Free Assessment: 331 Data Quality Things You Should Know

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 331 essential critical questions to check off in that domain.

The following domains are covered:

Data Quality, Quantitative data, Data profiling, Data governance, Analytical quality control, Measurement error, Data scrubbing, Data visualization, Application software, Data editing, Data storage, ISO 8000, Synthetic measure, Information privacy, Data warehousing, Shadow system, Electronic health record, Data farming, Data security, Qualitative data, Business operations, Data cleansing, Data warehouse, Computer data storage, Data integration, Decision making, Database normalization, Record linkage, Data loss, Business intelligence, Business rules engine, Wearable technology, Body area network, Data migration, Bounds checking, Analysis paralysis, Master data management, Data corruption, Customer relationship management, Data wrangling, Data curation, Data consistency, Customer service, Data validation, Database administration, Data Analysis, Data fusion, ISO 9000, Data mining, Information quality, Information systems, United States Postal Service, Supply chain management, Support Vector Machine, Data reduction, Kristo Ivanov, Data pre-processing, Data integrity, Cross tabulation, Health data, Random Forest, Linked Open Data, Data compression:

Data Quality Critical Criteria:

Interpolate Data Quality tactics and inform on and uncover unspoken needs and breakthrough Data Quality results.

– Would an information systems (is) group with more knowledge about a data production process produce better quality data for data consumers?

– Information on verification or evidence for the value and accuracy how can I check the value or have a confidence in it?

– What issues should you consider when determining whether existing data may possibly serve as a source of information?

– Are data timely enough to influence management decision-making (i.e., in terms of frequency and currency)?

– Establish benchmarks and baselines to help track Data Quality -is it deteriorating or remaining constant?

– Are there clearly defined and followed procedures to identify and reconcile discrepancies in reports?

– Does the reported data contain enough information to represent performance measure activities?

– Accuracy: does the data accurately represent reality or a verifiable source?

– Missing values and defaults are indistinguishable too many missing values?

– Can good algorithms, models, heuristics overcome Data Quality problems?

– Do you have a plan or procedure to collect and review data?

– What is the lowest level for which you have data?

– Does data meet the specifications you assumed?

– Is the frequency of review identified?

– How good does data have to be?

– How big should the sample be?

– What do we mean by data?

– Is the system flexible?

– Are records complete?

– Where to clean?

Quantitative data Critical Criteria:

Have a meeting on Quantitative data quality and differentiate in coordinating Quantitative data.

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

– What about Data Quality Analysis of results?

– Do we have past Data Quality Successes?

Data profiling Critical Criteria:

Face Data profiling leadership and find the ideas you already have.

– How can you negotiate Data Quality successfully with a stubborn boss, an irate client, or a deceitful coworker?

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

– What will drive Data Quality change?

– Do we do data profiling?

Data governance Critical Criteria:

Devise Data governance decisions and spearhead techniques for implementing Data governance.

– Data Considerations. Data governance, confidentiality, integrity and quality need to be preserved by the migration. Is the data bound by statutory compliance?

– How does your organization assess staff training needs and ensure job/role specific information governance training is provided to all staff?

– How is the chief executive or equivalent management board consulted and/or informed of information governance issues?

– What is the organizations most effective method of training for information governance knowledge and skills?

– Who has decision and/or input rights for the decisions that must be made concerning your key data processes?

– Does the expected cost/benefit of this new collection justify putting it in place?

– What is the cost (time, money, resources) associated with this new collection?

– What types of information should be included in the data dictionary?

– What activities does the governance board need to consider?

– Who knows the nitty-gritty details about your systems?

– Is Supporting Data Quality documentation required?

– What happens to projects after they are completed?

– How will decisions be made and monitored?

– Do you use the best tools money can buy?

– Why create a data governance system?

– What is your organizations purpose?

– Where are those databases located?

– Why have a data governance plan?

– Who is responsible ?

– Were not doing what?

Analytical quality control Critical Criteria:

Grasp Analytical quality control risks and define what our big hairy audacious Analytical quality control goal is.

– What are the key elements of your Data Quality performance improvement system, including your evaluation, organizational learning, and innovation processes?

