I see many businesses ‘warehousing’ huge quantities of data, without necessarily forming strategies to leverage value from them.
The infrastructure needed for processing and returning big data queries is not equivalent to that required for ordinary data storage and querying.
Optimizing hardware and software for dynamic and complex big data can be a challenging process. Yet the costs have some down significantly and the benefits are a great return on the investment.
As business intelligence tech innovations continue and the amount of data that can be captured, stored and managed escalates, I see more organizations placing a monetary valuation on their stored data, and to scrutinize how effectively they are extracting this value.
The return on investment includes benefits through efficiency, innovation and creation gains, driven by insights unlocked from the data.
There is no doubt that deploying a modern business intelligence / data warehouse infrastructure and adopting big data analytics solutions will result in organizations gaining a competitive advantage in their industry.
Becoming a data and evidence driven organization provides a significant competitive advantage.
On top of the requisite high-performance IT infrastructure, data specialists will also be needed by organizations seeking to reap the benefits of big data.
Organizations therefore have to be in a position to recruit from a pool of highly-skilled, data-savvy workers. This means that the increasing data focus among organizations needs to be matched in education and training, in order to provide a workforce qualified to seize the big data opportunity.
Bold action is therefore required to ensure that science, technology, engineering and mathematics (STEM) skills are regarded with the necessary high importance at all levels of education.
I suggest we urgently need to do two things:
1) Make a career in data science attractive to young people
2) Partner with high schools and universities to ensure a proper curriculum is offered
Big data is broadly considered anything that is over 10 Gigabytes in size. 90% of all the data on the planet was created in the last two years. In many cases, the analysis of data dwarfs the original data set.
Big data isn’t just about massive amounts of data. It’s about new, unstructured data sources. It’s about performing data and compute together to perform better and faster analysis.
intTENSITY extracts triples, facts, named entities, entity types, and events from unstructured data collected from over 75 million social media sources for trending and analysis.
Data exists on a time continuum. Data is no longer stationary. It moves from function to function within an organization.
Gone are the days where a piece of information arrives, gets locked away in an Oracle (or DB2, MSSQL, etc.) system and is left to grow old and cold.
Big Data is all about leveraging information for its total worth, and that means extracting the maximum value from data at various points in its life.
Profound insight. Data does indeed exists on a time continuum.
In fact, as data moves from function to function its relationship with other data at different points creates a new value proposition to extract.
The New York Times reports today that spending on IT is growing rapidly despite the doldrums of the economy at large. Total spending on IT is projected to reach USD $3.6 trillion in 2012, far above the expectations of most analysts.
Parabon provides platforms, tools, and services for distributed or “grid” computing.
We make it easy for organizations to transform existing IT infrastructure into a single, centrally managed virtual datacenter, able to provide dynamically provisioned cloud services, extreme-scale grid computing capacity, and system-wide resource profiling — all while maintaining current operational capabilities.
Digital Reasoning‘s Synthesys transforms structured and unstructured data into the underlying facts, entities, relationships, and associated terms so that analysts don’t need to read every document.
Enterprise and Government customers are awash with too much data. This data has three demanding characteristics – it is too big (volume), it is accumulating too fast (velocity) and it is located in many location and forms (variety). Solutions today have attempted to find ever better methods of getting the user to the “right” documents. As a result, data scientists and data analysts today are confronted with the dilemma of an ever-increasing need to read to understand. This is an untenable problem.
In response to this new set of market demands, Digital Reasoning has developed Synthesys. Instead of trying to find ever-better ways to deliver documents, Synthesys transforms structured and unstructured data into the underlying facts, entities, relationships, and associated terms to eliminate the need for data analysts to read in order to understand.
Synthesys delivers an entity oriented analytics solution that brings together the latest entity extraction and NLP technology, geo and temporal reasoning, and patented associative net along with extreme scalability in order to solve some of today’s biggest data analytics challenges. Synthesys works with our partners to deliver a solution that address the volume, velocity and variety of today’s big data challenges – Automated Understanding for Big Data.
Why do customers choose Synthesys?
Analyze with facts instead of more documents to read
Find non-obvious connections based on limited search parameters.
Analyze information in real-time
Visualize and Summarize critical relationships
Single point solution that can scale to an infinite number of servers
Data exists on a time continuum because the “things” we do with data are strongly correlated to its age.
Just after data is created, it is highly interactive. We want to perform high velocity operations on that data at this stage – how fast can we place a trade, or serve an ad, or inspect a record?
Shortly after creation, we are often interested in a specific data instance relative to other data that has also arrived recently – how is my network traffic trending, what is my composite risk by trading desk, what is the current state of my online game leader board?
Queries like these on high velocity data are commonly referred to as realtime analytics.
As data begins to age, our interest often changes from “hot” analytics to record-level storage and retrieval – store this URL, retrieve this user profile, etc. Write it to the database and read it back as necessary, but make sure those reads can be serviced very quickly because there’s often a user waiting for that data on the other side of a browser.
As data begins to age, its value does not diminish, but the nature of that value does begin to change.
Ultimately data becomes useful in a historical context.
Organizations have found countless ways to gain valuable insights – trends, patterns, anomalies – from data over long timelines. Business intelligence, reporting, back testing are all examples of what we do to extract value from historical data.
Additionally, we are now seeing an increasing use of data science – applications that explore data for increasingly deeper insights – not just observing trends, but discovering them.
Datameer is an analytics platform for Hadoop that lets end users work with Big Data intuitively without the IT department’s help.
Previously you needed a three step process for data analytics involving three different vendors, three teams of experts, and three different technologies. Datameer simplifies this complex environment into a single application on top of the powerful Hadoop platform.
Qbase develops data management tools to help you understand and improve your data.
