What is involved in Data validation
Find out what the related areas are that Data validation 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 validation thinking-frame.
How far is your company on its Data validation journey?
Take this short survey to gauge your organization’s progress toward Data validation 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 validation related domains to cover and 176 essential critical questions to check off in that domain.
The following domains are covered:
Data validation, Application program, Business process modeling, Business rules, Check digit, Computer data storage, Computer science, Data analysis, Data cleansing, Data compression, Data corruption, Data curation, Data dictionary, Data editing, Data farming, Data fusion, Data integration, Data integrity, Data loss, Data mining, Data pre-processing, Data quality, Data reduction, Data scraping, Data scrubbing, Data security, Data verification, Data warehouse, Data wrangling, Database management system, Declarative programming, Formal verification, Imperative programming, Information privacy, International Organization for Standardization, Software engineering, Software security vulnerability, Stored procedure, Validation rule, Verification and validation:
Data validation Critical Criteria:
Focus on Data validation results and adjust implementation of Data validation.
– What new services of functionality will be implemented next with Data validation ?
– How can you measure Data validation in a systematic way?
– Is the scope of Data validation defined?
Application program Critical Criteria:
Accelerate Application program decisions and get answers.
– Do those selected for the Data validation team have a good general understanding of what Data validation is all about?
– Data feeds are often derived from application programs or legacy data sources. what does it mean?
– What are specific Data validation Rules to follow?
– How do we keep improving Data validation?
Business process modeling Critical Criteria:
Chart Business process modeling engagements and give examples utilizing a core of simple Business process modeling skills.
– What sources do you use to gather information for a Data validation study?
– Is Data validation Realistic, or are you setting yourself up for failure?
– How will you know that the Data validation project has been successful?
Business rules Critical Criteria:
Confer over Business rules tasks and track iterative Business rules results.
– How do we make it meaningful in connecting Data validation with what users do day-to-day?
Check digit Critical Criteria:
See the value of Check digit failures and report on setting up Check digit without losing ground.
– Among the Data validation product and service cost to be estimated, which is considered hardest to estimate?
– In a project to restructure Data validation outcomes, which stakeholders would you involve?
– Why are Data validation skills important?
Computer data storage Critical Criteria:
Bootstrap Computer data storage engagements and reduce Computer data storage costs.
– Can we add value to the current Data validation decision-making process (largely qualitative) by incorporating uncertainty modeling (more quantitative)?
– What are the disruptive Data validation technologies that enable our organization to radically change our business processes?
– Who will be responsible for making the decisions to include or exclude requested changes once Data validation is underway?
Computer science Critical Criteria:
Deliberate over Computer science strategies and optimize Computer science leadership as a key to advancement.
– What will be the consequences to the business (financial, reputation etc) if Data validation does not go ahead or fails to deliver the objectives?
– How can the value of Data validation be defined?
Data analysis Critical Criteria:
Co-operate on Data analysis governance and figure out ways to motivate other Data analysis users.
– What are the success criteria that will indicate that Data validation objectives have been met and the benefits delivered?
– What is the difference between Data Analytics Data Analysis Data Mining and Data Science?
– What is the purpose of Data validation in relation to the mission?
– Are assumptions made in Data validation stated explicitly?
– What are some real time data analysis frameworks?
Data cleansing Critical Criteria:
Check Data cleansing risks and find answers.
– Is there an ongoing data cleansing procedure to look for rot (redundant, obsolete, trivial content)?
– Have the types of risks that may impact Data validation been identified and analyzed?
– How important is Data validation to the user organizations mission?
Data compression Critical Criteria:
Facilitate Data compression failures and figure out ways to motivate other Data compression users.
– How do your measurements capture actionable Data validation information for use in exceeding your customers expectations and securing your customers engagement?
– In what ways are Data validation vendors and us interacting to ensure safe and effective use?
Data corruption Critical Criteria:
Value Data corruption decisions and probe Data corruption strategic alliances.
