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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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When Pirates Meet Advanced Analytics

When Pirates Meet Advanced Analytics | Big Data Technology, Semantics and Analytics | Scoop.it
Seagoing criminals are always changing tactics. To catch them, uncover their hidden patterns.

Via Toni Sánchez
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Tony Agresta's comment, January 15, 2013 9:30 AM
This article is spot on - with so many different sources of data, identifying suspects can be challenging. The volume and variety of data stored in different systems makes the job of the intelligence officer almost impossible. But new approaches in big data technology allow data scientists to consolidate the data, including unstructured data. Tight integration with data visualization tools that talk to Enterprise NoSQL databases make it easier than ever to profile all of the data in support of identifying connections between people, events, locations and more. This approach has proven to work in some of the largest intelligence agencies in the world. It has been applied in local law enforcement, fraud detection, loss prevention, cyber security and social networking. Real time alerts make it possible to immediately notify analysts as big data streams into the NoSQL database meeting predefined conditions. But to do this you need an Enterprise approach to NoSQL one that is hardened, scales, meets security requirements and has disaster recovery and high availability as part of the foundational technology providing production grade implementations in less time.
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NoSQL, huh, what is it good for?…

…Actually quite a lot really. Say it again, y’all! In this post I try to dymystify NoSQL for the Relational DB crowd / average human, and give some real world examples of how NoSQL can ...

Via Dominic Spitz
Tony Agresta's insight:

To echo some of Adam's thoughts...In a recent key note by Mike Bowers of The Church of Latter Day Saints, Mike discussed why the NoSQL model is the best model for developing applications.  Here are some of his conclusions after years of working with all major classes of databases.

 

  • The NoSQL approach Increases developer productivity since it supports agile development without a schema
  • With NoSQL, you can handle rapidly changing requirements.
  • NoSQL handles deeply hierarchical, complex, and highly variable data structures
  • There is little-to-no impedance mismatch between application and database
  • JSON is the new lingua franca of the web
  • There's the potential to enable better search relevance including full-text search in context of document structure and full-featured queries of any data anywhere in a document.

 

Specific to the MarkLogic approach, Mike indicated that Enterprise Search capabilities clearly distinguished MarkLogic from the pack, a sentiment echoed by leading analysts around the world.    He went on to break down the most important aspects of this including:

 

  • Ability to Query to find multiple documents, a capability that is not as good in Mongo, poor in Riak and Cassandra.
  • MarkLogic stand alone in the category of search relevance and advanced search with facets, geospatial search and entity enrichment.
  • Data integration capabilities and data consistency are also unique differentiators and large contributors to developer productivity.

 

Enterprise readiness was discussed in detail during the key note.  More on that in another post.

 


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Why use a NoSQL Database, and why not?

I just conducted a review of the first 70 results from Google on the question “Why use a NoSQL database?”. In this post I show you the results in the for camp, and the against camp. The...
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How Large Enterprises are Benefiting from The Use of NoSQL Database

How Large Enterprises are Benefiting from The Use of NoSQL Database | Big Data Technology, Semantics and Analytics | Scoop.it
For the most part, the world of database management systems has been ruled by the SQL model for quite some time. There have been a few alternative methods that have temporarily popped up but SQL has maintained its dominance.
Tony Agresta's insight:

Quite true, the NoSQL movement is gaining momentum and this trend will continue for years to come.   But beware - not all NoSQL approaches are the same.   At MarkLogic, we distinguish between the open source vendors, some legacy relational players with nascent offerings and our Enterprise approach with 6 releases over 10 years.  Areas of distinction include but not are not limited to developer productivity, application performance and enterprise readiness.

 

Advanced search using facets, geospatial search, entity enrichment, data consistency, the ability to retrieve multiple documents, integration with the BI stack using SQL, real time data ingestion and security are just some of the areas that organizations evaluating this class of technology should look closely at.

 

Here are two links to additional information on these topics:

 

Important components of an Enterprise NoSQL Database:

http://www.marklogic.com/what-is-marklogic/enterprise-nosql/

 

Customer Success with enterprise grade deployments:

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

 

 

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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...

Via Mason Powers
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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, 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, 1:59 AM
Staggering: $1.9b is the 'settlement' amount? How much might the damage have been, full-tilt?
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Finding structure for unstructured data - FCW.com

Finding structure for unstructured data - FCW.com | Big Data Technology, Semantics and Analytics | Scoop.it
Fort Mills Times
Finding structure for unstructured data
FCW.com
If hype equaled performance, big data and cloud computing would already be unmatched cost-cutting, efficiency-increasing, bottom line-building tools in the federal space.

Via Toni Sánchez
Tony Agresta's insight:

There's a link to the paper entitled "Delivering on the Promise of Big Data in The Cloud" which goes into more detail on the Reference Data Architecture.   In that paper, the authors describe some of the techniques used to filter big data and visualize it in support of revealing meaning in the data.  Some of this visualization will involve charting, maps, timelines, tables and relationship graphs.  Big data is manageable given the right tools and methods of analysis.

