BUSA8030 Chap.7 Big Data, Analytics Types and AI Oversight
Big Data, Analytics Types and AI Oversight
Week 4 defines big data as data whose size exceeds what traditional database systems and analytics tools can handle, and describes it through five Vs: volume, velocity, variety, veracity and value. Managing it involves storage, processing, integration, quality management, security and privacy, governance, scalability, performance and backup.
A history of data-related systems shows each era chasing a new objective and inheriting a new problem, from automating subtasks to limiting human input while avoiding bias in AI.
The lecture lists ten challenges of big data, which this guide groups into data itself, infrastructure, people and responsibility.
It separates big data, the raw material, from analytics, the techniques that make sense of it, and stresses that each draws value from the other.
Five types of analytics follow: descriptive work describes the current situation, diagnostic work finds root causes, predictive work forecasts, prescriptive work recommends the best course of action, and autonomous work builds models that act with minimal human involvement.
The final part concerns oversight.
Machine learning is described as teaching computers to learn and act without explicit programming, and the lecture asks whether AI will replace business analysts, how humans keep control and who is accountable when something goes wrong. Managers hold different views on data versus human judgment.
Six activities, covering talent, analysis, strategy, change, ethics and continuous improvement, describe how analytics should be managed. The Week 4 summary ends on balance: analytics and big data must be moderated with human knowledge and judgment.
What this chapter covers
- 01
Big data and the five Vs
- 02
Managing big data
- 03
Ten challenges in four clusters
- 04
Big data versus analytics
- 05
Five types of analytics
- 06
Machine learning, oversight and accountability
- 07
Six activities for managing analytics
Worked example · free
AskSia practice weighting: classifying analytics types in a retail case
- 1Step 1 is descriptive: it reports what is happening.
- 1Step 2 is diagnostic: it finds the root cause of the spike.
- 1Step 3 is predictive: it estimates future returns.
- 1Step 4 is prescriptive: it recommends an allocation under constraints.
- 1Keep managerial approval on step 4, because allocation commits significant stock.
Key terms
- Big data
- Data whose size and complexity exceed the capabilities of traditional database and analytics tools.
- Veracity
- The credibility of data and its sources, requiring cleansing and verification.
- Diagnostic analytics
- Analysis that identifies the root causes of a problem.
- Prescriptive analytics
- Analysis that develops and assesses the best course of action using methods such as optimisation.
- Autonomous analytics
- Models that learn from data and act with minimal human involvement.
- Machine learning
- An approach in which algorithms adapt their models through experience to improve predictions.
Big Data, Analytics Types and AI Oversight FAQ
What are the five Vs of big data?
Volume, velocity, variety, veracity and value. The first three describe the data itself, veracity asks whether it can be trusted, and value asks whether anyone benefits from it.
How is big data different from analytics?
Big data is the large, fast and varied raw material; analytics is the set of techniques that explores it for insight. Without analytics big data has little value, and analytics without big data offers less than it could.
What separates predictive from prescriptive analytics?
Predictive analytics forecasts what is likely to happen. Prescriptive analytics goes further and recommends the best action, using simulation, optimisation or multi-criteria models, so a manager receives a recommended choice rather than only a forecast of what may happen.
Will AI replace the business analyst?
Week 4 poses the question rather than settling it, then emphasises human oversight and accountability. The summary concludes that analytics must be moderated with human knowledge and judgment.
What do managers think about data-based decisions?
The lecture reports three groups: many managers see some role for data, others think data-based decisions are generally superior, and a third group believes humans decide better because knowledge lives in the mind.
Assessment move
For any technology your case organisation uses, place it on the five-type scale and note who still makes the final call. This quickly shows whether the firm's claims about AI describe autonomous action or simply better reports, a distinction that sharpens critical analysis.
Use the four challenge clusters to structure the challenges section of your report.
Pick the cluster that most constrains the organisation, support it with evidence, and avoid a long list of generic risks that could describe any company.
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