Data Management Software — City & Guilds Limited End-Point Assessment Digital Skills & IT Revision

    This subtopic focuses on the effective use of data management software to create, modify, and maintain structured data records, ensuring accuracy and integ

    Topic Synopsis

    This subtopic focuses on the effective use of data management software to create, modify, and maintain structured data records, ensuring accuracy and integrity. It involves practical skills in entering and editing data through forms or datasheets, and retrieving specific information using queries and filters. Mastering these competencies enables learners to organise business information, generate tailored reports, and support data-driven decision-making in a vocational context.

    Key Concepts & Core Principles

    Exam Tips & Revision Strategies

    Common Misconceptions & Mistakes to Avoid

    Examiner Marking Points

    Data Management Software

    CITY & GUILDS LIMITED
    vocational

    This element focuses on using data management software to create, populate, and interrogate databases, ensuring learners can handle data accurately and present information to meet specified requirements. Practical competence involves structuring tables, inputting records, validating entries, and extracting relevant data through queries and reports, which are essential digital skills for administrative and IT support roles.

    24
    Learning Outcomes
    50
    Assessment Guidance
    58
    Key Skills
    21
    Key Terms
    62
    Assessment Criteria

    Assessment criteria

    City & Guilds Level 2 Diploma in IT User Skills
    City & Guilds Level 3 Award for IT Users (ITQ)
    City & Guilds Level 2 Award for IT Users (ITQ)
    City & Guilds Level 3 Certificate for IT Users (ITQ)
    City & Guilds Level 3 Diploma for IT Users (ITQ)
    City & Guilds Level 2 Diploma for IT Users (ITQ)
    City & Guilds Level 2 Certificate for IT Users (ITQ)
    City & Guilds Level 3 Diploma in IT User Skills
    City & Guilds Level 1 Award for IT Users - (ITQ)
    City & Guilds Level 1 Certificate for IT Users (ITQ)
    City & Guilds Level 1 Diploma for IT Users (ITQ)

    Topic Overview

    The City & Guilds Level 3 Diploma in IT User Skills is a comprehensive vocational qualification designed to equip students with advanced digital competencies essential for modern workplaces. This diploma covers a wide range of IT user skills, including word processing, spreadsheets, databases, presentation software, and digital communication tools. It emphasizes practical, hands-on proficiency rather than theoretical knowledge, preparing students for roles that require confident and efficient use of technology in business, administration, or further study.

    This qualification is particularly valuable because it aligns with the UK's National Occupational Standards for IT users, ensuring that students develop skills that are directly transferable to real-world job roles. The diploma is structured into mandatory and optional units, allowing learners to tailor their studies to specific career paths, such as digital marketing, data analysis, or office management. By completing this diploma, students demonstrate a high level of competence in using IT to solve problems, manage information, and communicate effectively, which are critical skills in today's digital economy.

    Within the broader context of Digital Skills & IT, this diploma serves as a stepping stone to higher-level qualifications, such as the City & Guilds Level 4 Diploma in IT, or direct entry into employment. It is recognized by employers across various sectors, including finance, healthcare, education, and government. The course also fosters essential soft skills like time management, attention to detail, and independent learning, as students work through practical assignments and projects that simulate real workplace scenarios.

    Key Concepts

    Core ideas you must understand for this topic

    • →Advanced spreadsheet functions: Using complex formulas (e.g., VLOOKUP, IF statements), pivot tables, and macros to analyse and present data efficiently.
    • →Database management: Designing relational databases, creating queries using SQL, and generating reports to extract meaningful information.
    • →Professional document production: Applying styles, templates, mail merge, and collaborative editing tools to create polished, accessible documents.
    • →Digital communication and collaboration: Using email, video conferencing, and cloud-based platforms (e.g., Microsoft Teams, Google Workspace) to work effectively in teams.
    • →Data security and GDPR: Understanding principles of data protection, secure password practices, and safe handling of sensitive information.