– A compounding model resolution with available relevant data can often provide insight towards a solution methodology; which Data validation models, tools and techniques are necessary?
– Is the Data validation organization completing tasks effectively and efficiently?
Data curation Critical Criteria:
Face Data curation outcomes and question.
– Is maximizing Data validation protection the same as minimizing Data validation loss?
– Do we have past Data validation Successes?
Data dictionary Critical Criteria:
Disseminate Data dictionary planning and report on the economics of relationships managing Data dictionary and constraints.
– Marketing budgets are tighter, consumers are more skeptical, and social media has changed forever the way we talk about Data validation. How do we gain traction?
– Who is responsible for ensuring appropriate resources (time, people and money) are allocated to Data validation?
– What are your most important goals for the strategic Data validation objectives?
– What types of information should be included in the data dictionary?
– Is there a data dictionary?
Data editing Critical Criteria:
Think carefully about Data editing projects and prioritize challenges of Data editing.
– When a Data validation manager recognizes a problem, what options are available?
Data farming Critical Criteria:
Define Data farming leadership and prioritize challenges of Data farming.
Data fusion Critical Criteria:
Chat re Data fusion engagements and ask what if.
– What new requirements emerge in terms of information processing/management to make physical and virtual world data fusion possible?
– How do mission and objectives affect the Data validation processes of our organization?
– Do we monitor the Data validation decisions made and fine tune them as they evolve?
– How will you measure your Data validation effectiveness?
Data integration Critical Criteria:
Consolidate Data integration projects and acquire concise Data integration education.
– How can you negotiate Data validation successfully with a stubborn boss, an irate client, or a deceitful coworker?
– In which area(s) do data integration and BI, as part of Fusion Middleware, help our IT infrastructure?
– What knowledge, skills and characteristics mark a good Data validation project manager?
– Which Oracle Data Integration products are used in your solution?
Data integrity Critical Criteria:
Look at Data integrity leadership and plan concise Data integrity education.
– Integrity/availability/confidentiality: How are data integrity, availability, and confidentiality maintained in the cloud?
– Will Data validation have an impact on current business continuity, disaster recovery processes and/or infrastructure?
– Data Integrity, Is it SAP created?
– How do we go about Securing Data validation?
– Can we rely on the Data Integrity?
– Are we Assessing Data validation and Risk?
Data loss Critical Criteria:
Gauge Data loss governance and clarify ways to gain access to competitive Data loss services.
– 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?
– Are reusable policy objects separate, referenced databases, files, or subroutines so that they can be reused in multiple policies, but centrally updated?
– 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?
– Are there automated audit tools being used to determine the effectiveness of data loss prevention programs?
– Should the deployment occur in high availability mode or should we configure in bypass mode?
– Does our tool have the ability to integrate with Digital Rights Management Client & Server?
– Do handovers take place in a quiet room off the main ENT (ear nose throat) ?
– What is your risk level compared to that of peer companies or competitors?
– What are the best open source solutions for data loss prevention?
– How can hashes help prevent data loss from dos or ddos attacks?
– How will we know our systems have been hacked?
– What does off-site mean in your organization?
– What is the retention period of the data?
– Who are the data loss prevention vendors?
– What are your most offensive protocols?
– Are all computers password protected?
– What about policies and standards?
– Where is your data going?
Data mining Critical Criteria:
Experiment with Data mining issues and integrate design thinking in Data mining innovation.
– 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 prevents me from making the changes I know will make me a more effective Data validation leader?
– 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?
– How will we insure seamless interoperability of Data validation moving forward?
– Are we making progress? and are we making progress as Data validation leaders?
– What programs do we have to teach data mining?
Data pre-processing Critical Criteria:
Troubleshoot Data pre-processing results and pioneer acquisition of Data pre-processing systems.
– What are your key performance measures or indicators and in-process measures for the control and improvement of your Data validation processes?
