223 Big Data Information Management Criteria for Multi-purpose Projects

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 223 essential critical questions to check off in that domain.

The following domains are covered:

Big Data, MIKE2.0 Methodology, Data curation, Dirty data, Business Operations, Barack Obama presidential campaign, 2012, Critical data studies, Machine intelligence, Software AG, Cluster analysis, Oracle NoSQL Database, Moore’s Law, Factor analysis, Public service broadcasting in the United Kingdom, Ulf-Dietrich Reips, Direct-attached storage, Data analysis, UC Berkeley, H. Eugene Stanley, Predictive analytics, Bharatiya Janata Party, Information Technology, Fiber connector, Extract, transform, load, Data lake, Data sharing, Cyber-physical system, Bridgeport, Connecticut, Small data, Large Synoptic Survey Telescope, Information privacy, Big Data, International Data Corporation, Storage area network, Association for Computing Machinery, Uwe Matzat, DNA database, International development, New York Times, TIME Magazine, Google Flu Trends, Indian general election, 2014, with mixed results, Application software, Data blending, Mobile device, Massive parallel processing, Data science, Government database, Internet of Things, Viktor Mayer-Schönberger, Unstructured data, Digital footprint, National Security Agency, Consumer privacy, Business informatics, SAP AG:

Big Data Critical Criteria:

Reason over Big Data goals and raise human resource and employment practices for Big Data.

– Do you see the need for actions in the area of standardisation (including both formal standards and the promotion of/agreement on de facto standards) related to your sector?

– How we make effective use of the flood of data that will be produced will be a real big data challenge: should we keep it all or could we throw some away?

– 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)?

– Looking at hadoop big data in the rearview mirror what would you have done differently after implementing a Data Lake?

– 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?

– What is the Quality of the Result if the Quality of the Data/Metadata is poor?

– How can the best Big Data solution be chosen based on use case requirements?

– What if the needle in the haystack happens to be a complex data structure?

– What new Security and Privacy challenge arise from new Big Data solutions?

– With more data to analyze, can Big Data improve decision-making?

– Can analyses improve with better system and environment models?

– What analytical tools do you consider particularly important?

– What is/are the corollaries for non-algorithmic analytics?

– Which Oracle applications are used in your project?

– How much data correction can we do at the edges?

– Is Big data different?

– How to deal with too much data?

– What about Volunteered data?

– Who is collecting what?

MIKE2.0 Methodology Critical Criteria:

Consolidate MIKE2.0 Methodology failures and slay a dragon.

– 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?

– Can we add value to the current Big Data decision-making process (largely qualitative) by incorporating uncertainty modeling (more quantitative)?

– What is the source of the strategies for Big Data strengthening and reform?

Data curation Critical Criteria:

Focus on Data curation leadership and develop and take control of the Data curation initiative.

– Which customers cant participate in our Big Data domain because they lack skills, wealth, or convenient access to existing solutions?

– What sources do you use to gather information for a Big Data study?

– How can the value of Big Data be defined?

Dirty data Critical Criteria:

Focus on Dirty data tasks and display thorough understanding of the Dirty data process.

– What will be the consequences to the business (financial, reputation etc) if Big Data does not go ahead or fails to deliver the objectives?

– In what ways are Big Data vendors and us interacting to ensure safe and effective use?

– Is there any existing Big Data governance structure?

Business Operations Critical Criteria:

Detail Business Operations governance and summarize a clear Business Operations focus.

– 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?

– 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?

– Is maximizing Big Data protection the same as minimizing Big Data loss?

– Who needs to know about Big Data ?

Barack Obama presidential campaign, 2012 Critical Criteria:

Trace Barack Obama presidential campaign, 2012 engagements and summarize a clear Barack Obama presidential campaign, 2012 focus.

– Who will be responsible for deciding whether Big Data goes ahead or not after the initial investigations?

– Who is the main stakeholder, with ultimate responsibility for driving Big Data forward?

– Is a Big Data Team Work effort in place?

Critical data studies Critical Criteria:

Deliberate over Critical data studies outcomes and find out.

– Among the Big Data product and service cost to be estimated, which is considered hardest to estimate?

– What threat is Big Data addressing?

– Why are Big Data skills important?

Machine intelligence Critical Criteria:

Air ideas re Machine intelligence quality and finalize the present value of growth of Machine intelligence.

