MM1041 Chap.2 Data Analytics: Process, Characteristics and History
Data Analytics: Process, Characteristics and History
A definition you can write from memory
The e-learning lesson treats data analytics and data analysis as the same thing and defines it through four actions: inspecting data, cleaning it, transforming it and modelling it. Its goal is to discover useful information, inform conclusions and support decision making.
The four verbs matter: each one is a stage where an analyst can add value or make a mistake, and an answer that says only that analytics means looking at data has left out three of them.
Analytics is the last stage of a pipeline
Before any analysis, raw data is collected from sources such as the internet, social media and transactions, stored in a database, a data warehouse or files, and retrieved so that only the relevant pieces are examined.
The lesson warns that trying to analyse everything quickly becomes unmanageable. In a business project retrieval is where the scope is set, and a well-scoped question is what makes the rest of the pipeline finishable.
Three characteristics
The lesson describes analytics through its input, method and output.
The input is data driven and, ideally, large scale, because conclusions from a small sample are less reliable. The method is interdisciplinary, built on mathematics and computer science, with domain knowledge added when needed. The output is knowledge or information used for decisions.
Learning these as input, method and output makes them easy to recall under exam conditions.
A history of cases
The history begins with a cholera outbreak in mid-nineteenth-century London. John Snow mapped deaths, saw a cluster, and used one outlying case, a woman who had her favourite well water delivered to a new home, to show that the disease came from a pump rather than bad air.
The 1880 United States census, covering about fifty million people, took more than seven years to tabulate by hand, while the Hollerith punched-card system processed the 1890 census in six weeks.
The Manhattan Project and the space programme later pushed computing-based analysis further.
Why now
The lesson explains today's popularity of analytics with three forces: better models, moving from rule-based through statistical approaches to deep learning; better computing resources such as faster processors and graphics chips; and far more data from the internet and social media.
It also names everyday applications, including product recommendation from purchase history, ranking web pages by the links pointing to them, and artificial intelligence itself.
What this chapter covers
- 01
Inspecting, cleaning, transforming and modelling data
- 02
Collection, storage, retrieval and analysis
- 03
Data driven input, interdisciplinary method, decision output
- 04
The cholera map and the outlier
- 05
From a seven-year census to six weeks
- 06
Better models, better computing, more data
- 07
Recommendation and page ranking
Worked example · free
Explain a recommender with the three characteristics
- 2Describe the input.
- 2Describe the method.
- 2Describe the output and the decision it supports.
Key terms
- Data Retrieval
- Selecting only the relevant data from storage before analysis, which sets the scope of the work.
- Data Warehouse
- A central store that holds large volumes of business data for later analysis.
- Hollerith Tabulating System
- A punched-card machine that processed the 1890 United States census in weeks rather than years.
- Outlier
- An observation that does not fit the main pattern and may either be an error or the key evidence.
- Product Recommendation
- Predicting which items a customer is likely to buy from the items they bought before.
- Interdisciplinary Method
- An approach that combines mathematics, computer science and domain knowledge to analyse data.
- Page Ranking
- Ordering web pages by importance, judged by how many other pages link to them.
Data Analytics: Process, Characteristics and History FAQ
Is data analysis the same as data analytics?
In this course, yes. The e-learning lesson uses the two terms as equivalents and gives one definition for both, built on four actions, inspecting, cleaning, transforming and modelling, aimed at useful information, sound conclusions and better decisions. Some textbooks draw a distinction, but an exam answer that follows the course definition is safe.
Why does the John Snow story keep appearing in data analytics courses?
Because it is a complete run from data to decision. Deaths were recorded, mapped into a visible cluster, tested against an outlying case that confirmed the water explanation, and turned into an action, disabling the pump. It also warns analysts that the case which does not fit may be the most informative one, not noise to delete.
Why has data analytics become so popular only recently if it is so old?
The lesson gives three reasons working together. Models improved from simple rules to statistical methods and deep learning, computers became powerful enough to run them, and the internet and social media produced vast amounts of data. Any one alone would not have been enough; the combination made analysis cheap and valuable at business scale.
What stage do students most often forget in the analytics process?
Retrieval. Between storing data and analysing it, the relevant pieces must be selected. Skipping it means either analysing far more than the question needs or analysing the wrong subset. In a project, stating what you retrieved and why is also how you show the marker that your scope was deliberate.
Exam move
Learn the definition as four verbs and the characteristics as input, method and output. Then retell the cholera story and the census story in three sentences each, ending with the decision that followed. Those two short narratives cover most history questions this chapter can produce.
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