What is involved in Big Data
Find out what the related areas are that Big Data connects with, associates with, correlates with or affects, and which require thought, deliberation, analysis, review and discussion. This unique checklist stands out in a sense that it is not per-se designed to give answers, but to engage the reader and lay out a Big Data thinking-frame.
How far is your company on its Big Data Information Management journey?
Take this short survey to gauge your organization’s progress toward Big Data Information Management leadership. Learn your strongest and weakest areas, and what you can do now to create a strategy that delivers results.
To address the criteria in this checklist for your organization, extensive selected resources are provided for sources of further research and information.
Start the Checklist
Below you will find a quick checklist designed to help you think about which Big Data related domains to cover and 234 essential critical questions to check off in that domain.
The following domains are covered:
Big Data, Small data, Horizon 2020, Information and communication technologies for development, Ulf-Dietrich Reips, Government of India, Association for Computing Machinery, Data science, Jure Leskovec, New York Times, Data silos, Massive parallel processing, Google Flu Trends, Sloan Digital Sky Survey, Big Data Maturity Model, Data fusion, Windermere Real Estate, Square Kilometre Array, Natural disaster, Financial Times, Data mining, BBC News, Framework Programmes for Research and Technological Development, Fiber connector, Software AG, Business Operations, Data acquisition, Business Intelligence, Oracle Corporation, Economic productivity, International development, Oracle NoSQL Database, Internet of Things, Digital camera, DNA database, Data curation, Data visualization, Systems management, Large Synoptic Survey Telescope, Mobile device, Bridgeport, Connecticut, Information privacy, Big Data, UC Berkeley, World Wide Web Conference, Search-based application, Integrated Authority File, Complex Systems, Automatic identification and data capture, Web search engine, Danah Boyd, Surveillance capitalism, DBC 1012, Data set, Seventh Framework Program, Personally identifiable information, McKinsey & Company, Machine intelligence, Data quality, Viktor Mayer-Schönberger, Large Hadron Collider, Computer data storage:
Big Data Critical Criteria:
Prioritize Big Data planning and define Big Data competency-based leadership.
– While a move from Oracles MySQL may be necessary because of its inability to handle key big data use cases, why should that move involve a switch to Apache Cassandra and DataStax Enterprise?
– Do you see the need to clarify copyright aspects of the data-driven innovation (e.g. with respect to technologies such as text and data mining)?
– Erp versus big data are the two philosophies of information architecture consistent complementary or in conflict with each other?
– What are the main obstacles that prevent you from having access to all the datasets that are relevant for your organization?
– What is (or would be) the added value of collaborating with other entities regarding data sharing in your sector?
– In which area(s) do data integration and BI, as part of Fusion Middleware, help our IT infrastructure?
– Does big data threaten the traditional data warehouse business intelligence model stack?
– How are the new Big Data developments captured in new Reference Architectures?
– Which other Oracle Business Intelligence products are used in your solution?
– What is the right technique for distributing domains across processors?
– What are the new developments that are included in Big Data solutions?
– Is data-driven decision-making part of the organizations culture?
– With more data to analyze, can Big Data improve decision-making?
– How much data is really relevant to the problem solution?
– Where do you see the need for standardisation actions?
– Wait, DevOps does not apply to Big Data?
– So how are managers using big data?
– what is Different about Big Data?
– What can it be used for?
– What are we collecting?
Small data Critical Criteria:
Pay attention to Small data tasks and correct better engagement with Small data results.
– In the case of a Big Data project, the criteria for the audit derive from implementation objectives. an audit of a Big Data project involves assessing whether the recommendations outlined for implementation have been met. in other words, can we track that any Big Data project is implemented as planned, and is it working?
– How will we insure seamless interoperability of Big Data moving forward?
– What are the long-term Big Data goals?
Horizon 2020 Critical Criteria:
Refer to Horizon 2020 leadership and report on developing an effective Horizon 2020 strategy.
