Minimum
Experience:
At least 3-5 years’ experience as Business Intelligence Analyst or equivalent (data analyst etc.) including experience in data analytics, BI methods and tools, data warehousing and the data life cycle.
Experience in identifying, quantifying, and subsequently delivering value on how to solve business challenges using dataÂ
Experience with stakeholder engagementÂ
Experience in working with data on cloud platforms, such as AWS or Microsoft AzureÂ
Experience in building data visualisations using tools such as Power BIÂ
Experience of using SQL to prepare and analyse data
Knowledge:
Knowledge and basic experience in using low/no code AutoML on modelling tools such as Power BI or AWS Sagemaker Â
Design thinking
Dimensional Modelling
The Data Product Life Cycle (DPLC)
Knowledge of data privacy and security regulations and best practicesÂ
Knowledge and understanding of data quality and incident management Â
Knowledge and experience of agile project management methodologyÂ
Ideal:
Previous experience working in business analysis in the banking sector
Knowledge of change management principles and practices
The data life cycle describes how data moves through an organisation.
It outlines each stage from creation to disposal.
The goal is to manage data effectively and securely.
Every stage has specific responsibilities.
Proper data management improves decision-making.
Stage 1: Data Generation
Data is generated through business activities.
It may come from humans or machines.
Examples include forms, sensors, systems, and transactions.
Good data starts with accurate collection.
Clear input rules help reduce errors.
Stage 2: Data Collection
Data is captured from its original source.
Tools include surveys, apps, scanners, and APIs.
Consistent formats improve quality.
Metadata is also collected at this stage.
Secure collection prevents early data loss.
Stage 3: Data Ingestion
Data moves from the source into storage systems.
ETL pipelines are often used.
ETL means Extract, Transform, Load.
Streaming systems may ingest real-time data.
Batch systems ingest large datasets periodically.
Validation often starts during ingestion.
Stage 4: Data Storage
Data is placed in databases or data lakes.
Storage must balance speed and cost.
Structured data fits into relational databases.
Unstructured data often sits in data lakes.
Backup policies protect data from loss.
Storage security is essential.
Stage 5: Data Processing
Raw data is cleaned and transformed.
Processing ensures data becomes usable.
Cleaning removes duplicates and errors.
Transformation creates consistent formats.
Aggregation summarises large datasets.
Business rules are applied at this point.
Stage 6: Data Quality Assurance
Data is reviewed for accuracy.
Quality dimensions include completeness.
They also include accuracy and consistency.
Timeliness is another important dimension.
High-quality data builds trust in reporting.
Poor data quality leads to bad decisions.
Stage 7: Data Integration
Data from multiple sources is combined.
Integration creates a single source of truth.
It reduces silos.
Tools include APIs, data warehouses, and middleware.
Proper integration improves analytics.
Stage 8: Data Analysis
Analysts extract insights from the data.
Analysis may be descriptive.
It may also be diagnostic.
Predictive analysis forecasts future events.
Prescriptive analysis recommends actions.
Visualisation helps communicate insights.
Stage 9: Data Use
Data is applied to solve business problems.
Teams use dashboards and reports.
Data supports decision-making.
Operations rely on accurate data.
Data may feed automated systems.
Customers may directly interact with data.
Stage 10: Data Sharing
Data is distributed to users or partners.
Sharing must follow policy.
Access should be based on roles.
APIs enable safe sharing.
Data governance ensures compliance.
Sensitive data must be protected.
Stage 11: Data Governance
Governance defines rules for managing data.
It covers quality, privacy, and security.
Policies ensure responsible use.
Data stewards manage specific domains.
Good governance protects the organisation.
Stage 12: Data Security
Security protects data from threats.
Encryption secures data at rest.
Encryption secures data in transit.
Access controls prevent unauthorised use.
Monitoring detects suspicious activity.
Compliance laws must be followed.
Stage 13: Data Archiving
Old data is moved into long-term storage.
Archiving reduces storage costs.
Archived data must remain accessible.
Regulations may require long retention.
Good archiving preserves historical value.
Stage 14: Data Disposal
Data that is no longer needed is deleted.
Disposal prevents unnecessary storage costs.
It also reduces security risks.
Disposal must follow legal requirements.
Secure deletion ensures data cannot be recovered.
The data life cycle is continuous.
When new data is generated, the cycle begins again.
Strong life-cycle management leads to smarter, safer organisations.
A data warehouse is a central repository for organisational data.
