What is involved in Data Quality
Find out what the related areas are that Data Quality 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 Data Quality thinking-frame.
How far is your company on its Data Quality journey?
Take this short survey to gauge your organization’s progress toward Data Quality 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 Data Quality related domains to cover and 322 essential critical questions to check off in that domain.
The following domains are covered:
Data Quality, Data loss, Body area network, Mainframe computer, Business operations, Quantitative data, Data Analysis, Data wrangling, Cross tabulation, ISO 9000, Wearable technology, Data scrubbing, Data corruption, Data integrity, Bounds checking, Business intelligence, Data pre-processing, Customer service, Random Forest, Data integration, United States Postal Service, Data visualization, Analysis paralysis, Master data management, Support Vector Machine, Data validation, Measurement error, Synthetic measure, Information privacy, Computer data storage, Data consistency, Database normalization, Record linkage, Analytical quality control, Data editing, Qualitative data, Business rules engine, Data fusion, Information systems, Shadow system, Data warehousing, Kristo Ivanov, Supply chain management, Linked Open Data, Data warehouse, Customer relationship management, Electronic health record, Quality Assurance, Data compression, Application software, Data mining, Data migration, Data security, Data scraping, Data profiling, Database administration, Health data, Data cleansing, Information quality, Data reduction, Data farming, Data curation, ISO 8000:
Data Quality Critical Criteria:
Collaborate on Data Quality strategies and improve Data Quality service perception.
– What percentage of eligibles are not included on the data file or what percentage of those mandated are not compliant?
– What issues should you consider when determining whether existing data may possibly serve as a source of information?
– At all levels at which data are aggregated, are procedures in place to reconcile discrepancies in reports?
– Can a decision (or estimate) be made with the desired level of certainty, given the quality of the data?
– Do you have policies and procedures which direct your data collection process?
– View before and after results : did the integration go the way you thought?
– What is the proportion of missing values for each field?
– Does the database contain what you think it contains?
– Do you clearly document your data collection methods?
– Are people involved in the development identified?
– Are you measuring what you intended to measure?
– You get a data set. what do you do with it?
– Scan individual records are there gaps?
– Have Data Quality objectives been met?
– What is the goal of data management?
– How does the data enter the system?
– What do we mean by Data Quality ?
– Are the attributes independent?
– Where is the domain expertise?
– How big should the sample be?
Data loss Critical Criteria:
Guard Data loss planning and innovate what needs to be done with Data loss.
– The goal of a disaster recovery plan is to minimize the costs resulting from losses of, or damages to, the resources or capabilities of your IT facilities. The success of any disaster recovery plan depends a great deal on being able to determine the risks associated with data loss. What is the impact to our business if the data is lost?
– Does the tool in use have the ability to integrate with Active Directory or sync directory on a scheduled basis, or do look-ups within a multi-domain forest in the sub-100-millisecond range?
– How do your measurements capture actionable Data Quality information for use in exceeding your customers expectations and securing your customers engagement?
– Does the tool we use have the ability to deep inspect a large number of file types for content matches (e.g., .pdf; .docx; .txt; .html; .xlsx, etc.)?
– Do you have guidelines or a policy in place defining the parameters for employees working on files outside of the office?
– Does the tool we use provide a task-based help function with recommendation settings for mail configuration options?
– Does the tool we use provide the ability for system-generated notification to arbitrator of email disposition?
– What is a standard data flow, and what should be the source and destination of the identified data?
– Does the tool we use provide the ability to prevent the forwarding of secure email?
– Do we ask the question, What could go wrong and what is the worst that can happen?
– Other than port blocking what sort of security does our host provider provide?
– Where does your sensitive data reside, both internally and with third parties?
– Do all computers have up-to-date anti-spam protection?
– Downtime and Data Loss: How much Can You Afford?
– Do we utilize security awareness training?
– Who are the data loss prevention vendors?
– Who is the System Administrator?
– Why Data Loss Prevention?
– Where is your data going?
– What Causes Data Loss?
Body area network Critical Criteria:
Accommodate Body area network goals and innovate what needs to be done with Body area network.
– what is the best design framework for Data Quality organization now that, in a post industrial-age if the top-down, command and control model is no longer relevant?
– What is our formula for success in Data Quality ?
Mainframe computer Critical Criteria:
Have a meeting on Mainframe computer tasks and secure Mainframe computer creativity.
– Who will be responsible for documenting the Data Quality requirements in detail?
– Are assumptions made in Data Quality stated explicitly?
– What are specific Data Quality Rules to follow?