– What are your key performance measures or indicators and in-process measures for the control and improvement of your Big Data processes?
– How can we incorporate support to ensure safe and effective use of Big Data into the services that we provide?
– What tools and technologies are needed for a custom Big Data project?
Information and communication technologies for development Critical Criteria:
Steer Information and communication technologies for development tasks and suggest using storytelling to create more compelling Information and communication technologies for development projects.
– What are your results for key measures or indicators of the accomplishment of your Big Data strategy and action plans, including building and strengthening core competencies?
– What are the success criteria that will indicate that Big Data objectives have been met and the benefits delivered?
– Who is responsible for ensuring appropriate resources (time, people and money) are allocated to Big Data?
Ulf-Dietrich Reips Critical Criteria:
Scrutinze Ulf-Dietrich Reips goals and assess what counts with Ulf-Dietrich Reips that we are not counting.
– Are there any easy-to-implement alternatives to Big Data? Sometimes other solutions are available that do not require the cost implications of a full-blown project?
– Is Big Data Realistic, or are you setting yourself up for failure?
Government of India Critical Criteria:
Consider Government of India outcomes and adjust implementation of Government of India.
– Record-keeping requirements flow from the records needed as inputs, outputs, controls and for transformation of a Big Data process. ask yourself: are the records needed as inputs to the Big Data process available?
– Think of your Big Data project. what are the main functions?
– How do we Lead with Big Data in Mind?
Association for Computing Machinery Critical Criteria:
Think about Association for Computing Machinery issues and customize techniques for implementing Association for Computing Machinery controls.
– What is the source of the strategies for Big Data strengthening and reform?
– To what extent does management recognize Big Data as a tool to increase the results?
Data science Critical Criteria:
Conceptualize Data science governance and shift your focus.
– What is the difference between Data Analytics Data Analysis Data Mining and Data Science?
– How will you know that the Big Data project has been successful?
– How is the value delivered by Big Data being measured?
Jure Leskovec Critical Criteria:
Contribute to Jure Leskovec planning and use obstacles to break out of ruts.
– What is the total cost related to deploying Big Data, including any consulting or professional services?
– Think about the functions involved in your Big Data project. what processes flow from these functions?
– What potential environmental factors impact the Big Data effort?
New York Times Critical Criteria:
Mix New York Times decisions and summarize a clear New York Times focus.
– What is our formula for success in Big Data ?
– Why should we adopt a Big Data framework?
Data silos Critical Criteria:
Categorize Data silos tasks and plan concise Data silos education.
– what is the best design framework for Big Data organization now that, in a post industrial-age if the top-down, command and control model is no longer relevant?
– Are we making progress? and are we making progress as Big Data leaders?
– How do we keep improving Big Data?
Massive parallel processing Critical Criteria:
Audit Massive parallel processing leadership and balance specific methods for improving Massive parallel processing results.
– Who will be responsible for documenting the Big Data requirements in detail?
– Are we Assessing Big Data and Risk?
Google Flu Trends Critical Criteria:
Judge Google Flu Trends outcomes and learn.
– Are there recognized Big Data problems?
– What threat is Big Data addressing?
Sloan Digital Sky Survey Critical Criteria:
Confer over Sloan Digital Sky Survey risks and assess what counts with Sloan Digital Sky Survey that we are not counting.
– Does Big Data create potential expectations in other areas that need to be recognized and considered?
– What are the record-keeping requirements of Big Data activities?
– Who will provide the final approval of Big Data deliverables?
Big Data Maturity Model Critical Criteria:
Generalize Big Data Maturity Model planning and create Big Data Maturity Model explanations for all managers.
– What are your most important goals for the strategic Big Data objectives?
– How important is Big Data to the user organizations mission?
– Is Supporting Big Data documentation required?
Data fusion Critical Criteria:
Look at Data fusion governance and differentiate in coordinating Data fusion.