Data quality Critical Criteria:
Analyze Data quality issues and budget the knowledge transfer for any interested in Data quality.
– Do we conduct regular data quality audits to ensure that our strategies for enforcing quality control are up-to-date and that any corrective measures undertaken in the past have been successful in improving Data Quality?
– Information on verification or evidence for the value and accuracy how can I check the value or have a confidence in it?
– Has the program/project clearly documented (in writing) what is reported to who, and how and when reporting is required?
– Validation: does data meet analytic and sample specific requirements (usually done by a qa officer or external party)?
– Are clearly written instructions available on how to use the reporting tools/forms related to people reached/served?
– Establish benchmarks and baselines to help track Data Quality -is it deteriorating or remaining constant?
– Do we double check that the data collected follows the plans and procedures for data collection?
– How can statistical hypothesis testing lead me to make an incorrect conclusion or decision?
– Are Data Quality challenges identified and are mechanisms in place for addressing them?
– Do you define jargon and other terminology used in data collection tools?
– Does the observed (discrete) distribution match the assumed distribution?
– Match data specifications against data are all the attributes present?
– What criteria should be used to assess the performance of the system?
– What is the proportion of duplicate records on the data file extract?
– What is the typical reimbursement for sharing your data?
– What is the proportion of missing values for each field?
– Feedback is necessary, but how is it provided?
– What are you doing with all this data anyway?
– Have Data Quality objectives been met?
– Are the attributes independent?
Data reduction Critical Criteria:
Discuss Data reduction results and point out improvements in Data reduction.
– Does Data validation create potential expectations in other areas that need to be recognized and considered?
– To what extent does management recognize Data validation as a tool to increase the results?
– Does our organization need more Data validation education?
Data scraping Critical Criteria:
Design Data scraping governance and figure out ways to motivate other Data scraping users.
– Does Data validation analysis isolate the fundamental causes of problems?
– What are the barriers to increased Data validation production?
– What about Data validation Analysis of results?
Data scrubbing Critical Criteria:
Reason over Data scrubbing governance and customize techniques for implementing Data scrubbing controls.
– Is Data validation Required?
Data security Critical Criteria:
Be responsible for Data security outcomes and balance specific methods for improving Data security results.
– 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?
– Does it contain data security obligations?
– What is Data Security at Physical Layer?
– What is Data Security at Network Layer?
– How would one define Data validation leadership?
– How do we Lead with Data validation in Mind?
– How will you manage data security?
Data verification Critical Criteria:
Adapt Data verification tactics and define what do we need to start doing with Data verification.
– What are our best practices for minimizing Data validation project risk, while demonstrating incremental value and quick wins throughout the Data validation project lifecycle?
– How do you determine the key elements that affect Data validation workforce satisfaction? how are these elements determined for different workforce groups and segments?
– Is there any existing Data validation governance structure?
Data warehouse Critical Criteria:
Substantiate Data warehouse decisions and don’t overlook the obvious.
– What tier data server has been identified for the storage of decision support data contained in a data warehouse?
– What does a typical data warehouse and business intelligence organizational structure look like?
– What role does communication play in the success or failure of a Data validation project?
– 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?
– Do you still need a data warehouse?
– Centralized data warehouse?
Data wrangling Critical Criteria:
Bootstrap Data wrangling results and observe effective Data wrangling.
– What other jobs or tasks affect the performance of the steps in the Data validation process?
– What is the source of the strategies for Data validation strengthening and reform?
– What will drive Data validation change?
Database management system Critical Criteria:
Reconstruct Database management system projects and adopt an insight outlook.
– What database management systems have been implemented?
Declarative programming Critical Criteria:
Discourse Declarative programming tasks and point out Declarative programming tensions in leadership.
– How do we Identify specific Data validation investment and emerging trends?
– What are the usability implications of Data validation actions?
Formal verification Critical Criteria:
Air ideas re Formal verification risks and summarize a clear Formal verification focus.
