Major challenges in delivering data projects - personal experience

 1. Data Quality

2. Data Ownership and Governance

3. Data Integration

4. Data Security

5. Data Scalability / performance

6. Regulatory compliance

7. Talent availability

AUSTRAC Compliance - AML / Anti-money laundering, Counter Terrorism Financing [CTF] Compliance Reporting

AUSTRAC Compliance - AML/CTF

Australian banks and other financial institutions are required to report certain transactions to AUSTRAC (Australian Transaction Reports and Analysis Centre) for anti-money laundering and counter-terrorism financing compliance purposes. 

Matter has to be reported to AUSTRAC within 10 days of the transaction. The reports include details about the transaction, such as the date, amount, account details, and customer identification information.

1. TTR: Transaction Threshold Reporting
2. SMR: Suspect Matter Reporting

TTR

Transaction threshold reporting involves reporting to AUSTRAC any cash transactions or transfers equal to or above a certain threshold amount. The specific thresholds are as follows:

1. Cash Transactions >=10k AUD
2. International Fund Transfers >= 10k AUD
3. E-currency transfers such as Bitcoin >= 10k AUD

SMR

SMR (Suspect Matter Report) reporting is another key anti-money laundering and counter-terrorism financing compliance obligation for Australian banks and financial institutions. It is submitted as soon as the transaction happens when the bank has reasonable grounds to suspect the transaction or an attempted transaction may be related to money laundering, terrorism financing or any other serious criminal offence, for example prostitution, blackmailing, etc.

SMR keypoints:

• Must be reported asap unlike TTR where there is a buffer of 10 days.
• Triggers are suspected activity.
• Must include details of the transaction or attempted transaction, as well as the reasons for suspicision.
• The fact that an SMR has been submitted is strictly confidential.

Gartner - Data quality challenges in AI

 

Data Project Manager - Key Areas to be aware of

Becoming a proficient data project manager requires a broad understanding of various aspects related to data management, analysis, and interpretation. Here's an elaboration on some crucial areas a data project manager should be aware of:

Data Governance: Data governance involves the overall management of the availability, usability, integrity, and security of the data used in an enterprise or organization. As a data project manager, you need to ensure that proper policies, procedures, and controls are in place to manage data assets effectively.

Data Quality Management: Ensuring data quality is crucial for reliable analysis and decision-making. Data project managers should be familiar with techniques and tools for assessing, monitoring, and improving data quality. This involves identifying and resolving issues related to accuracy, completeness, consistency, and timeliness of data.

Data Integration and ETL Processes: Understanding how data flows through different systems and processes is essential. Data project managers should have knowledge of Extract, Transform, Load (ETL) processes and data integration techniques to facilitate seamless data movement across various platforms and applications.

Data Warehousing and Data Lakes: Data project managers should be familiar with data warehousing concepts and technologies, which involve storing and managing large volumes of structured data for reporting and analysis purposes. Additionally, knowledge of data lakes, which store vast amounts of raw, unstructured data, can be valuable for certain projects.

Data Analysis and Visualization: While data analysts and scientists typically handle the technical aspects of data analysis, data project managers should have a basic understanding of statistical methods, data modeling techniques, and data visualization tools. This knowledge helps in effectively communicating insights derived from data to stakeholders.

Data Security and Privacy: Protecting sensitive data from unauthorized access, breaches, and misuse is paramount. Data project managers should understand data security best practices, compliance regulations (such as GDPR or HIPAA), and how to implement measures to safeguard data privacy and confidentiality.

Data Lineage and Metadata Management: Data lineage refers to the life cycle of data, from its origin to its current state and how it moves across systems. Metadata provides descriptive information about data, such as its structure, format, and context. Data project managers should understand the importance of data lineage and metadata management for tracking data provenance, ensuring data traceability, and facilitating data discovery.

Data Storage and Scalability: Knowledge of different storage technologies (e.g., relational databases, NoSQL databases, cloud storage) and their scalability features is essential for managing data effectively. Understanding the trade-offs between performance, cost, and scalability helps in selecting the right storage solutions for specific project requirements.

Data Access and Permissions: Controlling access to data and defining permissions based on user roles and responsibilities is critical for maintaining data security and integrity. Data project managers should be familiar with access control mechanisms, authentication methods, and authorization policies to ensure that data is accessed and used appropriately.

Data Ethics and Bias: Awareness of ethical considerations and potential biases in data collection, analysis, and interpretation is important. Data project managers should promote ethical practices and be mindful of biases that may arise from the data or the algorithms used in data-driven decision-making processes.

Salesforce Introduction - Coursera / University of California

Salesforce is a cloud-based CRM platform // Customer Success Platform that provides a comprehensive suite of tools to help business manage their: 

• Customer relationship
• Sales
• Marketing
• Other businesses

Essentially, you can sell, service, market, analyze, and connect with your customers.

It is: 

• Cloud based: are hosted on remote servers of Salesforce company. Users access it via Internet.
• SaaS model: Companies subsribe to service on a per-use / per-service basis. 

Variety of modules

Sales Cloud:

Function: Automates sales processes.
Features: Contact and account management, opportunity tracking, lead management, sales forecasting, and performance analytics.

Service Cloud:

Function: Enhances customer support and service operations.
Features: Case management, service automation, a knowledge base, customer portals, and multi-channel support (phone, email, chat, social media).

Marketing Cloud:

Function: Manages digital marketing efforts.
Features: Email marketing, social media marketing, advertising, customer journey management, and analytics.

Commerce Cloud:

Function: Supports e-commerce operations.
Features: Online store management, mobile commerce, order management, product recommendations, and personalization.

Community Cloud:

Function: Builds online communities for customers, partners, and employees.
Features: Discussion forums, user groups, knowledge sharing, and customer service communities.

Analytics Cloud (Einstein Analytics):

Function: Provides advanced data analysis and business intelligence.
Features: Data visualization, dashboards, predictive analytics, and AI-driven insights.

App Cloud:

Function: Enables custom application development.
Features: Development platforms like Force.com, Heroku, and Lightning for building custom apps that integrate with Salesforce.

Einstein AI:

Function: Adds artificial intelligence capabilities across Salesforce.
Features: Predictive analytics, natural language processing, automated data entry, and intelligent recommendations.

Integration Cloud:

Function: Ensures seamless integration with other enterprise systems.
Features: Tools for data synchronization, API management, and integration with third-party applications.