week 8

3.8 Data systems

3.8.1 Know the definition of data wrangling and understand its purpose andwhen it is used.

3.8.2 Know and understand the purpose of each step of data wrangling:

As explained in the previous youtube vidoe Data wrangling is the structured process of transforming raw, messy, and inconsistent data into a form that is reliable, accurate, and ready for analysis. Raw data often contains errors, missing values, and formatting problems that make it difficult to use. The purpose of data wrangling is to take this unrefined information and prepare it so that organisations can confidently draw conclusions, make decisions, or run automated systems. Each step - structuring, cleaning, validating, enriching, and outputting - reduces risk, improves quality, and ensures data is suitable for its end use, whether that is reporting, machine learning, cyber-security analysis, or a business decision. Below we look at these in further detail 

Structure

Structuring data is the process of organising raw information into a consistent and logical format (such as tables, fields, rows, and columns). It involves identifying the key attributes, deciding how information should be stored, and creating a shape that supports future tasks like searching, sorting, or linking datasets. Good structuring ensures the data is readable and usable across systems.

Case Study (Structure)

A college collects feedback from students in emails, text messages, and handwritten forms. Before analysing trends in satisfaction, the IT support team extracts the responses and places them into a single structured spreadsheet with columns for Student ID, Course, Rating, Comments, and Date.

Clean

Cleaning involves removing errors, inconsistencies, and unwanted data. This may include correcting spelling mistakes, removing duplicates, fixing formatting issues, filling or removing missing values, and ensuring all data uses the same units (e.g., all dates in DD/MM/YYYY). Cleaning is essential for accuracy, especially when decisions rely on precise information.

Case Study (Clean)

A cyber-security team logs device sign-ins from staff laptops. Some records contain blank usernames, others list the same person twice with slightly different spellings (“J. Smith” vs “John Smith”). Cleaning ensures all entries are standardised so unusual login behaviour can be accurately monitored.

Validate

Validation checks whether the data meets required rules (e.g., numbers within an acceptable range, postcodes in the right format, no negative ages, email addresses containing “@”). This step ensures that the data is logical, realistic, and trustworthy. Validation prevents bad data from entering business systems and causing incorrect results.

Case Study (Validate)

An IT helpdesk collects data on incidents reported by staff. A validation rule prevents users from submitting an incident with a “Resolution Time” longer than 365 days. When an incorrect value (e.g., 8,000 days) appears, the system rejects it and asks for correction.

Enrich

Enrichment adds additional useful information to a dataset by combining it with external or related data. This might include adding geographical data, linking customer records with purchase histories, or attaching risk levels to cyber-security events. Enrichment makes the dataset more meaningful and improves the quality of insights.

Case Study (Enrich)

A retail company collects customer purchase data. To better understand buying habits, they enrich the dataset by attaching customers’ regional information based on postcode. This helps the business identify which areas buy which products most often, supporting marketing and stock planning.

Output

The output stage provides the final, cleaned, structured, and enriched dataset in the correct format for its intended use. This might be a CSV file, a dashboard, a report, a visualisation, or a database entry. The purpose is to deliver the dataset in a form that other systems, analysts, or decision-makers can immediately use.

Case Study (Output)

A college attendance system compiles daily attendance records into a cleaned table. The final output is exported as a CSV file for the safeguarding team, who upload it into a monitoring system that highlights students at risk of persistent absence.

Data Wrangling Challenge - “Fix the Dataset”

Scenario:
You have been given a small sample of messy data from a fictional college’s student contact system. Your task is to act as a data specialist and apply the five stages of data wrangling to make the dataset usable.

Instructions
1. Download or create a simple table (8-10 rows) containing errors such as missing values, inconsistent dates, duplicated names, different phone number formats, and incomplete postcodes.

2. Structure the data
Reorganise the information into clear columns (e.g., Name, Student ID, Phone Number, Email, Postcode).

3. Clean the data
Fix spellings, remove duplicates, correct formats, and fill in values where possible.

4. Validate the data
Apply at least three validation checks (e.g., all emails contain “@”, postcodes use UK format, phone numbers have 11 digits).