– Where do ideas that reach policy makers and planners as proposals for Big Data strengthening and reform actually originate?

– What are the short and long-term Big Data goals?

Software AG Critical Criteria:

Merge Software AG visions and oversee Software AG requirements.

– When a Big Data manager recognizes a problem, what options are available?

– How will you know that the Big Data project has been successful?

Cluster analysis Critical Criteria:

Communicate about Cluster analysis strategies and assess and formulate effective operational and Cluster analysis strategies.

– What are our Big Data Processes?

– How can we improve Big Data?

Oracle NoSQL Database Critical Criteria:

Confer re Oracle NoSQL Database risks and probe using an integrated framework to make sure Oracle NoSQL Database is getting what it needs.

– What will drive Big Data change?

– Do we have past Big Data Successes?

Moore’s Law Critical Criteria:

Value Moore’s Law decisions and work towards be a leading Moore’s Law expert.

– In a project to restructure Big Data outcomes, which stakeholders would you involve?

– Does Big Data appropriately measure and monitor risk?

Factor analysis Critical Criteria:

Define Factor analysis leadership and describe which business rules are needed as Factor analysis interface.

– 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?

– Why is it important to have senior management support for a Big Data project?

– How do we Improve Big Data service perception, and satisfaction?

Public service broadcasting in the United Kingdom Critical Criteria:

Consult on Public service broadcasting in the United Kingdom tactics and observe effective Public service broadcasting in the United Kingdom.

– Does Big Data create potential expectations in other areas that need to be recognized and considered?

– How will we insure seamless interoperability of Big Data moving forward?

– Are there recognized Big Data problems?

Ulf-Dietrich Reips Critical Criteria:

Disseminate Ulf-Dietrich Reips leadership and optimize Ulf-Dietrich Reips leadership as a key to advancement.

– Does Big Data analysis show the relationships among important Big Data factors?

Direct-attached storage Critical Criteria:

Derive from Direct-attached storage strategies and find answers.

– Who is responsible for ensuring appropriate resources (time, people and money) are allocated to Big Data?

– What prevents me from making the changes I know will make me a more effective Big Data leader?

– How do mission and objectives affect the Big Data processes of our organization?

Data analysis Critical Criteria:

Paraphrase Data analysis strategies and integrate design thinking in Data analysis innovation.

– What is the difference between Data Analytics Data Analysis Data Mining and Data Science?

– What are some real time data analysis frameworks?

– How to Secure Big Data?

UC Berkeley Critical Criteria:

Air ideas re UC Berkeley risks and acquire concise UC Berkeley education.

– Do we cover the five essential competencies-Communication, Collaboration,Innovation, Adaptability, and Leadership that improve an organizations ability to leverage the new Big Data in a volatile global economy?

– What are the disruptive Big Data technologies that enable our organization to radically change our business processes?

– What are the success criteria that will indicate that Big Data objectives have been met and the benefits delivered?

H. Eugene Stanley Critical Criteria:

Check H. Eugene Stanley leadership and cater for concise H. Eugene Stanley education.

– Is Big Data dependent on the successful delivery of a current project?

– Does Big Data analysis isolate the fundamental causes of problems?

– Which individuals, teams or departments will be involved in Big Data?

Predictive analytics Critical Criteria:

Learn from Predictive analytics tactics and don’t overlook the obvious.

– What are direct examples that show predictive analytics to be highly reliable?

Bharatiya Janata Party Critical Criteria:

Judge Bharatiya Janata Party visions and correct Bharatiya Janata Party management by competencies.

– Have all basic functions of Big Data been defined?

Information Technology Critical Criteria:

Think about Information Technology governance and clarify ways to gain access to competitive Information Technology services.

– 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?

– Do the response plans address damage assessment, site restoration, payroll, Human Resources, information technology, and administrative support?

– Does your company have defined information technology risk performance metrics that are monitored and reported to management on a regular basis?

– If a survey was done with asking organizations; Is there a line between your information technology department and your information security department?

– How does new information technology come to be applied and diffused among firms?

– The difference between data/information and information technology (it)?

– What tools and technologies are needed for a custom Big Data project?

– When do you ask for help from Information Technology (IT)?

Fiber connector Critical Criteria:

Start Fiber connector tasks and prioritize challenges of Fiber connector.