– What new requirements emerge in terms of information processing/management to make physical and virtual world data fusion possible?
– What is the purpose of Big Data in relation to the mission?
Windermere Real Estate Critical Criteria:
Troubleshoot Windermere Real Estate decisions and devote time assessing Windermere Real Estate and its risk.
– What are the key elements of your Big Data performance improvement system, including your evaluation, organizational learning, and innovation processes?
– How to Secure Big Data?
Square Kilometre Array Critical Criteria:
X-ray Square Kilometre Array adoptions and get out your magnifying glass.
– Meeting the challenge: are missed Big Data opportunities costing us money?
Natural disaster Critical Criteria:
Study Natural disaster strategies and get answers.
– Which customers cant participate in our Big Data domain because they lack skills, wealth, or convenient access to existing solutions?
– Does cloud storage get affected during a natural disaster how can we ensure a secure disaster recovery for that?
– How much safe is data from Natural disaster?
Financial Times Critical Criteria:
Adapt Financial Times adoptions and know what your objective is.
– What are specific Big Data Rules to follow?
Data mining Critical Criteria:
See the value of Data mining governance and ask what if.
– How do your measurements capture actionable Big Data information for use in exceeding your customers expectations and securing your customers engagement?
– What types of transactional activities and data mining are being used and where do we see the greatest potential benefits?
– What is the difference between business intelligence business analytics and data mining?
– Is business intelligence set to play a key role in the future of Human Resources?
– Can Management personnel recognize the monetary benefit of Big Data?
– What are all of our Big Data domains and what do they do?
– What programs do we have to teach data mining?
BBC News Critical Criteria:
Detail BBC News outcomes and customize techniques for implementing BBC News controls.
– Marketing budgets are tighter, consumers are more skeptical, and social media has changed forever the way we talk about Big Data. How do we gain traction?
– How do we Identify specific Big Data investment and emerging trends?
Framework Programmes for Research and Technological Development Critical Criteria:
Transcribe Framework Programmes for Research and Technological Development tactics and drive action.
– What will be the consequences to the business (financial, reputation etc) if Big Data does not go ahead or fails to deliver the objectives?
– Who will be responsible for making the decisions to include or exclude requested changes once Big Data is underway?
– What are current Big Data Paradigms?
Fiber connector Critical Criteria:
Categorize Fiber connector adoptions and display thorough understanding of the Fiber connector process.
Software AG Critical Criteria:
Consult on Software AG decisions and reduce Software AG costs.
– Does Big Data appropriately measure and monitor risk?
– How can you measure Big Data in a systematic way?
– How do we maintain Big Datas Integrity?
Business Operations Critical Criteria:
Trace Business Operations tactics and adopt an insight outlook.
– What tools do you use once you have decided on a Big Data strategy and more importantly how do you choose?
– Is legal review performed on all intellectual property utilized in the course of your business operations?
– How to move the data in legacy systems to the cloud environment without interrupting business operations?
– Which individuals, teams or departments will be involved in Big Data?
– Do Big Data rules make a reasonable demand on a users capabilities?
Data acquisition Critical Criteria:
Systematize Data acquisition outcomes and pioneer acquisition of Data acquisition systems.
– What are the short and long-term Big Data goals?
Business Intelligence Critical Criteria:
Guard Business Intelligence leadership and acquire concise Business Intelligence education.
– What information can be provided in regards to a sites usage and business intelligence usage within the intranet environment?
– Can you easily add users and features to quickly scale and customize to your organizations specific needs?
– What are the main differences between a business intelligence team compared to a team of data scientists?
– What does a typical data warehouse and business intelligence organizational structure look like?
– Can you filter, drill down, or add entirely new data to your visualization with mobile editing?
– What is the biggest value proposition for new BI or analytics functionality at your company?
– What are some best practices for gathering business intelligence about a competitor?
– What is the difference between a data scientist and a business intelligence analyst?