5. Enrich the data
Add one new column using an external lookup (e.g., Region based on postcode, or Age Group based on date of birth).

6. Output your final dataset
Export the cleaned, validated, enriched version as a CSV or screenshot.

7. Reflect (3–4 sentences)
Explain the issues you identified and how each wrangling step improved the data.

Expected outcome:
A neat, accurate, structured dataset and a short written reflection demonstrating your understanding of the data wrangling process.

Messy Dataset csv

3.8.3 Know and understand the purpose of each core function of a data system:

A data system is more than just a data store; it is a set of interconnected services and functions that allow data to be entered, retrieved, persisted, combined, organised, presented, and improved over time. These core functions ensure that data becomes a useful asset rather than a liability. Without each part working well, data could be missing, inconsistent, inaccessible, or unreliable. For example, if you can’t search it, having tons of data doesn’t help; if you don’t integrate it with other sources, insights are limited; if you don’t produce output, stakeholders can’t act; and if you don’t build a feedback loop, the system can’t improve.

Input

Purpose & Discussion:
The input function is where raw data enters the system. This might be manual user entry, sensors, imports from external systems, or file uploads. The quality, completeness and accuracy of data at the input stage determine how reliable everything else will be. If the input is poorly handled (e.g., wrong format, missing values, incorrect units), the rest of the system will struggle. Good input mechanisms include validation at entry, standardised formats, and controlled sources.

Why it contributes to the system:

• It ensures data enters in a usable state rather than chaos.

• It sets the foundation for everything downstream: search, save, integrate, etc.

• It helps minimise garbage-in problems.

• It can enforce business rules at the earliest point (e.g., required fields, format checks).

Simple case study:

A college's student-registration portal allows students to input their details. If the "Date of Birth" is entered incorrectly (e.g., using US format instead of UK), the system might mis-calculate age. By enforcing the format at input, the data system prevents many downstream errors.

Search

Purpose & Discussion:
The search function allows users or systems to find relevant data within the system. It may involve indexing, full-text search, filters, queries, or metadata lookup. Effective search is critical for turning data into actionable information. Without it, data remains trapped and unusable.

Why it contributes to the system:

• It makes data accessible and useful to end-users.

• It supports efficiency: finding specific records, statistics or patterns.

• It supports decision-making, when analysts can retrieve the right data quickly.

• It supports other functions (integration, output) by locating relevant records.

Simple case study:
In a customer-services system, a support agent uses the search function to retrieve all tickets logged by a particular user ID. Without good search capability, the agent might miss relevant historical tickets or duplicates, hindering resolution.

Save

Purpose & Discussion:
The save function is about persisting data so it's stored reliably for future use. This involves databases, file systems, cloud storage, backups, and versioning. Its purpose is to ensure data is kept securely, can be retrieved later, and remains intact (integrity) over time.

Why it contributes to the system:

• It ensures data remains available and durable.

• It supports data continuity (not lost when system restarts).

• It enables historical tracking and audit capabilities.

• It provides the platform for other functions (search, integrate) to operate on stored data.

Simple case study:
An IT department logs network access events and saves them into a long-term log database. This ensures the data is available for compliance, audits, and forensic investigations. If events weren’t saved reliably, there might be gaps.

Integrate

Purpose & Discussion:
The integrate function brings together data from multiple sources into a coherent whole. This might be combining databases, linking records across systems, performing ETL (Extract, Transform, Load), or joining internal with external datasets. Integration is vital for richer insight-single siloed datasets often tell only part of the story.

Why it contributes to the system:

• It enables holistic views of data (e.g., combining sales + support + marketing).

• It supports data enrichment (linking related data).

• It reduces duplication, overlaps and inconsistencies across systems.

• It supports analytics and reporting at a more advanced level.

Simple case study:
A retail organisation merges customer purchase history (from e-commerce database) with support ticket data (from CRM system). By integrating these, the business can see which customers file frequent tickets and correlate this with purchase value, enabling better customer segmentation.

