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AD-702 (B) · Business Intelligence/Quick Revision Short Notes

Business Intelligence (AD-702 (B)) - Unit 2 Short Notes

How unit 2 is examined

This unit covers how a BI system is justified, measured, built, run and governed; no topic has been asked recently, so learn each definition and its key points.

Key Drivers

<span style="display:inline-block;padding:.16em .6em;border:1.5px solid currentColor;border-radius:999px;font-size:.68em;font-weight:700;letter-spacing:.06em;text-transform:uppercase;opacity:.75">Not asked since 2022</span>

Definition. <mark>Key drivers are the business needs and pressures that push an organisation to invest in a BI system.</mark>

Key points.

  1. Competitive pressure and fast-changing markets force firms to decide faster than rivals, and BI supplies the facts for that speed.
  2. Growing data volume from sales, web and sensors is useless unless it is converted into information, which BI does.
  3. Managers need one consistent version of the truth instead of conflicting departmental reports.
  4. Cost reduction, revenue growth, customer retention and regulatory compliance are the usual goals that justify the investment.
  5. Technology drivers such as cheaper storage, cloud platforms and self-service tools make BI affordable even for mid-sized firms.
  6. Globalisation and rising customer expectations create the need to understand customers and markets from data rather than from opinion.

Example. A retail chain sees falling margins and scattered store reports; the drivers are cost pressure and the need for one consolidated view, so it builds a BI system.

Key Performance Indicators and Performance Metrics

<span style="display:inline-block;padding:.16em .6em;border:1.5px solid currentColor;border-radius:999px;font-size:.68em;font-weight:700;letter-spacing:.06em;text-transform:uppercase;opacity:.75">Not asked since 2022</span>

Definition. <mark>A KPI is a measurable value that shows how effectively an organisation is achieving a key business objective; a metric is any measure of a process, and a KPI is a metric tied to a strategic goal.</mark>

Key points.

  1. Good KPIs are SMART: specific, measurable, achievable, relevant and time-bound.
  2. Leading KPIs predict future results (sales pipeline), while lagging KPIs report past results (monthly revenue).
  3. Typical KPIs are customer churn rate, average order value, on-time delivery and profit margin.
  4. The balanced scorecard groups KPIs into four perspectives: financial, customer, internal process, and learning and growth.
  5. A metric counts activity (website visits), whereas a KPI shows progress toward a target (conversion rate against a 3 percent goal).
  6. Every KPI needs an owner, a formula, a target and a review frequency so that it can be acted upon.
Perspective Sample KPI
Financial Return on investment, profit margin
Customer Customer satisfaction, churn rate
Internal process Order cycle time, defect rate
Learning and growth Training hours, employee retention

Formula. $\text{Churn rate} = \dfrac{\text{customers lost in period}}{\text{customers at start}} \times 100$

Example. Losing 50 of 1000 customers in a quarter gives a churn rate of $50/1000 \times 100 = 5\%$.

BI Architecture/Framework

<span style="display:inline-block;padding:.16em .6em;border:1.5px solid currentColor;border-radius:999px;font-size:.68em;font-weight:700;letter-spacing:.06em;text-transform:uppercase;opacity:.75">Not asked since 2022</span>

Definition. <mark>BI architecture is the layered structure that moves data from source systems to the decision maker: sources, ETL, data warehouse, analysis tools and presentation.</mark>

Key points.

  1. The data source layer holds operational databases, files and external feeds.
  2. ETL (extract, transform, load) cleans and integrates the data before storing it in the data warehouse or data marts.
  3. The analysis layer applies OLAP, data mining and reporting engines on the stored data.
  4. The presentation layer delivers dashboards, reports and alerts to users, and metadata plus security run across all layers.
  5. Data marts are smaller subject-specific warehouses (sales, finance) that serve one department quickly.
  6. A good architecture is scalable, secure and flexible, so new sources and users can be added without redesign.

Diagram.

