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 328 essential critical questions to check off in that domain.
The following domains are covered:
Data Quality, Analysis paralysis, Analytical quality control, Application software, Body area network, Bounds checking, Business intelligence, Business operations, Business rules engine, Computer data storage, Cross tabulation, Customer relationship management, Customer service, Data Analysis, Data cleansing, Data compression, Data consistency, Data corruption, Data curation, Data editing, Data farming, Data fusion, Data governance, Data integration, Data integrity, Data loss, Data migration, Data mining, Data pre-processing, Data profiling, Data reduction, Data scraping, Data scrubbing, Data security, Data validation, Data visualization, Data warehouse, Data warehousing, Data wrangling, Database administration, Database normalization, Decision making, Electronic health record, ISO 8000, ISO 9000, Information privacy, Information quality, Information systems, Kristo Ivanov, Mainframe computer, Master data management, Measurement error, Qualitative data, Quality Assurance, Quantitative data, Record linkage, Shadow system, Supply chain management, United States Postal Service, Wearable technology:
Data Quality Critical Criteria:
Graph Data Quality results and suggest using storytelling to create more compelling Data Quality projects.
– What are the known sources of errors in the administrative data (e.g. non-response, keying, coding errors)?
– Which quality elements and parameters do you test and what types of methods do you use to evaluate quality?
– Do we double check that the data collected follows the plans and procedures for data collection?
– How do you express quality with regard to making a decision from a statistical hypothesis test?
– What investigations/analyses have been conducted that reveal Data Quality characteristics?
– What are some of the different sources of error (variability) in my collected data?
– Does the observed (discrete) distribution match the assumed distribution?
– At what level is data first computerized (i.e., entered in a computer)?
– What criteria should be used to assess the performance of the system?
– What are the data quality requirements required by the business user?
– what is the difference between a field duplicate and a field split?
– Think through your sampling design what are you trying to show?
– What is the proportion of duplicate records on the data file?
– What is the proportion of missing values for each field?
– How do you determine the quality of data?
– Data Quality: how good is your data?
– How does the data enter the system?
– Do we do data profiling?
– Where do you clean data?
– Is the data relevant?
Analysis paralysis Critical Criteria:
Look at Analysis paralysis visions and intervene in Analysis paralysis processes and leadership.
– What sources do you use to gather information for a Data Quality study?
– Are accountability and ownership for Data Quality clearly defined?
– What is our formula for success in Data Quality ?
Analytical quality control Critical Criteria:
Collaborate on Analytical quality control governance and achieve a single Analytical quality control view and bringing data together.
– Does our organization need more Data Quality education?
– Is Supporting Data Quality documentation required?
– How do we keep improving Data Quality?
Application software Critical Criteria:
Align Application software goals and slay a dragon.
– 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?
– How do you manage the new access devices using their own new application software?
– How likely is the current Data Quality plan to come in on schedule or on budget?
– Is the process effectively supported by the legacy application software?
– Is the scope of Data Quality defined?
Body area network Critical Criteria:
Drive Body area network results and assess what counts with Body area network that we are not counting.
– Will Data Quality deliverables need to be tested and, if so, by whom?
– How is the value delivered by Data Quality being measured?
– How much does Data Quality help?
Bounds checking Critical Criteria:
Deliberate over Bounds checking goals and intervene in Bounds checking processes and leadership.
– How can you measure Data Quality in a systematic way?
– Is Data Quality Required?
Business intelligence Critical Criteria:
Inquire about Business intelligence failures and balance specific methods for improving Business intelligence results.
– Forget right-click and control+z. mobile interactions are fundamentally different from those on a desktop. does your mobile solution allow you to interact with desktop-authored dashboards using touchscreen gestures like taps, flicks, and pinches?
– What are the main differences between a business intelligence team compared to a team of data scientists?
– How is Business Intelligence affecting marketing decisions during the Digital Revolution?
– Does big data threaten the traditional data warehouse business intelligence model stack?
– What is the future scope for combination of Business Intelligence and Cloud Computing?
– What is the difference between a data scientist and a business intelligence analyst?
– Does creating or modifying reports or dashboards require a reporting team?
– Does your BI solution help you find the right views to examine your data?
– What are the best UI frameworks for Business Intelligence Applications?
– Is Business Intelligence a more natural fit within Finance or IT?