Organise (index)

Purpose & Discussion:
The organise function (often called indexing, categorising or structuring) arranges data so it becomes manageable and efficient. This includes creating metadata, indexes, hierarchies, taxonomies, and classification schemes. Well-organised data is easier to search, retrieve, manage and maintain.

Why it contributes to the system:

• It improves performance of search and retrieval (via indexing).

• It ensures consistency (categories, taxonomies).

• It supports governance and data quality (knowing what each data item means).

• It makes maintenance (archiving, purging) simpler.

Simple case study:
A library system indexes each book by author, title, subject and ISBN. Without this indexing structure, finding books would be slow or require scanning all entries; with the index the system can jump directly to relevant records.

Output

Purpose & Discussion:
The output function presents the processed data in a usable form. This could be reports, dashboards, exported files, visualisations, or data feeds to other systems. The goal is to deliver information that stakeholders or other systems can use to act.

Why it contributes to the system:

• It turns data into actionable insights.

• It supports decision-making and reporting to management.

• It enables sharing of data and findings.

• It completes the "value chain" of data: from raw to actionable.

Simple case study:
A marketing team receives a dashboard showing monthly website visits, campaign performance and conversion rates. The dashboard (output) is produced from integrated, organised, stored data and helps managers decide budgets and tactics.

Feedback loop.

Purpose & Discussion:
The feedback loop is the mechanism by which the system receives input about its performance or accuracy, and uses that to improve. For a data system this might mean logging errors, monitoring usage, feeding back corrections, re-training models, updating rules, or purging outdated data. It ensures the system adapts and remains fit for purpose.

Why it contributes to the system:

• It supports continuous improvement of data quality and processes.

• It helps detect errors or outdated information and correct them.

• It enables the system to evolve with changing requirements.

• It closes the loop between output (what users see) and input/integration (how data is collected or structured).

Simple case study:
After delivering a monthly sales report, the business notices that many entries were mis-labelled. A feedback loop is established where incorrect entries are flagged, the input form is modified to prevent the error, and future reports improve in accuracy and reliability.

"Loan Laptops"
You work for a college’s Digital Support Services team. The college has just launched a new system to track student equipment loans (laptops, tablets). Your job is to map out and apply the core functions of the data system.

Instructions:
1. Input (3 minutes):
Write down what data you would collect when a student borrows a piece of equipment (e.g., Student ID, Equipment ID, Date borrowed, Condition).

2. Search (2 minutes):
Describe how you (or a support staff) might search the system to find all equipment currently out on loan or all loans for a particular student.

3. Save (2 minutes):
State where and how the data will be saved (which database/table, what backups or archiving might be needed).

4. Integrate (3 minutes):
Think of one other system or dataset you could integrate with the equipment-loan data (e.g., student enrolment database, maintenance records). Write what benefit that integration gives.

5. Organise (Index) (3 minutes):
Decide how you would organise and index the loan data so it can be retrieved efficiently (e.g., index by Equipment ID, Student ID, Borrow Date).

6. Output (3 minutes):
Describe one output you would produce for managers (e.g., monthly report of overdue loans, dashboard of equipment utilisation).

7. Feedback Loop (2 minutes):
Explain how you would put a feedback loop in place (e.g., logging errors when items are returned late, updating input form to require condition field, review of lost-item trends).

Submission:
Write your answers in a short document or on a worksheet. Be ready to share which part you found most challenging and why.

3.8.4 Know the types of data entry errors and understand how and why they occur:

Data entry errors are mistakes that occur when information is manually or digitally entered into a system. These errors often seem small-

maybe a mistyped letter or a number in the wrong order-but they can cause major issues for organisations. These problems arise because data systems rely on accuracy: one incorrect value can affect calculations, decision-making, records, reports, and even customer service.

Errors usually happen because humans get tired, distracted, rushed, or misunderstand what data they are supposed to input. Poor forms, unclear labels, or complex interfaces also increase the chance of mistakes. Reflecting on these errors helps us understand how to design better systems, improve training, and reduce risks.

Transcription Errors

A transcription error happens when data is recorded incorrectly during the process of copying, typing, or transferring information from one place to another. This might include copying from a paper form to a database, listening to someone say information, or transferring data between systems.