<figure class="ds-fig" style="margin:1.4rem 0;overflow-x:auto"><svg xmlns="http://www.w3.org/2000/svg" id="dsfig-u2-01" viewBox="0 0 596 80" width="596" height="80" role="img" aria-label="BI architecture. Src = data sources, ETL = extract-transform-load, DW = data warehouse or marts, Ana = OLAP and mining, UI = dashboards, reports and alerts"><style>#dsfig-u2-01 .e{stroke:#454C5A;stroke-width:1.4;fill:none}#dsfig-u2-01 .e.hi{stroke:#2340B8;stroke-width:2.6}#dsfig-u2-01 .n{fill:#FFFFFF;stroke:#16181D;stroke-width:1.4}#dsfig-u2-01 .n.hi{fill:#E3E9FC;stroke:#2340B8;stroke-width:2.2}#dsfig-u2-01 .n.rb-b{fill:#16181D;stroke:#16181D}#dsfig-u2-01 .n.rb-r{fill:#BD3227;stroke:#BD3227}#dsfig-u2-01 text{font-family:"JetBrains Mono",ui-monospace,Menlo,Consolas,monospace;font-size:13px}#dsfig-u2-01 .t{fill:#16181D;font-weight:500}#dsfig-u2-01 .t.inv{fill:#FFFFFF;font-weight:700}#dsfig-u2-01 .kd{stroke:#16181D;stroke-width:1.2}#dsfig-u2-01 .dot{fill:#16181D}#dsfig-u2-01 .ann{fill:#2340B8;font-size:11px;font-weight:700}#dsfig-u2-01 .lbl{fill:#6F7787;font-family:system-ui,-apple-system,sans-serif;font-size:12px;font-weight:700}#dsfig-u2-01 .ptr{fill:#2340B8;font-size:12px;font-weight:700}#dsfig-u2-01 .ah{fill:#454C5A}#dsfig-u2-01 .ah.hi{fill:#2340B8}#dsfig-u2-01 .wl rect{fill:#FFFFFF;stroke:#DCE0E7}#dsfig-u2-01 .wl .t{font-size:12px;font-weight:700}#dsfig-u2-01 .wl.hi rect{fill:#2340B8;stroke:#2340B8}#dsfig-u2-01 .wl.hi .t{fill:#FFFFFF}html.dark #dsfig-u2-01 .e{stroke:#B1B7C3}html.dark #dsfig-u2-01 .e.hi{stroke:#8FA3FF}html.dark #dsfig-u2-01 .n{fill:#161920;stroke:#E6E8ED}html.dark #dsfig-u2-01 .n.hi{fill:#1E2748;stroke:#8FA3FF}html.dark #dsfig-u2-01 .n.rb-b{fill:#E6E8ED;stroke:#E6E8ED}html.dark #dsfig-u2-01 .n.rb-r{fill:#FF7E71;stroke:#FF7E71}html.dark #dsfig-u2-01 .t{fill:#E6E8ED}html.dark #dsfig-u2-01 .t.inv{fill:#0F1115}html.dark #dsfig-u2-01 .kd{stroke:#E6E8ED}html.dark #dsfig-u2-01 .dot{fill:#E6E8ED}html.dark #dsfig-u2-01 .ann{fill:#8FA3FF}html.dark #dsfig-u2-01 .lbl{fill:#858D9C}html.dark #dsfig-u2-01 .ptr{fill:#8FA3FF}html.dark #dsfig-u2-01 .ah{fill:#B1B7C3}html.dark #dsfig-u2-01 .ah.hi{fill:#8FA3FF}html.dark #dsfig-u2-01 .wl rect{fill:#161920;stroke:#2A2E37}html.dark #dsfig-u2-01 .wl.hi rect{fill:#8FA3FF;stroke:#8FA3FF}html.dark #dsfig-u2-01 .wl.hi .t{fill:#0F1115}</style><defs><marker id="ah4" viewBox="0 0 10 10" refX="9" refY="5" markerWidth="7" markerHeight="7" orient="auto-start-reverse"><path class="ah" d="M0,1 L9,5 L0,9 z"/></marker><marker id="ahh4" viewBox="0 0 10 10" refX="9" refY="5" markerWidth="7" markerHeight="7" orient="auto-start-reverse"><path class="ah hi" d="M0,1 L9,5 L0,9 z"/></marker></defs><path class="e" d="M59,40 L148,40" marker-end="url(#ah4)"/><path class="e" d="M188,40 L277,40" marker-end="url(#ah4)"/><path class="e" d="M317,40 L406,40" marker-end="url(#ah4)"/><path class="e" d="M446,40 L535,40" marker-end="url(#ah4)"/><circle class="n" cx="40" cy="40" r="18"/><text class="t" x="40" y="40" dy=".35em" text-anchor="middle">Src</text><circle class="n" cx="169" cy="40" r="18"/><text class="t" x="169" y="40" dy=".35em" text-anchor="middle">ETL</text><circle class="n" cx="298" cy="40" r="18"/><text class="t" x="298" y="40" dy=".35em" text-anchor="middle">DW</text><circle class="n" cx="427" cy="40" r="18"/><text class="t" x="427" y="40" dy=".35em" text-anchor="middle">Ana</text><circle class="n" cx="556" cy="40" r="18"/><text class="t" x="556" y="40" dy=".35em" text-anchor="middle">UI</text></svg><figcaption style="font-size:.82em;opacity:.72;margin-top:.45rem">BI architecture. Src = data sources, ETL = extract-transform-load, DW = data warehouse or marts, Ana = OLAP and mining, UI = dashboards, reports and alerts</figcaption></figure>

