology, Semantics and Analytics | Scoop.it
The announcement of a settlement on Tuesday came after state and federal authorities decided against indicting the British bank in a money-laundering case.
Tony Agresta's insight:

If you read this, notice one of the last paragraphs - 

 

"HSBC has since moved to bolster its safeguards. The bank doubled its spending on compliance functions and revamped its oversight, according to a spokesman. In January, HSBC hired Stuart A. Levey as chief legal officer to come up with stricter internal standards to thwart the illegal flow of cash. Mr. Levey was formerly an under secretary at the Treasury Department who focused on terrorism and financial intelligence."

 

Big Data Analytics is one way to do this.   But HSBC may have fallen into the trap where they focus on one form of analysis to detect money laundering.  Predictive models used to identify transactions that may be fraud or money laundering can be a useful way to detect this type of activity.   But all models contain some amount of error.  When network analysis, geospatial analysis and temporal analysis are also applied, money laundering schemes can be revealed using data visualization that show unusual patterns of behavior, linkages between people and events, fund transfers that take place at odd times and more.   Most of these institutions need to combine descriptive reporting, alerts which are triggered when outlier transactions are ready for approval, predictive models and interactive data visualization including link analysis to explore hidden relationships in data.   Without this comprehensive approach, this problem will continue to occur.  The data is all there.  Now it needs to be integrated (including unstructured data in the form of notes) and analyzed using all of the major techniques.

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WebMarketingStore's comment, May 2, 2014 1:59 AM
Staggering: $1.9b is the 'settlement' amount? How much might the damage have been, full-tilt?
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Shopping in the Big Data marketplace

Shopping in the Big Data marketplace | Big Data Technology, Semantics and Analytics | Scoop.it

"The Big Data marketplace is diverse and growing. It has a host of companies selling products that are called big data solutions but some of that is opportunism and some is deliberate obfuscation of the concept."

 

After reading this article, I couldn't help but add some more depth. "Shopping in the Big Data Marketplace" fails to mention the leader in this space, MarkLogic who has been providing powerful, accessible and trusted technology in this space longer than any of the vendors mentioned in the article. This isn't hype.

 

There's one absolute way to validate this - Read about the customers who use MarkLogic today including the BBC, MModal, Conde Naste, Zynx, countless government agencies. A more complete view is available here:

 

http://www.marklogic.com/customers/

 

Why did these organizations decide to use MarkLogic? It's an enterprise grade, hardened Big Data platform that works. Some of the capabilities provided out of the box include:

 

 - MarkLogic distributes Hadoop

 - User-Defined functions

 - In-database Analytic Functions

 - Visualization Widgets

 - Real-time, flexible indexes

 - Multilingual full-text search

 - Schemaless design

 - Scale out on commodity hardware with auto-sharding

 - Tiered storage

 - Alerting and event processing

-  Geospatial query

 - Java API

 - REST API

 - JSON Support

 - MarkLogic Content Pump

 - BI Tools Integration

 - Application Builder

 - Information Studio

 - Connector for Hadoop

 - Monitoring and Management

 - Support for Linux, Windows, Solaris, MacOSX, SUSE, VMWare

 - Superclusters

 - Transactions

 - Role-based Security

 - Automated failover

 - Replication

 - Journal archiving

 - Point-in-time recovery

 - Database rollback

 - Backup/Restore

 - Distributed transactions

 - Common Criteria for Information Technology Security Evaluation (ISO 15408)

 

Here's a link to a video you can watch about how the BBC Olympics Website was built on this proven, trusted technology:

 

http://resources.marklogic.com/library/media/scaling-to-olympic-proportions

 

 

 

 

 

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96 – The percentage of Canadian executives who think real time Big Data tools are important | Infomart

96 – The percentage of Canadian executives who think real time Big Data tools are important | Infomart | Big Data Technology, Semantics and Analytics | Scoop.it

It's the title of this post that caught my eye. There's no shortage of Big Data tools on the market today. But how many handle the real time aspect of big data with enterprise reliability? Here's an excerpt from a talk given recently at MarkLogic's NY Summit:

 

We are in the middle of a database revolution. NoSQL is disrupting the database world by innovating and Schema-less databases enable extreme agility of software development and rapid changes to huge data sets. But are all NOSQL approaches the same? Not at all. MarkLogic, for example, allows for real time ingestion of data and alerting. Indexes are derived instantly. ACID transactions are supported without the need to write additional code.

 

What is an ACID Transaction? From Wikipedia: ACID (atomicity, consistency, isolation, durability) is a set of properties that guarantee that database transactions are processed reliably. In the context of databases, a single logical operation on the data is called a transaction. For example, a transfer of funds from one bank account to another, even involving multiple changes such as debiting one account and crediting another, is a single transaction.

 

So, evaluate NoSQL approaches carefully. Real Time support of ACID transactions may be exactly what you need to compete at a time when instant and reliable access to data is essential.