– For your Data Quality project, identify and describe the business environment. is there more than one layer to the business environment?

– How do we ensure that implementations of Data Quality products are done in a way that ensures safety?

Measurement error Critical Criteria:

Win new insights about Measurement error visions and devote time assessing Measurement error and its risk.

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

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

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

Data scrubbing Critical Criteria:

Huddle over Data scrubbing projects and ask questions.

– Do Data Quality rules make a reasonable demand on a users capabilities?

– How can the value of Data Quality be defined?

– How much does Data Quality help?

Data visualization Critical Criteria:

Talk about Data visualization risks and innovate what needs to be done with Data visualization.

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

– What are the best places schools to study data visualization information design or information architecture?

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

– Is the scope of Data Quality defined?

Application software Critical Criteria:

Illustrate Application software governance and reduce Application software costs.

– 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 Data Quality in relation to the mission?

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

– How will you measure your Data Quality effectiveness?

Data editing Critical Criteria:

Ventilate your thoughts about Data editing outcomes and modify and define the unique characteristics of interactive Data editing projects.

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

– Do the Data Quality decisions we make today help people and the planet tomorrow?

– Have all basic functions of Data Quality been defined?

Data storage Critical Criteria:

Deliberate over Data storage visions and oversee Data storage management by competencies.

– 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 procedures does your intended long-term data storage facility have in place for preservation and backup?

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

– Think of your Data Quality project. what are the main functions?

– What are the data storage and the application logic locations?

ISO 8000 Critical Criteria:

Adapt ISO 8000 issues and proactively manage ISO 8000 risks.

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

– How can you measure Data Quality in a systematic way?

– What are current Data Quality Paradigms?

Synthetic measure Critical Criteria:

Reason over Synthetic measure strategies and finalize the present value of growth of Synthetic measure.

– What are your results for key measures or indicators of the accomplishment of your Data Quality strategy and action plans, including building and strengthening core competencies?

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

Information privacy Critical Criteria:

Infer Information privacy governance and create a map for yourself.

– What is the total cost related to deploying Data Quality, including any consulting or professional services?

Data warehousing Critical Criteria:

Graph Data warehousing results and find out.

– 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?

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

– What is the difference between Enterprise Information Management and Data Warehousing?

Shadow system Critical Criteria:

Unify Shadow system management and oversee Shadow system requirements.

– 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?

– Are we Assessing Data Quality and Risk?

Electronic health record Critical Criteria:

Disseminate Electronic health record leadership and probe using an integrated framework to make sure Electronic health record is getting what it needs.

– 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?

– Can we do Data Quality without complex (expensive) analysis?

Data farming Critical Criteria:

Ventilate your thoughts about Data farming tactics and question.

– What tools do you use once you have decided on a Data Quality strategy and more importantly how do you choose?

– How important is Data Quality to the user organizations mission?

– Are there Data Quality problems defined?

Data security Critical Criteria:

Tête-à-tête about Data security governance and test out new things.

– 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?

– 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?

– What are the challenges related to cloud computing data security?

– So, what should you do to mitigate these risks to data security?

– What vendors make products that address the Data Quality needs?

– 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?

Qualitative data Critical Criteria:

Collaborate on Qualitative data issues and adjust implementation of Qualitative data.

– Think about the functions involved in your Data Quality project. what processes flow from these functions?

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

Business operations Critical Criteria:

Depict Business operations adoptions and report on developing an effective Business operations strategy.

– Do we aggressively reward and promote the people who have the biggest impact on creating excellent Data Quality services/products?

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

– How to deal with Data Quality Changes?

Data cleansing Critical Criteria:

Talk about Data cleansing leadership and document what potential Data cleansing megatrends could make our business model obsolete.

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

– Is there an ongoing data cleansing procedure to look for rot (redundant, obsolete, trivial content)?

– Have you identified your Data Quality key performance indicators?

Data warehouse Critical Criteria:

Conceptualize Data warehouse goals and cater for concise Data warehouse education.

– 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?

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

– Does big data threaten the traditional data warehouse business intelligence model stack?

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

– 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?