    Learning Objectives

    What you need to know and understand

    • Create a database table with appropriate fields, data types and a primary key
    • Apply validation rules and input masks to ensure accurate data entry
    • Edit and delete records while maintaining referential integrity
    • Construct single and multi-table queries to retrieve specific data
    • Generate formatted reports that present data clearly for a given purpose
    • Demonstrate ability to sort and filter records within a datasheet view
    • Enter, edit and maintain data records in a data management system, Retrieve and display data records to meet requirements
    • Enter, edit and maintain data records in a data management system, Retrieve and display data records to meet requirements
    • Enter, edit and maintain data records in a data management system, Retrieve and display data records to meet requirements
    • Enter, edit and maintain data records in a data management system, Retrieve and display data records to meet requirements
    • Enter, edit and maintain data records in a data management system, Retrieve and display data records to meet requirements
    • Accurately enter data into database tables using appropriate data types and field properties.
    • Apply validation rules to ensure data accuracy and prevent input errors.
    • Modify existing records to maintain up-to-date and reliable information.
    • Construct basic queries to retrieve specific data subsets based on single and multiple criteria.
    • Generate formatted reports to display data clearly for specified audiences.
    • Apply data protection principles when handling personal or sensitive information.
    • Enter, edit and maintain data records in a data management system, Retrieve and display data records to meet requirements
    • DMSE:1 Enter, edit and maintain data records in a data management system, DMSE:2 Retrieve and display data records to meet requirements
    • DMSE:1 Enter, edit and maintain data records in a data management system, DMSE:2 Retrieve and display data records to meet requirements
    • DMSE:1 Enter, edit and maintain data records in a data management system, DMSE:2 Retrieve and display data records to meet requirements
    • Enter, edit and maintain data records in a data management system, Retrieve and display data records to meet requirements
    • Enter, edit and maintain data records in a data management system, Retrieve and display data records to meet requirements
    • Enter, edit and maintain data records in a data management system, Retrieve and display data records to meet requirements