It stores historical and current data for analysis.
A warehouse supports business intelligence (BI).
It is designed for querying, not transactions.
Data warehouses improve decision-making.
Data warehouses use structured, organised data.
They integrate data from multiple sources.
Common sources include ERP, CRM, HR, and finance systems.
Data is cleaned before entering the warehouse.
Accuracy is essential for trust.
A data warehouse is subject-oriented.
Subject areas include sales, customers, products, etc.
It is integrated across departments.
It is time-variant, storing years of history.
It is non-volatile — data doesn’t change once stored.
ETL is critical in data warehousing.
ETL stands for Extract, Transform, Load.
Extraction pulls data from source systems.
Transformation cleans and converts data.
Loading inserts the processed data into the warehouse.
Modern warehouses may use ELT instead.
ELT loads raw data first, then transforms it.
ELT is common in cloud environments.
It supports large-scale processing.
Tools include Snowflake, BigQuery, and Redshift.
Dimensional modelling is used to structure data.
It simplifies complex data for analysis.
Two main table types exist: facts and dimensions.
Fact tables store numeric metrics.
Dimension tables store descriptive attributes.
Star schemas are widely used.
A star schema has one fact table in the center.
Dimensions connect directly to the fact table.
It is simple and fast for querying.
Star schemas improve performance.
Snowflake schemas are another option.
Snowflaking normalises dimension tables.
It saves space.
But it increases query complexity.
Many teams prefer star schemas.
Data marts are subsets of the warehouse.
A data mart focuses on one department.
Examples include marketing, HR, or finance marts.
Marts speed up access for teams.
Marts reduce unnecessary complexity.
OLAP is used for advanced analytics.
OLAP stands for Online Analytical Processing.
OLAP cubes enable multidimensional analysis.
Users can drill down into details.
They can roll up to view summaries.
Data warehouses rely on metadata.
Metadata describes the structure of the data.
It includes table names, fields, and definitions.
Metadata improves governance.
It helps analysts understand data sources.
Data quality is vital in warehousing.
Quality checks include accuracy.
They also include completeness and consistency.
Poor quality affects analytics.
Automated validation helps maintain standards.
Data governance supports warehouse success.
Governance defines rules for data use.
It ensures compliance with regulations.
Roles include data owners and data stewards.
Governance increases trust in reporting.
Warehouses usually store historical data.
History enables trend analysis.
Slowly Changing Dimensions (SCDs) track changes.
SCD Type 1 overwrites old data.
SCD Type 2 stores previous versions.
Warehouses separate storage from computation.
This improves scalability.
Cloud warehouses handle large volumes easily.
They offer pay-as-you-go pricing.
Examples include Azure Synapse and AWS Redshift.
Performance tuning is essential.
Indexes can speed up queries.
Partitioning improves data management.
Caching boosts frequently used queries.
Clustered storage improves efficiency.
Security protects warehouse data.
Access controls restrict users.
Encryption protects sensitive information.
Auditing tracks data changes.
Compliance standards must be followed.
Data warehouse users include analysts.
Managers also rely on the warehouse.
Executives use dashboards for insights.
Data scientists use warehouses for modelling.
BI tools are commonly connected.
Popular BI tools include Power BI.
Others include Tableau and QlikView.
These tools visualise warehouse data.
Dashboards help communicate results.
Reports support strategic decisions.
Data warehousing is evolving.
Automation improves ETL efficiency.
Real-time streaming is becoming common.
AI enhances anomaly detection.
Data warehouses remain essential for analytics
Business Intelligence (BI) is the practice of turning data into insights.
BI helps organisations make better decisions.
It combines data analysis, reporting, and visualisation.
BI relies on both methods and tools.
Strong BI improves business performance.
BI Method 1: Reporting
Reporting summarises what happened.
Reports show KPIs and performance indicators.
Standard reports are scheduled regularly.
Ad hoc reports answer specific questions.
Reporting is foundational to BI.
BI Method 2: Data Visualisation
Visualisation presents data in charts and graphs.
It makes complex data easier to understand.
Dashboards show real-time performance.
Good visuals support better storytelling.
Tools automate visualisation creation.
BI Method 3: OLAP Analysis
OLAP stands for Online Analytical Processing.
OLAP enables multi-dimensional analysis.
Users can drill down into details.
They can roll up to see summaries.
Slicing and dicing helps comparison.
BI Method 4: Data Mining
Data mining discovers patterns in data.
It uses statistical and machine learning techniques.