Business operations Critical Criteria:
Administer Business operations adoptions and don’t overlook the obvious.
– In the case of a Data Quality project, the criteria for the audit derive from implementation objectives. an audit of a Data Quality project involves assessing whether the recommendations outlined for implementation have been met. in other words, can we track that any Data Quality project is implemented as planned, and is it working?
– How can we incorporate support to ensure safe and effective use of Data Quality into the services that we provide?
– 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?
– Will new equipment/products be required to facilitate Data Quality delivery for example is new software needed?
Quantitative data Critical Criteria:
Discourse Quantitative data governance and get answers.
– Is there a Data Quality Communication plan covering who needs to get what information when?
– What are your most important goals for the strategic Data Quality objectives?
Data Analysis Critical Criteria:
See the value of Data Analysis leadership and ask questions.
– Does Data Quality create potential expectations in other areas that need to be recognized and considered?
– What is the difference between Data Analytics Data Analysis Data Mining and Data Science?
– Is the Data Quality organization completing tasks effectively and efficiently?
– What are some real time data analysis frameworks?
Data wrangling Critical Criteria:
Face Data wrangling tactics and grade techniques for implementing Data wrangling controls.
– What sources do you use to gather information for a Data Quality study?
– What is the purpose of Data Quality in relation to the mission?
Cross tabulation Critical Criteria:
Merge Cross tabulation leadership and simulate teachings and consultations on quality process improvement of Cross tabulation.
– Think about the functions involved in your Data Quality project. what processes flow from these functions?
– In a project to restructure Data Quality outcomes, which stakeholders would you involve?
– How do we manage Data Quality Knowledge Management (KM)?
ISO 9000 Critical Criteria:
Discuss ISO 9000 quality and sort ISO 9000 activities.
– What management system can we use to leverage the Data Quality experience, ideas, and concerns of the people closest to the work to be done?
– What process management and improvement tools are we using PDSA/PDCA, ISO 9000, Lean, Balanced Scorecard, Six Sigma, something else?
– Do not ISO 9000 and CMM certifications loose their meaning when applied to the software industry?
– Is Data Quality dependent on the successful delivery of a current project?
– Is Supporting Data Quality documentation required?
Wearable technology Critical Criteria:
Check Wearable technology adoptions and change contexts.
– In what ways are Data Quality vendors and us interacting to ensure safe and effective use?
– What vendors make products that address the Data Quality needs?
– What threat is Data Quality addressing?
Data scrubbing Critical Criteria:
Explore Data scrubbing failures and look at the big picture.
– What are your current levels and trends in key measures or indicators of Data Quality 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 is the value delivered by Data Quality being measured?
Data corruption Critical Criteria:
Dissect Data corruption tasks and intervene in Data corruption processes and leadership.
– Do several people in different organizational units assist with the Data Quality process?
– Think of your Data Quality project. what are the main functions?
– What is our Data Quality Strategy?
Data integrity Critical Criteria:
Coach on Data integrity risks and balance specific methods for improving Data integrity results.
– Are there any easy-to-implement alternatives to Data Quality? Sometimes other solutions are available that do not require the cost implications of a full-blown project?
– Integrity/availability/confidentiality: How are data integrity, availability, and confidentiality maintained in the cloud?
– Are we making progress? and are we making progress as Data Quality leaders?
– Data Integrity, Is it SAP created?
– Can we rely on the Data Integrity?
– What are current Data Quality Paradigms?
Bounds checking Critical Criteria:
Think carefully about Bounds checking strategies and work towards be a leading Bounds checking expert.
– Do we monitor the Data Quality decisions made and fine tune them as they evolve?
– How to Secure Data Quality?
Business intelligence Critical Criteria:
Map Business intelligence planning and question.
– Does your BI solution honor distinctions with dashboards that automatically authenticate and provide the appropriate level of detail based on a users privileges to the data source?
– Does your bi solution require weeks of training before new users can analyze data and publish dashboards?
– What strategies will we pursue to ensure the success of the business intelligence competency center?
– What does a typical data warehouse and business intelligence organizational structure look like?
– Which core Oracle Business Intelligence or Big Data Analytics products are used in your solution?
– What are the key skills a Business Intelligence Analyst should have?
– What social media dashboards are available and how do they compare?
– Number of data sources that can be simultaneously accessed?
– What are the most efficient ways to create the models?
– What are the pillar concepts of business intelligence?
– Are there any on demand analytics tools in the cloud?
– How is Business Intelligence related to CRM?
– What are typical data-mining applications?