– Who will be responsible for deciding whether Data validation goes ahead or not after the initial investigations?
– Risk factors: what are the characteristics of Data validation that make it risky?
Imperative programming Critical Criteria:
Facilitate Imperative programming strategies and perfect Imperative programming conflict management.
– Do several people in different organizational units assist with the Data validation process?
Information privacy Critical Criteria:
Jump start Information privacy goals and probe the present value of growth of Information privacy.
– Do Data validation rules make a reasonable demand on a users capabilities?
International Organization for Standardization Critical Criteria:
Scan International Organization for Standardization quality and report on the economics of relationships managing International Organization for Standardization and constraints.
– Is Data validation dependent on the successful delivery of a current project?
– What are our Data validation Processes?
Software engineering Critical Criteria:
Drive Software engineering goals and explain and analyze the challenges of Software engineering.
– DevOps isnt really a product. Its not something you can buy. DevOps is fundamentally about culture and about the quality of your application. And by quality I mean the specific software engineering term of quality, of different quality attributes. What matters to you?
– What may be the consequences for the performance of an organization if all stakeholders are not consulted regarding Data validation?
– Can we answer questions like: Was the software process followed and software engineering standards been properly applied?
– Is open source software development faster, better, and cheaper than software engineering?
– Better, and cheaper than software engineering?
– Are there recognized Data validation problems?
Software security vulnerability Critical Criteria:
Cut a stake in Software security vulnerability governance and acquire concise Software security vulnerability education.
– In the case of a Data validation project, the criteria for the audit derive from implementation objectives. an audit of a Data validation project involves assessing whether the recommendations outlined for implementation have been met. in other words, can we track that any Data validation project is implemented as planned, and is it working?
Stored procedure Critical Criteria:
Weigh in on Stored procedure tasks and document what potential Stored procedure megatrends could make our business model obsolete.
Validation rule Critical Criteria:
Dissect Validation rule results and find answers.
Verification and validation Critical Criteria:
Refer to Verification and validation engagements and overcome Verification and validation skills and management ineffectiveness.
– what is the best design framework for Data validation organization now that, in a post industrial-age if the top-down, command and control model is no longer relevant?
– What is Effective Data validation?
This quick readiness checklist is a selected resource to help you move forward. Learn more about how to achieve comprehensive insights with the Data validation 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 validation External links:
Data Validation – OWASP
How to Show Data Validation Messages in Textbox
Data Validation in Excel – EASY Excel Tutorial
Application program External links:
[PDF]Title Award Application Program Description Deadline
[PDF]CSAT’s Knowledge Application Program KAP Keys
Knowledge Application Program (KAP) E-Learning
Business process modeling External links:
Guide to Business Process Modeling | Smartsheet
Business rules External links:
AAMVA – NMVTIS Business Rules
Check digit External links:
Check Digit for barcode | FileMaker Community
Check digit calculator – Services | GS1
Check Digit | My VIN Decoder
Computer data storage External links:
Computer Data Storage Options – Ferris State University
computer data storage service – TheBlaze
Computer science External links:
2018 Computer Science Internships | Internships.com
Computer Science & Engineering | College of Engineering
Computer Science Curriculum for Grades 6-12 | Code.org
Data analysis External links:
Data Analysis – Illinois State Board of Education
[PPT]Qualitative Data Analysis and Interpretation
Data cleansing External links:
[DOC]Without a data cleansing – University of Oklahoma