– Do you monitor the effectiveness of your Big Data activities?

– Which Big Data goals are the most important?

Extract, transform, load Critical Criteria:

Ventilate your thoughts about Extract, transform, load adoptions and research ways can we become the Extract, transform, load company that would put us out of business.

– Will new equipment/products be required to facilitate Big Data delivery for example is new software needed?

– Do we monitor the Big Data decisions made and fine tune them as they evolve?

Data lake Critical Criteria:

Adapt Data lake engagements and oversee Data lake management by competencies.

– Can the data be obtained at no cost, or is there a charge associated with access?

– What data is being licensed, and how or where is it being made available?

– Did it get exported, when, where how will it be used (organizational)?

– What kinds of use are permitted/prohibited by the license?

– Data Warehouse versus Data Lake (Data Swamp)?

– How strict to be with dimensional design?

– Can we realistically store everything?

– What processes touched my data?

– How is this data represented?

– What Is Data Governance ?

– Where did it come from?

– What is geostatistics ?

– MapReduce: forgotten?

– How old is this data?

– What is our Big Data Strategy?

– What method to use ?

Data sharing Critical Criteria:

Boost Data sharing planning and pay attention to the small things.

– What will be the policies for data sharing and public access (including provisions for protection of privacy, confidentiality, security, intellectual property rights and other rights as appropriate)?

– What is (or would be) the added value of collaborating with other entities regarding data sharing across economic sectors?

– Does the project require agreements related to organizational data sharing that havent yet been created?

– What is (or would be) the added value of collaborating with other entities regarding data sharing in your sector?

– What transformations will be necessary to prepare data for preservation / data sharing?

– Do you regularly audit 3rd parties with whom you have data sharing agreements with?

– What would be needed to support collaboration on data sharing across economic sectors?

– What would be needed to support collaboration on data sharing in your sector?

– How can we improve data sharing methodologies between departments?

– Do you have any data sharing agreements with any 3rd parties?

– What are the usability implications of Big Data actions?

Cyber-physical system Critical Criteria:

Experiment with Cyber-physical system governance and look at it backwards.

– 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?

– Who sets the Big Data standards?

Bridgeport, Connecticut Critical Criteria:

Accelerate Bridgeport, Connecticut projects and secure Bridgeport, Connecticut creativity.

– How is the value delivered by Big Data being measured?

Small data Critical Criteria:

Guard Small data visions and adopt an insight outlook.

– How do we manage Big Data Knowledge Management (KM)?

Large Synoptic Survey Telescope Critical Criteria:

Systematize Large Synoptic Survey Telescope tactics and get answers.

– Are we Assessing Big Data and Risk?

Information privacy Critical Criteria:

Powwow over Information privacy outcomes and find out.

– What are the business goals Big Data is aiming to achieve?

– What about Big Data Analysis of results?

Big Data Critical Criteria:

Grade Big Data management and sort Big Data activities.

– What are the particular research needs of your organization on big data analytics that you find essential to adequately handle your data assets?

– Do you see the need to address the issues of data ownership or access to non-personal data (e.g. machine-generated data)?

– What type(s) of data does your organization find relevant but has not yet been able to exploit?

– Technology Drivers – What were the primary technical challenges your organization faced?

– In which way does big data create, or is expected to create, value in the organization?

– Does the in situ hardware have the computational capacity to support such algorithms?

– How will systems and methods evolve to remove Big Data solution weaknesses?

– What is the right technique for distributing domains across processors?

– What are the new developments that are included in Big Data solutions?

– How do we track the provenance of the derived data/information?

– What is tacit permission and approval, anyway?

– So how are managers using big data?

– What are some impacts of Big Data?

– Hash tables for term management?

– what is Different about Big Data?

– Find traffic bottlenecks ?

– What s limiting the task?

International Data Corporation Critical Criteria:

Closely inspect International Data Corporation management and research ways can we become the International Data Corporation company that would put us out of business.

– What are your key performance measures or indicators and in-process measures for the control and improvement of your Big Data processes?

– Is the Big Data organization completing tasks effectively and efficiently?

Storage area network Critical Criteria:

Huddle over Storage area network visions and ask what if.

– Will Big Data deliverables need to be tested and, if so, by whom?

– How do we keep improving Big Data?

Association for Computing Machinery Critical Criteria:

Pilot Association for Computing Machinery failures and get answers.