– Do you offer formal user training?
– Do you support video integration?
– What is your annual maintenance?
– What is your products direction?
– Does your system provide apis?
– Using dashboard functions?
– Why BI?
Data pre-processing Critical Criteria:
Recall Data pre-processing outcomes and find out what it really means.
– Think about the kind of project structure that would be appropriate for your Data Quality project. should it be formal and complex, or can it be less formal and relatively simple?
– Do those selected for the Data Quality team have a good general understanding of what Data Quality is all about?
Customer service Critical Criteria:
Weigh in on Customer service results and intervene in Customer service processes and leadership.
– How do you feel about having to self-disclose personal information (e.g., social security or drivers license number or birth dates) in a Customer Service environment. do your views or preferences might affect the way that you provide service to others?
– How would you as an individual feel if you had made what you felt was a valid complaint, and the organization/company dismissed it as being of no concern and not worth sorting out?
– Why is Customer Service and helpdesks so undervalued given that this is a core part of branding and growth i e The head of the Service Desk is not a CxO level title?
– What element(s) of interpersonal communications do you believe are the most important in a Customer Service environment?
– What is the average supervisor to Customer Service representative ratio for a fixed route call center?
– Since you do not have a relationship with a new client, what will you do to get off to a solid start?
– What specific functionality is our Customer Service Management system required to provide?
– In what methods are you contacted by customers (i.e., e-mail, fax, phone, in-person)?
– How important is real time for providing social media Customer Service?
– Ow often do you hear people complaining about poor Customer Service?
– How do we know if we are measuring or meeting our customer s needs?
– CRM and Customer Service: Strategic Asset or Corporate Overhead?
– Do we think that we are on the right track with our responses?
– Do we respond appropriately to Customer Service complaints?
– What are the pros and cons of outsourcing Customer Service?
– Why should the customer be interested in your problems?
– What is required in a Customer Service job?
– Do we track problems, so we can improve?
– What Does Marketing Have To Do With It?
– Who is the customer to us?
Random Forest Critical Criteria:
Revitalize Random Forest decisions and observe effective Random Forest.
– Why is it important to have senior management support for a Data Quality project?
– Which Data Quality goals are the most important?
– Is the scope of Data Quality defined?
Data integration Critical Criteria:
Adapt Data integration quality and achieve a single Data integration view and bringing data together.
– In which area(s) do data integration and BI, as part of Fusion Middleware, help our IT infrastructure?
– Which Oracle Data Integration products are used in your solution?
– Have all basic functions of Data Quality been defined?
United States Postal Service Critical Criteria:
Troubleshoot United States Postal Service decisions and test out new things.
– What are the success criteria that will indicate that Data Quality objectives have been met and the benefits delivered?
– Meeting the challenge: are missed Data Quality opportunities costing us money?
Data visualization Critical Criteria:
See the value of Data visualization governance and get going.
– What are the best places schools to study data visualization information design or information architecture?
– Who is the main stakeholder, with ultimate responsibility for driving Data Quality forward?
Analysis paralysis Critical Criteria:
Gauge Analysis paralysis planning and sort Analysis paralysis activities.
– How do you incorporate cycle time, productivity, cost control, and other efficiency and effectiveness factors into these Data Quality processes?
– What are the Essentials of Internal Data Quality Management?
– Are there Data Quality Models?
Master data management Critical Criteria:
Map Master data management adoptions and separate what are the business goals Master data management is aiming to achieve.
– What are our best practices for minimizing Data Quality project risk, while demonstrating incremental value and quick wins throughout the Data Quality project lifecycle?
– What are some of the master data management architecture patterns?
– Why should we use or invest in a Master Data Management product?
– What Is Master Data Management?
Support Vector Machine Critical Criteria:
Guide Support Vector Machine engagements and define what our big hairy audacious Support Vector Machine goal is.
– Do Data Quality rules make a reasonable demand on a users capabilities?
Data validation Critical Criteria:
Revitalize Data validation quality and find out what it really means.
– What are the top 3 things at the forefront of our Data Quality agendas for the next 3 years?
Measurement error Critical Criteria:
Consider Measurement error projects and mentor Measurement error customer orientation.
– Risk factors: what are the characteristics of Data Quality that make it risky?
– How do we go about Comparing Data Quality approaches/solutions?
Synthetic measure Critical Criteria:
Talk about Synthetic measure planning and observe effective Synthetic measure.
– How do we make it meaningful in connecting Data Quality with what users do day-to-day?
– What are internal and external Data Quality relations?