    Assessment Criteria

    Key criteria assessors look for in your portfolio

    • Award credit for correctly creating a table structure with at least four fields, including appropriate data types and a primary key.
    • Evidence should show accurate entry of a minimum of ten records, with no typographical errors.
    • Look for correct application of at least one validation rule (e.g., range check or format check) and an input mask where specified.
    • Queries must include relevant criteria and produce correct results; credit for using logical operators (AND/OR) appropriately.
    • Reports must be generated directly from queries or tables, with appropriate headers, sorting, and layout as per requirements.
    • Marks for demonstrating the ability to edit an existing record and delete a record safely without breaking relationships.
    • Award credit for demonstrating accurate data entry using forms, including appropriate validation techniques (e.g., input masks, drop-down lists) to minimise errors.
    • Credit for evidence of editing records while preserving referential integrity, such as updating linked fields across related tables.
    • Award marks for maintaining data consistency through batch updates and deletion of duplicates using query tools.
    • Credit for retrieving data using complex queries involving multiple criteria and sorting to meet precise requirements.
    • Award credit for displaying data through formatted reports, including appropriate headers, grouping, and summaries that align with the provided specification.
    • Award credit for demonstrating the accurate entry of new records with no typographical errors and correct field types.
    • Award credit for evidencing the editing of existing records, such as updating address fields, while maintaining data consistency.
    • Award credit for showing the use of queries to filter and display records that meet specific criteria, and for generating formatted reports.
    • Award credit for demonstrating accurate entry of new records with full compliance to data validation rules.
    • Credit given when learners show ability to edit existing records without compromising referential integrity or linked data.
    • Evidence of maintaining data consistency by updating multiple records through batch processes or queries should be recognised.
    • Retrieval tasks should include filtering and sorting data according to given criteria, with output formatted appropriately (e.g., report, export).
    • Display of data must match requirements, including correct use of fields, calculated values, and layout as per specification.
    • Award credit for accurate data entry into appropriate fields, including adherence to data types and formats as defined in the table design.
    • Evidence of editing records using both datasheet view and customised forms, demonstrating efficient navigation and selection techniques.
    • Demonstrate application of validation rules (e.g., required fields, input masks) to maintain data integrity when adding or modifying records.
    • Produce a query that correctly filters and sorts data based on given criteria, including multi-table joins where specified.
    • Generate a report that groups, summarises, and formats data to meet a stated purpose, with appropriate headers, footers, and layout.
    • Award credit for demonstrating accurate data entry using appropriate field types and formats as defined in the data structure.
    • Award credit for effectively applying data validation rules to minimise input errors and maintain data consistency.
    • Award credit for constructing and executing queries or filters that retrieve records precisely matching given criteria.
    • Award credit for presenting retrieved data in a clear, formatted layout (e.g., sorted, grouped, or reported) that meets the specified output requirements.
    • Award credit for demonstrating consistent and accurate data entry with correct use of field types and formats.
    • Expect evidence of using validation tools such as dropdown lists, input masks, or validation rules.
    • Look for efficient retrieval using filters, sorts, and queries with correct logical operators.
    • Assess whether reports are clearly formatted with appropriate headings, grouping, and relevant field selection.
    • Award credit for demonstrating accurate data entry using appropriate field types and validation rules to maintain data consistency.
    • Look for evidence of maintaining records through editing and updating, with attention to version control and audit trails where applicable.
    • Assess the ability to construct and execute complex queries to retrieve relevant data, ensuring the output matches the given requirements.
    • Credit should be given for presenting retrieved data in a clear, professional format (e.g., reports, forms) suitable for the intended audience.
    • Award credit for entering complete and accurate records, free from typographical errors, with all required fields populated as per the task brief.
    • Award credit for successfully editing existing records, ensuring changes are saved and reflected correctly without introducing inconsistencies.
    • Award credit for demonstrating appropriate maintenance techniques, such as deleting obsolete records or updating stale information, to preserve data currency.
    • Award credit for correctly using search and filter tools to retrieve specific subsets of data that exactly match given criteria.
    • Award credit for displaying retrieved data in a clear, organized format (e.g., sorted ascending/descending, appropriate column layout) as specified in the requirements.
    • Award credit for demonstrating the ability to create new records and enter data accurately into appropriate fields without omissions or errors.
    • Look for evidence of editing existing records, such as changing field values and deleting redundant entries, while preserving overall data structure.
    • Assess the use of data retrieval methods, including searching on single criteria and sorting data alphabetically or numerically to produce required outputs.
    • Expect the learner to display or print selected data records clearly, showing only the necessary fields for the given purpose.
    • Award credit for demonstrating accurate data entry with no typographical errors and consistent formatting across records.
    • Credit should be given for using appropriate software functions to edit and update existing records without corrupting data relationships.
    • Assessors should look for evidence of maintaining data integrity, such as applying validation rules or identifying and correcting duplicate entries.
    • For retrieval, award credit for constructing simple queries or filters to extract data that meets specified criteria.
    • When displaying data, credit is awarded for presenting results in a clear, organised format (e.g., table, report) with appropriate labels and headers.
    • Award credit for demonstrating the ability to enter new data records accurately, using appropriate data types and adhering to any field validation rules (e.g., date format, drop-down lists).
    • Award credit for showing the editing of existing records, including modifying individual fields without corrupting other data, and saving changes correctly.
    • Award credit for maintaining data records by performing tasks such as deleting obsolete records, updating linked tables, or ensuring referential integrity where applicable.
    • Award credit for retrieving data by correctly constructing simple queries or sorts (e.g., filtering by a criterion, sorting alphabetically) and displaying the output in a specified view or report format.
    • Award credit for demonstrating accurate and consistent data entry, including correct use of field types (e.g., date, numeric, currency).
    • Award credit for showing ability to edit existing records without altering unrelated data, ensuring version control or audit trail where applicable.
    • Award credit for applying appropriate sorting and filtering techniques to retrieve data that precisely matches given criteria.
    • Award credit for generating clear, well-formatted reports or on-screen displays that meet stated business requirements, such as summary tables or printed lists.
    • Award credit for demonstrating accurate data entry with consistent formatting and no typographical errors.
    • Award credit for correctly applying editing functions such as replacing, deleting, or appending records while maintaining data integrity.
    • Award credit for using appropriate retrieval methods (e.g., filters, queries, sorts) to extract records that exactly match given criteria.
    • Award credit for presenting retrieved data in a clear, professional layout that meets the specified requirements, including headers and page setup.

