You are reading the 2026 curriculum (exams up to November 2026). Sitting in February 2027 or later? Go to the 2027 Quantitative Methods guide.
Big Data, the four Vs and alternative data
Big Data is defined by volume (how much), velocity (how fast it arrives) and variety (how many forms it takes); for investment use add veracity (whether it can be trusted). It includes alternative data generated by individuals, by business processes and by sensors, much of it unstructured.
On the exam
- Describe aspects of “fintech” that are directly relevant for the gathering and analyzing of financial data
- Describe Big Data, artificial intelligence, and machine learning
Describe the aspects of fintech relevant to financial data, and describe Big Data and its characteristics. Expect "which V does this describe?" or "which source category is this data?". No calculations.
How deeply to learn each part
- Know the idea
- Understand it in general. It frames other topics, but you will not calculate with it.
- Use it
- Understand it well enough to solve a question with it. Recognize when it applies.
- Memorize
- Learn it by heart. The exam will not give it to you.
You know this one. The full lesson is folded away. The takeaways, formulas and checkpoint below are your quick refresher, and the checkpoint is a fair test of whether you still do.
You tested strong here. The full lesson is folded away. The takeaways, formulas and checkpoint below are your quick refresher, and the checkpoint is a fair test of whether you still know it.
What does fintech change for investors?
Fintech is technology-driven innovation in how financial services and products are designed and delivered. For the exam, only two parts matter: the analysis of very large datasets, including new non-traditional data, and analytical tools such as artificial intelligence that can handle them. Cheap storage, faster computers and better algorithms made both possible. Early fintech simply automated routine tasks; today’s tools learn from data.
Both quantitative managers and fundamental managers now use these tools, often in a hybrid style.
What makes data “big”?
Big Data is the vast, fast-growing stream of information produced by markets, companies, governments, individuals and devices. Three features define it, and a fourth matters when you rely on it.
- Volume Memorize how much
- Velocity Memorize how fast it is created and delivered
- Variety Memorize how many forms
- Structured tables, fixed fields
- Semi-structured tagged pages such as HTML
- Unstructured text, images, audio, video
-
- Veracity Memorize credibility and reliability, added for inference
| V | Question it answers | Example |
|---|---|---|
| Volume | How much data? | Billions of card transactions a year |
| Velocity | How fast is it created and needed? | Order-book updates every millisecond |
| Variety | How many forms? | Prices, PDFs, satellite photos, audio |
| Veracity | Can we trust it? | Social-media posts written by bots |
Volume is the number of data points, now millions or billions. Velocity is the speed and frequency of recording and transmission, with real-time or near-real-time data now common. Variety is the mix of sources and formats. Veracity is the credibility and reliability of a source. It is the exam’s favorite confusion partner for variety: variety asks “what shape?”, veracity asks “is it true?”.
Exam tip Fake reviews, bots, biased samples and missing values are veracity. PDFs, audio and images in one dataset are variety.
Structured or not?
Structured data fit rows and columns with the same fields in every record: prices, financial statements, Structured Query Language (SQL) tables. Semi-structured data, such as web pages with HyperText Markup Language (HTML) tags, have some of both. Unstructured data cannot be put in a table: text, email, photos, voice recordings, video. They usually need specialized software before an analyst can use them.
Memorize
Where does alternative data come from?
Alternative data are the non-traditional data types that sit beside traditional ones (market prices, financial statements, regulatory filings, economic releases). There are three main sources.
- Individuals Memorize mostly unstructured
- Social media, news, reviews
- Web searches, clicks
-
- Business processes Memorize mostly structured
- Transactions and sales data
- Corporate exhaust supply chain, banking records, scanner data
-
- Sensors Memorize huge volume
- Satellites, cameras, phones
- Internet of Things connected devices and buildings
-
- Individuals produce text, photos, video and audio, and also clicks and time on a page. This is mostly unstructured and growing quickly as more people shop, post and search online.
- Business processes produce information flows from companies and public bodies: direct sales data such as card transactions, plus corporate exhaust , the by-products of doing business (supply-chain records, banking records, retail point-of-sale scanner data). It is mostly structured.
- Sensors are devices such as phones, cameras, radio-frequency identification (RFID) chips and satellites. Their data are mostly unstructured and many orders of magnitude larger than the other two. Networks of embedded devices, from smart buildings to vehicles, form the internet of things .
What are the limits?
Alternative data are not free insight. The main cautions are quality (selection bias, gaps, outliers), whether the volume is sufficient, whether the dataset suits the question, and the cost of sourcing and cleaning, which is hardest for unstructured data. Traditional statistics cannot always cope with such data, which is why AI and machine learning (next lesson) are used alongside them. Legal and ethical limits apply too. Scraping , the automated collection of data from web pages, can capture personal information that is protected by regulation or was published without the individual’s knowledge, and rules differ between jurisdictions.