Why transcription errors occur

• Fatigue or distraction

• Poor handwriting on original documents

• Rushed data entry

• Mishearing information

• Misreading similar characters (e.g., 0 vs O, 1 vs I)

Examples of transcription errors

• Typing “Baker Street” as “Baket Street”

• Entering £530.00 as £350.00

• Recording a phone number as 07982 613441 instead of 07982 613441

• Copying a passport number incorrectly because the handwriting was unclear

In practice

A college administrator enters student enrolment details into the management system. The student’s surname is “Harrington”, but the administrator types “Harington”. This leads to the student’s emails and login details failing to generate correctly, causing delays.

Transposition Errors

A transposition error happens when the correct characters or numbers are used, but placed in the wrong order. This type of error is especially common when dealing with long numbers or codes.

Why transposition errors occur

• Typing too quickly

• Mis-hitting keys on a keyboard

• Losing place in a long numerical sequence

• Visual fatigue when reading long codes or IDs

Examples of transposition errors

• Entering 81 instead of 18

• Typing £1,294 as £1,249

• Recording a product code A473B as A437B

• Entering a date as 12/03/2025 instead of 13/02/2025

In practice

A library assistant enters book barcodes into the system for inventory. The correct barcode is 496721, but it is entered as 469721. This causes the wrong book to appear as “missing” in the system.

Case Study: Hospital Appointment System Failure

A hospital experienced significant problems due to transcription and transposition errors in its patient appointment system.

Administrative staff manually entered patient NHS numbers, appointment times, and treatment codes.
Several transposition errors occurred where NHS numbers were typed in the wrong order.
Some transcription errors caused patients’ names and dates of birth to be incorrectly recorded.

Consequences

Patients received incorrect letters, including appointment times for different people.

Medical records were temporarily mismatched, risking incorrect treatment plans.

Appointments were missed, leading to delays in diagnosis and treatment.

The hospital faced complaints and had to conduct a large internal audit, costing money, staff time, and reputational damage.

Reflection
This situation highlights that even small errors made during routine data entry tasks can create large-scale risks and operational failures. Good system design, staff training, and automated validation tools are essential to reduce the frequency and impact of these mistakes.

3.8.5 Know and understand methods to reduce data entry errors:

Data entry errors occur when information is typed, selected or recorded incorrectly within a system, database, spreadsheet, or form. These mistakes can include typing the wrong value, selecting the wrong option, missing data, or even entering information in the wrong field. Although some errors might seem small, they can lead to serious issues such as inaccurate records, poor decision-making, failed transactions, incorrect reporting, or even legal and compliance breaches—especially in professional environments where accuracy is essential.

Reducing data entry errors is important because reliable data underpins every digital system. If the data is wrong from the start, any process or analysis that relies on it will also be unreliable. For example, a misspelled email address can prevent a customer receiving an order confirmation; an incorrect stock number can cause shortages; and errors in medical or financial systems can have serious consequences. Preventing these errors saves time, lowers costs, improves efficiency, and increases trust in the organisation’s data.

To help avoid these mistakes, digital systems use several methods to reduce human error during data entry. These methods provide structure, checks and support for the user, guiding them to enter accurate, complete and appropriate information.

Validation of User Input

Validation is the process of checking data before it is accepted by the system. The system tests whether the input meets specific rules—such as being in the correct format, within a certain range, or containing the right type of characters.

How it works:
When a user enters information, validation rules check it against criteria. If the input does not match, the system gives an error message and asks the user to correct it.

Examples of validation rules:

• Format check: A postcode must follow a valid UK format (e.g., ME4 6AB).

• Range check: Age must be between 0 and 120.

• Presence check: A required field must not be left blank.

• Length check: A phone number must have the correct number of digits.

• Data type check: A price must be a number, not text.

Why it reduces errors:
Validation stops incorrect or inappropriate data before it enters the system. It forces users to correct mistakes immediately, which prevents inaccurate data being stored and used later.

"Stop the Garbage"

Scenario: You have been hired by a local gardening group to update their database to include validation to remove inaccurate data entries. This is important to them as, without valid information going into the system, they cannot guarantee the correct information being generated.