– How to deal with Big Data Changes?

Uwe Matzat Critical Criteria:

Disseminate Uwe Matzat failures and probe Uwe Matzat strategic alliances.

– 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?

DNA database Critical Criteria:

Weigh in on DNA database strategies and sort DNA database activities.

– To what extent does management recognize Big Data as a tool to increase the results?

– Who are the people involved in developing and implementing Big Data?

International development Critical Criteria:

Track International development outcomes and create International development explanations for all managers.

New York Times Critical Criteria:

Match New York Times planning and achieve a single New York Times view and bringing data together.

– What other jobs or tasks affect the performance of the steps in the Big Data process?

– Is Big Data Required?

TIME Magazine Critical Criteria:

Generalize TIME Magazine visions and look in other fields.

– How do you incorporate cycle time, productivity, cost control, and other efficiency and effectiveness factors into these Big Data processes?

– What may be the consequences for the performance of an organization if all stakeholders are not consulted regarding Big Data?

– Does Big Data systematically track and analyze outcomes for accountability and quality improvement?

Google Flu Trends Critical Criteria:

Accumulate Google Flu Trends risks and assess what counts with Google Flu Trends that we are not counting.

– What role does communication play in the success or failure of a Big Data project?

Indian general election, 2014, with mixed results Critical Criteria:

Mix Indian general election, 2014, with mixed results decisions and forecast involvement of future Indian general election, 2014, with mixed results projects in development.

– What potential environmental factors impact the Big Data effort?

– Who will provide the final approval of Big Data deliverables?

Application software Critical Criteria:

Concentrate on Application software tactics and catalog Application software activities.

– How do you manage the new access devices using their own new application software?

– Is the process effectively supported by the legacy application software?

– What is the purpose of Big Data in relation to the mission?

Data blending Critical Criteria:

Collaborate on Data blending management and revise understanding of Data blending architectures.

– How do we go about Comparing Big Data approaches/solutions?

Mobile device Critical Criteria:

Debate over Mobile device failures and do something to it.

– 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?

– Who will be responsible for making the decisions to include or exclude requested changes once Big Data is underway?

– Do several people in different organizational units assist with the Big Data process?

– Can your bi solution quickly locate dashboard on your mobile device?

– Will your product work from a mobile device?

Massive parallel processing Critical Criteria:

Scrutinze Massive parallel processing issues and probe using an integrated framework to make sure Massive parallel processing is getting what it needs.

– What business benefits will Big Data goals deliver if achieved?

– How would one define Big Data leadership?

Data science Critical Criteria:

Refer to Data science governance and ask questions.

– 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?

Government database Critical Criteria:

Grade Government database goals and question.

Internet of Things Critical Criteria:

Deliberate over Internet of Things adoptions and get going.

– New objects as the plethora of different device types, devices, gateways and IoT platforms need to be maintained because they are decentralized trust servers of the organizations using them. Management and governance enables organizations to meet both compliance and business requirements. Will your IAM system handle the increased number of relationships between users, devices, services and policies?

– When developing and capitalizing on IoT solutions, do we as owners consider the societal cost, systemic risk, and risk externality to avoid what may be called designer hubris. In other words, why add features when theyre not needed and contribute to the insecurity/fragility of the whole system?

– Sensors and the IoT add to the growing amount of monitoring data that is available to a wide range of users. How do we effectively analyze all of this data and ensure that meaningful and relevant data and decisions are made?

– Even the most security-conscious sectors may be unprepared for the security impact that IoT connected devices can have. So what can we do to protect IoT solutions?

– Does the internet of things need a scale-of-blame to help manage security incidents during the years until technology solves the security problem?

– How could FCC rules be changed to make it easier for small businesses to participate in the Internet of Things?

– How can sluggish supply chains be empowered by IoT to make them more transparent and responsive?

– Do you believe that additional principles and requirements are necessary for IoT applications?

– What type of training is required for users prior to receiving access to the information?

– Fog networking: how to connect every component of the fog at large scale, such as IoT?

– How will it help your business compete in the context of Digital Marketing?

– How would a society benefit from an AI that passes the Turing test?

– How can the RoI of IoT applications be assessed and measured?

– Which user group(s) will have access to the system?

– What information is shared and for what purpose?

– How is the information transmitted or disclosed?