Computer data storage Critical Criteria:

Guard Computer data storage leadership and gather Computer data storage models .

– Are accountability and ownership for Data Quality clearly defined?

Data integration Critical Criteria:

Give examples of Data integration engagements and arbitrate Data integration techniques that enhance teamwork and productivity.

– 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?

– In which area(s) do data integration and BI, as part of Fusion Middleware, help our IT infrastructure?

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

– What are the record-keeping requirements of Data Quality activities?

– Which Oracle Data Integration products are used in your solution?

Decision making Critical Criteria:

Face Decision making decisions and spearhead techniques for implementing Decision making.

– 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?

– What s the protocol for interaction, decision making, project management?

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

– 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?

– What threat is Data Quality addressing?

Database normalization Critical Criteria:

Conceptualize Database normalization projects and slay a dragon.

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

– What is our formula for success in Data Quality ?

Record linkage Critical Criteria:

Reorganize Record linkage planning and secure Record linkage creativity.

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

– What are the long-term Data Quality goals?

Data loss Critical Criteria:

Have a session on Data loss quality and spearhead techniques for implementing Data loss.

– Are we doing adequate due diligence before contracting with third party providers -particularly in regards to involving audit departments prior to contractual commitments?

– Is a technical solution for data loss prevention -i.e., systems designed to automatically monitor for data leakage -considered essential to enterprise risk management?

– 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?

– Could you lose your service when an investigation into data loss of another customer starts to affect your privacy and data?

– What types of transactional activities and data mining are being used and where do we see the greatest potential benefits?

– Does the tool we use provide the ability to combine multiple Boolean operators and regular expressions into policies?

– Are IT and executive management cognizant and being responsive to protecting organizations from data loss breaches?

– Do you know where your organizational data comes from, where it is stored, and how it is used?

– Does our tool have the ability to integrate with Digital Rights Management Client & Server?

– Are audit plans and programs being modified / created to address data loss prevention?

– Does the tool we use provide the ability to prevent the forwarding of secure email?

– What is the impact of the economy on executing our audit plans?

– What processes are in place to govern the informational flow?

– Do all computers have up-to-date antivirus protection?

– When was your last SWOT analysis for Internal Audit?

– If applicable, is the wireless WEP or WPA encrypted?

– Are Incident response plans documented?

– How many copies must be off-line?

– Why Data Loss Prevention?

– What is the data?

Business intelligence Critical Criteria:

Focus on Business intelligence issues and frame using storytelling to create more compelling Business intelligence projects.

– What are the potential areas of conflict that can arise between organizations IT and marketing functions around the deployment and use of business intelligence and data analytics software services and what is the best way to resolve them?

– Does the software let users work with the existing data infrastructure already in place, freeing your IT team from creating more cubes, universes, and standalone marts?

– When users are more fluid and guest access is a must, can you choose hardware-based licensing that is tailored to your exact configuration needs?

– Does the software provide fast query performance, either via its own fast in-memory software or by directly connecting to fast data stores?

– Can you easily add users and features to quickly scale and customize to your organizations specific needs?

– What are some best practices for gathering business intelligence about a competitor?

– 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 is the process of data transformation required by your system?

– What are the pros and cons of outsourcing Business Intelligence?

– What are the best use cases for Mobile Business Intelligence?

– What is your anticipated learning curve for Report Users?

– What type and complexity of system administration roles?

– Does your software integrate with active directory?

– Is your software easy for IT to manage and upgrade?

– How stable is it across domains/geographies?

– Describe any training materials offered?

Business rules engine Critical Criteria:

Dissect Business rules engine tasks and develop and take control of the Business rules engine initiative.

– Why is Data Quality important for you now?

Wearable technology Critical Criteria:

Review Wearable technology planning and inform on and uncover unspoken needs and breakthrough Wearable technology results.

– How do we know that any Data Quality analysis is complete and comprehensive?

– Are there Data Quality Models?

Body area network Critical Criteria:

Consolidate Body area network issues and summarize a clear Body area network focus.

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

– Is Data Quality Required?

Data migration Critical Criteria:

Pay attention to Data migration tactics and catalog what business benefits will Data migration goals deliver if achieved.