Best Practices

<span style="display:inline-block;padding:.16em .6em;border:1.5px solid currentColor;border-radius:999px;font-size:.68em;font-weight:700;letter-spacing:.06em;text-transform:uppercase;opacity:.75">Not asked since 2022</span>

Definition. <mark>BI best practices are proven guidelines that raise the chance of a BI project delivering business value.</mark>

Key points.

  1. Align the project with clear business goals and get executive sponsorship before any technology is chosen.
  2. Ensure data quality and governance, since wrong data gives wrong decisions.
  3. Start small with a pilot, deliver quickly and grow in iterations.
  4. Train users, keep dashboards simple and monitor usage so that BI is actually adopted.
  5. Involve business users from the start so that requirements are real and the result is trusted.
  6. Define common definitions of KPIs and data ownership, so that every report shows the same number.
  7. Measure the return on investment of BI and improve continuously.

Business Decision Making

<span style="display:inline-block;padding:.16em .6em;border:1.5px solid currentColor;border-radius:999px;font-size:.68em;font-weight:700;letter-spacing:.06em;text-transform:uppercase;opacity:.75">Not asked since 2022</span>

Definition. <mark>Business decision making is choosing the best course of action among alternatives, and BI supports it by giving timely, fact-based information.</mark>

Key points.

  1. Decisions are strategic (top management, long term), tactical (middle management) or operational (day-to-day).
  2. Simon's stages are intelligence, design, choice and implementation; BI mainly strengthens the intelligence stage.
  3. Decisions may be structured, semi-structured or unstructured, and a decision support system (DSS) helps the last two.
  4. Fact-based decisions reduce guesswork, bias and delay compared with intuition alone.
  5. Decision makers use BI outputs such as dashboards, what-if analysis and forecasts to compare alternatives before choosing.
  6. Good decisions still need human judgement, because data shows what happened but the manager decides what to do.
Level Who Example
Strategic Top management Enter a new market
Tactical Middle management Set next quarter's budget
Operational Supervisors Reorder stock today

Styles of BI - Event-Driven Alerts

<span style="display:inline-block;padding:.16em .6em;border:1.5px solid currentColor;border-radius:999px;font-size:.68em;font-weight:700;letter-spacing:.06em;text-transform:uppercase;opacity:.75">Not asked since 2022</span>

Definition. <mark>Event-driven BI monitors data continuously and automatically alerts users when a predefined event or threshold occurs.</mark>

Key points.

  1. The main BI styles are static reporting, ad hoc querying, dashboards and scorecards, OLAP analysis, and event-driven alerts.
  2. Alerts are triggered by rules, for example stock falling below a reorder level or a sudden fraud pattern.
  3. They are delivered by email, SMS or app notification, so the user need not open a report.
  4. This style is proactive and near real time, unlike periodic reports, which are pulled by the user.
  5. Alerts should be limited to important events, because too many alerts cause users to ignore them.
  6. Event-driven BI depends on real-time or near real-time data integration and a rules engine.

Example. A bank system texts the customer and flags the transaction when a card is used in two cities within an hour.

A Cyclic Process of Intelligence Creation

<span style="display:inline-block;padding:.16em .6em;border:1.5px solid currentColor;border-radius:999px;font-size:.68em;font-weight:700;letter-spacing:.06em;text-transform:uppercase;opacity:.75">Not asked since 2022</span>

Definition. <mark>Intelligence creation is a repeating loop in which data is turned into information, knowledge and action, and the results feed the next round.</mark>

Key points.

