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Performance Management · Big data and data analytics

Sources of Big Data and Data Types for ACCA PM

Updated 11 October 2026 · Fact-checked

Big data comes from internal sources (transaction systems, sensors, staff records) and external sources (social media, public data, partners). It takes three forms: structured (fits rows and columns), unstructured (no set format, such as emails or video) and semi-structured (tagged but flexible, such as XML). In exams, classify the data, then link it to a business use.

Understand Sources of Big Data and Data Types

Big data means very large, fast-growing and varied data sets that normal software cannot easily store or analyse. Organisations collect it because patterns in it can improve decisions about customers, costs and risks.

Start with where data comes from. Internal sources sit inside the organisation. Examples are sales and accounting systems, inventory records, payroll, customer loyalty records, website logs, machine sensors and delivery vehicle trackers. The organisation controls these and usually owns them.

External sources come from outside. Examples are social media posts, customer reviews, government and public statistics, competitor websites, credit agency data, weather data, market research and data bought from third-party providers. They add context that internal data lacks, but quality and legal use are less certain.

Now the types. Structured data has a fixed format and fits neatly into tables with defined fields, such as a sales ledger or a price list. It is easy to search and analyse. Unstructured data has no predefined format, such as emails, photos, video, audio and free-text comments. Most big data is of this kind and needs special tools to analyse. Semi-structured data has some tags or markers that give it organisation but does not fit a rigid table. Examples are XML or JSON files, emails with sender and date fields plus free text, and web pages with tags.

Organisations collect data through transaction processing, sensors and the Internet of Things, website and app tracking, scanning and loyalty cards, social media monitoring, surveys, and purchase or licensing of data sets. The collection method often shows whether the source is internal or external, and what type results.

Key rules to remember

Source classification
Internal = generated inside the organisation; External = obtained from outside it
Ask who created the data and where it was captured. Bought data is external even once stored in your system.
Structured data
Structured = fixed format, rows and columns, predefined fields
Examples: ledgers, inventory tables, payroll records.
Unstructured data
Unstructured = no predefined format or data model
Examples: emails body text, images, video, audio, social media posts.
Semi-structured data
Semi-structured = tags or markers give partial organisation, no rigid table
Examples: XML, JSON, emails with header fields, web pages.

How to solve Sources of Big Data and Data Types questions

Use this method for any question on sources or types of big data.

  1. 1Read the scenario and list each data item mentioned.
  2. 2For each item, decide who generated it: inside the organisation (internal) or outside (external).
  3. 3Decide the format: does it fit a table (structured), have no set format (unstructured), or have tags with flexible content (semi-structured)?
  4. 4State the classification in one clear phrase, such as 'external, unstructured'.
  5. 5Explain how the organisation would collect it, for example sensors, web tracking or purchase.
  6. 6Link the data to a specific use in the scenario, such as pricing, cost control or customer targeting.
  7. 7Add one limitation if the question asks for evaluation, such as quality, cost, privacy or storage.

Quickest way: Two-question test

When to use it: Use in Section A and Section B objective questions where time is short.

  1. Ask 'Who made it?' Inside means internal, outside means external.
  2. Ask 'Could it sit in a spreadsheet with fixed columns?' Yes means structured.
  3. If no columns but there are tags or fields, choose semi-structured.
  4. If it is free text, image, audio or video with no tags, choose unstructured.
  5. Check the wording of the options and pick the one matching both answers.

Common mistakes in Sources of Big Data and Data Types

  • Calling all big data unstructured.

    Textbooks stress that most big data is unstructured, so students overgeneralise.

    Fix: Big data includes all three types. Classify each item on its own format.

  • Treating an email as purely unstructured.

    The body text is free form, so students ignore the sender, date and subject fields.

    Fix: Say an email is semi-structured if the question considers its fields. Unstructured fits the body text alone.

  • Classifying purchased data as internal because it is stored in the company database.

    Students focus on where it is kept, not where it came from.

    Fix: Source depends on origin. Data bought from a third party is external.

  • Listing sources without linking to the scenario.

    Students recall a memorised list instead of reading the case.

    Fix: Name data the business would actually collect, such as store loyalty cards for a retailer, and say what decision it supports.

  • Mixing up data types with the Vs of big data.

    Both are in the same chapter, and 'variety' touches on data types.

    Fix: Types describe format. Volume, velocity, variety and veracity describe characteristics. Answer what is asked.

Worked examples

Example 1

A retail chain records every till sale in its accounting system, tracks customer comments on social media, and buys regional weather data. Classify each item as internal or external, and as structured, unstructured or semi-structured. (6 marks)

Show the solution
  1. Till sales: generated by the chain's own systems, so internal. Each sale has set fields such as date, product and price, so structured.
  2. Social media comments: written by customers on external platforms, so external. Free text with no fixed format, so unstructured.
  3. Weather data: bought from a provider, so external. Usually supplied as a table of dates, places and measures, so structured. If supplied as XML or JSON feeds, it would be semi-structured.
  4. State a use: sales data supports inventory planning, comments show customer sentiment, weather data helps forecast demand for seasonal goods.

Answer: Till sales: internal, structured. Social media comments: external, unstructured. Weather data: external, structured (semi-structured if supplied as XML or JSON). Each supports a decision such as stock planning or demand forecasting.

Example 2

A logistics company fits sensors to its trucks and keeps driver emails and maintenance reports. Explain two internal sources of big data here and how each could help management accountants. (4 marks)

Show the solution
  1. Source one: truck sensors. They are internal because the company owns the trucks and captures the data. They collect location, speed and fuel use continuously, often in a structured or semi-structured form.
  2. Use of sensor data: management accountants can measure fuel cost per kilometre, identify inefficient routes and set better cost standards.
  3. Source two: maintenance reports. They are internal records produced by the company. They may be structured fields (date, cost, part) with free-text notes, so partly unstructured.
  4. Use of maintenance data: link repair costs to vehicle age or usage to support replacement decisions and budget maintenance costs.

Answer: Truck sensors give real-time data on fuel and routes, helping control transport costs. Maintenance reports show repair patterns, supporting replacement decisions and maintenance budgets. Both are internal sources.

Exam tips

  • Read each data item and decide source and type separately. Questions often test both at once.
  • In Section C, always tie the data to the business in the scenario. Generic lists score poorly.
  • Watch for options where one is internal but the wrong type. Only one option matches both.
  • Mention a limitation such as cost, privacy, quality or storage when asked to evaluate.
  • Use the exact terms structured, unstructured and semi-structured, not 'messy' or 'organised'.

Practice questions from Big data and data analytics

Sources of Big Data and Data Types in other exams

The same ground in other exams, if you are preparing for more than one or want another angle on it.

Sources of Big Data and Data Types: frequently asked questions

What is the difference between structured and unstructured data?

Structured data fits a fixed format of rows and columns, such as a sales ledger. Unstructured data has no predefined format, such as video, photos or social media posts. Structured data is easier to analyse with standard tools.

What are examples of semi-structured data for accountants?

Examples are XML or JSON files used to exchange financial data, emails with header fields plus free text, and web pages with tags. They have some organisation but not a rigid table.

What are the main internal and external sources of big data?

Internal sources include transaction systems, sensors, website logs and payroll. External sources include social media, public statistics, competitor information and bought data sets.

Is data bought from a third party internal or external?

It is external, because it originates outside the organisation. Storing it in your own system does not change the source.