– 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?

– How do you determine the key elements that affect Data Quality workforce satisfaction? how are these elements determined for different workforce groups and segments?

– 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?

– Are there data migration issues?

Bounds checking Critical Criteria:

Chart Bounds checking issues and use obstacles to break out of ruts.

– Does the Data Quality task fit the clients priorities?

Analysis paralysis Critical Criteria:

X-ray Analysis paralysis failures and report on the economics of relationships managing Analysis paralysis and constraints.

– How do your measurements capture actionable Data Quality information for use in exceeding your customers expectations and securing your customers engagement?

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

– Are assumptions made in Data Quality stated explicitly?

Master data management Critical Criteria:

Analyze Master data management strategies and maintain Master data management for success.

– How do we make it meaningful in connecting Data Quality with what users do day-to-day?

– What new services of functionality will be implemented next with Data Quality ?

– 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?

Data corruption Critical Criteria:

Probe Data corruption outcomes and customize techniques for implementing Data corruption controls.

– Is there any existing Data Quality governance structure?

Customer relationship management Critical Criteria:

Discuss Customer relationship management issues and point out Customer relationship management tensions in leadership.

– Has your organization ever had to invoke its disaster recovery plan which included the CRM solution and if so was the recovery time objective met and how long did it take to return to your primary solution?

– Why is it the case that crm sfa sales force automation and hr systems are moving to cloud while scm manufacturing financial packages or systems are not moving to cloud?

– What is the value of integrating social intelligence listening and engagement into the CRM your business is using?

– What methodology do you use for measuring the success of your social media programs for clients?

– Does the average call time provided include both inbound and outbound calls?

– Is there an iphone app for mobile scrm or customer relationship management?

– When shipping a product, do you send tracking information to the customer?

– Do you have any proprietary tools or products related to social media?

– What is our core business and how will it evolve in the future?

– How can mobile users access services transparently?

– Does the current CRM system contain a Web Portal?

– Is the offline synching performance acceptable?

– What Type of Information May be Released?

– Do they always buy the same thing?

– Is the processor speed sufficient?

– How many cases have been resolved?

– What happens to reports?

– Can metadata be loaded?

– What is on-demand CRM?

Data wrangling Critical Criteria:

Match Data wrangling failures and remodel and develop an effective Data wrangling strategy.

– What are our Data Quality Processes?

Data curation Critical Criteria:

Coach on Data curation outcomes and know what your objective is.

Data consistency Critical Criteria:

Discourse Data consistency tasks and customize techniques for implementing Data consistency controls.

– 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?

– Who will be responsible for documenting the Data Quality requirements in detail?

Customer service Critical Criteria:

Reorganize Customer service tasks and ask what if.

– Do we do this…As you approach the front desk, the Customer Service professional stops what he is doing, makes eye contact with you, offers a warm smile, and asks, hello, how may I help you?

– Good Customer Service at a fast food drive through window is fairly easy to visualize. but what about good Customer Service in our organization?

– Why would potential clients outsource their business to us if they can perform the same level of Customer Service in house?

– In general, would you say the Customer Service experiences you have with companies usually…?

– What are acceptable techniques for directing a customer to the Customer Service department?

– What qualities would a manager who is focused on outstanding Customer Service possess?

– What are good examples of us utilizing SMS as a Customer Service mechanism?

– Do you have any complaint filtering in operation within your organization?

– Is the telephone service covered by the Customer Service guarantee (csg)?

– In what ways have you seen modesty in others exhibited in the past?

– CRM and Customer Service: Strategic Asset or Corporate Overhead?

– Will your customer know what to do after receiving our replies?

– Customer Service: How can social CRM improve service quality?

– Customer, User, direct and indirect is there a difference?

– How do you plan to address Customer Service?

– Who should use this self assessment?

– Why is Customer Service substandard?

– How would you define attitude?

– Why does your company exist?

– Who is the customer to us?

Data validation Critical Criteria:

Disseminate Data validation decisions and oversee Data validation management by competencies.

– How can we incorporate support to ensure safe and effective use of Data Quality into the services that we provide?