Steps:
Using the provided Database file below, add the following validation rules
1. Automatically capitalise an expert's surname
2. A rose height can only be between 0.6 and 1.82 

Rose Database

Verification of User Input by Double Entry

Verification by double entry is a method where the system requires the user to enter the same piece of data twice to confirm accuracy. The system automatically checks whether both entries match.

How it works:
When signing up for an account, a user may need to re-enter their email or password. If the two entries are different, the system highlights the mismatch.

Example:
Typing an email address in two separate boxes—if one is mistyped (e.g., “.co.uk” vs “.couk”), the system detects the difference.

Why it reduces errors:
Double entry verification is especially useful for critical fields where even a small error could cause major issues—such as email addresses, bank account numbers, or passwords. It forces the user to spot and correct errors through repetition.

Drop-Down Menus (Selection Lists)

Drop-down menus allow users to select data from a predefined list instead of typing. This method eliminates typing errors and ensures consistent formatting.

How it works:
Instead of typing a location, job role, or product category, the user clicks a menu and selects from available options.

Examples:

• Selecting a department name from a list: “IT Support”, “HR”, “Finance”.

• Choosing a delivery method: “Standard”, “Next Day”, “Click & Collect”.

• Selecting a country or title from a controlled set of values.

Why it reduces errors:
Drop-down menus:

• Prevent spelling errors (e.g., “Maidstone” vs “Madistone”).

• Ensure consistency—everyone uses the same wording.

• Reduce confusion by showing only valid options.

• Speed up data entry.

They are especially useful in systems where categorisation and consistency are important-like stock systems or HR databases.

Pre-Filled Data Entry Boxes (Auto-Fill / Default Values)

Pre-filled boxes contain information provided by the system automatically-either based on previous records, user accounts, or common default settings. The user only needs to confirm or adjust the information.

How it works:
When filling in a form, fields such as name, date, department, or location may already be completed based on stored profile data or the most common choice.

Examples:

• A customer’s address auto-fills after entering their postcode.

• “Today's date” automatically appears on a report form.

• A device serial number is pre-filled for internal IT support requests.

• Default values such as “United Kingdom” or “Quantity: 1”.

Why it reduces errors:
Pre-filled information:

• Minimises the amount of typing required.

• Ensures frequently used data is always correct.

• Prevents users from entering invalid or inconsistent information.

• Speeds up the overall process.

This method is especially helpful when users repeat similar tasks or when the system already holds reliable background data.

This activity is designed to help you practise reducing data entry errors using common techniques found in real digital systems. You will work with a spreadsheet that includes validation rules, double-entry verification, drop-down menus and pre-filled fields. These features are used in workplaces such as IT support, HR, finance, retail systems and online forms.

By completing the form correctly, you will learn how each method helps prevent mistakes, improves accuracy and makes data more reliable. You’ll also reflect on which types of errors you personally found easier or harder to avoid, helping you understand the importance of entering accurate data in any digital role.

The aim is to give you hands-on experience with the same tools professionals rely on every day to ensure data is correct, consistent and fit for purpose.
Download file
Download file
 

3.8.6 Know and understand the factors that impact implementation of data entry:

Time needed to create the screens

The amount of time required to design and build data-entry screens directly affects how quickly the organisation can implement the process.
If screens are complex, contain many validation rules, or need to be accessible on different devices, the development time increases. Longer development time can delay the rollout of the system and slow down the wider project. In T-Level examples, changes to digital systems often require careful planning and adaptation time, which impacts how quickly solutions can be deployed .
Efficient screen creation is important because delays can reduce productivity and increase costs before the new process is even used.

Expertise needed to create screens

The skills required to build data-entry screens also influence implementation.
Screen creation may require knowledge of UX design, database structure, validation rules, and possibly programming or form-building tools. If the organisation does not have staff with the right expertise, the process slows down or becomes more expensive because training or external support is needed.
This mirrors exam content where learners must consider how technical skills and staff capability affect the successful adoption of digital systems and processes .
A lack of expertise can also result in poorly designed interfaces, which increases user errors and reduces efficiency.