– What are typical responsibilities of someone in the role of Business Analyst?
– what is the difference between Data analytics and Business Analytics If Any?
– What are some software and skills that every Data Scientist should know?
– What information needs of managers are satisfied by the new BI system?
– Who prioritizes, conducts and monitors business intelligence projects?
– Is data warehouseing necessary for our business intelligence service?
– What type and complexity of system administration roles?
– Where is the business intelligence bottleneck?
– What are typical data-mining applications?
– How is business intelligence disseminated?
– Types of data sources supported?
Oracle Corporation Critical Criteria:
Give examples of Oracle Corporation results and handle a jump-start course to Oracle Corporation.
– Think about the kind of project structure that would be appropriate for your Big Data project. should it be formal and complex, or can it be less formal and relatively simple?
– Is the scope of Big Data defined?
Economic productivity Critical Criteria:
Guide Economic productivity visions and correct Economic productivity management by competencies.
– What may be the consequences for the performance of an organization if all stakeholders are not consulted regarding Big Data?
International development Critical Criteria:
Troubleshoot International development governance and research ways can we become the International development company that would put us out of business.
– How do we ensure that implementations of Big Data products are done in a way that ensures safety?
– What are our needs in relation to Big Data skills, labor, equipment, and markets?
– Is a Big Data Team Work effort in place?
Oracle NoSQL Database Critical Criteria:
Chat re Oracle NoSQL Database projects and stake your claim.
– What are the top 3 things at the forefront of our Big Data agendas for the next 3 years?
– Risk factors: what are the characteristics of Big Data that make it risky?
Internet of Things Critical Criteria:
Interpolate Internet of Things risks and customize techniques for implementing Internet of Things controls.
– Fog Computing is internet computing where the devices responsible for the computing surround us. Instead of having a data center where all of the processing and storage occurs, fog computing would allow us to bring the devices closer to us and these devices would be responsible for their own processing and storage. So how does this concept help us deal with the problems created by the IoT, and what benefits would this provide us that upgrading the cloud infrastructure couldnt?
– Designing internet of things (IoT) solutions can unlock innovation, increase efficiencies and create new competitive advantages. but in an emerging marketplace of mostly unknown and untested solutions, where do we start?
– How can sluggish supply chains be empowered by IoT to make them more transparent and responsive?
– Are there any agreements concerning the security and privacy of the data once it is shared?
– If a component fails what (if any) functions must the application continue to provide?
– Is any form of notice provided to the individual prior to collection of information?
– Are our executives are aware of the transformational potential of the Internet of Things?
– Is the social web being irreversibly corrupted by automation tools?
– How would a society benefit from an AI that passes the Turing test?
– Will Big Data deliverables need to be tested and, if so, by whom?
– What are the best examples of the Internet of things?
– If the Contractor installs, what shall this entail?
– What market segment(s) are served by the company?
– Will contractors install the necessary equipment?
– Where does the network need to be in 3-5 years?
– Is there a need/way to authenticate a thing?
– Will we find gold in iiot?
– Why Is IoT Important?
– Who owns the data?
– What is a thing?
Digital camera Critical Criteria:
Experiment with Digital camera tactics and create Digital camera explanations for all managers.
– Is the Big Data organization completing tasks effectively and efficiently?
– Have you identified your Big Data key performance indicators?
DNA database Critical Criteria:
Learn from DNA database goals and probe the present value of growth of DNA database.
– Do several people in different organizational units assist with the Big Data process?
– Do the Big Data decisions we make today help people and the planet tomorrow?
Data curation Critical Criteria:
Deduce Data curation visions and integrate design thinking in Data curation innovation.
– In a project to restructure Big Data outcomes, which stakeholders would you involve?
– Have all basic functions of Big Data been defined?
Data visualization Critical Criteria:
Read up on Data visualization engagements and perfect Data visualization conflict management.