 

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Analytics Lessons from Spy Work: Machine Learning Applied to Unstructured Data

Analytics Lessons from Spy Work: Machine Learning Applied to Unstructured Data | Big Data Technology, Semantics and Analytics | Scoop.it

Intelligence agencies always seek to be more proactive, said Gary Bloom, CEO of MarkLogic. “They look to be able to predict events in order to be able to either prevent them or manage them to best effect.” In business, competitive advantage often lies in the ability to spot trends that can be capitalized on, he said.

 

The reliability and security of data analytics systems is another area where intelligence services and private sector needs align. “In the intelligence community, if you get the answer wrong, lives might be at stake,” said Bloom of MarkLogic. These agencies can’t afford to lose data or experience transactional inconsistency that would lead to inaccurate data being used, he said. While businesses don’t typically deal with life-and-death situations, system and data reliability are essential as integrating data sources becomes a mission critical part of a company’s core applications and systems, he said.


Via Dominic Spitz
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US Electoral Compass 2012

US Electoral Compass 2012 | Big Data Technology, Semantics and Analytics | Scoop.it

The US Electoral Compass - 2012.  Twitter Streams have been used to analyze what voters are talking about in each state.

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Here is how cloud computing, big data, and some innovation can reinvent healthcare

Here is how cloud computing, big data, and some innovation can reinvent healthcare | Big Data Technology, Semantics and Analytics | Scoop.it
No matter if you were for or against the new healthcare regulatory changes, the end result is that more people will be tossed into a system that is already at capacity. You can either ration the c...

Via Mason Powers
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Mason Powers's curator insight, December 27, 2012 2:09 PM

Interesting article with 2 recommendations for Healthcare Providers & Payers in leveraging big data technologies:

 

1. Manage patient data holistically, and in new, innovative ways. 

 

This is exactly what Informatics Corporation of America is doing through their industry leading Health Information Exchange.  By powering this exchange with MarkLogic's enterprise-hardened NoSQL database technology, ICA is able to provide a holistic view of the patient across all delivery networks. 

 

2.  Combine data into huge analytical data sets that exist on cloud computing providers, providing universal open access.

 

This continues the discussion of the available technologies which can now combine ALL data:  diagnostic, treatment, and outcome data into a single, searchable repository (i.e. MarkLogic 6).  Analysts can combine this data, better understand factors of quality of care, and keep healthcare costs low.  Low healthcare costs = healthy Americans; this inverse relationship should be the most obvious and striking point to motivate both providers and payers to adopt a big data strategy that focuses on lowering costs.  Whereas lower costs leads to healthier citizens, it also leads to higher margins.   

 

The next generation of technologies is here:  technologies that can manage vast amounts of unstructured, strucutred, and poly-structured data, all from the same platform; technologies that can maintain performance, speed, and scale under constant ingest and high frequency queries; technologies that work better, enable innovation, and do so for much, much cheaper.  

 

Please take a look at www.marklogic.com to learn how we are helping leading Healthcare organizations leverage their big data and improve quality of care through shortened billing cycles with better outcomes. 

Tony Agresta's comment, December 28, 2012 10:19 AM
Good insight Mason. If you like Mason's post, you will also like this: http://www.slideshare.net/tagresta/marklogic-applications-in-healthcare
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The Contentious State of B2B Content Marketing [Infographic] | Business 2 Community

The Contentious State of B2B Content Marketing [Infographic] | Business 2 Community | Big Data Technology, Semantics and Analytics | Scoop.it
Our friends over at Marketing Profs and the Content Marketing Institute recently released The State of B2B Content Marketing in North America. It’s a
Tony Agresta's insight:

Only 6% of companies claim their B to B marketing content strategy is effective and yet 54% plan to increase their efforts this coming year. Some of the top challenges faced include producing enough content, producing engaging content and producing a variety of content.  I think most of these challenges can be overcome through improved packaging and content re-use.   Aligning your strategy with phases in the sales cycle (awareness, solution development, evaluation and commitment) helps educate prospective buyers leading to customer success and advocacy.

 

 

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2012 Year in Review: Big Data

2012 Year in Review: Big Data | Big Data Technology, Semantics and Analytics | Scoop.it
The way big data has shaped the way we work and live in 2012.
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Big data: 2012 in review

Big data: 2012 in review | Big Data Technology, Semantics and Analytics | Scoop.it
2012 was the year that big data went from the server room to the board room
Tony Agresta's insight:

"We're moving to a new generation of database, NoSQL, designed to process the 80 per cent of the world's data that's not in a relational database. NoSQL isn't going to replace RDBMS, it's going to complement RDBMS, do the things RDBMS can't do, or can't do well." - Gary Bloom, CEO, MarkLogic

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Press release: MarkLogic opens up RSC data

Tony Agresta's insight:

Big data applications continue to radically transform industry, science and our lives.  I never would have guessed that 170 years of information could be unlocked and available on line.  But in so doing, the RSC has exceeded the expectations of their customers creating loyalty driven by content and a positive user experience.   Now data has been integrated, assembled and organized using different formats.   If this isn't enough, the automation used to tag the content allows users to not only gain access quickly but to do so within context.  It's this point around "context" that is transforming applications like these today.   With context comes understanding allowing the human mind to quickly interpret meaning and take action.   Dynamic publishing applications like these and the others the RSC has developed provide a ground breaking competitive advantage.  This article is worth reading, especially the ROI metrics in paragraph 9.  Enterprise ready, hardened, scalable, secure big data technology allows organizations like the RSC to dramatically improve their productivity today. They are publishing 3 times as many journals and 4 times as many articles over 2006.  New applications are bound to increase exposure and help chemists, students and researchers.   In so doing, it helps all of us. 

