Informatics, structured and unstructured data, and alternative data: HSC Enterprise Computing Data Science
“Investigate how informatics supports the development of a deeper understanding of data; interpret and present data using graphs, infographics, dashboards, reports, network diagrams and maps; investigate structured and unstructured datasets; explore the use of likes, emoticons and memes as forms of alternative data as sources of feedback”
Informatics turns data into information and knowledge by linking, organising and analysing it. Present data in the format that fits its shape and audience. Structured data has fixed fields; unstructured data does not and needs extra processing. Likes, emoticons and memes are fast alternative feedback but are ambiguous, biased and easy to inflate.
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What this dot point is asking
This group of content points is about turning data into understanding. You need to explain what informatics adds, choose an appropriate way to present data, tell structured from unstructured datasets, and evaluate social media reactions as a form of feedback.
The answer
Informatics
Informatics is the study and practice of how data is collected, stored, processed, retrieved and communicated so that people can turn it into information and knowledge. It combines computing with the needs of a field (health informatics, business informatics, sports informatics).
Informatics supports a deeper understanding of data by:
- linking data from different sources (sales, weather, staff rosters) so patterns across them become visible;
- designing how data is organised and labelled (metadata) so it can be found and trusted;
- applying analysis and visualisation so people can act on the results.
The chain to remember is data (raw facts) to information (processed data with context) to knowledge (understanding that guides decisions).
Presenting data
| Format | Best for |
|---|---|
| Graph or chart | Trends over time (line), comparing categories (bar), relationships (scatter) |
| Infographic | Telling a simple story to a general audience with icons, numbers and short text |
| Dashboard | Monitoring several key measures at once, often live and interactive |
| Report | Detailed analysis with explanation, tables and recommendations |
| Network diagram | Relationships and connections between people, devices or web pages |
| Map | Data with a location, such as sales by region or live vehicle positions |
Choose by asking who the audience is, what decision they need to make, and what shape the data has (time, category, location, connection).
Structured and unstructured datasets
- Structured data fits a fixed model of fields and records (database tables, spreadsheets, sensor logs). It is easy to sort, filter, query with SQL and analyse statistically.
- Unstructured data has no predefined model (emails, reviews, social media posts, images, audio, video). It makes up most of the data enterprises hold and needs extra processing, such as text mining, image recognition or manual coding, before analysis.
- Semi-structured data sits between them (JSON, XML, emails with header fields).
Alternative data as feedback
Alternative data is non-traditional data used as a signal. Likes, emoticons and memes are fast, cheap, high-volume feedback that shows reach and emotional reaction.
Limitations to discuss:
- Ambiguity: the same emoji can express approval, irony or mockery.
- Validity: a like measures attention, not satisfaction or intention to buy.
- Bias: only active users respond, and algorithms decide who sees content.
- Manipulation: bots and paid engagement inflate counts.
A council posts a proposal for a new skate park. It receives 1,200 likes, 300 angry-face reactions and a meme mocking the design.
- Structure: the reaction counts are structured; the comments and meme are unstructured.
- Interpretation: likes suggest broad support, but angry reactions and the meme show a vocal opposing group. Reading the comments (manual coding into themes such as noise, cost and location) explains why.
- Limitation: people who do not use the council's page are not represented.
- Action: combine the social data with a formal consultation survey and a map of where respondents live.
- Treating likes as votes
- They measure engagement, not agreement or intention.
- Saying unstructured data cannot be analysed
- It can, but it needs processing first.
- Choosing a pie chart by default
- Match the format to the data shape and audience.
Practice questions
Original practice questions graded from foundation to exam level, each with a full worked solution. Try them before revealing the solution.
foundation3 marksClassify each as structured, semi-structured or unstructured data: (a) a table of customer orders, (b) a folder of product review videos, (c) a JSON file returned by a weather API.Show worked solution →
(a) Structured: fixed fields and records in a table.
(b) Unstructured: video has no predefined fields that can be queried directly.
(c) Semi-structured: JSON uses tags (keys) to label values but has no fixed table schema.
Marking guide: 1 mark each.
core4 marksA regional bus company wants to present (i) the number of passengers per route each month to its board and (ii) live bus locations to commuters. Recommend a presentation format for each and justify it.Show worked solution →
(i) A report with a bar chart or a dashboard. The board needs to compare routes and months at a glance; a bar chart compares categories, and a report adds commentary and recommendations.
(ii) An interactive map. Commuters need to see where buses are in relation to their stop, and location data is best understood spatially. Updating the map in real time supports decisions such as when to leave home.
Marking guide: 1 mark per suitable format, 1 mark per justification linked to the audience and data.
exam6 marksA cosmetics brand plans to measure the success of a product launch using likes, emoji reactions and memes shared on social media instead of a customer survey. Evaluate this plan.Show worked solution →
- Strengths
- Alternative data is available immediately and in large volume, costs little to collect, and captures spontaneous reactions rather than answers to leading questions. Tracking likes and shares over time shows reach and momentum, and memes show whether the product has entered popular culture.
- Limitations
- The data is unstructured and ambiguous: a laughing emoji or a meme may mock the product rather than praise it, and sarcasm defeats simple sentiment analysis. Likes measure attention, not purchase or satisfaction. The audience is self-selected (followers and active users), bots and paid engagement can inflate counts, and platform algorithms decide who sees posts, so the sample is biased.
- Judgement
- Alternative data is a useful early signal of reach and sentiment, but it should not replace a survey. The brand should combine it with structured data such as sales, return rates and a short survey of actual buyers, and use sentiment analysis checked by human review.
Marking guide: 2 marks for strengths, 3 marks for limitations (ambiguity, validity, bias), 1 mark for a justified judgement.