– Think about the people you identified for your Big Data project and the project responsibilities you would assign to them. what kind of training do you think they would need to perform these responsibilities effectively?
– What are the best places schools to study data visualization information design or information architecture?
– Is Big Data dependent on the successful delivery of a current project?
Systems management Critical Criteria:
Graph Systems management risks and catalog Systems management activities.
– Does Big Data include applications and information with regulatory compliance significance (or other contractual conditions that must be formally complied with) in a new or unique manner for which no approved security requirements, templates or design models exist?
– What are the Essentials of Internal Big Data Management?
– Which Big Data goals are the most important?
Large Synoptic Survey Telescope Critical Criteria:
Exchange ideas about Large Synoptic Survey Telescope adoptions and inform on and uncover unspoken needs and breakthrough Large Synoptic Survey Telescope results.
– What are your current levels and trends in key measures or indicators of Big Data product and process performance that are important to and directly serve your customers? how do these results compare with the performance of your competitors and other organizations with similar offerings?
– How do mission and objectives affect the Big Data processes of our organization?
Mobile device Critical Criteria:
Extrapolate Mobile device engagements and prioritize challenges of Mobile device.
– Imagine you work in the Human Resources department of a company considering a policy to protect its data on employees mobile devices. in advising on this policy, what rights should be considered?
– If mobile technologies are supported, how is the software optimized for use on smartphone, tables, and other mobile devices?
– Does the tool we use provide the ability for mobile devices to access critical portions of the management interface?
– Can your bi solution quickly locate dashboard on your mobile device?
– What business benefits will Big Data goals deliver if achieved?
– Will your product work from a mobile device?
– Do we all define Big Data in the same way?
Bridgeport, Connecticut Critical Criteria:
Scrutinze Bridgeport, Connecticut strategies and mentor Bridgeport, Connecticut customer orientation.
– For your Big Data project, identify and describe the business environment. is there more than one layer to the business environment?
– How to deal with Big Data Changes?
Information privacy Critical Criteria:
Derive from Information privacy outcomes and test out new things.
Big Data Critical Criteria:
Derive from Big Data projects and clarify ways to gain access to competitive Big Data services.
– Do you see the need to address the issues of data ownership or access to non-personal data (e.g. machine-generated data)?
– Should we use data without the permission of individual owners, such as copying publicly available data?
– Future: Given the focus on Big Data where should the Chief Executive for these initiatives report?
– Which core Oracle Business Intelligence or Big Data Analytics products are used in your solution?
– Wheres the evidence that using big data intelligently will improve business performance?
– What are the legal risks in using Big Data/People Analytics in hiring?
– How can the best Big Data solution be chosen based on use case requirements?
– Are there any best practices or standards for the use of Big Data solutions?
– How can the benefits of Big Data collection and applications be measured?
– Which Oracle Data Integration products are used in your solution?
– What (additional) data do these algorithms need to be effective?
– Does your organization buy datasets from other entities?
– What are our tools for big data analytics?
– Can analyses improve with more data to process?
– How much data might be lost to pruning?
– Does Big Data Really Need HPC?
– How to use in practice?
UC Berkeley Critical Criteria:
Map UC Berkeley strategies and stake your claim.
– What are our best practices for minimizing Big Data project risk, while demonstrating incremental value and quick wins throughout the Big Data project lifecycle?
– How do we measure improved Big Data service perception, and satisfaction?
World Wide Web Conference Critical Criteria:
Consider World Wide Web Conference risks and create a map for yourself.
– What other jobs or tasks affect the performance of the steps in the Big Data process?
– What are the usability implications of Big Data actions?
Search-based application Critical Criteria:
Systematize Search-based application engagements and ask what if.
– Why is it important to have senior management support for a Big Data project?
Integrated Authority File Critical Criteria:
Test Integrated Authority File failures and simulate teachings and consultations on quality process improvement of Integrated Authority File.
– How do senior leaders actions reflect a commitment to the organizations Big Data values?
