How hardware and software changed data analytics, and OLAP: HSC Enterprise Computing Data Visualisation
“Investigate the impact of the evolution of hardware and software on the field of data analytics, including processing power, storage/memory and communication media; describe online analytical processing (OLAP)”
Faster processors, GPUs and cloud computing, cheaper and faster storage and memory, and faster networks moved data analytics from overnight batch reports to real-time, interactive and predictive dashboards. OLAP stores data in multidimensional cubes of dimensions and measures and supports roll-up, drill-down, slice, dice and pivot for fast analysis.
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What this dot point is asking
You need to explain how changes in hardware and software (processing power, storage and memory, communication media) have shaped data analytics, and describe OLAP, the technology behind fast multidimensional analysis.
The answer
Processing power
- Early analytics ran as overnight batch jobs on mainframes, producing printed summary reports.
- Multi-core CPUs, GPUs (thousands of parallel cores) and cloud computing (renting thousands of servers on demand) now process huge datasets in seconds.
- Impact: real-time analytics, interactive dashboards, and machine learning on big data became practical and affordable even for small enterprises.
Storage and memory
- Storage cost per gigabyte has fallen dramatically, and capacities have grown from megabytes to petabytes.
- SSDs read data much faster than spinning hard drives, and large RAM allows in-memory analytics, where entire datasets are held in memory for instant queries.
- Impact: enterprises keep detailed historical data (not just summaries), build data warehouses and data lakes, and explore data interactively.
Communication media
- Faster networks (fibre, 4G and 5G, Wi-Fi, satellite) and cloud services move data from sensors, stores and apps to central systems continuously.
- Impact: live dashboards, access to analytics from any device, collaboration across locations, and streaming data from IoT devices.
Software evolved alongside hardware: spreadsheets, then relational databases and SQL, then business intelligence platforms, big data frameworks and cloud analytics services with drag-and-drop visualisation.
Online analytical processing (OLAP)
OLAP lets users analyse large amounts of historical data from many perspectives quickly.
- Data is organised in a multidimensional cube. Dimensions are the perspectives (time, product, location, customer type); measures are the numbers analysed (sales, units, profit).
- Values are often pre-aggregated, so summaries at any level appear instantly.
- Roll-up: aggregate to a higher level (month to year).
- Drill-down: move to more detail (year to month to day).
- Slice: fix one dimension to a single value (only 2025).
- Dice: select a sub-cube using values on several dimensions (2025, two states, one product line).
- Pivot (rotate): swap which dimensions appear on rows and columns.
OLAP differs from OLTP (online transaction processing), which handles many small, fast updates for daily operations (processing a sale). OLAP usually runs on a data warehouse fed from OLTP systems, and powers the drill-down charts and pivot tables in dashboards.
A university analyses enrolments in an OLAP cube with dimensions Year, Faculty and Student type, and the measure Enrolments.
- Roll-up: total enrolments per year (2021 to 2026) show overall growth.
- Drill-down: into 2026 by faculty; Engineering has grown fastest.
- Slice: Student type = International; Engineering's growth comes mostly from international students.
- Pivot: faculties as columns and years as rows to compare trends side by side in a chart.
- Describing OLAP as a chart type
- It is a way of structuring and querying data; charts display its results.
- Confusing OLAP with OLTP
- Analysis versus day-to-day transactions.
- Listing hardware improvements without the impact on analytics
- Always say what became possible.
Practice questions
Original practice questions graded from foundation to exam level, each with a full worked solution. Try them before revealing the solution.
foundation3 marksDescribe how improvements in storage and memory have affected data analytics.Show worked solution →
Storage has become far cheaper and larger (cloud storage, large SSDs), so enterprises can keep years of detailed data rather than summaries. Faster SSDs and large amounts of RAM allow in-memory analytics, where whole datasets are loaded into memory and queried in seconds rather than hours. This makes interactive, detailed analysis of big data practical.
Marking guide: 1 mark for cheaper and larger storage, 1 mark for speed and in-memory analytics, 1 mark for the effect on analysis.
core4 marksA retail OLAP cube has dimensions Time, Store and Product, and the measure Sales. Describe a roll-up, a drill-down, a slice and a pivot a manager might perform.Show worked solution →
- Roll-up: view total sales by quarter instead of by day.
- Drill-down: from state totals, drill into sales for each store in Victoria.
- Slice: show only the Product value "Footwear" across all stores and times.
- Pivot: swap rows and columns, so stores become columns and months become rows, to compare stores side by side.
Marking guide: 1 mark per correct operation with an example.
exam6 marksAnalyse how the evolution of processing power, storage and communication media has changed the way a national airline uses data analytics and visualisation.Show worked solution →
- Processing power
- Multi-core CPUs, GPUs and cloud computing let the airline process millions of bookings, flight sensor readings and customer interactions quickly. Pricing models can recalculate fares continuously instead of weekly, and machine learning predicts delays from weather and aircraft data.
- Storage and memory
- Cheap cloud storage means years of detailed booking and maintenance data are kept in a data warehouse. In-memory analytics lets analysts query this history interactively through OLAP, drilling from annual route profits down to individual flights.
- Communication media
- Fibre links and satellite and mobile connectivity stream aircraft engine data during flights and deliver live dashboards to operations staff, pilots and managers anywhere. Customers see real-time flight status on their phones.
- Overall impact
- Analytics has moved from delayed, summary reports to real-time, detailed and predictive dashboards. This improves decisions (rerouting, dynamic pricing, predictive maintenance) but also increases dependence on reliable networks and raises data security demands.
Marking guide: 2 marks each for processing, storage and communication analysed in context (6 marks), with credit for an overall impact statement within these.