– What is your anticipated learning curve for Report Users?
– How would you broadly categorize the different BI tools?
– What type and complexity of system administration roles?
– To create parallel systems or custom workflows?
– Where is the business intelligence bottleneck?
– What level of training would you recommend?
– What is your licensing model and prices?
– How are you going to manage?
Business operations Critical Criteria:
Deliberate over Business operations adoptions and describe the risks of Business operations sustainability.
– How do your measurements capture actionable Data Quality information for use in exceeding your customers expectations and securing your customers engagement?
– 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?
– How can you negotiate Data Quality successfully with a stubborn boss, an irate client, or a deceitful coworker?
Business rules engine Critical Criteria:
Conceptualize Business rules engine planning and prioritize challenges of Business rules engine.
– Does Data Quality create potential expectations in other areas that need to be recognized and considered?
– Who will provide the final approval of Data Quality deliverables?
– How do we go about Comparing Data Quality approaches/solutions?
Computer data storage Critical Criteria:
Confer re Computer data storage issues and diversify by understanding risks and leveraging Computer data storage.
– At what point will vulnerability assessments be performed once Data Quality is put into production (e.g., ongoing Risk Management after implementation)?
– What potential environmental factors impact the Data Quality effort?
– Think of your Data Quality project. what are the main functions?
Cross tabulation Critical Criteria:
Collaborate on Cross tabulation management and catalog Cross tabulation activities.
– Is Data Quality dependent on the successful delivery of a current project?
– How will you measure your Data Quality effectiveness?
Customer relationship management Critical Criteria:
Air ideas re Customer relationship management projects and get answers.
– How to ensure high data availability in mobile computing environment where frequent disconnections may occur because the clients and server may be weakly connected?
– Can we establish a new market segmentation strategy focused on potential profitability and willingness to purchase?
– Support – how can we drive support for using the escalation processes for service, support and billing issues?
– How does our CRM collaboration software integrate well with Google services like Google Apps and Google Docs?
– What platforms are you unable to measure accurately, or able to provide only limited measurements from?
– What IT infrastructure do we have and what do we need to support the future organization needs?
– What is your approach to server analytics and community analytics for program measurement?
– Is there an iphone app for mobile scrm or customer relationship management?
– When shipping a product, do you send tracking information to the customer?
– How do you calculate the cost of servicing a customer in a SaaS business?
– What are the roles of suppliers and supply chain partners in CRM?
– How often do you fully test your disaster recovery capabilities?
– What were the factors that caused CRM to appear when it did?
– Are abandons included in your service level denominator?
– What is the recovery time objective for the application?
– What type of information may be released to whom?
– What is your live agent queue abandon rate?
– How does CRM fit in our overall strategy?
– How do you structure your account teams?
– What happens to reports?
Customer service Critical Criteria:
Check Customer service decisions and drive action.
– Customer Service What is the future of CRM with regards to Customer Service five years from now, What Technologies would affect it the most and what trends in Customer Service landscape would we see at that time?
– Has it re-engineered or redesigned processes, and leveraged technologies to improve responsiveness, Customer Service and customer satisfaction?
– How many attempts do you make before you reach the correct person (e.g., number of phone transfers, e-mail forwards, etc.)?
– How might the key characteristics of your service change in the future if the customers and their expectations change?
– What is the average supervisor to Customer Service representative ratio for a fixed route call center?
– Which of the following are reasons you use social media when it comes to Customer Service?
– What kind of qualities would staff members who deliver stellar Customer Service possess?
– Do customers receive the same service from each place/site within your organization?
– What do you do when you loose your temper with a Customer Service professional?
– Do we End each day with a sense of personal accomplishment and fulfillment?
– Do we have a vision for our staff, and do they know what the target is?
– In what ways have you seen modesty in others exhibited in the past?
– Will your customer know what to do after receiving our replies?
– Do we think that we are on the right track with our responses?
– Do we meet our Customer Service goals and timelines?
– What is the percentage of calls transferred to you?
– Are there any gaps in or problems with the system?
– How Do You Know What customers Want and Need?
– Who is Responsible for Customer Service?
– What is Effective Customer Service?
Data Analysis Critical Criteria:
Differentiate Data Analysis tasks and secure Data Analysis creativity.