Qbase has numerous federally-focused solutions to overcome data quality challenges, convert legacy data to new formats, and transform data into actionable information.
With Qbase, you can:
-analyze and condition data
-cleanse and migrate data from legacy environments
-integrate multiple, disparate data sources into one source
-get results from “day one”
Qbase has expertise in all data formats, including structured and unstructured, full-text, binary and image data.
There’s no need to license expensive software, purchase more hardware, or train personnel to become data experts. Qbase has a systematic process for revealing and understanding the condition of your data. This process has proven invaluable in driving unprecedented accuracy with data integration efforts.
250 Veronia Drive
Springfield, OH 45505
888 458 0345
Thetus’s Savanna is a powerful, open, model-driven geo-cultural and spatio-temporal analysis platform.
Simplify analysis, produce high-value information and accelerate decision-making.
Savanna is a multi-source, model-enabled analysis solution for correlating situational awareness to support strategic and actionable analysis. Built on existing investments and programs, Savanna brings together various analysis techniques to enable faster, more informed decision-making. Savanna is a solution that provides an integrated user experience for search, visualization, discovery, and output of intelligence all while providing robust, analytic capabilities.
In addition to a suite of analysis tools, Savanna now offers structured data operations and statistical analysis, the ability to create visual representations of data sets through charts, a case management system to organize and preview knowledge assets, as well as a dual-pane interface to optimize workflow.
The value of data changes from the individual item to the aggregate over this time line. Here is an example to demonstrate how the value of that data changes over the same timeline.
When I buy my new iPad, that individual transaction (data item) is highly valuable – to me, to Apple and to Visa. Over the course of a week, Apple may use that item with other near term items for realtime analytics – inventory control perhaps or regional trending. But the value of that individual item will rapidly decline. In 6 months, there will be almost no value in knowing that I bought my iPad in the San Francisco Apple store on July 11, 2012.
However, there will be tremendous value to Apple when my purchase is looked at relative to many others like me over a six-month period – that is, the value of data is high in the aggregate. Finding people of a similar cohort, who buy additional products or services, are valuable insights for Apple to use to sell additional products to large consumer populations (something Apple is very good at doing).
Over time, the value of that one data item (my iPad purchase) is now exclusively in its relationship to many other related purchases.
Data is no longer purely static. It lives on a continuum where different actions are taken on it and value is derived from it in different ways.
Clearly, we can’t use a single datastore to service our needs across the entire continuum.
Our mission is to identify, design, customize and implement smart technologies / systems that can interact with the human race faster, cheaper and better.
Application Performance Monitoring
Application Security Testing
Backup Recovery Software
Benefits Of Data Virtualization
Business Cloud Strategy
Business Improvement Priorities
Business Improvement Priorities
Business Intelligence And Analytics Platform
Business Process Analysis Tools
Business Smartphone Selection
Business Technologies Watchlist
Client Management Tools
Cloud Assessment Framework
Cloud Business Usage Index
Cloud Deployment Model
Cloud Deployment Model Attributes
Cloud Strategies Online Collaboration
Core Technology Rankings
Corporate Learning Systems
Crm Multichannel Campaign Management
Customer Communications Management
Customer Management Contact Center Bpo
Customer Relationship Management
Customer Service Contact Centers
Data Analytics Lifecycle
Database Management Systems
Data Center. Database
Data Center Outsourcing
Data Center Outsourcing And Infrastructure Utility Services
Data Integration Tools
Data Loss Prevention
Data Management Stack
Data Quality Tools
Data Volume Variety Velocity
Data Volume Variety Velocity Veracity
Data Warehouse Database Management Systems
Dr. David Ferrucci
Dr. John Kelly
E Discovery Software
Emerging Technologies And Trends
Employee-Owned Device Program
Employee Performance Management
Endpoint Protection Platforms
Enterprise Architecture Management Suites
Enterprise Architecture Tools
Enterprise Content Management
Enterprise Data Warehousing Platforms
Enterprise Mobile Application Development
Enterprise Resource Planning
Enterprise Service Bus
Enterprise Social Platforms
Global It Infrastructure Outsourcing 2011 Leaders
Global Knowledge Networks
Global Network Service Providers
Hadoop Technology Stack
Hadoop Technology Stack
Hardware As A Service
Health Care And Big Data
Hidden Markov Models
High Performance Computing
Ibm Big Data Platform
Information Capabilities Framework
Infrastructure As A Service
Infrastructure Utility Services
Integrated It Portfolio Analysis Applications
Integrated Software Quality Suites
Internet Of Things
Internet Trends 2011
It Innovation Wave
Key Performance Indicators
Kindle Fire Tablet
Long Term Evolution Network Infrastructure
Managed Security Providers
Marketing Resource Management
Marketing Resource Management
Master Data Management
Microsoft Big Data Platform
Microsoft Dynamics Ax
Mobile App Internet
Mobile Application Development
Mobile Business Application Priorities
Mobile Business Intelligence
Mobile Consumer Application Platforms
Mobile Data Protection
Mobile Development Tool Selection
Mobile Device Management
Mobile Device Management Software Magic Quadrant 2011
Mobile Internet Trends
Mobile Payment System
Modular Disk Arrays
Natural Language Processing
N-gram Language Modeling
Pioneering The Science Of Information
Platform As A Service
Primary Storage Reduction Technologies
Real Time Analytics
Real-time Bidding Ad Exchange
Retail Marketing Analytics
Sales Force Automation
Sap Big Data Platform
Scenario-Based Enterprise Performance Management (EPM)
Security Information & Event Management
Self-Service Business Intelligence
Service Oriented Architecture
Software As A Service
Sony Tablet S
Survey Most Important It Priorities
Technology Industry Report Card
Technology M&A Deals
Vendor Due Diligence
Vertical Industry It Growth
Web Content Management