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 186 essential critical questions to check off in that domain.
The following domains are covered:
Data validation, Application program, Business process modeling, Business rules, 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, Imperative programming, Information privacy, Software engineering, Software security vulnerability, Stored procedure, Validation rule, Verification and validation:
Data validation Critical Criteria:
Face Data validation goals and track iterative Data validation results.
– What management system can we use to leverage the Data validation experience, ideas, and concerns of the people closest to the work to be done?
– What are the record-keeping requirements of Data validation activities?
– What are our Data validation Processes?
Application program Critical Criteria:
Devise Application program quality and separate what are the business goals Application program is aiming to achieve.
– Which customers cant participate in our Data validation domain because they lack skills, wealth, or convenient access to existing solutions?
– For your Data validation project, identify and describe the business environment. is there more than one layer to the business environment?
– Data feeds are often derived from application programs or legacy data sources. what does it mean?
– Does Data validation appropriately measure and monitor risk?
Business process modeling Critical Criteria:
Systematize Business process modeling strategies and report on setting up Business process modeling without losing ground.
– How can skill-level changes improve Data validation?
– What will drive Data validation change?
– Is the scope of Data validation defined?
Business rules Critical Criteria:
Devise Business rules projects and devote time assessing Business rules and its risk.
– Do we aggressively reward and promote the people who have the biggest impact on creating excellent Data validation services/products?
– How is the value delivered by Data validation being measured?
Computer data storage Critical Criteria:
Examine Computer data storage projects and grade techniques for implementing Computer data storage controls.
– What are the key elements of your Data validation performance improvement system, including your evaluation, organizational learning, and innovation processes?
– Do we monitor the Data validation decisions made and fine tune them as they evolve?
– Why is Data validation important for you now?
Computer science Critical Criteria:
Familiarize yourself with Computer science planning and figure out ways to motivate other Computer science users.
– How can we incorporate support to ensure safe and effective use of Data validation into the services that we provide?
– How do senior leaders actions reflect a commitment to the organizations Data validation values?
– Does the Data validation task fit the clients priorities?
Data analysis Critical Criteria:
Derive from Data analysis governance and visualize why should people listen to you regarding Data analysis.
– 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 the difference between Data Analytics Data Analysis Data Mining and Data Science?
– What are the short and long-term Data validation goals?
– What are some real time data analysis frameworks?
Data cleansing Critical Criteria:
Administer Data cleansing projects and raise human resource and employment practices for Data cleansing.
– What are the success criteria that will indicate that Data validation objectives have been met and the benefits delivered?
– Think about the functions involved in your Data validation project. what processes flow from these functions?
– Is there an ongoing data cleansing procedure to look for rot (redundant, obsolete, trivial content)?
– Think of your Data validation project. what are the main functions?
Data compression Critical Criteria:
Deliberate over Data compression issues and diversify disclosure of information – dealing with confidential Data compression information.
– Does Data validation create potential expectations in other areas that need to be recognized and considered?
– What are the business goals Data validation is aiming to achieve?
– How do we go about Securing Data validation?
Data corruption Critical Criteria:
Troubleshoot Data corruption tasks and learn.
– What will be the consequences to the business (financial, reputation etc) if Data validation does not go ahead or fails to deliver the objectives?
– Are there any disadvantages to implementing Data validation? There might be some that are less obvious?
– Are accountability and ownership for Data validation clearly defined?
Data curation Critical Criteria:
Do a round table on Data curation quality and intervene in Data curation processes and leadership.
– What are your current levels and trends in key measures or indicators of Data validation product and process performance that are important to and directly serve your customers? how do these results compare with the performance of your competitors and other organizations with similar offerings?
– How do we make it meaningful in connecting Data validation with what users do day-to-day?
– Do we all define Data validation in the same way?
Data dictionary Critical Criteria:
Troubleshoot Data dictionary risks and oversee implementation of Data dictionary.
– At what point will vulnerability assessments be performed once Data validation is put into production (e.g., ongoing Risk Management after implementation)?
– What types of information should be included in the data dictionary?
– What are internal and external Data validation relations?
– How do we Lead with Data validation in Mind?
– Is there a data dictionary?
Data editing Critical Criteria:
Inquire about Data editing engagements and get the big picture.