Go deeper Why unstructured data dominate growth
Every phone is a camera, microphone and location sensor, and every connected appliance adds a stream. These devices create images, audio and readings, not neat tables. Structured data grow too, but far more slowly, so the share of data an analyst can use without specialized tools keeps shrinking. That is why the next lesson’s tools matter.
Go deeper Alternative data and the Standards
Data that are not public raise a question the Standards of Professional Conduct address: could the information be material and non-public? A dataset assembled from licensed, lawful sources is a research input. A feed that leaks a company’s unreleased results is not. Always ask how a vendor obtained the data and whether individuals consented to its use.
Memorize this
MemorizeThe four Vs
| V | Asks | Think |
|---|---|---|
| Volume | How much data? | millions or billions of data points |
| Velocity | How fast is it created and needed? | real time or near real time |
| Variety | How many forms? | structured, semi-structured, unstructured |
| Veracity | Can it be trusted? | credibility and reliability of the source |
Memory hook V-V-V then the one that is about trust: three describe the data, the fourth (veracity) describes whether you should believe it. Variety = forms, Veracity = verify.
Know it cold Three Vs define Big Data; veracity is added when Big Data is used for inference or prediction.
MemorizeThree sources of alternative data
| Source | Usually | Examples |
|---|---|---|
| Individuals | unstructured | reviews, posts, web searches, clicks |
| Business processes | structured | card transactions, scanner data, supply-chain records (corporate exhaust) |
| Sensors (incl. IoT) | unstructured, enormous volume | satellites, phones, cameras, smart meters |
Memory hook People, Processes, Probes. Ask who or what created the data.
Know it cold Card transaction data are business-process data. Business-process data are often leading, near real-time indicators; company reports lag.
- Volume Memorize how much
- Velocity Memorize how fast it is created and delivered
- Variety Memorize how many forms
- Structured tables, fixed fields
- Semi-structured tagged pages such as HTML
- Unstructured text, images, audio, video
-
- Veracity Memorize credibility and reliability, added for inference
- Individuals Memorize mostly unstructured
- Social media, news, reviews
- Web searches, clicks
-
- Business processes Memorize mostly structured
- Transactions and sales data
- Corporate exhaust supply chain, banking records, scanner data
-
- Sensors Memorize huge volume
- Satellites, cameras, phones
- Internet of Things connected devices and buildings
-
How it is asked
-
Which V is the concern?
An analyst fears many online reviews are fake. Which characteristic of Big Data is at issue?
Trust in the data is veracity. Trap: variety (forms of data).
-
Classify the source
Night-time satellite images show how brightly a carmaker’s plants are lit. Source category?
A device collects it, so sensors. Trap: business processes (it is about a firm, but the firm did not record it).
-
Compared with company reports
How do card transaction data compare with quarterly reports?
Closer to real time (leading), while reports lag. Trap: unstructured text.
-
Structured or not?
Which data are unstructured: price table, earnings-call audio, or financial statements?
Audio. Tables with consistent fields are structured.
Exam traps
- Confusing variety (many forms) with veracity (reliability).
- Classifying card transaction data as individual data. A person makes the purchase, but in the alternative data classification the record is business-process data.
- Assuming alternative data are structured. Individual and sensor data mostly are not.
- Treating the fourth V as part of the definition. The three core Vs define Big Data; veracity matters when you use it to infer or predict.
Checkpoint
4 exam-style questions. About 90 seconds each.
1An analyst worries that a large set of online product reviews contains many fake posts. This concern relates to Big Data's:
2A fund uses night-time satellite images to gauge how busy a carmaker's factories are. These images are best classified as data generated by:
3Compared with traditional company reports, business-process data such as card transactions are most likely to be:
4Which of the following is most likely unstructured data?
Key takeaways
- Volume, velocity and variety define Big Data; veracity (can you trust it?) matters for inference and prediction.
- Alternative data come from individuals, business processes and sensors.
- Much alternative data are unstructured and need cleaning and specialized tools before analysis.
- Legal and ethical limits apply, especially to scraped web data and personal information.
Questions about this lesson?
Where this comes back
- AI and machine learning: supervised, unsupervised, deep Introduction to Big Data Techniques
Questions candidates ask
What are the characteristics of Big Data?
Volume (the amount of data), velocity (the speed at which data are created and transmitted) and variety (many sources and formats, from structured to unstructured). When Big Data is used for inference or prediction, veracity (credibility and reliability) is added as a fourth V.
What is alternative data in investing?
Non-traditional data outside prices, financial statements and economic releases, such as social media, card transactions, satellite images and sensor readings. Investors use it to look for additional insight into consumers, firms and trends.
What are the sources of alternative data?
Three: individuals (posts, reviews, searches), business processes (transactions, scanner data, supply-chain records) and sensors (satellites, phones, connected devices). Individual and sensor data are usually unstructured; business-process data usually structured.
Spotted an error? Tell us and we will fix it.