– 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 difference between Data Analytics Data Analysis Data Mining and Data Science?
– What vendors make products that address the Data Quality needs?
– What are some real time data analysis frameworks?
Data cleansing Critical Criteria:
Apply Data cleansing strategies and catalog what business benefits will Data cleansing goals deliver if achieved.
– How can we incorporate support to ensure safe and effective use of Data Quality into the services that we provide?
– Is there an ongoing data cleansing procedure to look for rot (redundant, obsolete, trivial content)?
Data compression Critical Criteria:
Devise Data compression engagements and modify and define the unique characteristics of interactive Data compression projects.
– In what ways are Data Quality vendors and us interacting to ensure safe and effective use?
– Have you identified your Data Quality key performance indicators?
Data consistency Critical Criteria:
Differentiate Data consistency tactics and reinforce and communicate particularly sensitive Data consistency decisions.
– Which Data Quality goals are the most important?
– How do we maintain Data Qualitys Integrity?
Data corruption Critical Criteria:
Reorganize Data corruption issues and find out what it really means.
– Do we cover the five essential competencies-Communication, Collaboration,Innovation, Adaptability, and Leadership that improve an organizations ability to leverage the new Data Quality in a volatile global economy?
– Why should we adopt a Data Quality framework?
Data curation Critical Criteria:
Wrangle Data curation results and clarify ways to gain access to competitive Data curation services.
– What are your key performance measures or indicators and in-process measures for the control and improvement of your Data Quality processes?
– What is the total cost related to deploying Data Quality, including any consulting or professional services?
– Is a Data Quality Team Work effort in place?
Data editing Critical Criteria:
Canvass Data editing risks and explore and align the progress in Data editing.
– Do you monitor the effectiveness of your Data Quality activities?
– What are specific Data Quality Rules to follow?
Data farming Critical Criteria:
Add value to Data farming visions and diversify disclosure of information – dealing with confidential Data farming information.
– What are our needs in relation to Data Quality skills, labor, equipment, and markets?
Data fusion Critical Criteria:
Consolidate Data fusion results and look at the big picture.
– What new requirements emerge in terms of information processing/management to make physical and virtual world data fusion possible?
– Who is the main stakeholder, with ultimate responsibility for driving Data Quality forward?
– Does the Data Quality task fit the clients priorities?
Data governance Critical Criteria:
Check Data governance risks and oversee Data governance requirements.
– Before any rule is created or any data-related decision is made, a prior decision must be addressed Who will have to live with the decision?
– Can data in your non-production environments be protected, yet still be usable for training, application development and testing?
– Does the expected return on investment (roi) of this new collection justify putting it in place?
– How is the organization kept informed of information/data governance issues or decisions?
– What was the most important criteria/item in the personnel system project?
– Document distribution how does taxonomy shape match that of content?
– How can the data element influence practice, policy, or research?
– How do you know if decisions have reached the necessary people?
– Where does your sensitive data reside across the enterprise?
– What happens to projects after they are completed?
– Do programmers have quiet working conditions?
– Are there too many documents in a category?
– Should the data be made readily available?
– How will decisions be made and monitored?
– How to govern its use and maintenance?
– What was the project manager best at?
– Is the information identifiable?
– Who determines access controls?
– Who should be represented?
– Were not doing what?
Data integration Critical Criteria:
Jump start Data integration adoptions and find the ideas you already have.
– 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?
– How can the value of Data Quality be defined?
– What is Effective Data Quality?
Data integrity Critical Criteria:
Check Data integrity results and devise Data integrity key steps.
– 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?
– 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?
– Integrity/availability/confidentiality: How are data integrity, availability, and confidentiality maintained in the cloud?
– Who will be responsible for deciding whether Data Quality goes ahead or not after the initial investigations?
– Can we rely on the Data Integrity?
– Data Integrity, Is it SAP created?
Data loss Critical Criteria:
Do a round table on Data loss adoptions and probe using an integrated framework to make sure Data loss is getting what it needs.
– Are there any other areas of CCM that could be used for more effective audits and timely identification of aberrant activities -e.g., monitoring IT controls?
– 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.)?
– Does the tool we use provide the ability to delegate role-based user administration to Agency Administrator by domain?
– Does the tool we use provide a task-based help function with recommendation settings for mail configuration options?
– Do you know where your organizational data comes from, where it is stored, and how it is used?