  1. The cycle runs: analysis of the business problem, data collection and integration, analysis, insight, decision, action, and measurement of the result.
  2. The measured outcome becomes new input, so the process is continuous rather than one-off.
  3. Each round refines KPIs, data sources and models, so intelligence improves with every loop.
  4. It follows the DIKW idea: data, information, knowledge, wisdom.
  5. Feedback from the results is what turns the straight pipeline into a cycle.

Diagram.

<figure class="ds-fig" style="margin:1.4rem 0;overflow-x:auto"><svg xmlns="http://www.w3.org/2000/svg" id="dsfig-u2-02" viewBox="0 0 424 252" width="424" height="252" role="img" aria-label="Cycle of intelligence creation. Dat = data, Inf = information, Kno = knowledge and insight, Act = decision and action, Res = measured result, Nxt = refined needs for the next round"><style>#dsfig-u2-02 .e{stroke:#454C5A;stroke-width:1.4;fill:none}#dsfig-u2-02 .e.hi{stroke:#2340B8;stroke-width:2.6}#dsfig-u2-02 .n{fill:#FFFFFF;stroke:#16181D;stroke-width:1.4}#dsfig-u2-02 .n.hi{fill:#E3E9FC;stroke:#2340B8;stroke-width:2.2}#dsfig-u2-02 .n.rb-b{fill:#16181D;stroke:#16181D}#dsfig-u2-02 .n.rb-r{fill:#BD3227;stroke:#BD3227}#dsfig-u2-02 text{font-family:"JetBrains Mono",ui-monospace,Menlo,Consolas,monospace;font-size:13px}#dsfig-u2-02 .t{fill:#16181D;font-weight:500}#dsfig-u2-02 .t.inv{fill:#FFFFFF;font-weight:700}#dsfig-u2-02 .kd{stroke:#16181D;stroke-width:1.2}#dsfig-u2-02 .dot{fill:#16181D}#dsfig-u2-02 .ann{fill:#2340B8;font-size:11px;font-weight:700}#dsfig-u2-02 .lbl{fill:#6F7787;font-family:system-ui,-apple-system,sans-serif;font-size:12px;font-weight:700}#dsfig-u2-02 .ptr{fill:#2340B8;font-size:12px;font-weight:700}#dsfig-u2-02 .ah{fill:#454C5A}#dsfig-u2-02 .ah.hi{fill:#2340B8}#dsfig-u2-02 .wl rect{fill:#FFFFFF;stroke:#DCE0E7}#dsfig-u2-02 .wl .t{font-size:12px;font-weight:700}#dsfig-u2-02 .wl.hi rect{fill:#2340B8;stroke:#2340B8}#dsfig-u2-02 .wl.hi .t{fill:#FFFFFF}html.dark #dsfig-u2-02 .e{stroke:#B1B7C3}html.dark #dsfig-u2-02 .e.hi{stroke:#8FA3FF}html.dark #dsfig-u2-02 .n{fill:#161920;stroke:#E6E8ED}html.dark #dsfig-u2-02 .n.hi{fill:#1E2748;stroke:#8FA3FF}html.dark #dsfig-u2-02 .n.rb-b{fill:#E6E8ED;stroke:#E6E8ED}html.dark #dsfig-u2-02 .n.rb-r{fill:#FF7E71;stroke:#FF7E71}html.dark #dsfig-u2-02 .t{fill:#E6E8ED}html.dark #dsfig-u2-02 .t.inv{fill:#0F1115}html.dark #dsfig-u2-02 .kd{stroke:#E6E8ED}html.dark #dsfig-u2-02 .dot{fill:#E6E8ED}html.dark #dsfig-u2-02 .ann{fill:#8FA3FF}html.dark #dsfig-u2-02 .lbl{fill:#858D9C}html.dark #dsfig-u2-02 .ptr{fill:#8FA3FF}html.dark #dsfig-u2-02 .ah{fill:#B1B7C3}html.dark #dsfig-u2-02 .ah.hi{fill:#8FA3FF}html.dark #dsfig-u2-02 .wl rect{fill:#161920;stroke:#2A2E37}html.dark #dsfig-u2-02 .wl.hi rect{fill:#8FA3FF;stroke:#8FA3FF}html.dark #dsfig-u2-02 .wl.hi .t{fill:#0F1115}</style><defs><marker id="ah5" viewBox="0 0 10 10" refX="9" refY="5" markerWidth="7" markerHeight="7" orient="auto-start-reverse"><path class="ah" d="M0,1 L9,5 L0,9 z"/></marker><marker id="ahh5" viewBox="0 0 10 10" refX="9" refY="5" markerWidth="7" markerHeight="7" orient="auto-start-reverse"><path class="ah hi" d="M0,1 L9,5 L0,9 z"/></marker></defs><path class="e" d="M59,40 L191,40" marker-end="url(#ah5)"/><path class="e" d="M231,40 L363,40" marker-end="url(#ah5)"/><path class="e" d="M384,59 L384,191" marker-end="url(#ah5)"/><path class="e" d="M365,212 L233,212" marker-end="url(#ah5)"/><path class="e" d="M193,212 L61,212" marker-end="url(#ah5)"/><path class="e" d="M40,193 L40,61" marker-end="url(#ah5)"/><circle class="n" cx="40" cy="40" r="18"/><text class="t" x="40" y="40" dy=".35em" text-anchor="middle">Dat</text><circle class="n" cx="212" cy="40" r="18"/><text class="t" x="212" y="40" dy=".35em" text-anchor="middle">Inf</text><circle class="n" cx="384" cy="40" r="18"/><text class="t" x="384" y="40" dy=".35em" text-anchor="middle">Kno</text><circle class="n" cx="384" cy="212" r="18"/><text class="t" x="384" y="212" dy=".35em" text-anchor="middle">Act</text><circle class="n" cx="212" cy="212" r="18"/><text class="t" x="212" y="212" dy=".35em" text-anchor="middle">Res</text><circle class="n" cx="40" cy="212" r="18"/><text class="t" x="40" y="212" dy=".35em" text-anchor="middle">Nxt</text></svg><figcaption style="font-size:.82em;opacity:.72;margin-top:.45rem">Cycle of intelligence creation. Dat = data, Inf = information, Kno = knowledge and insight, Act = decision and action, Res = measured result, Nxt = refined needs for the next round</figcaption></figure>