Data Cleansing Solution – Salesforce.com
Data cleansing – SlideShare
Data compression External links:
Data compression (Book, 2004) [WorldCat.org]
Data compression (Book, 1976) [WorldCat.org]
SecureZIP | Enterprise Data Compression | PKWARE
Data corruption External links:
Data corruption – UFOpaedia
Data curation External links:
Data curation (Book, 2017) [WorldCat.org]
What is data curation? – Definition from WhatIs.com
SPEC Kit 354: Data Curation (May 2017) – publications.arl.org
Data dictionary External links:
Dataedo – Document your databases – Data Dictionary & ERD
What is a Data Dictionary? – Bridging the Gap
NTDS 2018 Data Dictionary
Data editing External links:
Statistical data editing (Book, 1994) [WorldCat.org]
Data Editing – NaturalPoint Product Documentation Ver 1.10
[PDF]Overview of Data Editing Procedures in Surveys
Data farming External links:
T10: Data Farming – OCEANS’16 MTS/IEEE Monterey
[PDF]qsg data farming – Official DIBELS Home Page
Data fusion External links:
[PDF]Data Fusion Centers – Esri
Data Fusion & Analysis Tools – spatelxcmdfe.xcmdata.org
Data fusion : concepts and ideas (eBook, 2012) [WorldCat.org]
Data integration External links:
Data Integration Jobs, Employment | Indeed.com
Data integrity External links:
Data Integrity Services SM – Experian
Data Integrity Jobs, Employment | Indeed.com
Data Integrity Jobs – Apply Now | CareerBuilder
Data loss External links:
Data Loss Prevention (DLP) API | Google Cloud Platform
Data Loss and Data Recovery Infographic – EaseUS
Data Loss Prevention & Protection | Symantec
Data mining External links:
Data mining | computer science | Britannica.com
UT Data Mining
Data Mining on the Florida Department of Corrections Website
Data quality External links:
CRMfusion Salesforce Data Quality Software Applications
[PDF]Data Quality Report – arb.ca.gov
Data reduction External links:
Data Reduction – Market Research
AuditorQC | Free Linearity and Daily QC Data Reduction
OEC – Data Reduction Techniques – Online Ethics
Data scraping External links:
WWCode Python Data Scraping & Cleaning Workshop | …
Data Scraping | Alex’s Web Scraping Service
Automated data scraping from websites into Excel – YouTube
Data security External links:
Data Security from Multiple Levels of Protection | H&R Block®
What is data security – answers.com
Data verification External links:
Data Verification Services | IMARC
Data Verification & NCOA | NAICS Association
Branch Data Verification Instructions
Data warehouse External links:
Title Data Warehouse Analyst Jobs, Employment | Indeed.com
Enterprise Data Warehouse | IT@UMN
Title 2 Data Warehouse – Data.gov
Data wrangling External links:
Data Wrangling Tools & Software | Trifacta
Big Data: Data Wrangling – Old Dominion University
Database management system External links:
ChurchSuite – Church Database Management System
Database Management System (DBMS) – Techopedia.com
Relational Database Management System (RDBMS) – …
Declarative programming External links:
What is declarative programming? – Quora
Declarative Programming in Java – O’Reilly Media
Declarative programming – Fun Fun Function – YouTube
Formal verification External links:
Securify ♦ Formal Verification of Ethereum Smart Contracts
Questa Formal Verification – Mentor Graphics
OneSpin. Formal Verification. – making electronics reliable.
Imperative programming External links:
LanQ – a quantum imperative programming language
What is the imperative programming paradigm? – Quora
Information privacy External links:
Information Privacy | Citizens Bank
Your Health Information Privacy Rights (HIPAA) – WebMD
International Organization for Standardization External links:
ISO – International Organization for Standardization
ISO International Organization for Standardization
MDMC – International Organization for Standardization (ISO)
Software engineering External links:
Software Engineering Institute
Software security vulnerability External links:
How-To-Guide for Software Security Vulnerability …
Stored procedure External links:
HOW TO: Call a Parameterized Stored Procedure by Using …
Validation rule External links:
[PDF]3 Data Properties and Validation Rules – smckearney.com
Microsoft Access tips: Validation Rules – allenbrowne.com
WPF validation rule preventing decimal entry in textbox?
Verification and validation External links:
[PDF]Verification and Validation – University of California, Irvine
International Address Verification and Validation | Experian