It identifies correlations and trends.
It helps find hidden insights.
Data mining supports predictive analytics.
BI Method 5: Predictive Analytics
Predictive analytics forecasts future outcomes.
It uses historical data to build models.
Predictive models estimate probabilities.
They support risk management.
Forecasts help in planning and strategy.
BI Method 6: KPI Monitoring
KPIs measure performance against goals.
BI dashboards display KPI trends.
Alerts notify teams of issues.
KPIs align with business objectives.
Monitoring ensures accountability.
BI Method 7: Benchmarking
Benchmarking compares performance over time.
It can also compare against competitors.
It identifies gaps.
It motivates improvement.
BI tools automate comparisons.
BI Method 8: Statistical Analysis
Statistical analysis quantifies data relationships.
It tests hypotheses.
It measures significance.
It identifies trends.
It improves reliability of BI outputs.
BI Method 9: Real-Time Analytics
Real-time analytics uses streaming data.
It supports immediate decision-making.
It powers modern operational dashboards.
It is crucial in industries like banking.
Tools support high-speed processing.
BI Method 10: Self-Service BI
Self-service BI allows users to create their own reports.
It reduces dependency on IT.
It empowers business teams.
Drag-and-drop interfaces simplify usage.
Data governance keeps it secure.
BI Tools Overview
BI tools automate analysis and visualisation.
Tools connect to data sources.
They create dashboards and reports.
They support collaboration.
Many tools integrate AI features.
Tool: Microsoft Power BI
Power BI is widely used across industries.
It connects to many data sources.
It offers strong visualisation capabilities.
DAX enables advanced calculations.
Power BI supports cloud dashboards.
Tool: Tableau
Tableau excels in visual analytics.
It offers rich interactive dashboards.
Drag-and-drop functionality is central.
It handles large datasets effectively.
Tableau creates powerful storytelling visuals.
Tool: Qlik Sense
Qlik uses an associative data engine.
It allows flexible data exploration.
Users can navigate relationships dynamically.
Qlik offers self-service analytics.
It is strong in real-time analysis.
Tool: SAP BusinessObjects
SAP BO supports enterprise reporting.
It integrates deeply with SAP systems.
It is ideal for large organisations.
It offers advanced report scheduling.
It supports governed BI environments.
Tool: Looker (Google Cloud)
Looker uses a modelling language called LookML.
It centralises data definitions.
It integrates well with cloud data warehouses.
Looker supports modern embedded analytics.
Data analytics is the process of examining data to find insights.
It helps organisations make data-driven decisions.
Analytics answers business questions using facts.
It involves collecting, cleaning, analysing, and interpreting data.
Strong analytics improve performance and strategy.
There are four types of data analytics.
Descriptive analytics explains what happened.
Diagnostic analytics explains why it happened.
Predictive analytics forecasts what will happen.
Prescriptive analytics recommends what to do.
Data analytics starts with a clear question.
Questions guide the entire analytical process.
Good questions focus on measurable outcomes.
Analysts translate business needs into analytical tasks.
Clear objectives improve results.
Data collection gathers information from various sources.
Sources include databases, APIs, files, and sensors.
Accurate data collection is essential.
Consistent formats reduce complexity.
Metadata helps analysts understand the data structure.
Data cleaning prepares raw data for analysis.
Cleaning removes duplicates.
It fixes errors and inconsistencies.
It handles missing values.
Cleaning improves reliability.
Analysts use transformation steps.
Transformation standardises formats.
It derives new calculated fields.
Aggregation summarises data for easier analysis.
Transformation creates usable datasets.
Exploratory data analysis (EDA) is a key step.
EDA uncovers patterns.
It identifies trends in the data.
It reveals outliers.
Visualisation tools support EDA.
Analysts use statistics for deeper insight.
Statistics help quantify relationships.
Measures include mean, median, and mode.
Variance measures data spread.
Correlation reveals associations.
Data analytics relies heavily on visualisation.
Charts simplify complex information.
Dashboards show real-time performance.
Good visuals tell a story.
Visualisation improves communication.
Analysts work with BI tools.
Tools automate reporting.
Common tools include Power BI.
Others include Tableau and Qlik.
BI tools help create interactive dashboards.
Programming is often required.
Python is widely used for analytics.
R is also popular.
SQL is essential for querying databases.
Programming automates complex tasks.
Machine learning enhances analytics.
ML finds patterns that humans may miss.
It improves predictive models.
Supervised learning uses labelled data.