Time needed to enter the data.

The amount of time required for staff to input data affects day-to-day productivity.
If forms contain too many fields, require manual typing, or load slowly, the time to enter each record increases. This can reduce staff efficiency and can increase the likelihood of errors because users may rush or become fatigued — consistent with how the mark schemes explain links between user actions, accuracy, and the need for error-prevention methods (validation, verification, drop-downs, pre-filled fields) .
Reducing input time through better screen design, automation or pre-filled fields can therefore improve accuracy and speed.

Explain two factors that impact the implementation of data entry screens within a digital system. Your answer should refer to:

 - time needed to create the screens
- expertise needed to create the screens
 - time needed to enter the data

(4 marks)

Improving Data Entry Efficiency

Scenario:
You have been asked by a digital support technician to evaluate the data entry process used in a small organisation. The current process uses slow, manually typed forms that were built several years ago by staff with limited design experience.

Your Task:

A. Evaluation Task (10 minutes)
Review the three factors below:
Time needed to create the screens
Expertise needed to create the screens
Time needed to enter the data

For each factor, write:
What the issue is in the scenario
How this issue affects staff, accuracy, or efficiency
One improvement you would suggest (e.g., validation, simpler layout, automation, drop-downs)

B. Quick Reflection (5–10 minutes)

Answer the following:
Which factor has the biggest impact on data quality in the scenario, and why?
Which factor could be fixed most quickly?
Which factor needs long-term planning?

3.8.7 Understand the relationship between factors that impact data entry and data quality and make judgements about the suitability of methods to reduce data entry errors in digital support and security.

High-quality data underpins secure, reliable and compliant digital-support operations. The accuracy of data entered into a system directly affects its usefulness for troubleshooting, monitoring, reporting, cyber-security processes, and compliance with legal requirements such as GDPR. Several factors influence the effectiveness and accuracy of data entry, and these have a direct relationship with the quality of the resulting dataset.

1. Time Required to Enter Data - Impacts Accuracy and Completeness

When data entry is rushed (e.g., technicians under time pressure logging incidents during busy shifts), errors become more frequent. These may include:

• Transcription errors – incorrect characters entered

• Transposition errors – swapping of characters (seen in Paper 1 Q2b: Transposition )

• Omitted or incomplete fields

Lower time investment → Higher error probability → Reduced quality.

2. Expertise and Skill Level of the User - Impacts Validity and Consistency

Users with low digital literacy may misinterpret prompts, choose incorrect items from dropdown menus, or misunderstand technical terminology.
This leads to:

• Invalid data types

• Incorrect categorical selections

• Misformatted entries (dates, IP addresses, device names)

The mark scheme in Paper 1 (Question 2c) highlights how validation and verification methods exist specifically to mitigate errors from user inaccuracy or misunderstanding.

3. Complexity of the Data Entry Screens - Impacts Usability and Error Rates

Screens that are cluttered, poorly labelled or require multi-stage navigation increase the cognitive load on the user, leading to more errors.
Complex screens may:

• Confuse users

• Encourage guess-work

• Slow down workflows

• Increase the likelihood of wrong selections in drop-down boxes

4. Environmental and Organisational Factors - Impacts Reliability

In digital support and security, data entry often happens during:

• Incident response

• Customer interaction

• Fault logging and system monitoring

If technicians work in stressful or noisy environments, quality falls due to distraction and pressure.

Judgement: Suitability of Methods to Reduce Data Entry Errors

The Pearson mark schemes expect reasoning that includes a method + linked justification (e.g., Paper 1, Q2c and Paper 2 Q3). The following assessment applies this structure.

1. Validation (Highly Suitable)

Why: Ensures data follows the correct rules (e.g., integers only, valid date format).
Impact on quality: Prevents incorrect data types, ensuring datasets remain usable for analytics, monitoring and security auditing.
Suitability: Very suitable – works reliably for structured, predictable data.