Ethics of Business Intelligence

<span style="display:inline-block;padding:.16em .6em;border:1.5px solid currentColor;border-radius:999px;font-size:.68em;font-weight:700;letter-spacing:.06em;text-transform:uppercase;opacity:.75">Not asked since 2022</span>

Definition. <mark>BI ethics is the responsible collection, use and sharing of data so that individuals are not harmed and the law is respected.</mark>

Key points.

  1. Privacy: personal data must be collected with consent and used only for the stated purpose.
  2. Security: data must be protected against breach and unauthorised access.
  3. Fairness: models must not discriminate, and biased or wrong data must not be used to judge people.
  4. Transparency and compliance: users should know how data is used, and the firm must follow laws such as the IT Act and data protection rules.
  5. Data must be accurate and kept only as long as needed, since old or wrong data can harm people.
  6. Competitive intelligence must use legal, open sources and never spying or deception.

Example. Using a customer's purchase history to sell them relevant offers is acceptable; selling it to a third party without consent is unethical.

Last-minute revision

  • Key drivers: competition, data growth, one version of truth, cost and revenue goals.
  • KPI is a metric tied to a strategic goal; SMART criteria.
  • Balanced scorecard has four perspectives: financial, customer, internal process, learning and growth.
  • BI layers: source, ETL, warehouse, analysis, presentation.
  • Best practice number one: align with business goals and start with a pilot.
  • Decision levels: strategic, tactical, operational.
  • Simon's stages: intelligence, design, choice, implementation.
  • Event-driven BI is proactive and rule-triggered.
  • Intelligence creation is a cycle, not a straight line.
  • Ethics: privacy, security, fairness, transparency.

Memory hooks

  • "SMART KPI": Specific, Measurable, Achievable, Relevant, Time-bound.
  • "S-E-W-A-P": Source, ETL, Warehouse, Analysis, Presentation.
  • "IDCI": Intelligence, Design, Choice, Implementation.
  • "PSFT" for ethics: Privacy, Security, Fairness, Transparency.

Coverage checklist

  • Key Drivers: no past questions.
  • Key Performance Indicators and Performance Metrics: no past questions.
  • BI Architecture/Framework: no past questions.
  • Best Practices: no past questions.
  • Business Decision Making: no past questions.
  • Styles of BI - vent-Driven alerts: no past questions.
  • A cyclic process of Intelligence Creation: no past questions.
  • Ethics of Business Intelligence: no past questions.
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