– What will drive Data Quality change?
Information privacy Critical Criteria:
Nurse Information privacy outcomes and balance specific methods for improving Information privacy results.
– How will we insure seamless interoperability of Data Quality moving forward?
– What business benefits will Data Quality goals deliver if achieved?
Computer data storage Critical Criteria:
Reason over Computer data storage engagements and overcome Computer data storage skills and management ineffectiveness.
– Have you identified your Data Quality key performance indicators?
– Can we do Data Quality without complex (expensive) analysis?
– Will Data Quality deliverables need to be tested and, if so, by whom?
Data consistency Critical Criteria:
Have a round table over Data consistency failures and intervene in Data consistency processes and leadership.
– Will Data Quality have an impact on current business continuity, disaster recovery processes and/or infrastructure?
– Do you monitor the effectiveness of your Data Quality activities?
– How do we maintain Data Qualitys Integrity?
Database normalization Critical Criteria:
Discourse Database normalization tactics and ask questions.
– What is the total cost related to deploying Data Quality, including any consulting or professional services?
– Why should we adopt a Data Quality framework?
Record linkage Critical Criteria:
Dissect Record linkage risks and finalize the present value of growth of Record linkage.
– A compounding model resolution with available relevant data can often provide insight towards a solution methodology; which Data Quality models, tools and techniques are necessary?
– What are the record-keeping requirements of Data Quality activities?
– Are there recognized Data Quality problems?
Analytical quality control Critical Criteria:
Examine Analytical quality control engagements and test out new things.
– Are there any disadvantages to implementing Data Quality? There might be some that are less obvious?
– To what extent does management recognize Data Quality as a tool to increase the results?
Data editing Critical Criteria:
Talk about Data editing issues and devote time assessing Data editing and its risk.
– When a Data Quality manager recognizes a problem, what options are available?
Qualitative data Critical Criteria:
Deliberate Qualitative data failures and cater for concise Qualitative data education.
– How can skill-level changes improve Data Quality?
Business rules engine Critical Criteria:
Sort Business rules engine quality and reinforce and communicate particularly sensitive Business rules engine decisions.
– What knowledge, skills and characteristics mark a good Data Quality project manager?
– How will you measure your Data Quality effectiveness?
Data fusion Critical Criteria:
Scan Data fusion leadership and secure Data fusion creativity.
– What new requirements emerge in terms of information processing/management to make physical and virtual world data fusion possible?
– Why are Data Quality skills important?
Information systems Critical Criteria:
Own Information systems tactics and get the big picture.
– Have we developed a continuous monitoring strategy for the information systems (including monitoring of security control effectiveness for system-specific, hybrid, and common controls) that reflects the organizational Risk Management strategy and organizational commitment to protecting critical missions and business functions?
– On what terms should a manager of information systems evolution and maintenance provide service and support to the customers of information systems evolution and maintenance?
– Has your organization conducted a cyber risk or vulnerability assessment of its information systems, control systems, and other networked systems?
– Are information security events and weaknesses associated with information systems communicated in a manner to allow timely corrective action to be taken?
– Would an information systems (is) group with more knowledge about a data production process produce better quality data for data consumers?
– What does the customer get from the information systems performance, and on what does that depend, and when?
– What are the principal business applications (i.e. information systems available from staff PC desktops)?
– Why Learn About Security, Privacy, and Ethical Issues in Information Systems and the Internet?
– What are information systems, and who are the stakeholders in the information systems game?
– What role does communication play in the success or failure of a Data Quality project?
– What tools and technologies are needed for a custom Data Quality project?
– Is unauthorized access to information held in information systems prevented?
– Is authorized user access to information systems ensured?
– How are our information systems developed ?
– Is security an integral part of information systems?
– Is Data Quality Required?
Shadow system Critical Criteria:
Confer over Shadow system leadership and diversify by understanding risks and leveraging Shadow system.
– Who sets the Data Quality standards?
Data warehousing Critical Criteria:
Deduce Data warehousing planning and devise Data warehousing key steps.
– Consider your own Data Quality project. what types of organizational problems do you think might be causing or affecting your problem, based on the work done so far?
– What is the difference between Enterprise Information Management and Data Warehousing?
Kristo Ivanov Critical Criteria:
Scrutinze Kristo Ivanov results and know what your objective is.
Supply chain management Critical Criteria:
Talk about Supply chain management risks and ask questions.
– How do supply chain management systems coordinate planning, production, and logistics with suppliers?
– What makes cloud computing well suited for supply chain management applications?
– What is TESCM tax efficient supply chain management?