Complex Systems Critical Criteria:
Exchange ideas about Complex Systems decisions and grade techniques for implementing Complex Systems controls.
– How much testing is necessary in order to expose all the potential failure modes and situations of highly integrated complex systems?
– Complex interventions or complex systems?
Automatic identification and data capture Critical Criteria:
Interpolate Automatic identification and data capture tasks and achieve a single Automatic identification and data capture view and bringing data together.
– What are the business goals Big Data is aiming to achieve?
Web search engine Critical Criteria:
Use past Web search engine leadership and adopt an insight outlook.
Danah Boyd Critical Criteria:
Discuss Danah Boyd outcomes and oversee Danah Boyd management by competencies.
– What new services of functionality will be implemented next with Big Data ?
– How would one define Big Data leadership?
Surveillance capitalism Critical Criteria:
Contribute to Surveillance capitalism quality and suggest using storytelling to create more compelling Surveillance capitalism projects.
– Can we add value to the current Big Data decision-making process (largely qualitative) by incorporating uncertainty modeling (more quantitative)?
– How much does Big Data help?
DBC 1012 Critical Criteria:
Pilot DBC 1012 quality and research ways can we become the DBC 1012 company that would put us out of business.
Data set Critical Criteria:
Review Data set failures and look in other fields.
– For hosted solutions, are we permitted to download the entire data set in order to maintain local backups?
– How was it created; what algorithms, algorithm versions, ancillary and calibration data sets were used?
– Is data that is transcribed or copied checked for errors against the original data set?
– What needs to be in the plan related to the data capture for the various data sets?
– Is someone responsible for migrating data sets that are in old/outdated formats?
– You get a data set. what do you do with it?
Seventh Framework Program Critical Criteria:
Explore Seventh Framework Program governance and gather Seventh Framework Program models .
Personally identifiable information Critical Criteria:
Coach on Personally identifiable information results and look at the big picture.
– When sharing data, are appropriate procedures, such as sharing agreements, put in place to ensure that any Personally identifiable information remains strictly confidential and protected from unauthorized disclosure?
– Does the company collect personally identifiable information electronically?
– What is Personal Data or Personally Identifiable Information (PII)?
McKinsey & Company Critical Criteria:
Talk about McKinsey & Company tasks and devote time assessing McKinsey & Company and its risk.
Machine intelligence Critical Criteria:
See the value of Machine intelligence tactics and question.
Data quality Critical Criteria:
Map Data quality tasks and overcome Data quality skills and management ineffectiveness.
– Do we conduct regular data quality audits to ensure that our strategies for enforcing quality control are up-to-date and that any corrective measures undertaken in the past have been successful in improving Data Quality?
– Information on verification or evidence for the value and accuracy how can I check the value or have a confidence in it?
– Validation: does data meet analytic and sample specific requirements (usually done by a qa officer or external party)?
– What issues should you consider when determining whether existing data may possibly serve as a source of information?
– Are clearly written instructions available on how to use the reporting tools/forms related to people reached/served?
– Integrity: is the structure of data and relationships among entities and attributes maintained consistently?
– How do you express quality with regard to making a decision from a statistical hypothesis test?
– Are data maintained in accordance with international or national confidentiality guidelines?
– Do you have policies and procedures which direct your data collection process?
– Does the data clearly and adequately represent the intended result?
– How can you control the probability of making decision errors?
– What is the proportion of duplicate records on the data file?
– What is the typical reimbursement for sharing your data?
– Does the database contain what you think it contains?
– Verification: is the data complete and correct?
– Timeliness: is data available when needed?
– Can Data Quality be improved?
– How big should the sample be?
– What makes up a good record?
– Is the system acceptable?
Viktor Mayer-Schönberger Critical Criteria:
Win new insights about Viktor Mayer-Schönberger leadership and tour deciding if Viktor Mayer-Schönberger progress is made.
