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

Business Intelligence (AD-702 (B)) - Unit 1 Short Notes

How unit 1 is examined

This unit covers what BI is, its scope, components, tools, data mining and warehousing, OLAP, the DIKW pyramid and the analytics project methodology; no topic was asked in the supplied papers, so every topic is short but complete.

Business Intelligence (BI)

<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 Intelligence is the set of processes, technologies and tools that turn raw business data into meaningful information so that managers can take better, faster decisions.</mark>

Key points.

  1. BI collects data from internal sources (sales, finance, CRM) and external sources (market, competitors) and stores it centrally.
  2. It analyses the data with reporting, querying, OLAP and data mining to reveal trends, patterns and exceptions.
  3. Results reach managers as reports, dashboards, scorecards and alerts.
  4. The aim is fact-based decisions that raise profit, cut cost and improve competitiveness.

Scope of BI solutions and their fitting into existing infrastructure

<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>The scope of a BI solution is the range of business functions and data it covers; it must be integrated with the existing databases, ERP and applications rather than replace them.</mark>

Key points.

  1. BI applies across sales, marketing, finance, operations, supply chain and human resources.
  2. It fits into existing infrastructure by extracting data from ERP, CRM and legacy databases through ETL into a warehouse.
  3. Integration needs compatible data formats, common definitions, security and scalable hardware.
  4. A phased rollout, department by department, lowers cost and risk.

BI Components

<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 components are the layers that carry data from source systems to the decision maker: data sources, ETL, data warehouse, analysis engines (OLAP, data mining) and the presentation layer.</mark>

Key points.

  1. Data sources are operational databases, flat files and external feeds.
  2. ETL extracts, transforms (cleans, integrates) and loads the data into the warehouse.
  3. The data warehouse stores integrated, historical, subject-oriented data.
  4. OLAP and data mining analyse it, and dashboards and reports present the results to users.