Linked Open Data Critical Criteria:
Canvass Linked Open Data adoptions and define what do we need to start doing with Linked Open Data.
– Does Data Quality systematically track and analyze outcomes for accountability and quality improvement?
Data warehouse Critical Criteria:
Unify Data warehouse governance and secure Data warehouse creativity.
– What tier data server has been identified for the storage of decision support data contained in a data warehouse?
– Do we need an enterprise data warehouse, a Data Lake, or both as part of our overall data architecture?
– Does big data threaten the traditional data warehouse business intelligence model stack?
– Is data warehouseing necessary for our business intelligence service?
– Is Data Warehouseing necessary for a business intelligence service?
– What is the difference between a database and data warehouse?
– What is the purpose of data warehouses and data marts?
– What are alternatives to building a data warehouse?
– Do we offer a good introduction to data warehouse?
– Data Warehouse versus Data Lake (Data Swamp)?
– Do you still need a data warehouse?
– Centralized data warehouse?
Customer relationship management Critical Criteria:
Exchange ideas about Customer relationship management management and budget the knowledge transfer for any interested in Customer relationship management.
– How to ensure high data availability in mobile computing environment where frequent disconnections may occur because the clients and server may be weakly connected?
– How can we truly understand and predict our customers needs to the point where we can design products and services that suit their needs?
– Are there any restrictions within the standard support and maintenance agreement on the number of staff that can request support?
– Is a significant amount of your time taken up communicating with existing clients to resolve issues they are having?
– Will the Exchange provide the call volumes and average handle time for the Tier 1 and Tier II calls?
– It is often said that CRMs complexity is due to its quantity of functions. How do we handle this?
– What is our approach to Risk Management in the specific area of social media?
– Have you integrated your call center telephony to your crm application?
– Can visitors/customers opt out of sharing their personal information?
– How is the emergence of new CRM solutions offered factored in?
– Will the customer have access to a development environment?
– What are the key application components of our CRM system?
– Do you have any monthly volumes of Outbound Calls?
– What type of information may be released to whom?
– Is the user a member of an existing organization?
– Can the current CRM track calls by call type?
– How much e-mail should be routed?
– Is the memory load acceptable?
– Is there a known outage?
– When do they buy?
Electronic health record Critical Criteria:
Check Electronic health record issues and get out your magnifying glass.
– Record-keeping requirements flow from the records needed as inputs, outputs, controls and for transformation of a Data Quality process. ask yourself: are the records needed as inputs to the Data Quality process available?
– How can we improve Data Quality?
Quality Assurance Critical Criteria:
Chart Quality Assurance goals and triple focus on important concepts of Quality Assurance relationship management.
– Is the Quality Assurance function recognized to be different from implicit and continuous quality control during fabrication, in that it is discrete, explicit following production, and ignores the sequence or nature of the fabrication steps or processes?
– Does the quality plan prescribe the type(s) of Quality Assurance activities (such as reviews, audits, inspections) to be performed to achieve the objectives of the quality plan?
– What is your Quality Assurance process to ensure that the large volumes of data gathered in the monitoring process are handled efficiently?
– How does the company manage the design and delivery of products and services that promise a high level of customer satisfaction?
– Is software Quality Assurance done by an independently reporting agency representing the interests of the eventual user?
– Have records center personnel received training on the records management aspects of the Quality Assurance program?
– Has the board demonstrated a willingness to provide appropriate resources to Quality Assurance programs?
– Are records maintained in fireproof enclosures that are sealed to prevent moisture intrusion?
– Is there a chance the planning/registering activity may lose ownership of any items suddenly?
– How do you assure that repairs and/or preventative maintenance were completed?
– What is/are the major contractors qa plan(s), and is it/are they implemented?
– Does the Quality Assurance record center contain the selected documents?
– How does improper/incomplete documentation affect disciplinary actions?
– Is the system/component adequately labeled for ease of operation?
– How do we Identify specific Data Quality investment and emerging trends?
– Can the test data be processed in a timely manner?
– Who is responsible for overseeing this process?
– Who provides training for any new protocol?
– How are complaints tracked?
– What is the qa plan?
Data compression Critical Criteria:
Add value to Data compression adoptions and proactively manage Data compression risks.
– Marketing budgets are tighter, consumers are more skeptical, and social media has changed forever the way we talk about Data Quality. How do we gain traction?
– What are the disruptive Data Quality technologies that enable our organization to radically change our business processes?
Application software Critical Criteria:
Pay attention to Application software failures and remodel and develop an effective Application software strategy.