– 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?
– What are the top 3 things at the forefront of our Data validation agendas for the next 3 years?
Data farming Critical Criteria:
Distinguish Data farming issues and describe which business rules are needed as Data farming interface.
– Will new equipment/products be required to facilitate Data validation delivery for example is new software needed?
– How to deal with Data validation Changes?
– How do we keep improving Data validation?
Data fusion Critical Criteria:
Differentiate Data fusion decisions and test out new things.
– What new requirements emerge in terms of information processing/management to make physical and virtual world data fusion possible?
– Who is responsible for ensuring appropriate resources (time, people and money) are allocated to Data validation?
– Who is the main stakeholder, with ultimate responsibility for driving Data validation forward?
– What are current Data validation Paradigms?
Data integration Critical Criteria:
Test Data integration quality and summarize a clear Data integration focus.
– 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 would one define Data validation leadership?
– How can we improve Data validation?
Data integrity Critical Criteria:
Confer over Data integrity risks and pioneer acquisition of Data integrity systems.
– How do you incorporate cycle time, productivity, cost control, and other efficiency and effectiveness factors into these Data validation processes?
– Integrity/availability/confidentiality: How are data integrity, availability, and confidentiality maintained in the cloud?
– How do mission and objectives affect the Data validation processes of our organization?
– Who needs to know about Data validation ?
– Data Integrity, Is it SAP created?
– Can we rely on the Data Integrity?
Data loss Critical Criteria:
Match Data loss goals and clarify ways to gain access to competitive Data loss services.
– The goal of a disaster recovery plan is to minimize the costs resulting from losses of, or damages to, the resources or capabilities of your IT facilities. The success of any disaster recovery plan depends a great deal on being able to determine the risks associated with data loss. What is the impact to our business if the data is lost?
– Are we doing adequate due diligence before contracting with third party providers -particularly in regards to involving audit departments prior to contractual commitments?
– 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 delegate role-based user administration to Agency Administrator by domain?
– Is website access and maintenance information accessible by the ED and at least one other person (e.g., Board Chair)?
– Does the tool we use provide the ability for mobile devices to access critical portions of the management interface?
– Do you know where your organizational data comes from, where it is stored, and how it is used?
– Does the tool we use provide the ability to delegate role-based user administration by scope?
– Should the deployment occur in high availability mode or should we configure in bypass mode?
– Where does your sensitive data reside, both internally and with third parties?
– What are the physical location requirements for each copy of our data?
– How can hashes help prevent data loss from DoS or DDoS attacks?
– How will the setup of endpoints with the DLP manager occur?
– If applicable, is the wireless WEP or WPA encrypted?
– How will we know our systems have been hacked?
– What is the retention period of the data?
– Do any copies need to be off-site?
– What about spot-checking instead?
– What is your most important data?
Data mining Critical Criteria:
Model after Data mining visions and balance specific methods for improving Data mining results.
– 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 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 you know that the Data validation project has been successful?
– What are the barriers to increased Data validation production?
– What programs do we have to teach data mining?
– Do we have past Data validation Successes?
Data pre-processing Critical Criteria:
Cut a stake in Data pre-processing governance and display thorough understanding of the Data pre-processing process.
– Why is it important to have senior management support for a Data validation project?
– How can you measure Data validation in a systematic way?
Data quality Critical Criteria:
Gauge Data quality management and forecast involvement of future Data quality projects in development.
– Can we describe the data architecture and relationship between key variables. for example, are data stored in a spreadsheet with one row for each person/entity, a relational database, or some other format?
– Does sufficient documentation exist on implementing partner activities commensurate with the level of upstream support that is being claimed?
– Validation: does data meet analytic and sample specific requirements (usually done by a qa officer or external party)?
– Does the reported data contain enough information to represent performance measure activities?
– What kinds of practical constraints on collecting data should you identify?
– Do we regularly review and update its Data Quality control procedures?
– What are the data quality requirements required by the business user?
– What is the proportion of duplicate records on the data file extract?
– Can good algorithms, models, heuristics overcome Data Quality problems?
– Has management performed regular Data Quality assessments?
– Scan individual records are there gaps?
– Has your tool delivered a positive roi?
– Is the frequency of review identified?
– Have Data Quality objectives been met?