Large Hadron Collider Critical Criteria:
Deliberate Large Hadron Collider leadership and explore and align the progress in Large Hadron Collider.
– Who are the people involved in developing and implementing Big Data?
– How do we go about Securing Big Data?
Computer data storage Critical Criteria:
Concentrate on Computer data storage quality and summarize a clear Computer data storage focus.
– When a Big Data manager recognizes a problem, what options are available?
– Are accountability and ownership for Big Data clearly defined?
This quick readiness checklist is a selected resource to help you move forward. Learn more about how to achieve comprehensive insights with the Big Data Information Management Self Assessment:
Author: Gerard Blokdijk
CEO at The Art of Service | theartofservice.com
Gerard is the CEO at The Art of Service. He has been providing information technology insights, talks, tools and products to organizations in a wide range of industries for over 25 years. Gerard is a widely recognized and respected information expert. Gerard founded The Art of Service consulting business in 2000. Gerard has authored numerous published books to date.
To address the criteria in this checklist, these selected resources are provided for sources of further research and information:
Small data External links:
What are the Small Data Plans? | Verizon Community
Design issues – Sending small data segments over TCP …
LEGRAND Small Data Box,Ivory,Boxes – …
Horizon 2020 External links:
Horizon 2020 – How to apply – YouTube
Horizon 2020 Energy Info Days
Horizon 2020: What to Expect and Where to Apply in 2018- …
Ulf-Dietrich Reips External links:
Ulf-Dietrich Reips – Google Scholar Citations
Ulf-Dietrich Reips | Colorado PROFILES
Government of India External links:
eProcurement System Government of India
Association for Computing Machinery External links:
Association for Computing Machinery Student Chapter …
Association for Computing Machinery – Official Site
Association for Computing Machinery
Data science External links:
Yhat: End-to-End Data Science Platform
The Data Science Conference®, Seattle Sept. 2017
University of Wisconsin Data Science Degree Online
Jure Leskovec External links:
[PDF]Jure Leskovec – Stanford University
Jure Leskovec – Google Scholar Citations
New York Times External links:
The New York Times – Official Site
The School of The New York Times
Today’s Paper – The New York Times
Data silos External links:
Why We Need More Clinical Data – Medical Data Silos
Bridging the Data Silos – BrightTALK
Turn Your Data Silos into Smart Data | Cendyn Marketing Cloud
Google Flu Trends External links:
Google Flu Trends
Google Flu Trends – Track Influenza Faster Than the CDC
Google Flu Trends Is Under The Weather, Study Says
Data fusion External links:
Data Fusion Solutions
Algorithms and Data Fusion – Quora
Global Data Fusion, a Background Screening Company
Windermere Real Estate External links:
Windermere Real Estate Golf for Housing Hope
Windermere Real Estate
Square Kilometre Array External links:
Square Kilometre Array
Square Kilometre Array – Home | Facebook
Square Kilometre Array Italia – Home | Facebook
Natural disaster External links:
Natural Disaster Survival – Roblox
Natural disaster – Customer Assistance – Chase
Natural Disaster Claims | Safeco Insurance
Financial Times External links:
The Financial Times: Uber is doomed / Boing Boing
Financial Times Diaries
Data mining External links:
Data Mining : the Textbook (eBook, 2015) [WorldCat.org]
Data Mining – University of Texas at Austin
Job Titles in Data Mining – kdnuggets.com
BBC News External links:
Home – BBC News
BBC News (@bbcnews) • Instagram photos and videos
Fiber connector External links:
Alibaba – Fiber connector,cable box
Software AG External links:
Digital Marketplace | Software AG
Jobs at Software AG
Home – OneVision Software AG
Business Operations External links:
Business Operations & Finance Support / Overview
UofL Business Operations
How much does a business operations manager make?