– How do you manage the new access devices using their own new application software?
– Is the process effectively supported by the legacy application software?
– Is there any existing Data Quality governance structure?
Data mining Critical Criteria:
Discourse Data mining management and pioneer acquisition of Data mining systems.
– How do you determine the key elements that affect Data Quality workforce satisfaction? how are these elements determined for different workforce groups and segments?
– 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)?
– 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?
– What programs do we have to teach data mining?
Data migration Critical Criteria:
Add value to Data migration management and raise human resource and employment practices for Data migration.
– The process of conducting a data migration involves access to both the legacy source and the target source. The target source must be configured according to requirements. If youre using a contractor and provided that the contractor is under strict confidentiality, do you permit the contractor to house copies of your source data during the implementation?
– Data migration does our organization have a resource (dba, etc) who understands your current database structure and who can extract data into a pre-defined file and format?
– With the traditional approach to data migration, delays due to specification changes are an expected (and accepted) part of most projects. does this sound familiar?
– Data migration are there any external users accounts existing and will these user accounts need to be migrated to the new lms?
– Who will be responsible for deciding whether Data Quality goes ahead or not after the initial investigations?
– Are there data migration issues?
Data security Critical Criteria:
Judge Data security goals and look for lots of ideas.
– Does the cloud solution offer equal or greater data security capabilities than those provided by your organizations data center?
– What are the minimum data security requirements for a database containing personal financial transaction records?
– What tools do you use once you have decided on a Data Quality strategy and more importantly how do you choose?
– Do these concerns about data security negate the value of storage-as-a-service in the cloud?
– What are the challenges related to cloud computing data security?
– So, what should you do to mitigate these risks to data security?
– Does it contain data security obligations?
– What is Data Security at Physical Layer?
– What is Data Security at Network Layer?
– How will you manage data security?
Data scraping Critical Criteria:
Pilot Data scraping visions and finalize the present value of growth of Data scraping.
– Which customers cant participate in our Data Quality domain because they lack skills, wealth, or convenient access to existing solutions?
– What is Effective Data Quality?
Data profiling Critical Criteria:
Model after Data profiling visions and achieve a single Data profiling view and bringing data together.
– Think about the people you identified for your Data Quality 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 is the source of the strategies for Data Quality strengthening and reform?
– Do we have past Data Quality Successes?
– Do we do data profiling?
Database administration Critical Criteria:
Recall Database administration failures and prioritize challenges of Database administration.
– Rapid application development (rad) techniques have been around for about two decades now and have been used with varying degrees of success. sometimes rad is required for certain projects. but rad can be bad for database design. why?
– Disaster recovery planning, also called contingency planning, is the process of preparing your organizations assets and operations in case of a disaster. but what do we define as a disaster?
– What are our disaster recovery goal prioritazations? Do we want to get the system up as quickly as possible?
– Who should be called in case of Disaster Recovery?
Health data Critical Criteria:
Group Health data leadership and get the big picture.
– Do the Data Quality decisions we make today help people and the planet tomorrow?
– Is a Data Quality Team Work effort in place?
Data cleansing Critical Criteria:
Define Data cleansing visions and balance specific methods for improving Data cleansing results.
– How can you negotiate Data Quality successfully with a stubborn boss, an irate client, or a deceitful coworker?
– Is there an ongoing data cleansing procedure to look for rot (redundant, obsolete, trivial content)?
Information quality Critical Criteria:
Accommodate Information quality governance and describe which business rules are needed as Information quality interface.
– Do we all define Data Quality in the same way?
Data reduction Critical Criteria:
Revitalize Data reduction issues and adjust implementation of Data reduction.
– Who will provide the final approval of Data Quality deliverables?
Data farming Critical Criteria:
Disseminate Data farming governance and maintain Data farming for success.
– How likely is the current Data Quality plan to come in on schedule or on budget?
– Is maximizing Data Quality protection the same as minimizing Data Quality loss?
Data curation Critical Criteria:
Steer Data curation issues and pay attention to the small things.
– What are the Key enablers to make this Data Quality move?
– How do we keep improving Data Quality?
ISO 8000 Critical Criteria:
Exchange ideas about ISO 8000 tactics and use obstacles to break out of ruts.
– Does Data Quality 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?
– How do we know that any Data Quality analysis is complete and comprehensive?
This quick readiness checklist is a selected resource to help you move forward. Learn more about how to achieve comprehensive insights with the Data Quality 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.
