Business Intelligence and Data Analytics

After completing this topic, you should be able to explain how data can be analysed to provide business intelligence.

The previous topics have examined the meaning of data and the role of finance professionals in ensuring high-quality data is obtained, collected, and stored ethically and effectively.
The role of finance professionals has evolved from merely recording transactions to becoming business partners who provide insights that shape organizational strategies and values.
The accountant influences decision-making to achieve organizational goals.
Overview of the topic will cover:
- Data analytics as integral to finance roles.
- Ensuring high-quality information for businesses.
- Types of data analytics available and techniques used in accounting.
- Specific types of information from data analysis.

Definition of Business Intelligence: Actionable insights into improving business systems and increasing value.
- Managers need business intelligence to make informed decisions.
Finance professionals act as business partners, providing data-driven insights to management.
They work alongside business leaders to develop, implement strategies for enhanced capabilities and goals achievement.

These activities guide the role of finance professionals in producing quality insights.

A vital skill for finance professionals is to communicate findings effectively to decision-makers.
This requires:
- Understanding the qualities of good information.
- Using visualization tools like graphs and dashboards to present periodic summaries of key performance information.

Data collection involves:
- Identifying potential sources.
- Evaluating available data.
- Assembling useful data for analysis.
Related topics: ‘Meaning of Data,’ ‘Data Collection and Storage,’ and ‘Data Ethics.’

Data must be evaluated to extract actionable business insights.
- Example: Analyzing competitor performance to understand higher sales.

Finance professionals use analytics to support business decisions:
- Drawing up detailed business cases.
- Producing budgets and resource allocations.
- Developing appropriate performance measures.

Collect data on performance post-implementation.
Analyze this data to understand overall business impact.
Derive insights for future strategies and enhance decision-making for greater business value.

Finance professionals ensure high-quality business intelligence is presented:
- High-quality datasets do not guarantee equal informational value.
Quality information can be remembered with the mnemonic ACCURATE:
- Accurate
- Complete
- Cost-effective
- User specific
- Reliable
- Accessible
- Timely
- E understandable

Accuracy: Information must be free of errors and biases; relevant details must not be omitted.
- Example: A pricing strategy report requires competitor prices and economic forecasts along with costs.
Cost-effectiveness: The value derived from information must outweigh acquisition costs.
User-specific: Tailored to the intended recipient's needs and communicated at the required detail level.
- Example: Managers require detailed performance reports; board members need summarised reports.
Reliability: Confidence in the sources of data used; ensure references for outdated or unreliable data.
Timeliness: Information must be available when needed, enabling meaningful actions;
- With AI and real-time analytics, timeliness means ‘at once’ for decision-making.
Understandability: Use visualization tools to make information comprehensible.
- Avoid jargon that may confuse non-finance stakeholders.

Four main types of data analytics provide different insights:
1. Descriptive Analytics: Describes what has happened.
2. Diagnostic Analytics: Explains why something has happened.
3. Predictive Analytics: Forecasts what is likely to happen.
4. Prescriptive Analytics: Suggests or even decides what to do next.

Examines data to provide a picture of past events, reporting historic data and current performance.
Often used to provide management with key performance indicators (KPIs).
Tools used include:
- Frequencies: Measure occurrences.
- Averages: Compare outcomes against a standard.
- Variability: Measure data dispersion.
- Standard Deviations and Variances: Understand deviation from averages in production contexts.

Interprets insights from descriptive analytics to discover causes.
Known as root cause analysis.
Techniques include:
- Correlations: Assessing relationships to determine cause and effect.
- Variance Analysis: Identifying discrepancy between budgeted and actual performance.
- Exception Identification: Detecting anomalies indicating issues or potential fraud.

Uses past data and patterns to forecast future relevant outcomes.
Applications include:
- Regression Analysis: Models relationships to predict outputs.
- Simulations: Evaluates variable impacts.
- Decision Trees and Scenario Analysis: Maps potential outcomes.

Analyzes data to recommend actions for optimal business outcomes.
May operate automatically without human intervention (e.g., self-driving cars).
Applications include:
- Advertising campaign selection.
- Production plan optimizations.
- Currency holding recommendations.

The topic focused on the role of analytics in achieving data-driven decisions by finance professionals.
Discussed ensuring quality information for decision-making.
Reviewed the four types of analytics: descriptive, diagnostic, predictive, and prescriptive, emphasizing the techniques used and the importance of combining these analytics for problem-solving.