2. Verification by Double Entry (Moderately Suitable)

Why: Requires the user to type the same value twice, checking for mismatch.
Impact: Reduces typographical errors but doubles input time.
Suitability: Suitable for critical fields (e.g., device IDs, account usernames) but unsuitable for large volumes of data due to time burden.

3. Drop-Down Menus (Highly Suitable)

Why: Limits choices to valid options, making many errors impossible.
Impact: Improves consistency and eliminates spelling/format errors.
Suitability: Very suitable, especially for categorised or structured data (incident type, device model, location).

4. Pre-Filled Boxes / Auto-Completion (Suitable in Repetitive Contexts)

Why: Reduces typing and speeds up workflows where common answers dominate.
Impact: Minimises human error but may introduce “confirmation bias” if users accept default values incorrectly.
Suitability: Good, but requires careful design to avoid complacency.

5. User Training (Essential but Variable)

Why: Improves understanding of data requirements and system UX.
Impact: Raises overall accuracy but is dependent on user motivation and retention.
Suitability: Essential baseline measure, though not error-proof.

6. Interface Simplification / UX Redesign (Highly Suitable)

Why: Reduces cognitive load, guiding users toward correct behaviour.
Impact: Can significantly reduce error rates and increase speed.
Suitability: Very suitable, especially when combined with validation.

Case Study: Data Quality Risks in a Security Support Environment

Context:
A Digital Support Technician at "SecurePoint IT Services" logs all security-related incidents into a centralised security ticketing platform. The organisation has recently experienced rising error rates in incident logs, such as:

• Incorrect device IDs

• Wrong IP addresses

• Incorrect categorisation of threat types

• Missing timestamps

Events:

1. A technician rushes through logging suspicious traffic alerts during a DDoS mitigation event.

   • Time pressure leads to an incorrect IP range being entered.

   • As a result, the security team blocks the wrong subnet, disrupting internal VoIP traffic.

2. Less-experienced staff misinterpret the "Threat Vector" dropdown and repeatedly select unrelated categories.

   • The security analytics dashboard becomes unreliable.

   • Weekly reports misrepresent the type of attacks being faced.

3. Complex multi-screen navigation causes technicians to skip mandatory fields.

   • Incident logs are incomplete, limiting root-cause analysis.

Solutions Implemented:

• Dropdown menus for threat types and device categories.

• Validation rules enforcing correct IP formats.

• Revised screen layout with clearer grouping and progressive disclosure.

• Short, targeted refresher training.

• Reasonable time allocations for post-incident logging.

Outcome:
Error frequency drops by 62%. Incident analysis becomes more reliable, and the SOC (Security Operations Centre) prevents two near-miss escalation events caused by earlier poor-quality data.

“How Data Entry Factors Influence Data Quality in Digital Support & Security”

Activity:
Students work in pairs to produce a 5–7 minute presentation explaining the relationship between data entry factors and data quality, including a justified evaluation of methods used to reduce data entry errors.

Areas You Must Cover (Mirrors Exam Expectations)
Explanation of factors affecting data entry:
 - Time
- User expertise
- Screen/interface complexity
- Environmental influences

Analysis of how these factors affect data quality:
- Accuracy
- Validity
- Completeness
- Consistency
- Reliability

Judgement of methods to reduce data entry errors:
- Validation
- Verification
- Drop-down menus
- Pre-filled/auto-complete
- UX design
- Training

Use linked justifications
Reflecting the “one mark for explanation + one mark for justification” style in Pearson papers (e.g., Paper 1 Question 2c).

Apply content to a realistic digital support/security scenario
e.g., SOC incident logging, IT helpdesk ticketing, compliance reporting.

Final judgement/conclusion
- Which methods are most effective?
- How do they improve data quality in a security-focused environment?

Success Criteria (Teacher-Facing but Student-Friendly)
- Clear explanation of relationships between factors and data quality
- Accurate use of terminology (validation, verification, accuracy, reliability, transposition etc.)
- Evidence-based evaluation
- Realistic security-linked examples
- Professional, structured presentation delivery

3.8.8 Understand the relationship between factors that impact implementation of data entry and make judgements about the suitability of implementing data entry in digital support and security.

Last Updated
2025-11-28 11:19:51