– What are the best Internet of Things use cases?

– From whom is the information collected?

– How can we drive IoT at every level?

– Why Is IoT Important?

Viktor Mayer-Schönberger Critical Criteria:

Boost Viktor Mayer-Schönberger governance and question.

– Are there any disadvantages to implementing Big Data? There might be some that are less obvious?

– Are there Big Data problems defined?

Unstructured data Critical Criteria:

Categorize Unstructured data outcomes and cater for concise Unstructured data education.

– What tools do you consider particularly important to handle unstructured data expressed in (a) natural language(s)?

– Does your organization have the right tools to handle unstructured data expressed in (a) natural language(s)?

– Why should we adopt a Big Data framework?

Digital footprint Critical Criteria:

Inquire about Digital footprint adoptions and figure out ways to motivate other Digital footprint users.

National Security Agency Critical Criteria:

Adapt National Security Agency visions and look for lots of ideas.

– How likely is the current Big Data plan to come in on schedule or on budget?

Consumer privacy Critical Criteria:

Own Consumer privacy issues and adopt an insight outlook.

Business informatics Critical Criteria:

Conceptualize Business informatics failures and intervene in Business informatics processes and leadership.

– Risk factors: what are the characteristics of Big Data that make it risky?

– How does the organization define, manage, and improve its Big Data processes?

– What are the long-term Big Data goals?

SAP AG Critical Criteria:

Investigate SAP AG issues and grade techniques for implementing SAP AG controls.

– How do we go about Securing Big Data?


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 | http://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.

External links:

To address the criteria in this checklist, these selected resources are provided for sources of further research and information:

Big Data External links:

Pepperdata: DevOps for Big Data

Business Intelligence and Big Data Analytics Software

Data curation External links:

CiteSeerX — Data Curation at Scale: The Data Tamer System

Title: Data Curation APIs – arXiv

Dirty data External links:

Dirty Data in your CRM? What’s the real cost?

What is dirty data? – Definition from WhatIs.com

What is Dirty Data? – Definition from Techopedia

Business Operations External links:

Business Operations | Department of Energy

UofL Business Operations

What is Business Operations – Answers.com

Barack Obama presidential campaign, 2012 External links:

Barack Obama presidential campaign, 2012 – revolvy.com
https://www.revolvy.com/topic/Barack Obama presidential campaign, 2012

Machine intelligence External links:

Artists and Machine Intelligence – AMI

finbox.io | Fundamental Investing + Machine Intelligence

Machine Intelligence for analytics – Hyper Anna

Software AG External links:

Software AG – Empower

[PDF]GlobeRanger Corporation v. Software AG – United …

Lab Animal Management Software – a-tune software AG

Cluster analysis External links:

Cluster Analysis With JMP – YouTube

Chapter 9: Cluster analysis Flashcards | Quizlet

Oracle NoSQL Database External links:

oracle nosql database Jobs – Monster.com

The Oracle NoSQL Database – CERN Document Server

Moore’s Law External links:

Moore’s Law and The Secret World Of Ones And Zeroes

Zeno | Scaling Beyond Moore’s Law

Factor analysis External links:

Factor Analysis – Bureau of Labor Statistics

Factor Analysis – SPSS (part 1) – YouTube

[PDF]Confirmatory Factor Analysis using Amos, LISREL, …

Ulf-Dietrich Reips External links:

Ulf-Dietrich Reips – Hindawi Publishing Corporation

Ulf-Dietrich Reips | Colorado PROFILES

Ulf-Dietrich Reips

Data analysis External links:

Regional Data Warehouse/Data Analysis Site

AnswerMiner – Data analysis made easy

Data Analysis Examples – IDRE Stats

UC Berkeley External links:

UC Berkeley Career Center | Career Center

Home | UC Berkeley Extension

Homecoming | UC Berkeley

H. Eugene Stanley External links:

H. Eugene Stanley – The Mathematics Genealogy Project

H. Eugene Stanley – Panjury, A Social Review Site

H. Eugene Stanley | Boston University Physics

Predictive analytics External links:

PredictX Homepage – Predictive Analytics and …

Tookitaki – Predictive Analytics Platform

Predictive Analytics Solutions & Automated Big Data

Bharatiya Janata Party External links:

BJP Logo Images | Bharatiya Janata Party Logo

Bharatiya Janata Party (BJP) – Home | Facebook

Information Technology External links:

Rebelmail | UNLV Office of Information Technology (OIT)

OHIO: Office of Information Technology |About Email

SOLAR | Division of Information Technology

Fiber connector External links:

SC/PC Fiber Connector: Multimode – Thorlabs

Extract, transform, load External links:

ETL Tools (Extract, Transform, Load) – Information Builders

Data lake External links:

Data Lake | Microsoft Azure

How to Design a Successful Data Lake – Knowledgent

Data Lake Analytics | Microsoft Azure

Data sharing External links:


LETTR | Proven Data Sharing. Any RMS.