    Assessment Guidance

    Guidance for achieving higher grades

    • 💡Practice navigating the software efficiently; using keyboard shortcuts can save time during timed assessments.
    • 💡Always preview a report before printing or submitting to ensure it fits on the page and includes all required fields.
    • 💡Double-check query criteria by running the query and comparing results against expectations; use parameter queries if the brief asks for flexible input.
    • 💡When editing data, ensure changes commit properly; some software requires explicit saving of records after editing.
    • 💡Always read the requirements for data retrieval carefully: note exactly which fields, filters, and sort orders are needed before building queries.
    • 💡When entering data, first ensure any lookup fields or external data sources are correctly configured to maintain referential integrity.
    • 💡For report outputs, check that all labelling, grouping, and totals match the client’s specifications exactly—marks are often deducted for missing detail.
    • 💡Practice using wildcard characters (e.g., *, ?) and parameter queries to demonstrate flexible data retrieval skills.
    • 💡Always review assessment criteria carefully: ensure your evidence includes screenshots of before and after edits, not just the final database state.
    • 💡Practice constructing queries with multiple criteria (AND/OR logic) as this is frequently tested in practical assignments.
    • 💡Always read the assignment brief or task instructions thoroughly to identify all required fields, formats, and output specifications before starting.
    • 💡Practice building queries with multiple criteria and sorting levels, as these are frequently assessed in retrieval tasks.
    • 💡Document your steps, including screenshots and annotations, to provide evidence of editing and maintenance processes; this can differentiate between pass and higher grades.
    • 💡Verify the final output against the original requirements, checking for correct ordering, completeness, and adherence to any formatting guidelines.
    • 💡Always read the scenario carefully to determine the exact data entry, editing, or retrieval tasks required; align outputs strictly to user needs.
    • 💡Practice using a variety of query types (select, parameter, action) and report features (grouping, calculations) to handle common assessment tasks swiftly.
    • 💡Check your work systematically: verify data accuracy, query results, and report formatting against the specification before final submission.
    • 💡Always read the scenario requirements fully to identify exactly which fields need updating, what criteria to use for retrieval, and how the output should be displayed.
    • 💡Use the software’s built-in validation and field property features rather than manual checks to demonstrate proficiency in maintaining data quality.
    • 💡Practice constructing queries with multiple criteria and logical operators (AND/OR) to efficiently extract specific subsets of data.
    • 💡Before submitting an assignment, test your data entry with sample records to ensure the outputs match the given specification.
    • 💡Always verify entered data against source documents to minimise transcription errors.
    • 💡Use sort and filter functions to explore data before building formal queries.
    • 💡Practise writing queries with different logical operators (AND, OR) to refine result sets.
    • 💡When designing reports, consider the intended audience and ensure all required fields are visible and well labelled.
    • 💡Always plan your data structure before entering records—normalise tables and define relationships to avoid redundancy.
    • 💡Practise building queries using multiple criteria and logical operators, as these are frequently assessed in task-based scenarios.
    • 💡During assessments, save your work frequently and double-check that all outputs are labelled and clearly match the brief.
    • 💡Read the assessment brief carefully to identify exactly which fields need to be entered or edited, and cross-check your entries against source materials.
    • 💡Before submitting, verify that all retrieved data meets the stated requirements by re-reading the instructions and confirming your filters, sorts, and displayed fields.
    • 💡Use the software's built-in validation tools (e.g., spell check, data type checks) to minimize simple but costly errors that could lose marks.
    • 💡When maintaining records, adopt a systematic approach: locate, confirm necessity, update or delete as appropriate, and save—this demonstrates clear evidence of a planned process.
    • 💡Practice on sample datasets to become familiar with the software's interface and common functions like sort, filter, and form-based entry.
    • 💡Always double-check data entry by proofreading for typos and verifying against source documents to minimize errors.
    • 💡Capture screenshots or printouts at key stages as evidence for your portfolio, clearly showing before-and-after states for editing tasks.
    • 💡When retrieving data, plan the required fields and criteria in advance to avoid unnecessary steps and demonstrate systematic working.
    • 💡Practice entering a variety of data types (text, numbers, dates) to build speed and accuracy.
    • 💡Always double-check that you have saved or committed changes to records after editing; understand the difference between auto-save and manual save in the software.
    • 💡Before filtering, note down the exact criteria required; test your filter on a copy of the data if possible.
    • 💡For displaying results, use the software’s reporting tools to generate professional-looking outputs; include titles, field labels, and ensure data fits appropriately on the page.
    • 💡During your observed assessment, verbalise your actions as you perform tasks (e.g., 'I am now entering a new record using the form, ensuring the Postcode field uses a validated input mask') to demonstrate understanding.
    • 💡Always check that your retrieved data matches the given requirements exactly—if asked for all customers in 'London', ensure the output excludes similar names like 'Londonderry' unless specified.
    • 💡Build a portfolio of screenshots showing step-by-step completion of data entry, editing, and retrieval tasks, with annotations explaining how you met each assessment criterion.
    • 💡Always read the data entry task carefully—note any specified formats for dates, currencies, or reference numbers.
    • 💡For retrieval tasks, double-check that your filter conditions account for edge cases (e.g., exact match vs. partial match).
    • 💡When displaying results, preview the output and remove unnecessary columns to improve clarity; ensure the layout is suitable for the intended use (e.g., printed report vs. screen).
    • 💡Always read retrieval instructions carefully and break them down into logical criteria before applying filters or queries.
    • 💡Practice using the software's built-in data validation and input masks to reduce entry errors and save correction time.
    • 💡Before submission, check a sample of your entered records against the source data to ensure accuracy and completeness.
    • 💡Learn the key differences between 'AND' and 'OR' conditions in queries; many assessment tasks hinge on correctly combining multiple criteria.
    • 💡In practical assessments, pay close attention to the marking criteria: marks are often awarded for using efficient methods (e.g., keyboard shortcuts, formula auditing tools) and for demonstrating independent problem-solving, not just completing tasks.
    • 💡For written assignments, always reference specific features of the software you used (e.g., 'I used conditional formatting to highlight overdue invoices') and explain why your approach was appropriate for the given scenario.
    • 💡Manage your time carefully during exams: allocate time to plan your approach, especially for complex tasks like creating a pivot table or designing a database query. Rushing often leads to errors that lose marks.