<figure class="ds-fig" style="margin:1.4rem 0;overflow-x:auto"><svg xmlns="http://www.w3.org/2000/svg" id="dsfig-u1-01" viewBox="0 0 603 252" width="603" height="252" role="img" aria-label="BI components: Src = data sources, ETL = extract transform load, DW = data warehouse, Rep = reports and dashboards"><style>#dsfig-u1-01 .e{stroke:#454C5A;stroke-width:1.4;fill:none}#dsfig-u1-01 .e.hi{stroke:#2340B8;stroke-width:2.6}#dsfig-u1-01 .n{fill:#FFFFFF;stroke:#16181D;stroke-width:1.4}#dsfig-u1-01 .n.hi{fill:#E3E9FC;stroke:#2340B8;stroke-width:2.2}#dsfig-u1-01 .n.rb-b{fill:#16181D;stroke:#16181D}#dsfig-u1-01 .n.rb-r{fill:#BD3227;stroke:#BD3227}#dsfig-u1-01 text{font-family:"JetBrains Mono",ui-monospace,Menlo,Consolas,monospace;font-size:13px}#dsfig-u1-01 .t{fill:#16181D;font-weight:500}#dsfig-u1-01 .t.inv{fill:#FFFFFF;font-weight:700}#dsfig-u1-01 .kd{stroke:#16181D;stroke-width:1.2}#dsfig-u1-01 .dot{fill:#16181D}#dsfig-u1-01 .ann{fill:#2340B8;font-size:11px;font-weight:700}#dsfig-u1-01 .lbl{fill:#6F7787;font-family:system-ui,-apple-system,sans-serif;font-size:12px;font-weight:700}#dsfig-u1-01 .ptr{fill:#2340B8;font-size:12px;font-weight:700}#dsfig-u1-01 .ah{fill:#454C5A}#dsfig-u1-01 .ah.hi{fill:#2340B8}#dsfig-u1-01 .wl rect{fill:#FFFFFF;stroke:#DCE0E7}#dsfig-u1-01 .wl .t{font-size:12px;font-weight:700}#dsfig-u1-01 .wl.hi rect{fill:#2340B8;stroke:#2340B8}#dsfig-u1-01 .wl.hi .t{fill:#FFFFFF}html.dark #dsfig-u1-01 .e{stroke:#B1B7C3}html.dark #dsfig-u1-01 .e.hi{stroke:#8FA3FF}html.dark #dsfig-u1-01 .n{fill:#161920;stroke:#E6E8ED}html.dark #dsfig-u1-01 .n.hi{fill:#1E2748;stroke:#8FA3FF}html.dark #dsfig-u1-01 .n.rb-b{fill:#E6E8ED;stroke:#E6E8ED}html.dark #dsfig-u1-01 .n.rb-r{fill:#FF7E71;stroke:#FF7E71}html.dark #dsfig-u1-01 .t{fill:#E6E8ED}html.dark #dsfig-u1-01 .t.inv{fill:#0F1115}html.dark #dsfig-u1-01 .kd{stroke:#E6E8ED}html.dark #dsfig-u1-01 .dot{fill:#E6E8ED}html.dark #dsfig-u1-01 .ann{fill:#8FA3FF}html.dark #dsfig-u1-01 .lbl{fill:#858D9C}html.dark #dsfig-u1-01 .ptr{fill:#8FA3FF}html.dark #dsfig-u1-01 .ah{fill:#B1B7C3}html.dark #dsfig-u1-01 .ah.hi{fill:#8FA3FF}html.dark #dsfig-u1-01 .wl rect{fill:#161920;stroke:#2A2E37}html.dark #dsfig-u1-01 .wl.hi rect{fill:#8FA3FF;stroke:#8FA3FF}html.dark #dsfig-u1-01 .wl.hi .t{fill:#0F1115}</style><defs><marker id="ah1" 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="ahh1" 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="M59,126 L148,126" marker-end="url(#ah1)"/><path class="e" d="M188,126 L277,126" marker-end="url(#ah1)"/><path class="e" d="M313.8,115.5 L403.7,55.5" marker-end="url(#ah1)"/><path class="e" d="M313.8,136.5 L403.7,196.5" marker-end="url(#ah1)"/><path class="e" d="M448.6,54.4 L538.5,114.4" marker-end="url(#ah1)"/><path class="e" d="M448.6,197.6 L538.5,137.6" marker-end="url(#ah1)"/><circle class="n" cx="40" cy="126" r="18"/><text class="t" x="40" y="126" dy=".35em" text-anchor="middle">Src</text><circle class="n" cx="169" cy="126" r="18"/><text class="t" x="169" y="126" dy=".35em" text-anchor="middle">ETL</text><circle class="n" cx="298" cy="126" r="18"/><text class="t" x="298" y="126" dy=".35em" text-anchor="middle">DW</text><rect class="n" x="402" y="25" width="50" height="30" rx="15"/><text class="t" x="427" y="40" dy=".35em" text-anchor="middle">OLAP</text><rect class="n" x="402" y="197" width="50" height="30" rx="15"/><text class="t" x="427" y="212" dy=".35em" text-anchor="middle">Mine</text><circle class="n" cx="556" cy="126" r="18"/><text class="t" x="556" y="126" dy=".35em" text-anchor="middle">Rep</text></svg><figcaption style="font-size:.82em;opacity:.72;margin-top:.45rem">BI components: Src = data sources, ETL = extract transform load, DW = data warehouse, Rep = reports and dashboards</figcaption></figure>

Future 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>The future of BI is a move towards AI-driven, self-service, real-time and cloud-based analytics that every employee can use, not only specialists.</mark>

Key points.

  1. AI and machine learning automate insight discovery, prediction and anomaly detection.
  2. Self-service BI lets non-technical users build their own reports without waiting for IT.
  3. Cloud BI and real-time streaming reduce cost and give up-to-the-minute data.
  4. Natural-language queries, mobile access and embedded analytics widen adoption.

Functional areas and description of BI tools

<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 tools are software that query, analyse and present business data, used across functional areas such as sales, marketing, finance, production and human resources.</mark>

Key points.

  1. Dashboards show key indicators visually on one screen for quick monitoring.
  2. Reporting and query tools produce scheduled and ad hoc reports.
  3. OLAP tools allow multidimensional slicing of data, and data mining tools find hidden patterns.
  4. Common products are Power BI, Tableau and QlikView, used for sales analysis, budgeting, inventory control and customer segmentation.