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

Database administration Critical Criteria:

Learn from Database administration issues and differentiate in coordinating 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?

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

Data Analysis Critical Criteria:

Consolidate Data Analysis outcomes and point out improvements in Data Analysis.

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

– What are some real time data analysis frameworks?

– How would one define Data Quality leadership?

– How to Secure Data Quality?

Data fusion Critical Criteria:

Reason over Data fusion planning and create a map for yourself.

– What new requirements emerge in terms of information processing/management to make physical and virtual world data fusion possible?

ISO 9000 Critical Criteria:

Distinguish ISO 9000 quality and look at it backwards.

– 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?

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

Data mining Critical Criteria:

Bootstrap Data mining tasks and work towards be a leading Data mining expert.

– 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)?

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

– 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?

Information quality Critical Criteria:

Exchange ideas about Information quality governance and improve Information quality service perception.

– What are our needs in relation to Data Quality skills, labor, equipment, and markets?

Information systems Critical Criteria:

Categorize Information systems strategies and suggest using storytelling to create more compelling Information systems projects.

– 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?

– Will Data Quality have an impact on current business continuity, disaster recovery processes and/or infrastructure?

– Are information systems and the services of information systems things of value that have suppliers and customers?

– 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?

United States Postal Service Critical Criteria:

Disseminate United States Postal Service management and budget for United States Postal Service challenges.

Supply chain management Critical Criteria:

Distinguish Supply chain management issues and find answers.

– Do those selected for the Data Quality team have a good general understanding of what Data Quality is all about?

– How do supply chain management systems coordinate planning, production, and logistics with suppliers?

– What makes cloud computing well suited for supply chain management applications?

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

– What is TESCM tax efficient supply chain management?

Support Vector Machine Critical Criteria:

Do a round table on Support Vector Machine strategies and stake your claim.

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

Data reduction Critical Criteria:

Transcribe Data reduction visions and oversee Data reduction requirements.

Kristo Ivanov Critical Criteria:

Analyze Kristo Ivanov leadership and finalize specific methods for Kristo Ivanov acceptance.

– What are our best practices for minimizing Data Quality project risk, while demonstrating incremental value and quick wins throughout the Data Quality project lifecycle?

Data pre-processing Critical Criteria:

Canvass Data pre-processing strategies and probe the present value of growth of Data pre-processing.

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

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

Data integrity Critical Criteria:

Co-operate on Data integrity strategies and test out new things.

– Integrity/availability/confidentiality: How are data integrity, availability, and confidentiality maintained in the cloud?

– Which Data Quality goals are the most important?

– Data Integrity, Is it SAP created?

– Can we rely on the Data Integrity?

Cross tabulation Critical Criteria:

Judge Cross tabulation tactics and report on the economics of relationships managing Cross tabulation and constraints.

– What are the usability implications of Data Quality actions?

Health data Critical Criteria:

Concentrate on Health data risks and define Health data competency-based leadership.

– What are specific Data Quality Rules to follow?

Random Forest Critical Criteria:

Dissect Random Forest quality and oversee implementation of Random Forest.

– What are internal and external Data Quality relations?

Linked Open Data Critical Criteria:

Examine Linked Open Data failures and overcome Linked Open Data skills and management ineffectiveness.

– Who sets the Data Quality standards?

Data compression Critical Criteria:

Have a meeting on Data compression tasks and explore and align the progress in Data compression.

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


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 |

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:

Data Quality External links:

A3-4-02: Data Quality and Integrity (10/24/2016) – Fannie Mae

[PDF]Data Quality Assessment Checklist

Quantitative data External links:

[PDF]Course Title: Quantitative Data Analysis

What does quantitative data mean? |

Data profiling External links:

18 Performing Data Profiling – Oracle

Data Analysis | Data Profiling | Experian Data Quality

What is data profiling? – Definition from

Data governance External links:

Collibra University | Data Governance Training

DGPO | Data Governance Professionals Organization

Data Governance – Do Job Titles Matter? – DATAVERSITY

Analytical quality control External links:

[PDF]Internal Analytical Quality Control Using the Cusum …

Analytical Quality Control with Certipur® Reagents – …

Measurement error External links:

[PDF]Measurement Error 2: Scale Construction (Very Brief …

Measurement Error Webinar Series – National Cancer …

Which statement describes a common measurement error…

Data visualization External links:

NCHS Data Visualization Gallery – Homepage

Power BI | Interactive Data Visualization BI Tools

Data Visualization: What it is and why matters | SAS

Application software External links:

Application software – ScienceDaily

Ch. 3 Application Software Flashcards | Quizlet

Title application software Free Download for Windows

Data editing External links:

[PDF]Overview of Data Editing Procedures in Surveys

Statistical data editing (Book, 1994) []

Data Editing – NaturalPoint Product Documentation Ver 1.10

Data storage External links:

NetApp Careers – Data Storage Jobs | NetApp

Optimized Enterprise Data Storage and Protection | Leonovus

Data Storage Systems – Data Storage Arrays | NetApp

ISO 8000 External links:

ISO 8000 fast rauschfrei – YouTube

About ISO 8000 – Eccma

Synthetic measure External links:

[PDF]Chapter 11 A Synthetic Measure for the Assessment of …

A Synthetic Measure of Response Time –

Information privacy External links:

Information Privacy | Citizens Bank

What is Information Privacy? – Definition from Techopedia

[PDF]Information Privacy Policy –

Data warehousing External links:

Data Warehousing Dummies – AbeBooks

Data Warehousing on AWS | Directions Training

Data warehousing (Book, 2001) []

Shadow system External links:

How to Shadow System Fonts in Cricut Design Space – YouTube

U21: Mordor – Light/Shadow System (Radiance 2.0)

2-D. Shadow System Reports –

Electronic health record External links:

myD-H | eD-H Electronic Health Record of Dartmouth-Hitchcock

ChARM EHR Login – Electronic Health Record

What is electronic health record (EHR)? – Definition …

Data farming External links:

T10: Data Farming – OCEANS’16 MTS/IEEE Monterey

Data Farming: How Big Data Is Revolutionizing Big Ag

Data security External links:


What is Data Security? – Definition from Techopedia

Qualitative data External links:

[PDF]Qualitative Data Analysis – SAGE Pub

[PDF]Analyzing Qualitative Data: With or without software

[PDF]Tips & Tools #18: Coding Qualitative Data

Business operations External links:

Business Operations – ASAE

U.S. Forest Service – Business Operations

How much does a business operations manager make?

Data cleansing External links:

Data Cleansing Solution –

Data warehouse External links:

HRSA Data Warehouse Home Page

Title Data Warehouse Analyst Jobs, Employment |

Enterprise Data Warehouse | IT@UMN

Computer data storage External links:

Computer Data Storage Options – Ferris State University

Dell Computer Data Storage & Backup Devices | Dell …

Computer Data Storage Jobs, Employment |

Data integration External links:

Data Integration Specialist Jobs – Apply Now | CareerBuilder


Data Integration – Kettle | Hitachi Vantara Community

Decision making External links:

Effective Decision Making | SkillsYouNeed

Essays on decision making – Rutgers University

Database normalization External links:

Database Normalization Essays –

Description of the database normalization basics

An Introduction to Database Normalization — Mike …

Data loss External links:

How to Use Data Loss Prevention in Office 365 | SherWeb

Data Loss Prevention & Protection | Symantec

Data Loss and Data Recovery Infographic – EaseUS

Business intelligence External links:

EnsembleIQ | The premier business intelligence resource

Business Intelligence Tools & Software | Square

Business Intelligence Software – ERP & Project …

Business rules engine External links:

Corticon Business Rules Engine – Progress

Quickly Build a Business Rules Engine using C# and …

Code Effects: .NET Business Rules Engine

Wearable technology External links:

Wearable Technology from AT&T

Android Tablets & Smartphones | Wearable Technology – Sony

Wearables: Wearable Technology and Devices – Fossil

Body area network External links:

Body area network – YouTube

Data migration External links:

Azure Import/Export – Data Migration | Microsoft Azure

Data Migration Jobs – Apply Now | CareerBuilder

Data Migration Specialist Jobs, Employment |

Bounds checking External links:

Bounds Checking – Central Connecticut State University

LowFat: Lean C/C++ Bounds Checking with Low-Fat Pointers

7.3: No Bounds Checking in C++ Flashcards | Quizlet

Analysis paralysis External links:

How to rid yourself of analysis paralysis – Reliable Plant

How to Stop Analysis Paralysis: 8 Important Tips

Analysis Paralysis –

Master data management External links:

Best Master Data Management (MDM) Software – G2 Crowd

MDM Platform | Master Data Management Platform | Profisee

Data corruption External links:

Data corruption and file size overflows in pg_rewind

How to Recover from Outlook Data Corruption: 6 Steps

Customer relationship management External links:

Oracle – Siebel Customer Relationship Management

Customer Relationship Management

1workforce – Customer Relationship Management …

Data wrangling External links:

What Is Data Wrangling? – Datawatch Corporation

Data Wrangling Tools & Software | Trifacta

Data curation External links:

Data curation (Book, 2017) []

What is data curation? – Definition from

Customer service External links:

Customer Service – Kohl’s

Customer Service | Progressive

CW Title – customer service

Data validation External links:

Data Validation in Excel – EASY Excel Tutorial

Excel Data Validation Messages for Users – Contextures Inc.

Description and examples of data validation in Excel

Database administration External links:

What is Database Administration? – Definition from Techopedia

Data Analysis External links:

How to Write a Data Analysis | Bizfluent

What is Data Analysis? (with pictures) – wiseGEEK

[PDF]Data analysis and interpretation – assignment

Data fusion External links:

Global Data Fusion, a Background Screening Company

[PDF]Information Integration for Data Fusion

[PDF]Data Fusion Centers – Esri

ISO 9000 External links:

List of Accredited Registrars, ISO 9000, ISO 14000, …

What is ISO 9000? – Definition from

Find How ISO 9000 and ISO 9001 differ from one another

Data mining External links:

What is Data Mining in Healthcare?

What is data mining? | SAS

Nebraska Oil and Gas Conservation Commission – GIS Data Mining

Information quality External links:

Information Quality Guidelines –

Information quality (eBook, 2005) []

Information quality (eBook, 2005) []

Information systems External links:

NTREIS | North Texas Real Estate Information Systems, Inc.

Defense Information Systems Agency – Official Site

Mediware Information Systems

United States Postal Service External links:

United States Postal Service – Service Standards

United States Postal Service – Abbreviations

The history of the United States Postal Service – USPS

Supply chain management External links:

What is supply chain management (SCM)? – Definition …

Resilinc | Supply Chain Management

Support Vector Machine External links:

SVM Support Vector Machine Statistical Learning Theory

Train support vector machine classifier – MATLAB svmtrain

Introduction to Support Vector Machines¶ – OpenCV

Data reduction External links:

What is DATA REDUCTION – Science Dictionary

Data Reduction Registration Form – Verichem …

LISA data reduction | JILA Science

Kristo Ivanov External links:

Kristo Ivanov (@LuxTransO) | Twitter

Kristo Ivanov | Facebook

Kristo Ivanov Profiles | Facebook

Data pre-processing External links:

R Data Pre-Processing & Data Management – Shape your …

Data integrity External links:


Data Integrity Jobs – Apply Now | CareerBuilder

Data Integrity Jobs, Employment |

Cross tabulation External links:

Cross Tabulation – FREE download Cross Tabulation

Cross tabulation – myDBR

R: Cross Tabulation and Table Creation – ETH Z

Health data External links:

Welcome to NM-IBIS – New Mexico’s Public Health Data …

NC SCHS: Interactive Health Data: Health Data Query System

State of New York | Open Data Health | Health Data NY

Random Forest External links:

How does random forest work for regression? – Quora

What is a random forest? – ResearchGate

How Random Forest algorithm works – YouTube

Linked Open Data External links:

Linked Open Data at SAAM | Smithsonian American Art …

Linked Open Data – YouTube

Getty Vocabularies as Linked Open Data – Google Groups

Data compression External links:

Data compression (Book, 2004) []

The Data Compression Guide –

PKZIP | Data Compression | PKWARE