 

 

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The role of the Data Scientist in Big Data | TechRepublic

The role of the Data Scientist in Big Data | TechRepublic | Big Data Technology, Semantics and Analytics | Scoop.it
The role of the Data Scientist can be wide ranging while critical to large-scale Big Data efforts. Will Kelly peers into the role of the Data Scientis
Tony Agresta's insight:

As I read this article a few additional points came to mind.   As Data Scientists form a data plan, they typically take an inventory of available data including what may be "dark data", unstructured data that the organization is not using today.   The data inventory will likely include disparate sources of data. The analytical advantages of integrating this data could improve the chances of the exceeding business goals.  During the inventory process, Data Scientists will need to assess how complete each field of data is.  How dirty is it?  Do you have what you think you'll need to achieve your objectives or do you need to collect new information?   The data inventory is directly tied to analysis of the data which is, in turn, tied to your goals.  One way to think about this is as follows.  For each goal the business has, form a set of questions that need to be resolved through big data analysis.  Resolution to these questions will prove or disprove the hypothesis you have formulated.   The data visualization you perform, the predictive models that you build, the dashboards you derive should all be in support of resolving the questions and therefore the hypothesis you are testing.    A data inventory is essential.   Listing a set of questions you want to resolve in support of organizational goals is essential.  Using a variety of analytical approaches to answer these questions will help you create and manage a complete big data program.

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The 5 most influential data visualizations of all time

Data visualization allows us all to see and understand our data more deeply. That understanding breeds good decisions. Without data visualization and data anal
Tony Agresta's insight:

Data visualization is the key to unlocking meaning inside of big data.   The ability to explore the data in an unconstrained way through interactive charting allows analysts to uncover insights rapidly.  Related features such as filtering allow users to focus on subsets of big data for further analysis. Collaboration extends this process through force multiplication of efforts.  

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Tony Agresta's comment, December 16, 2012 9:49 AM
Most of the data visualization approaches today fall into five classes: charting, tables, geo-spatial, network and time lines. The vast majority of business analysts focus on the charting type and, as you know, there are hundreds of different ways to express data visually using charts. Tables have also been used extensively used in conjunction with icons that show direction for a specific metric using arrows or lines. This approach answers the question "Is the metric in the table trending upward or downward?"Geo-spatial visualizations continue to grow in popularity typically showing concentrations of events or people within a geographic area alongside other key landmarks or metrics. Networks of connected entities (people, places, events such as phone calls or log in access or database access) are beginning to be used more extensively outside of core markets such as government intelligence, law enforcement, cyber security and commercial fraud analysis. Most of these "link-node" or "link analysis" networks are used to identify groups of people that would have otherwise gone undetected without this form of visualization. And with the tremendous growth in social networks, these forms of visualization will continue to be used by commercial and government organizations. Time line analysis, or temporal analysis, is one of the less common forms of visualization and also one of the most revealing since analysts can detect patterns or trends in activity over time. But the fact that is that no single visualization can tell the entire story. This is where interactivity between the visualizations and the ability to explore the big data space in an unconstrained manner including looking across data sets is very important. This interactivity leads to faster learning. The human mind is able to recognize outliers and interesting patterns. Subsets of data can be created to examine the attributes of this new data spin-off. Conclusions can be drawn and shared with the business to make informed decisions.
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Big Data Analytics: Descriptive, Diagnostic, Predictive, & Prescriptive Analytics!

Big Data Analytics: Descriptive, Diagnostic, Predictive, & Prescriptive Analytics! | Big Data Technology, Semantics and Analytics | Scoop.it
Tony Agresta's insight:

Great source of research on a variety of topics including Big Data.   I have worked with Hypatia in the past and recommmend them to anyone.

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The Big Data Tidal Wave | The World Financial Review

Feature By Tony Agresta Technology to store, manage, search and analyze Big Data leaps to the top of the agenda for Financial Institutions as enterprise
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Situational Awareness - Blog Post by Adam Fowler that discusses the definition and applications of situational awareness with an emphasis on real time and alerting. Very good post for those inter...

Every new platform has a killer application that sets it out from the crowd. Situational Awareness, I believe, is the killer app for NoSQL databases with real time alerting, like MarkLogic. In this...
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Big Data's holy grail | | Commentary | Business Spectator

Big Data analytics has the potential to turn customers into an 'audience of one', matching consumer preferences to create breakthroughs in profitability.
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Searching for Value in Big Data’s V’s

Searching for Value in Big Data’s V’s | Big Data Technology, Semantics and Analytics | Scoop.it
Big Data Value: Among the velocity, volume and variety, there is a missing element that may push away business opportunity...
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