– What are the risks associated with third party processing that are of most concern?
– What is your risk level compared to that of peer companies or competitors?
– What are the physical location requirements for each copy of our data?
– How can hashes help prevent data loss from dos or ddos attacks?
– Downtime and Data Loss: How much Can You Afford?
– What does off-site mean in your organization?
– Who are the data loss prevention vendors?
– Who has (or can have) access to my data?
– What is considered sensitive data?
– Do any copies need to be off-site?
– Do you need to pre-filter traffic?
– How many copies must be off-line?
– Who is the System Administrator?
– Why Data Loss Prevention?
– What is Data Protection?
Data migration Critical Criteria:
Reconstruct Data migration strategies and prioritize challenges of 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?
– What are the usability implications of Data Quality actions?
– Are there data migration issues?
Data mining Critical Criteria:
Check Data mining decisions and give examples utilizing a core of simple Data mining skills.
– 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?
– What will drive Data Quality change?
– How to deal with Data Quality Changes?
Data pre-processing Critical Criteria:
Jump start Data pre-processing governance and find the ideas you already have.
– To what extent does management recognize Data Quality as a tool to increase the results?
– What are the barriers to increased Data Quality production?
– Does Data Quality appropriately measure and monitor risk?
Data profiling Critical Criteria:
Experiment with Data profiling goals and transcribe Data profiling as tomorrows backbone for success.
– How do senior leaders actions reflect a commitment to the organizations Data Quality values?
Data reduction Critical Criteria:
Test Data reduction strategies and sort Data reduction activities.
– 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 new services of functionality will be implemented next with Data Quality ?
– Is Data Quality Realistic, or are you setting yourself up for failure?
Data scraping Critical Criteria:
Bootstrap Data scraping leadership and pioneer acquisition of Data scraping 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?
– What is our Data Quality Strategy?
Data scrubbing Critical Criteria:
Group Data scrubbing quality and document what potential Data scrubbing megatrends could make our business model obsolete.
Data security Critical Criteria:
Huddle over Data security goals and change contexts.
– What other organizational variables, such as reward systems or communication systems, affect the performance of this Data Quality process?
– 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?
– Do these concerns about data security negate the value of storage-as-a-service in the cloud?
– Do several people in different organizational units assist with the Data Quality process?
– Meeting the challenge: are missed Data Quality opportunities costing us money?
– 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 validation Critical Criteria:
Transcribe Data validation leadership and find the essential reading for Data validation researchers.
– 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?
– What are all of our Data Quality domains and what do they do?
– How would one define Data Quality leadership?
Data visualization Critical Criteria:
Win new insights about Data visualization tasks and point out Data visualization tensions in leadership.
– What are the best places schools to study data visualization information design or information architecture?
– When a Data Quality manager recognizes a problem, what options are available?
– How do we Lead with Data Quality in Mind?
Data warehouse Critical Criteria:
Distinguish Data warehouse management and grade techniques for implementing Data warehouse controls.
– Do we need an enterprise data warehouse, a Data Lake, or both as part of our overall data architecture?
– What does a typical data warehouse and business intelligence organizational structure look like?
– 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)?
– What are the long-term Data Quality goals?
– Do you still need a data warehouse?
Data warehousing Critical Criteria:
Be responsible for Data warehousing planning and use obstacles to break out of ruts.
– What is the difference between Enterprise Information Management and Data Warehousing?
– What are the business goals Data Quality is aiming to achieve?
Data wrangling Critical Criteria:
Air ideas re Data wrangling quality and differentiate in coordinating Data wrangling.
Database administration Critical Criteria:
Investigate Database administration strategies and reduce Database administration costs.
– 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?
– What role does communication play in the success or failure of a Data Quality project?
– How do we Improve Data Quality service perception, and satisfaction?
– Who should be called in case of Disaster Recovery?
Database normalization Critical Criteria:
Derive from Database normalization outcomes and pioneer acquisition of Database normalization systems.
– How do we know that any Data Quality analysis is complete and comprehensive?
– What threat is Data Quality addressing?
Decision making Critical Criteria:
Design Decision making failures and clarify ways to gain access to competitive Decision making services.
– Is there a timely attempt to prepare people for technological and organizational changes, e.g., through personnel management, training, or participatory decision making?