Unsupervised learning discovers hidden groups.
Predictive analytics estimates future outcomes.
Forecasting uses historical trends.
Models include regression.
Time-series models analyse seasonal trends.
Classification predicts categories.
Prescriptive analytics recommends actions.
It uses optimisation techniques.
It helps choose the best option.
It supports decision-making.
It is common in pricing and logistics.
Analysts need strong domain knowledge.
Domain knowledge improves interpretation.
Understanding business context matters.
It helps generate relevant insights.
It ensures solutions align with goals.
Data storytelling is an important skill.
Storytelling connects data to decisions.
It explains insights clearly.
It simplifies complex results.
Good storytelling influences decisions.
Data ethics must be considered.
Analytics must follow data privacy laws.
Sensitive information must be protected.
Analysts must avoid bias.
Ethical analytics builds trust.
Collaboration is essential.
Analysts work with business teams.
They also work with IT teams.
Strong communication improves outcomes.
Collaboration ensures solutions are practical.
Automation enhances analytics efficiency.
Automated pipelines speed up reporting.
Scheduled reports ensure consistency.
Alerts notify users of unusual patterns.
Automation supports scalability.
Data analytics supports all industries.
It is used in finance.
It is used in healthcare and retail.
It is used in marketing and operations.
Data analytics continues to grow in importance.
I have experience gathering business requirements from stakeholders.
I translate business problems into analytical tasks.
I work closely with operations, finance, sales, and marketing teams.
I identify key metrics needed for decision-making.
I design BI solutions that align with organisational goals.
I build dashboards that provide actionable insights.
I create interactive visualisations for executives.
I use drill-down features to help users explore data.
I track KPIs through automated reports.
I design dashboards that reduce manual reporting.
I develop strong SQL queries to retrieve data.
I optimise SQL queries for speed and performance.
I create views, stored procedures, and functions.
I work with relational databases such as SQL Server and MySQL.
I ensure data extraction is accurate and efficient.
I perform data cleaning to eliminate errors.
I handle missing values and outliers.
I standardise inconsistent data formats.
I document data transformations clearly.
I ensure data quality before analysis.
I conduct exploratory data analysis to identify trends.
I uncover hidden patterns in data.
I validate business assumptions using analytics.
I investigate anomalies and unusual movements.
I identify performance gaps through data.
I use statistical techniques to enhance analysis.
I apply correlation analysis to find relationships.
I use regression to predict future performance.
I calculate growth rates and performance indicators.
I use hypothesis testing for data validation.
I work with BI tools such as Power BI.
I create DAX measures for complex calculations.
I design efficient data models.
I use summarisation, hierarchies, and KPIs in dashboards.
I publish dashboards to the cloud for organisation-wide use.
I also have experience with Tableau.
I create interactive storyboards.
I connect Tableau to multiple data sources.
I use calculated fields for customised metrics.
I optimise dashboard performance.
I work closely with data engineers.
I define data pipeline requirements.
I ensure ETL processes meet reporting needs.
I test ETL outputs for accuracy.
I collaborate to integrate new data sources.
I understand data warehouse concepts.
I work with star and snowflake schema designs.
I identify facts and dimensions for modelling.
I ensure data models support analytics.
I contribute to data governance processes.
I create automated reporting systems.
I schedule daily and monthly reports.
I set up data alerts for key thresholds.
I ensure reports are delivered reliably.
I eliminate manual spreadsheet work.
I conduct business performance reviews.
I analyse trends across months and quarters.
I compare performance to targets.
I identify weaknesses in business processes.
I recommend data-driven improvements.
I support strategy development with analytics.
I provide insights for budgeting and forecasting.
I help define new KPIs for business units.
I measure the impact of operational changes.
I support senior leadership with data narratives.
I explain data insights to non-technical users.
I create clear visual stories to drive action.
I simplify complex analytical results.
I train teams to use BI tools effectively.
I build strong relationships with stakeholders.
I maintain strict data confidentiality.
I follow data privacy policies.
I ensure compliance with governance frameworks.
I manage user permissions in BI platforms.
I protect sensitive financial and customer data.
I measure BI project outcomes.
I track adoption of dashboards.
I gather user feedback for improvement.
I refine dashboards to meet evolving needs.
I drive continuous improvement in BI systems.
I support automation of business workflows.
I use Power Query for data preparation.
I integrate BI with operational systems.
I help reduce manual reports across departments.
I support digital transformation initiatives.