To address the criteria in this checklist, these selected resources are provided for sources of further research and information:
Data Quality External links:
CLIENTSFirst Consulting – Data Quality Consultants | …
CWS Data Quality Portal
Data loss External links:
How to: New DLP (data loss prevention) policy template
Technical Overview of DLP (data loss prevention) in Exchange
Data Loss Prevention & Protection | Symantec
Body area network External links:
IEEE releases new standard for body area network | ZDNet
Mainframe computer External links:
Mainframe Computer Operator Jobs – Apply Now | CareerBuilder
Business operations External links:
Business Operations Manager Jobs, Employment | Indeed.com
UofL Business Operations
Business Operations Center
Quantitative data External links:
What’s Quantitative Data? | DataWorks – AdAge
Data Analysis External links:
How to Write a Data Analysis | Bizfluent
Data Analysis – Illinois State Board of Education
Learn Data Analysis – Intro to Data Analysis | Udacity
Data wrangling External links:
Big Data: Data Wrangling – Old Dominion University
Cross tabulation External links:
R: Cross Tabulation and Table Creation – ETH Z
Cross Tabulation – FREE download Cross Tabulation
Cross tabulation – myDBR
ISO 9000 External links:
Benefits of ISO 9000 – Perry Johnson Registrars, Inc.
What is ISO 9000? – Definition from WhatIs.com
List of ISO 9000 Registered Companies – 9000 Store
Wearable technology External links:
Wearable Technology – T-Mobile
Infographic: The History of Wearable Technology
Wearables: Wearable Technology and Devices – Fossil
Data corruption External links:
Data corruption – UFOpaedia
How to Recover from Outlook Data Corruption: 6 Steps
Data integrity External links:
[PDF]IMPROVING TITLE I DATA INTEGRITY FOR …
Data Integrity Specialist Jobs, Employment | Indeed.com
Data Integrity Services SM – Experian
Bounds checking External links:
[PDF]Scalable and Efﬁcient Bounds Checking for Large …
7.3: No Bounds Checking in C++ Flashcards | Quizlet
Bounds Checking – Central Connecticut State University
Business intelligence External links:
Oracle Business Intelligence – RCI
Mortgage Business Intelligence Software :: Motivity Solutions
Business Intelligence and Big Data Analytics Software
Customer service External links:
Capital One Customer Service | Contact Us
CW Title – customer service
Customer Service | Progressive
Random Forest External links:
GCD.5 – Random Forest | STAT 897D
Data integration External links:
Data Integration Jobs – Apply Now | CareerBuilder
Data Integration Jobs, Employment | Indeed.com
KingswaySoft – Data Integration Solutions
United States Postal Service External links:
United States Postal Service – Abbreviations
1.22.2 United States Postal Service (USPS) Classes of …
United States Postal Service – District Business Mail Entry
Data visualization External links:
Data Visualization | FEMA.gov
Power BI | Interactive Data Visualization BI Tools
NCHS Data Visualization Gallery – Homepage
Analysis paralysis External links:
How to Stop Analysis Paralysis: 8 Important Tips
Analysis Paralysis – investopedia.com
Analysis Paralysis | Mark Manson
Master data management External links:
Master Data Management/PIM Advisory & Implementation
What is master data management (MDM)? – Definition …
Support Vector Machine External links:
Introduction to Support Vector Machines¶ – OpenCV
Data validation External links:
Description and examples of data validation in Excel
Data Validation Monitoring Overview
Data Validation in Excel – EASY Excel Tutorial
Measurement error External links:
Measurement Error Webinar Series – National Cancer …
[PDF]Review Exercises Chapter 6 — Measurement Error …
[PDF]6 Measurement Error in Surveys of the Low-Income …
Synthetic measure External links:
A Synthetic Measure of Response Time – ir.canterbury.ac.nz
Information privacy External links:
Computer data storage External links:
Computer data storage unit conversion – non-SI quantity
Computer Data Storage Options – Ferris State University
Database normalization External links:
Database Normalization Essays – ManyEssays.com
Database Normalization – Studytonight
[PDF]Database Normalization And Design Techniques
http://faculty.ksu.edu.sa/KHaouam/database/Database Normalization Example.pdf
Record linkage External links:
Electronic Record Linkage to Identify Deaths Among …
[PDF]Generalized Record Linkage Program Generator …
PHARMO Record Linkage System – National Cancer Institute
Analytical quality control External links:
IAEA-EL – Analytical Quality Control Services
Data editing External links:
Statistical data editing (Book, 1994) [WorldCat.org]