– Can Data Quality be improved?
– What makes up a good record?
– Where do you clean data?
– Where to clean?
Data reduction Critical Criteria:
Ventilate your thoughts about Data reduction visions and achieve a single Data reduction view and bringing data together.
– Are there any easy-to-implement alternatives to Data validation? Sometimes other solutions are available that do not require the cost implications of a full-blown project?
Data scraping Critical Criteria:
Differentiate Data scraping tasks and find the essential reading for Data scraping researchers.
– 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?
– 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?
– Do those selected for the Data validation team have a good general understanding of what Data validation is all about?
Data scrubbing Critical Criteria:
Distinguish Data scrubbing issues and stake your claim.
– Will Data validation have an impact on current business continuity, disaster recovery processes and/or infrastructure?
– Does Data validation analysis show the relationships among important Data validation factors?
– Will Data validation deliverables need to be tested and, if so, by whom?
Data security Critical Criteria:
Look at Data security quality and assess and formulate effective operational and Data security strategies.
– 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?
– How likely is the current Data validation plan to come in on schedule or on budget?
– 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 verification Critical Criteria:
Revitalize Data verification projects and probe Data verification strategic alliances.
– Think about the kind of project structure that would be appropriate for your Data validation project. should it be formal and complex, or can it be less formal and relatively simple?
– Is there any existing Data validation governance structure?
Data warehouse Critical Criteria:
Test Data warehouse governance 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?
– 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?
– Does big data threaten the traditional data warehouse business intelligence model stack?
– What are our needs in relation to Data validation skills, labor, equipment, and markets?
– 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?
– Are we Assessing Data validation and Risk?
Data wrangling Critical Criteria:
Study Data wrangling governance and maintain Data wrangling for success.
– Does Data validation systematically track and analyze outcomes for accountability and quality improvement?
Database management system Critical Criteria:
Think carefully about Database management system failures and describe which business rules are needed as Database management system interface.
– Can we add value to the current Data validation decision-making process (largely qualitative) by incorporating uncertainty modeling (more quantitative)?
– How important is Data validation to the user organizations mission?
– What database management systems have been implemented?
– Which Data validation goals are the most important?
Declarative programming Critical Criteria:
Drive Declarative programming tasks and get answers.
– How do we ensure that implementations of Data validation products are done in a way that ensures safety?
Imperative programming Critical Criteria:
Analyze Imperative programming decisions and achieve a single Imperative programming view and bringing data together.
– What are all of our Data validation domains and what do they do?
Information privacy Critical Criteria:
Adapt Information privacy engagements and stake your claim.
– How do we know that any Data validation analysis is complete and comprehensive?
– Who are the people involved in developing and implementing Data validation?
Software engineering Critical Criteria:
Have a meeting on Software engineering leadership and shift your focus.
– 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?
– Can we answer questions like: Was the software process followed and software engineering standards been properly applied?
– Among the Data validation product and service cost to be estimated, which is considered hardest to estimate?
– Is open source software development faster, better, and cheaper than software engineering?
– Who will be responsible for documenting the Data validation requirements in detail?
– Better, and cheaper than software engineering?
Software security vulnerability Critical Criteria:
Bootstrap Software security vulnerability leadership and gather Software security vulnerability models .
– Who will be responsible for making the decisions to include or exclude requested changes once Data validation is underway?
Stored procedure Critical Criteria:
Judge Stored procedure risks and oversee Stored procedure management by competencies.
– Meeting the challenge: are missed Data validation opportunities costing us money?
– How does the organization define, manage, and improve its Data validation processes?
Validation rule Critical Criteria:
Disseminate Validation rule adoptions and inform on and uncover unspoken needs and breakthrough Validation rule results.
– To what extent does management recognize Data validation as a tool to increase the results?
– What vendors make products that address the Data validation needs?
Verification and validation Critical Criteria:
Distinguish Verification and validation tactics and change contexts.
– Think about the people you identified for your Data validation 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?
– Who will be responsible for deciding whether Data validation goes ahead or not after the initial investigations?