I manage multiple BI projects simultaneously.
I organise tasks based on priority.
I communicate timelines to stakeholders.
I ensure deliverables are met on schedule.
I maintain documentation for future reference.
I stay updated on BI trends and tools.
I explore new visualisation techniques.
I experiment with AI-assisted analytics.
I participate in data community events.
I share knowledge with colleagues.
I work well in cross-functional teams.
I focus on delivering measurable value.
I ensure insights support real business outcomes.
I continuously improve my analytical skills.
I bring a data-driven mindset to every project I work on.
Working with cloud platforms allows scalable data processing.
Cloud services support high availability and reliability.
Cloud tools reduce the need for physical infrastructure.
They help organisations manage large data volumes.
Cloud platforms enable modern analytics and AI.
I work extensively with AWS data services.
AWS provides end-to-end data pipelines.
I design ingestion processes using AWS Glue.
I use Glue Crawlers to detect schema.
I write ETL jobs to transform raw data.
I store structured and unstructured data in Amazon S3.
S3 provides low-cost, scalable storage.
I use bucket policies to secure data.
I manage versioning for backup and protection.
I optimise S3 storage classes for cost.
I build data warehouses using Amazon Redshift.
Redshift supports fast SQL analytics.
I design fact and dimension tables.
I optimise Redshift queries using sort keys.
I automate loads from S3 into Redshift.
I use AWS Athena for serverless querying.
Athena allows querying S3 data directly.
I define tables using Glue Data Catalog.
I optimise queries with Parquet formats.
Athena reduces the need for data movement.
I use AWS Lambda for automated workflows.
Lambda functions trigger on S3 events.
They automate data validation tasks.
Lambda supports lightweight transformations.
I integrate Lambda with other AWS services.
I work with AWS IAM for identity management.
IAM roles secure access to data services.
I follow least privilege principles.
I manage user access to sensitive resources.
I use encryption for data protection.
I use AWS CloudWatch to monitor pipelines.
CloudWatch tracks resource usage.
It helps detect ETL failures.
It sends alerts for unusual activity.
It supports continuous improvement.
I also work with Microsoft Azure data services.
Azure provides enterprise-strength data tools.
I use Azure Data Factory (ADF) for pipelines.
ADF orchestrates repeated data workflows.
I design pipelines with triggers and linked services.
I use ADF to ingest data from multiple sources.
Sources include SQL databases, files, and APIs.
I build mapping data flows for transformation.
I parameterise pipelines for reusability.
I set up pipeline alerts using ADF monitoring.
I use Azure Blob Storage for data storage.
Blob Storage supports hierarchical organisation.
I use lifecycle management to optimise cost.
I secure data using private endpoints.
I manage access through SAS tokens.
I work with Azure Data Lake Storage Gen2.
Data Lake enables big data analytics.
I structure data into Bronze, Silver, and Gold layers.
I optimise for high-throughput analytics.
I secure lake data with RBAC and ACLs.
I use Azure Synapse Analytics for warehousing.
Synapse unifies big data and SQL analytics.
I design SQL pools for large-scale reporting.
I create partitioned tables for performance.
I connect Synapse to BI tools for reporting.
I also use Synapse Pipelines for integration.
They complement ADF features.
I integrate pipelines with notebooks.
I automate end-to-end data flows.
I monitor workloads in Synapse Studio.
I use Azure Databricks for scalable analytics.
Databricks is strong for big data processing.
I write notebooks in Python and SQL.
I use Delta Lake for ACID transactions.
I build ML-ready datasets in Databricks.
I implement security using Azure Active Directory.
I manage identity through role assignments.
I enforce MFA for sensitive access.
I follow corporate security policies.
I safeguard personal data with encryption.
I use Azure Monitor for pipeline tracking.
It provides logs, metrics, and alerts.
I use Application Insights to detect failures.
I build dashboards to summarise performance.
Monitoring supports service reliability.
I work with cloud networking concepts.
I use virtual networks to secure data flows.
I configure private links for secure communication.
I restrict resources to internal networks.
I follow best practices in cloud architecture.
I optimise cloud costs using monitoring tools.
I select the right storage tiers.
I schedule compute workloads to reduce waste.
I use cost alerts to avoid overspending.
I review monthly usage patterns.
I document cloud data workflows thoroughly.
I prepare architecture diagrams for teams.
I maintain version-controlled scripts.
I ensure knowledge transfer to stakeholders.
I continuously upgrade my cloud skills to stay current.