[DOC]Data Sharing MOU – Draft v3.docx – Office of the CISO

Cyber-physical system External links:

Cyber-Physical Systems Security | Udacity

EuroCPS | Cyber-Physical Systems

Bridgeport, Connecticut External links:

Bridgeport, CT – Bridgeport, Connecticut Map & …

Small data External links:

Design issues – Sending small data segments over TCP …

LEGRAND Small Data Box,Ivory,Boxes – …

What are the Small Data Plans? | Verizon Community

Large Synoptic Survey Telescope External links:

BNL | Large Synoptic Survey Telescope (LSST)

Information privacy External links:

https://www.hsscreeningreg.com/upload/IH Privacy Practices 3-2015.pdf

Information Privacy | Citizens Bank

Big Data External links:

Pepperdata: DevOps for Big Data

Business Intelligence and Big Data Analytics Software

Storage area network External links:

Storage Area Network | Washington Technology Solutions

EMC SAN Tutorials for the Beginners | Storage Area Network

Support for booting from a Storage Area Network (SAN)

Association for Computing Machinery External links:

Association for Computing Machinery – Official Site

Association for Computing Machinery | Climate for Change

About ACM – Association for Computing Machinery

DNA database External links:

TxDPS – Statewide CODIS DNA Database Program, Overview

DNA Database FAQs | Colorado Bureau of Investigation


International development External links:

Economic and International Development – El Paso, Texas

U.S. Agency for International Development

Community Colleges for International Development …

New York Times External links:

Today’s Paper – The New York Times

The School of The New York Times

TIME Magazine External links:

Is the ‘Liar in Chief’ Time Magazine Cover Real? – Snopes.com

Break Time Magazine – City of Chandler, Arizona

Donald Trump Time Magazine Covers: See Them All | Time.com

Google Flu Trends External links:

Google Flu Trends Overview – YouTube

Google Flu Trends Failure Shows Drawbacks of Big Data | Time

Google Flu Trends|Centers for Disease Control and …

Application software External links:

Application software – ScienceDaily

Application software (Book, 1990) [WorldCat.org]

Chapter 3 – Application Software

Data blending External links:

data blending | Drawing with Numbers

Data blending is a process that is gaining attention among analysts and analytic companies due to the fact that it is a quick and straightforward method used to extract value from multiple data sources.
http://Reference: datawatch.com/what-is-data-blending

Data Blending For Dummies – Free Computer, …

Mobile device External links:

Restart the Apple Mobile Device Service (AMDS) on Windows

Data science External links:

UW Data Science Master’s Program – Seattle

DataScience.com | Enterprise Data Science Platform …

A New Path to Your Success Via Human Data Science – IQVIA

Internet of Things External links:

AT&T M2X: Build solutions for the Internet of Things

Physical Web Touchpoint Browsing for the Internet of Things

Unstructured data External links:

Isilon Scale-Out NAS Storage-Unstructured Data | Dell …

Digital footprint External links:

Digital Footprint | Wisconsin Department of Public Instruction

What’s in Your Digital Footprint? – YouTube

10 steps to erase your digital footprint | ZDNet

National Security Agency External links:

Internships / National Security Agency (NSA)

National Security Agency for Intelligence Careers

Consumer privacy External links:

IBERIABANK | Consumer Privacy Notice

Consumer Privacy Pledge | Privacy Policies | U.S. Bank

U.S. Consumer Privacy Notice from Bank of America

Business informatics External links:

Healthcare Business Informatics | Business …

Bachelor of Science in Business Informatics

SAP AG External links:

User Management, SAP AG – Ohio

User Management, SAP AG – haascnc.com

User Management, SAP AG – Welcome! Please Sign in.