Data acquisition External links:
AstroNova Test & Measurement | Data Acquisition Solutions
Manager, Data Acquisition – Columbus, OH – Quantum …
Hi-Techniques Inc. – High Performance Data Acquisition …
Business Intelligence External links:
List of Business Intelligence Skills – The Balance
[PDF]Position Title: Business Intelligence Analyst – ttra
Oracle Corporation External links:
Oracle Corporation – The New York Times
Oracle Corporation – ORCL – Stock Price Today – Zacks
Economic productivity External links:
Economic productivity has declined in some countries as …
[PDF]FMSIB: Moving Freight for Economic Productivity
International development External links:
U.S. Agency for International Development
Economic and International Development – El Paso, Texas
International Development with Foundation for …
Oracle NoSQL Database External links:
The Oracle NoSQL Database – CERN Document Server
Oracle Blogs | Oracle NoSQL Database Blog
Internet of Things External links:
Leverege: Internet of Things (IoT) Platform and Solutions
Internet of Things World Forum – IoTWF Home
AT&T M2X: Build solutions for the Internet of Things
Digital camera External links:
Best Digital Camera Reviews – Consumer Reports
DNA database External links:
Utah Offender DNA Database
TxDPS – Statewide CODIS DNA Database Program, Overview
DNA Database FAQs | Colorado Bureau of Investigation
Data curation External links:
Data curation (Book, 2017) [WorldCat.org]
Data visualization External links:
QuickBooks Data Visualization And Business Intelligence
Data Visualization & Business Intelligence Tool | datapine
Systems management External links:
AppDetails – Application and Systems Management …
HSMN – Health Systems Management Network
Large Synoptic Survey Telescope External links:
About LSST | The Large Synoptic Survey Telescope
Mobile device External links:
Mobile Tracker – a mobile device IP address tracking
Duke Health Mobile Device Manager
Restart the Apple Mobile Device Service (AMDS) on Windows
Bridgeport, Connecticut External links:
Bridgeport, CT – Bridgeport, Connecticut Map & …
Information privacy External links:
Information Privacy | Citizens Bank
Big Data External links:
Pepperdata: DevOps for Big Data
Qognify: Big Data Solutions for Physical Security & …
Big Data Business Intelligence & Visualization | Arcadia Data
UC Berkeley External links:
UC Berkeley Committee for Protection of Human Subjects
UC Berkeley Graduate School of Education – Official Site
Research & Faculty | UC Berkeley Physics
World Wide Web Conference External links:
25th World Wide Web Conference – Conference overview
[PDF]World Wide Web conference opens, 20 years after its …
Integrated Authority File External links:
MEDLARS indexing: integrated authority file
Complex Systems External links:
About Complex Systems | NECSI
ICTT-Systems Engineering for Complex Systems
Model-Based Design of Complex Systems
Web search engine External links:
Openfos – The Business to Business Web Search Engine
Magelln Web Search Engine :: Your Web Search Engine
Danah Boyd External links:
danah boyd | ParentMap
danah boyd at Microsoft Research
danah boyd – Data & Society: Points
Data set External links:
OpenFEMA Dataset: OpenFEMA Data Sets – V1 | FEMA.gov
Limited Data Set | HHS.gov
Seventh Framework Program External links:
Seventh Framework Program | Fortnightly
Personally identifiable information External links:
Personally Identifiable Information (PII) – RMDA
McKinsey & Company External links:
India | McKinsey & Company
Machine intelligence External links:
ByteGain | Machine Intelligence for the rest of us.
Research – Machine Intelligence Research Institute
finbox.io | Fundamental Investing + Machine Intelligence
Data quality External links:
Webbula – The Data Quality Experts
Viktor Mayer-Schönberger External links:
Viktor Mayer-Schönberger, Delete: The Virtue of …
Viktor Mayer-Schönberger | Facebook
Large Hadron Collider External links:
Large Hadron Collider – CNN
The Large Hadron Collider (eVideo, 2015) [WorldCat.org]
Computer data storage External links:
Computer Data Storage Options – Ferris State University