 

When using this tools, here are some of the steps I've seen applied in the past with success -

 

  • Take an inventory of your data identifying data sources, tables, fields, missing values and the range of values for each field you will be working with.  How clean is the data?   Do you have what your need for the analysis?
  • Form your hypothesis and a detailed set of questions you want to answer about the data.  What are you trying to uncover?  What questions do you need to ask of the data?
  • Filter your data to arrive at a set of data to visualize.  This may need to be done in stages. For example, if you're using a visualization tool, you may be able to pre-process the data selecting a subset for your analysis. After some initial charting, you may identify a sub segment of data you want to look at more closely.  After looking at that data in different forms (maps, timelines, tables), you may decide it can be refined even further.
  • Use relationship graphing (link analysis) to connect the dots.   You may decide you need to see relationships between people and the flights they took, or people, where they live and where they work.  Any number of connection points can be analyzed with commercially available tools.   Too many connections will make the graph overly complex.   Three to six is probably ideal.
  • From that point, there are ways to highlight meaning in the data - scaling the size of the nodes and links based on other measures such as amount of deposits, number of calls, number of connections to other nodes – all of these will point you toward parts of the graph you need to explore in more detail.  Labeling and showing direction between the nodes (Did Jim call Steve or did Steve call Jim?) are other ways to analyze the data. 
  • Most tools that perform link analysis allow you to draw the graphs using different layouts. For example, draw the graph with the node that has the greatest number of connection points at the center.   Social network metrics like centrality and betweeness help you identify important nodes on the graph.  The entire space should be searchable providing you another level of filtering to identify individuals of interest.
  • There's usually a time dimension that's important in this type of analysis.  For example, when accounts were opened and closed, when phone calls were made or when flights were taken may provide additional insights.   Some tools allow you to build the graph based on this time dimension as if you're watching a movie. The graph is constructed based on these important events.
  • Share your results along with the insights you've found.  See if other analysts have additional things to add.

 

There are a ton of techniques you can use when it comes to big data visualization.    This is just a small sample but hopefully it helps analysts naviagte through large sets of data using data visualization and link analysis.

 


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Real world NoSQL performance pitfalls…

I’ve said for a long time that I am concerned about the accuracy of performance claims on Open Source NoSQL databases. In this blog post I give you a couple of links to people who have tried ...
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Tony Agresta's comment, January 7, 2013 5:37 PM
More insights from Adam Fowler's Blog
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'Big data' – the crown jewel or another missed opportunity for telcos? - Insight - News | Analysys Mason

'Big data' – the crown jewel or another missed opportunity for telcos? - Insight - News | Analysys Mason | Big Data Technology, Semantics and Analytics | Scoop.it
Analysys Mason delivers strategy advice, operations support, and market intelligence worldwide to leading commercial and public sector organisations in telecoms, IT, and media.
Tony Agresta's insight:

With vast amounts of data about consumer usage, cost effective advertising, churn reduction and effective cross sell seem well suited to be addressed with big data technology in the telco space.    Now that data streams can be captured in real time as data is pushed to communication service providers, this data can be connected to data pulled from other databases to create vivid usage patterns.   When anonymized and profiled correctly, it represents a perfect fit for advertisers, especially with any geospatial attributes available.  According to this article, Telefonica is doing just that.  


With enterprise search available through indexed data management, telcos can track pattern changes in real time while displaying results in dashboards. It’s this dynamic data analysis that can lead to more targeted advertising.   Load balancing based on network usage can also be monitored with big data triggers in place to avoid thresholds from reaching certain levels.   In other words, service interruptions can be avoided as part of this process.   Out of home advertising will become more pervasive in 2013 and 2014 as taxis and other forms of transportation begin to dynamically advertise based on geo location. If the advertising is relevant, timely, has the proper incentive and respects privacy, it has the potential to create new forms of revenue for the telco industry as well.

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NYT: Big Data Is Great, but Don't F

NYT: Big Data Is Great, but Don't F | Big Data Technology, Semantics and Analytics | Scoop.it
It is easier than ever to measure and monitor people and machines, but the technology of Big Data is not without its shortcomings.
Tony Agresta's insight:

Intuition helps but there's no substitute for big data technology and analysis.  Technology to ingest, store, search and analyze big data will proliferate across government and commerical sectors in 2013 and beyond.

 

Techniques for segmentation, clustering and modeling data allow organizations to revel meaning hidden inside massive amount of data.

 

Experienced analysts can recommend independent attributes for inclusion in the analysis, variables that have explanatory power in a model or segmentation scheme.  When this experience is coupled with data discovery methods to explore data in an unconstrained way, analysts can pinpoint data elements and relationships that may be correlated with a specific outcome and therefore improve the accuracy of the model.  It’s the combination of intuition, proven experience, flexible discovery tools, proven statistical methods and a full set of data that lead to the fastest, most significant insights.

 

This freedom to explore data has other benefits including identifying missing or poor quality data yielding improved standards and collection processes.  Most of this can be discovered through profiles and histograms of each data field.

 

Data discovery tools, when combined with the approaches referenced above, allow analysts to confirm findings and expand the way analyst’s model data. During this process, the analyst may discover new ways to transform data, group continuous data into categorical data or calculate new data attributes to be used in the analysis. 

 

This class of tools has the added advantage of telling a story about your data using a full complement of visualizations designed to focus the audience on insights and conclusions.  Once the data story is presented to the business, they rapidly draw conclusions to shape programs.

 

Look at the work MarkLogic has done with Tableau Software to better understand the enhanced power of data discovery using the full breadth of both unstructured and structured data. Sure, intuition is important.  But proven analytical methods that leverage new forms of data in real time will give you the most bang for your buck.  

 

http://resources.marklogic.com/library/media/big-data-marklogic-tableau-insights

 

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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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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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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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