– What kind of processes and tools could serve both the vertical and horizontal analysis and decision making?
– Does Data Quality systematically track and analyze outcomes for accountability and quality improvement?
– What s the protocol for interaction, decision making, project management?
– What role do analysts play in the decision making process?
– Who will be involved in the decision making process?
– Are the data needed for corporate decision making?
Electronic health record Critical Criteria:
Adapt Electronic health record decisions and adopt an insight outlook.
– 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?
– Will new equipment/products be required to facilitate Data Quality delivery for example is new software needed?
– How do we measure improved Data Quality service perception, and satisfaction?
ISO 8000 Critical Criteria:
Trace ISO 8000 leadership and shift your focus.
– What is the purpose of Data Quality in relation to the mission?
ISO 9000 Critical Criteria:
Group ISO 9000 risks and forecast involvement of future ISO 9000 projects in development.
– 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?
Information privacy Critical Criteria:
Dissect Information privacy visions and optimize Information privacy leadership as a key to advancement.
– How do we ensure that implementations of Data Quality products are done in a way that ensures safety?
Information quality Critical Criteria:
Guide Information quality engagements and define what our big hairy audacious Information quality goal is.
– How important is Data Quality to the user organizations mission?
Information systems Critical Criteria:
Have a meeting on Information systems projects and create a map for yourself.
– 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?
– 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?
Kristo Ivanov Critical Criteria:
Scrutinze Kristo Ivanov results and revise understanding of Kristo Ivanov architectures.
– What is the source of the strategies for Data Quality strengthening and reform?
– How do we Identify specific Data Quality investment and emerging trends?
Mainframe computer Critical Criteria:
Air ideas re Mainframe computer tactics and use obstacles to break out of ruts.
Master data management Critical Criteria:
Extrapolate Master data management failures and achieve a single Master data management view and bringing data together.
– 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?
Measurement error Critical Criteria:
Match Measurement error strategies and finalize the present value of growth of Measurement error.
– Among the Data Quality product and service cost to be estimated, which is considered hardest to estimate?
– Do Data Quality rules make a reasonable demand on a users capabilities?
Qualitative data Critical Criteria:
Learn from Qualitative data governance and correct Qualitative data management by competencies.
– In a project to restructure Data Quality outcomes, which stakeholders would you involve?
– How do mission and objectives affect the Data Quality processes of our organization?
Quality Assurance Critical Criteria:
Coach on Quality Assurance risks and improve Quality Assurance service perception.
– How do employee selection and development practices, as well as staff performance management, well-being, motivation, satisfaction, and compensation, contribute to the growth of the organization?
– Are there any circumstances/cases for which dissolution and/or disintegration testing may no longer be needed/provide any additional values to product Quality Assurance at release?
– Have we established unit(s) whose primary responsibility is internal audit, Quality Assurance, internal control or quality control?
– What is the availability of and cost of internal Quality Assurance manpower necessary to monitor each performance indicator?
– Are records maintained in fireproof enclosures that are sealed to prevent moisture intrusion?
– What is the process for grading the application of qa requirements for activities?
– Are Quality Assurance records marked or labelled to ensure traceability?
– Is the system or component adequately labeled for ease of operation?
– Is the system/component adequately labeled for ease of operation?
– How does the qa plan cover all aspects of the organization?
– Can the test data be processed in a timely manner?
– How does automation fit into Quality Assurance?
– How often are your current policies evaluated?
– Is the system or component user friendly?
– How much does Quality Assurance cost?
– How often are the protocols reviewed?
– Is specialized equipment necessary?
– How are complaints tracked?
– Quality Assurance for whom?
– What is the qa plan?
Quantitative data Critical Criteria:
Do a round table on Quantitative data strategies and improve Quantitative data service perception.
Record linkage Critical Criteria:
Consolidate Record linkage management and prioritize challenges of Record linkage.
– What are the top 3 things at the forefront of our Data Quality agendas for the next 3 years?
Shadow system Critical Criteria:
Generalize Shadow system planning and know what your objective is.
– Think about the functions involved in your Data Quality project. what processes flow from these functions?
– Is maximizing Data Quality protection the same as minimizing Data Quality loss?
Supply chain management Critical Criteria:
Review Supply chain management adoptions and look in other fields.
– 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?
– Who needs to know about Data Quality ?