Data Editing – NaturalPoint Product Documentation Ver 1.10
Data Editing – NaturalPoint Product Documentation Ver 2.0
Qualitative data External links:
[PDF]Analyzing Qualitative Data: With or without software
Home | Qualitative Data Repository
[PPT]Qualitative Data Analysis and Interpretation
Business rules engine External links:
Business Rules Engine – BizTalk Server | Microsoft Docs
Business Rules Engine Software | Pega
What are the best Business rules engine? – Quora
Data fusion External links:
[PDF]Data Fusion Centers – Esri: GIS Mapping Software, …
Global Data Fusion, a Background Screening Company
Data Fusion Solutions
Information systems External links:
CompFlo – Valuation Information Systems
Metropolitan Regional Information Systems – MRIS
Surgical Information Systems – Health Management …
Shadow system External links:
2-D. Shadow System Reports – sarm.unm.edu
In many ways, the lymphatic system is a shadow system …
How to Shadow System Fonts in Cricut Design Space – YouTube
Data warehousing External links:
Data Warehousing | Data Warehouse | Metadata
Data warehousing (Book, 2001) [WorldCat.org]
Data Warehousing – Concepts – Tutorials Point
Kristo Ivanov External links:
Kristo Ivanov (@LuxTransO) | Twitter
Supply chain management External links:
Logistics, Supply Chain Management and Order …
Department of Supply Chain Management | Eli Broad …
Linked Open Data External links:
Linked Open Data at SAAM | Smithsonian American Art …
Linked Open Data – What is it? on Vimeo
Data warehouse External links:
[PDF]Data Warehouse – Utility’s Smart Grid Clearinghouse
http://smartgrid.epri.com/UseCases/DW – Utility DOE SG Clearhouse_ph2add.pdf
Data Warehouse Specialist Salaries – Salary.com
Title 2 Data Warehouse – Data.gov
Customer relationship management External links:
Oracle – Siebel Customer Relationship Management
Oracle – Siebel Customer Relationship Management
Customer Relationship Management Software | SugarCRM
Electronic health record External links:
Paragon Electronic Health Record (EHR) System | McKesson
myD-H | eD-H Electronic Health Record of Dartmouth-Hitchcock
What is electronic health record (EHR)? – Definition …
Quality Assurance External links:
Quality assurance titles Jobs – Yakaz
Think of quality assurance as before the goods or services have been produced and quality control is during the production of the goods or services. The former ensures that there will be quality and the latter controls the execution to ensure that there was quality.
Quality Assurance | AmeriTitle Inc.
Data compression External links:
Data Compression | Data Compression | Code
Data compression (Book, 2004) [WorldCat.org]
PKZIP | Data Compression | PKWARE
Application software External links:
Title application software Free Download for Windows
Data mining External links:
Data Mining | Definition of Data Mining by Merriam-Webster
UT Data Mining
Data Mining on the Florida Department of Corrections Website
Data migration External links:
Download Intel® Data Migration Software
Data Migration Specialist Jobs, Employment | Indeed.com
Data security External links:
Data Security – ADP
What is data security – answers.com
Data scraping External links:
Data Scraping from PDF and Excel – Stack Overflow
WWCode Python Data Scraping & Cleaning Workshop | …
Data profiling External links:
What is data profiling? – Definition from WhatIs.com
Data Analysis | Data Profiling | Experian Data Quality
Data Profiling | Trifacta
Database administration External links:
[PPT]Database Administration (DBA) – albany.edu
What is Database Administration? – Definition from Techopedia
Health data External links:
State of New York | Open Data Health | Health Data NY
Welcome to NM-IBIS – New Mexico’s Public Health Data …
Data cleansing External links:
Data Cleansing Solution – Salesforce.com
Data cleansing – SlideShare
Data Cleansing Services | Database Cleaning | Data …
Information quality External links:
Information Quality | DOJ | Department of Justice
Information Quality Guidelines | NTIA
Information quality (eBook, 2005) [WorldCat.org]
Data reduction External links:
Data Reduction – Market Research
LISA data reduction | JILA Science
What is DATA REDUCTION – Science Dictionary
Data farming External links:
T10: Data Farming – OCEANS’16 MTS/IEEE Monterey
[PDF]qsg data farming – Official DIBELS Home Page
CiteSeerX — Data Farming: A Primer
Data curation External links:
What is data curation? – Definition from WhatIs.com
SPEC Kit 354: Data Curation (May 2017) – publications.arl.org
Data curation (Book, 2017) [WorldCat.org]
ISO 8000 External links:
About ISO 8000 – Eccma