F200 TA1: Understanding Data

1 State two differences between structured and unstructured data. 2

One mark for each of the following, to a maximum of 2.

• Structured data fits a fixed schema of rows and columns
• Unstructured data has no predefined model, with an example

Model answer. Structured data fits a fixed schema of rows and columns, such as a database table, whereas unstructured data has no predefined data model at all, such as free text, images or video.

2 Explain the difference between data, information and knowledge, using one example throughout. 4

One mark for each of the following, to a maximum of 4.

• Data defined as raw facts without context
• Information defined as data given context
• Knowledge defined as information plus experience or interpretation
• A single example carried through all three

Model answer. Data is a raw fact with no context attached: the value 37.2 on its own says nothing, because it could be a temperature, a price or a percentage. Information is that data given context and structure, so 'patient A1402's temperature was 37.2 degrees Celsius on 14 March' now means something, because it has a subject, a unit and a time. Knowledge is that information combined with experience and interpretation: a clinician knows that 37.2 is within the normal range for an adult, which is a judgement the database itself cannot hold. Each step adds something the previous one lacked, and the whole purpose of an analytics process is to move from the first to the third.

3 A retailer exports its daily sales to a file that is read by a spreadsheet, a reporting tool and a Python script. Justify the use of CSV for this export, and identify two risks it introduces. 6

One mark for each of the following, to a maximum of 6.

• CSV is readable by every tool without special software
• It is small and simple to produce
• Risk: no type information
• The consequence of that risk
• Risk: no nesting, or delimiter collisions
• A mitigation is offered

Model answer. CSV is a reasonable choice here because all three consumers can read plain text without any shared library or licence: the spreadsheet opens it directly, the reporting tool imports it, and the Python script reads it in two lines. The files are small, trivial to generate from a database query and easy for a human to inspect when something looks wrong, which matters for a daily operational export. The first risk is that CSV carries no type information, so every consumer guesses: a product code of 0123 is read as the number 123, and a date written 03/04/2026 is March in one tool and April in another, and both failures are silent. The second risk is that the format is flat and delimited, so a product description containing a comma breaks the row alignment unless fields are properly quoted, and nested data such as an order with several line items has to be flattened by repeating the order header. Both risks are manageable: publish a data dictionary declaring each column's type and format, use ISO dates, quote all text fields, and move to Parquet or a database export if the volumes grow.

4 Discuss the problems an organisation faces in using the unstructured data it already holds, such as emails, support calls and documents. 8

One mark for each of the following, to a maximum of 8.

• Unstructured data has no schema to query
• It must be processed before analysis
• Volume is a problem
• Storage and cost implications
• Quality and consistency problems
• Legal or privacy problems specific to this data
• A realistic approach is proposed
• A judgement weighing value against cost

Model answer. The first problem is that unstructured data cannot be queried. A relational table can be aggregated with a single statement, whereas a folder of support emails has no fields, so there is nothing to group by and nothing to sum. Anything useful has to be created first, by extracting entities, classifying each message by topic and sentiment, transcribing recorded calls and attaching metadata, and all of that is a processing pipeline that has to be built, tuned and maintained rather than bought. The second problem is scale: unstructured content is the majority of what most organisations hold, and it is large per item, so storing it in a form that can be reprocessed means a data lake and the cloud costs that go with it. Quality is the third problem and the most underestimated. Free text is inconsistent by nature: the same product is spelled four ways, the same complaint is phrased differently by every customer, and sarcasm defeats sentiment analysis, so any measure derived from it carries error that is hard to quantify. Fourth, this data is legally the most dangerous the organisation holds. Support emails contain personal data the customer volunteered, sometimes including health information, which is special category data under the UK GDPR; it was collected to answer a query, not to train a model, so reusing it for analytics engages purpose limitation, and retention schedules are almost never applied to mailboxes. A workable approach is therefore narrow rather than total: pick one question worth answering, such as which product generates the most support contact, sample rather than process everything, classify that sample with a tested model, and keep a human check on the output. Judged overall, unstructured data is worth the effort only when a specific decision depends on it, because the cost of making it analysable is paid up front and in full, while the value is speculative until the first question has actually been answered.