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 | 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:
Reporting Guidance and Data Validation
Data Validation « WordPress Codex
Data Validation – OWASP
Application program External links:
COPS Application Program
[PDF]Title Award Application Program Description Deadline
[PDF]REQUEST FOR APPLICATION Program Guidelines …
Business process modeling External links:
About the Business Process Modeling Notation …
[PDF]Business Process Modeling and Standards
Business rules External links:
Business Rules Engine Software | Pega
Computer data storage External links:
Computer Data Storage Options – Ferris State University
Computer science External links:
MyComputerCareer | Computer Science | IT Jobs | …
Purdue University – Department of Computer Science
Department of Computer Science
Data analysis External links:
Seven Bridges Genomics – The biomedical data analysis …
Data Analysis Examples – IDRE Stats
Data cleansing External links:
[DOC]Without a data cleansing – University of Oklahoma
[DOC]Wave 1 – Data Cleansing Strategy – South Carolina
Data compression External links:
SecureZIP | Enterprise Data Compression | PKWARE
Data compression (Book, 1976) [WorldCat.org]
Data corruption External links:
Repair Logger Data Corruption – Zimbra :: Tech Center
Data curation External links:
CiteSeerX — Data Curation at Scale: The Data Tamer System
Title: Data Curation APIs – arXiv
Data dictionary External links:
OpenAir Data Dictionary
16 Work with Data Dictionary – Oracle
What is a Data Dictionary? – Definition from Techopedia
Data editing External links:
Statistical data editing (Book, 1994) [WorldCat.org]
Data Editing – NaturalPoint Product Documentation Ver 1.10
Data fusion External links:
[PDF]Data Fusion – NASA
Global Data Fusion’s Background Screening Products …
[PDF]Revisions to the JDL Data Fusion Model
Data integration External links:
ADP Marketplace | The Source for HR Data Integration
Best Cloud Data Integration Software in 2017 | G2 Crowd
IBM Data Integration – IBM Analytics
Data integrity External links:
Data Integrity Specialist Jobs, Employment | Indeed.com
[PDF]data integrity statement – AAUDE
Data Integrity Jobs – Apply Now | CareerBuilder
Data loss External links:
Data Loss and Data Backup Statistics?
[PDF]Data Loss Prevention – WatchGuard
GTB Technologies – Enterprise Data Loss Prevention …
Data mining External links:
[USC04] 42 USC 2000ee-3: Federal agency data mining reporting
Data Mining – RMIT University
Data pre-processing External links:
Module 1: Data Pre-processing – YouTube
Data quality External links:
Webbula – The Data Quality Experts
Data reduction External links:
AuditorQC | Free Linearity and Daily QC Data Reduction
Data Reduction – Market Research
Data Reduction Registration Form – Verichem …
Data scraping External links:
Data Scraping | Alex’s Web Scraping Service
Automated data scraping from websites into Excel – YouTube
Automatic Data Scraping and Extraction Software – …
Data security External links:
Data Security | Federal Trade Commission
[PDF]Automotive Data Security – SAE International
Visitor & Access Management – TDS – Time Data Security
Data verification External links:
Data verification | FileMaker Community
Data Verification Specialist Jobs – Apply Now | CareerBuilder
Branch Data Verification Instructions
Data warehouse External links:
Title Data Warehouse Analyst Jobs, Employment | Indeed.com
Cloud Data Warehouse | Snowflake
Data wrangling External links:
Pandas Cheat Sheet: Data Science and Data Wrangling in …
Database management system External links:
Database Management System | Lucidea
Petroleum Database Management System (PDMS)
10-7 Operating System, Database Management System, …
Declarative programming External links:
What is declarative programming? – Quora
Declarative programming, Sasbachwalden 1991 : …
Imperative programming External links:
Imperative programming “MONTH PRINTING USING …
Information privacy External links:
[PDF]INTERACTIVE HEALTH INFORMATION PRIVACY …
www.hsscreeningreg.com/upload/IH Privacy Practices 3-2015.pdf
Information Privacy | Citizens Bank
Software engineering External links:
Codesmith | Software Engineering & Machine Learning
Academy for Software Engineering / Homepage
Software Engineering Institute
Stored procedure External links:
sql – What is a stored procedure? – Stack Overflow
sql server – Stored procedure modified time – Stack Overflow
Validation rule External links:
Creating a Validation Rule in an Access Table – YouTube
Coding a custom validation rule for a web performance test