Data mining & warehouse

<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 data warehouse is a subject-oriented, integrated, time-variant and non-volatile collection of data that supports management decisions; data mining is the extraction of hidden patterns and knowledge from large data sets.</mark>

Key points.

  1. The warehouse holds historical data from many sources, kept separate from operational systems.
  2. Non-volatile means data is added and read but not updated or deleted.
  3. Data mining, the core step of KDD (knowledge discovery in databases), uses classification, clustering, association rules and prediction.
  4. The warehouse supplies the clean, integrated data on which mining works.
Feature Data warehouse Operational database
Purpose Analysis and decisions Daily transactions
Data Historical, summarised Current, detailed
Updates Periodic load, read mostly Frequent insert and update
Design Subject-oriented Application-oriented

OLAP

<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>OLAP (Online Analytical Processing) allows fast, interactive multidimensional analysis of warehouse data, viewed as a cube of dimensions (time, product, region) and measures (sales).</mark>

Key points.

  1. Roll-up aggregates to a higher level, such as month to year; drill-down goes to finer detail.
  2. Slice fixes one dimension, dice selects a sub-cube on several dimensions, and pivot rotates the view.
  3. MOLAP stores data in multidimensional arrays; it is fast but limited in size.
  4. ROLAP works on relational tables and scales to large data but is slower; HOLAP combines both.

Drawing insights from data: DIKW pyramid

<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>The DIKW pyramid shows how Data is refined into Information, then Knowledge, then Wisdom, each level adding context and value.</mark>

Key points.

  1. Data is raw, unorganised facts, such as individual sales figures.
  2. Information is data with context, such as monthly sales by region.
  3. Knowledge is understanding of patterns in information, such as why sales fall in the monsoon.
  4. Wisdom is applying knowledge to choose the right action.

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Business Analytics project methodology

<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>The business analytics project methodology is a phased approach, as in CRISP-DM, that takes a project from the business problem to a deployed and monitored solution.</mark>

Key points.

  1. Business understanding defines the objective, scope and success criteria.
  2. Data understanding and data preparation collect, explore and clean the data.
  3. Modelling builds analytical models, and evaluation tests them against the business objective.
  4. Deployment puts the result into use, followed by monitoring and review, which may restart the cycle.

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Last-minute revision

  • BI turns raw data into information that supports decisions.
  • BI components in order: data sources, ETL, data warehouse, OLAP or data mining, reports and dashboards.
  • ETL stands for extract, transform, load.
  • A data warehouse is subject-oriented, integrated, time-variant and non-volatile.
  • Data mining is the analysis step of KDD, finding hidden patterns.
  • OLAP operations are roll-up, drill-down, slice, dice and pivot.
  • MOLAP uses arrays, ROLAP uses relational tables, HOLAP mixes both.
  • DIKW order is Data, Information, Knowledge, Wisdom.
  • CRISP-DM phases are business understanding, data understanding, data preparation, modelling, evaluation, deployment.
  • Popular BI tools are Power BI, Tableau and QlikView.

Memory hooks

  • Warehouse is SITE-like: Subject-oriented, Integrated, Time-variant, non-volatile (data is never erased).
  • DIKW: "Do I Know Well" gives Data, Information, Knowledge, Wisdom.
  • Cube moves: Roll up, Drill down, Slice, Dice, Pivot.
  • CRISP-DM: Business, Data, Prepare, Model, Evaluate, Deploy, then loop back.

Coverage checklist

  • Business Intelligence (BI): definition and four key points; no past questions.
  • Scope of BI solutions and their fitting into existing infrastructure: scope, integration through ETL; no past questions.
  • BI Components: five layers with the flow diagram; no past questions.
  • Future of Business Intelligence: AI, self-service, cloud, real time; no past questions.
  • Functional areas and description of BI tools: dashboards, reporting, OLAP, mining, products; no past questions.
  • Data mining & warehouse: warehouse properties, KDD, comparison table; no past questions.
  • OLAP: cube operations, MOLAP, ROLAP, HOLAP; no past questions.
  • Drawing insights from data: DIKW pyramid: four levels with the pyramid; no past questions.
  • Business Analytics project methodology - detailed description of each phase: CRISP-DM phases with the cycle; no past questions.
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