United States Postal Service Critical Criteria:
Reconstruct United States Postal Service engagements and innovate what needs to be done with United States Postal Service.
– Which individuals, teams or departments will be involved in Data Quality?
Wearable technology Critical Criteria:
Paraphrase Wearable technology decisions and pioneer acquisition of Wearable technology systems.
– Have the types of risks that may impact Data Quality been identified and analyzed?
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 | 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:
Data Quality Management for Utilities | Experian
CLIENTSFirst Consulting – Data Quality Consultants | …
Analysis paralysis External links:
Analysis Paralysis – investopedia.com
How to Stop Analysis Paralysis: 8 Important Tips
Keep Moving or Die: 3 Tips to Prevent Analysis Paralysis
Application software External links:
wiTECH Diagnostic Application Software Download – …
Body area network External links:
[PDF]A Scalable Wireless Body Area Network for Bio …
Body area network financial definition of Body area network
Bounds checking External links:
7.3: No Bounds Checking in C++ Flashcards | Quizlet
LowFat: Lean C/C++ Bounds Checking with Low-Fat Pointers
Bounds Checking – Central Connecticut State University
Business intelligence External links:
Mortgage Business Intelligence Software :: Motivity Solutions
Business Intelligence and Big Data Analytics Software
GENCO Business Intelligence Gateway
Business operations External links:
Business Operations & Finance Support / SubHub
UofL Business Operations
Business Operations Center
Business rules engine External links:
Business Rules Engine – BizTalk Server | Microsoft Docs
Business Rules Engine Software | Pega
Corticon Business Rules Engine – Progress
Computer data storage External links:
ELSYM5 Manual | Computer Data Storage | Materials
Cross tabulation External links:
Cross Tabulation of Survey Items – Custominsight
Customer relationship management External links:
Customer Relationship Management | CRM Software – Vtiger
Bourns Customer Relationship Management Login
CDK CRM – Automotive Customer Relationship Management
Customer service External links:
Customer Service – Fidelity Investments
Online Customer Service | eBay
Login Form – ODJFS | Child Support Customer Service …
Data Analysis External links:
Data Analysis in Excel – Easy Excel Tutorial
Seven Bridges Genomics – The biomedical data analysis …
Regional Data Warehouse/Data Analysis Site
Data cleansing External links:
MRO Data Management | MRO Data Cleansing | SDI
Experian | Data Cleansing | Data View
Data compression External links:
SecureZIP | Enterprise Data Compression | PKWARE
PKZIP | Data Compression | PKWARE
Data corruption External links:
What Causes Revit Data Corruption? – Microsol Resources
How to Recover from Outlook Data Corruption: 6 Steps
Data curation External links:
Role of Libraries in Data Curation
Research Data Curation – UC San Diego Library Home Page
Data editing External links:
NBT Data Editing : MCPE – reddit.com
Popup data editing example for Kendo UI Grid widget
[PDF]Overview of Data Editing Procedures in Surveys
Data farming External links:
SEED Center Hosts International Data Farming Workshop
Data Farming: How Big Data Is Revolutionizing Big Ag
Data Farming (@data_farming) | Twitter
Data fusion External links:
Global Data Fusion, a Background Screening Company
Global Data Fusion’s Background Screening Products …
Data Fusion & Analysis Tools
Data governance External links:
Dataguise | Sensitive Data Governance
7 Best Practices for Data Governance in Healthcare
What is data governance (DG)? – Definition from …
Data integration External links:
KingswaySoft – Data Integration Solutions
Data Integration Specialist | Superbadge
Customer Data Integration – Just another Tamr Inc. Sites site
Data integrity External links:
[PDF]Data Integrity and Compliance With CGMP Guidance …
Data integrity and data governance solutions | Infogix
Data loss External links:
A1Logic – Data Loss Prevention
GTB Technologies – Enterprise Data Loss Prevention …
Data Loss Prevention (DLP) API | Google Cloud Platform
Data migration External links:
Data Migration Server – Cirrus Data Solutions Inc.
Data migration from PRO to KidKare | KidKare
Data Migration Resources | DMR
Data mining External links:
What is Data Mining in Healthcare?