    Common Mistakes

    Common errors to avoid in your coursework

    • Forgetting to set a primary key, leading to duplicate records and query errors.
    • Misunderstanding data types, such as using text for numeric or date fields, causing sorting and calculation issues.
    • Entering query criteria incorrectly, e.g., using equals (=) when a wildcard is needed, or mismatching date formats.
    • Failing to consider referential integrity when editing or deleting records, resulting in orphaned data.
    • Producing reports that do not match requirements because the wrong query or table was selected.
    • Students often overlook data validation rules when entering records, leading to inconsistent or invalid entries that affect downstream queries.
    • A common error is editing data directly in table datasheets without considering locking or transaction controls, risking data corruption.
    • Many learners fail to back up data before performing bulk updates or deletions, resulting in irreversible data loss.
    • Confusing the use of 'AND' versus 'OR' in query criteria, which produces incorrect result sets.
    • Presenting raw data without formatting or summarisation in reports, making it hard for end-users to interpret.
    • Students often neglect to validate data types, leading to errors when sorting or querying fields (e.g., mixing text and number formats).
    • Misunderstanding field properties such as primary keys or unique constraints can cause duplicate records or data integrity issues.
    • Failing to save queries correctly or confusing a query with a report, resulting in incomplete evidence for retrieval tasks.
    • Learners often fail to set proper data types and validation, leading to erroneous entries that are accepted by the system.
    • Commonly overlook the need to back up data before performing bulk updates, risking irreversible corruption.
    • Misinterpret requirements for retrieval, producing overly complex queries that omit essential criteria or return redundant data.
    • Neglect to check data accuracy after import or manual entry, assuming that software will catch all errors.
    • Assuming data entry is simply typing; forgetting to check for correct data type assignments (e.g., entering text into a numeric field).
    • Using direct datasheet entry for all modifications, neglecting to design effective forms that improve accuracy and efficiency.
    • Overlooking validation settings when importing or bulk-updating data, leading to integrity issues.
    • Writing query criteria that return incorrect or empty result sets due to logic errors (e.g., using AND instead of OR).
    • Creating reports without previewing for layout issues, such as truncated fields or missing grouping levels, causing failure to meet requirements.
    • Entering data without first checking field properties, leading to type mismatches or truncated entries.
    • Confusing editing at the table level with form-based entry, resulting in accidental overwrites or deletions of records.
    • Using inefficient or incorrect criteria in queries, which returns incomplete or excessive data sets.
    • Failing to save or backup data after bulk edits, risking data loss if software crashes.
    • Inconsistent data formatting, especially with dates and numeric values, leading to retrieval errors.
    • Entering duplicate records due to lack of unique identifiers or primary keys.
    • Misinterpreting query criteria, resulting in incomplete or incorrect result sets.
    • Overlooking data backup before performing bulk edits or deletions.
    • Entering data directly into tables without using forms, increasing the risk of errors and duplication.
    • Using incorrect or inconsistent data formats (e.g., dates as text) leading to query failures and unreliable reporting.
    • Failing to apply primary keys and relationships, causing orphan records and compromised data integrity.
    • Designing queries that return excessive irrelevant data, slowing performance and missing the specified search criteria.
    • Forgetting to save changes after editing records, leading to loss of updates or reliance on outdated information.
    • Misunderstanding field types (e.g., entering text into a numeric field) causing validation errors or incorrect data representation.
    • Overlooking the need to apply consistent formatting, such as date formats or capitalization, resulting in messy and unprofessional data presentation.
    • Applying filters incorrectly, for instance using 'equals' when a range or wildcard is needed, thus retrieving the wrong records.
    • Failing to save changes after entering or editing data, leading to loss of work and inaccurate records.
    • Mixing up field types, for example entering text into a number field, which prevents correct sorting and searching.
    • Not using the search function efficiently, resulting in manual scrolling through large datasets rather than applying filters or queries.
    • Displaying all fields when only a subset is required, which clutters the output and may breach data confidentiality principles.
    • Students often confuse saving a database file with saving individual records, leading to data loss.
    • A common error is entering data without checking for consistency, such as inconsistent date formats or spelling variations for categorical data.
    • When retrieving data, learners may apply filters incorrectly, resulting in missing or extraneous records.
    • Displaying data without adequate formatting, such as no column headings or poor alignment, is a typical oversight.
    • Entering data inconsistently, such as using different date formats (DD/MM/YYYY vs. MM/DD/YYYY) within the same field, leading to sorting and filtering errors.
    • Forgetting to save changes after editing records, resulting in loss of work when the software is closed or when moving to a different view.
    • Confusing field types (e.g., entering text into a numeric field), which can cause validation errors or prevent calculations.
    • Applying filters incorrectly, such as using 'equals' instead of 'contains', which may yield no results or incomplete data retrieval.
    • Entering data in inconsistent formats (e.g., mixing date styles) leading to retrieval errors or sorting issues.
    • Deleting or overwriting entire records when only partial updates are needed, causing loss of historical data.
    • Using manual scanning instead of querying or filtering tools, resulting in inefficient retrieval and potential omissions.
    • Displaying all fields without consideration of the audience's needs, thus cluttering the output with irrelevant information.
    • Misinterpreting retrieval requirements, such as applying an incorrect filter condition that excludes relevant records or includes irrelevant ones.
    • Failing to validate data during entry, leading to inconsistent formats (e.g., dates as text, mixed case in names) that compromise later analysis.
    • Overwriting original data accidentally when attempting edits, often due to not using forms or confirmation prompts.
    • Assuming that saving a data file automatically creates a backup, risking data loss if the file becomes corrupted.
    • Misconception: 'Spreadsheet skills are just about entering data and basic formulas.' Correction: Advanced spreadsheets involve data modelling, what-if analysis, and automation using macros, which are crucial for business decision-making.
    • Misconception: 'Databases are the same as spreadsheets.' Correction: Databases are designed for storing and querying large volumes of structured data with relationships, while spreadsheets are better for calculations and small datasets.
    • Misconception: 'IT user skills are only about using software, not about theory.' Correction: While practical, the diploma also requires understanding of concepts like data integrity, accessibility standards, and legal frameworks (e.g., copyright, GDPR).