UT Data Mining
Data Mining Extensions (DMX) Reference | Microsoft Docs
Data pre-processing External links:
Data Pre-processing · GitHub
R Data Pre-Processing & Data Management – Shape your …
Data profiling External links:
Data Analysis | Data Profiling | Experian Data Quality
Data reduction External links:
Login – AuditorQC | Free Linearity and Daily QC Data Reduction
Data scraping External links:
Data Scraping from PDF and Excel – Stack Overflow
WWCode Python Data Scraping & Cleaning Workshop | …
Data security External links:
Data Security – Heartland Payment Systems
Data Security | Federal Trade Commission
Data validation External links:
Data Validation Guidance | NHSN | CDC
Data Validation in Excel – Easy Excel Tutorial
Data Validation Monitoring Overview
Data visualization External links:
AstroNova | Data Visualization Technology & Solutions
What is data visualization? – Definition from WhatIs.com
Data warehouse External links:
HRSA Data Warehouse Home Page
Regional Data Warehouse/Data Analysis Site
Welcome to the USC DATA WAREHOUSE
Data warehousing External links:
HEDW – Higher Education Data Warehousing Forum
Data Warehousing for Business Intelligence | Coursera
15-1199.07 – Data Warehousing Specialists – O*NET OnLine
Data wrangling External links:
Big Data: Data Wrangling – Old Dominion University
Database administration External links:
Oracle Database Administration for Absolute Beginners
WinSQL – A database administration and developement tool
Database normalization External links:
Database Normalization Flashcards | Quizlet
Description of the database normalization basics
Absolute Beginner’s Guide to Database Normalization | …
Decision making External links:
Mayo Clinic Shared Decision Making National Resource Center
Cloverpop: Better Decision Making Across Your Company
Effective Decision Making | SkillsYouNeed
Electronic health record External links:
EHR Electronic Health Record Flashcards | Quizlet
myD-H | eD-H Electronic Health Record of Dartmouth-Hitchcock
What is electronic health record (EHR)? – Definition …
ISO 8000 External links:
About ISO 8000 – Eccma
ISO 9000 External links:
Contact us today for your ISO 9000 Certification
What is ISO 9000? – Definition from WhatIs.com
Benefits of ISO 9000 – Perry Johnson Registrars, Inc.
Information privacy External links:
740 ILCS 14/ Biometric Information Privacy Act.
Information Privacy | Citizens Bank
Information quality External links:
Information Quality – USDA-Farm Service Agency Home …
Information Quality | Food and Nutrition Service
Information Quality Guidelines | United States …
Information systems External links:
Defense Information Systems Agency – Official Site
Geographic Information Systems (GIS) – Flathead County …
Mediware Information Systems
Kristo Ivanov External links:
Kristo Ivanov | Facebook
Kristo Ivanov Profiles | Facebook
Kristo Ivanov (@LuxTransO) | Twitter
Mainframe computer External links:
Mainframe Computer Operator Jobs – Apply Now | CareerBuilder
Master data management External links:
Best Master Data Management (MDM) Software – G2 Crowd
Measurement error External links:
What always affects measurement error in an …
Which statement describes a common measurement error…
Measurement Error Webinar Series – National Cancer …
Qualitative data External links:
Home | Qualitative Data Repository
[PDF]Tips & Tools #18: Coding Qualitative Data
Quality Assurance External links:
Nursing Care Quality Assurance Commission :: …
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.
AHCA: Health Quality Assurance Licensure Forms
Quantitative data External links:
Get Quantitative Data of Your Program—Starling by VersaMe
GPS Visualizer map input form: Plot quantitative data
What’s Quantitative Data? | DataWorks – AdAge
Record linkage External links:
PHARMO Record Linkage System – National Cancer Institute
[PDF]MATCHING AND RECORD LINKAGE William E. …
Electronic Record Linkage to Identify Deaths Among …
Shadow system External links:
How to Shadow System Fonts in Cricut Design Space – YouTube
2-D. Shadow System Reports – sarm.unm.edu
Supply chain management External links:
Supply Chain Management — FedEx Freight Customer …
Biagi Bros – SUPPLY CHAIN MANAGEMENT
United States Postal Service External links:
United States Postal Service – Abbreviations
The history of the United States Postal Service
Wearable technology External links:
Wearable Technology from AT&T
RecoHero – Wearable Technology at your Fingertips
Wearables: Wearable Technology and Devices – Fossil