    Frequently Asked Questions

    Common questions students ask about this topic

    Before You Start

    Prior knowledge that will help with this topic

    • •Basic digital literacy: Familiarity with operating systems (e.g., Windows, macOS), file management, and common software applications like word processors and web browsers.
    • •Level 2 IT User Skills or equivalent: A foundational understanding of IT concepts, such as creating simple documents, spreadsheets, and presentations.
    • •English and maths at Level 2: Good communication skills for interpreting assignment briefs and basic numeracy for data analysis tasks.

    Key Terminology

    Essential terms to know

    • Database structure and table design
    • Data entry and validation
    • Query creation and modification
    • Report generation and formatting
    • Data integrity and maintenance
    • Enter, edit and maintain data records in a data management system, Retrieve and display data records to meet requirements
    • Enter, edit and maintain data records in a data management system, Retrieve and display data records to meet requirements
    • Enter, edit and maintain data records in a data management system, Retrieve and display data records to meet requirements
    • Enter, edit and maintain data records in a data management system, Retrieve and display data records to meet requirements
    • Enter, edit and maintain data records in a data management system, Retrieve and display data records to meet requirements
    • Data entry and validation
    • Record maintenance and editing
    • Query design and execution
    • Report generation and presentation
    • Enter, edit and maintain data records in a data management system, Retrieve and display data records to meet requirements
    • DMSE:1 Enter, edit and maintain data records in a data management system, DMSE:2 Retrieve and display data records to meet requirements
    • DMSE:1 Enter, edit and maintain data records in a data management system, DMSE:2 Retrieve and display data records to meet requirements
    • DMSE:1 Enter, edit and maintain data records in a data management system, DMSE:2 Retrieve and display data records to meet requirements
    • Enter, edit and maintain data records in a data management system, Retrieve and display data records to meet requirements
    • Enter, edit and maintain data records in a data management system, Retrieve and display data records to meet requirements
    • Enter, edit and maintain data records in a data management system, Retrieve and display data records to meet requirements

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