We will achieve our success factors through five programs of work:
- Break down silos and barriers
- Enrich our data profiles of regulated entities and their industry environment
- Build confidence and trust in our data
- Create a faster path to decisions and actions
- Standardise our data environment
1. Break down silos and barriers
Unlock data and organisational knowledge siloed within teams and systems to fully leverage data assets.
Key projects:
- Projects to uplift ASIC’s data operating model – including developing a data service catalogue, service level agreements and self-service tools – and develop data literacy through training plans and change management.
- ´óÏóÊÓƵData Dictionary, detailing an enterprise data and metadata model to govern the way ´óÏóÊÓƵteams read, use, interpret and communicate data.
- ´óÏóÊÓƵKnowledge Finder, providing a central enterprise information portal for ´óÏóÊÓƵteam members.
- Connected Workforce, aimed at connecting ´óÏóÊÓƵteams through shared knowledge, automation, smart alerts and workflow management.
Key objectives
- Make data discoverable and consumable across teams through an ´óÏóÊÓƵdata dictionary and knowledge sharing.
- Integrate systems to give ´óÏóÊÓƵstaff a comprehensive view of external and internal work to drive efficiencies and enable operational reporting.
- Uplift ASIC’s data operating model and data literacy to empower staff to make data-informed decisions.
2. Enrich our data profiles of regulated entities and their industry environment
Collect and surface up-to-date insights on the entities and markets we regulate, arming our regulatory teams with information that is easy to use, understand and verify.
Key projects
- Recurrent Data Collection, collaborating with industry to phase in more frequent and more granular reporting of financial services data. Includes capability for external data sharing.
- Entity and Adviser 360, collating all information collected by ´óÏóÊÓƵabout each regulated entity including relevant interactions, insights and relationships.
- Market and Industry Insights, developing a centralised solution for extracting real-time market and industry data from relevant external sources and structuring for internal consumption, reporting and analysis.
Key objectives
- Provide a comprehensive, user-friendly view of all the information ´óÏóÊÓƵcollects on entities and the industries and markets in which they operate.
- Correlate market and entity data to strengthen our understanding of the regulated population.
- Harness recurrent and timely data collection for internal and external users, improving speed-to-insight and timely detection of threats and harms.
3. Build confidence and trust in our data
Roll out data quality and access solutions for internal and external users.
Key projects
- Data Quality Improvement, collecting and profiling ´óÏóÊÓƵreference data to improve consistency across the organisation.
- ´óÏóÊÓƵService Portal, a one-stop-shop to publish relevant and reliable information for the public and enable enquiries.
Key objectives
- Embed data standards to ensure quality, completeness, accuracy, availability and timeliness of data to drive data-led decision making.
- Promote confident and informed participation in the financial system by facilitating requests and delivering valuable insights to the regulated population and consumers.
4. Create a faster path to decisions and actions
Leverage analytics, insights and automation to increase organisational responsiveness.
Key projects
- ´óÏóÊÓƵBusiness Insights, implementing standardised, user-friendly data visualisation interfaces.
- Digital Agents, enabling artificial intelligence (AI) and cognitive automation capabilities to reduce human intervention during data capture and analysis.
- Smart Monitoring, implementing automated detection and reporting to intervene in potentially harmful behaviours.
- Rapid Value Factory, an ongoing program of work to deliver ‘quick win’ data solutions to facilitate efficiency gains.
- Agency Knowledge Exchange, providing an efficient and secure platform for data exchange and inter-agency collaboration.
Key objectives
- Expose cross-organisation data assets to support seamless data exploration and reporting.
- Leverage our investments in regulatory technology (regtech) and supervisory technology (suptech) with smart monitoring and risk-scoring tools.
- Increase automation of data capture and analysis.
- Enhance data sharing between local and international regulators.
5. Standardise our data environment
Establish a strong data foundation, architecture and operating model to guide, direct and govern the delivery and outcomes from our data portfolio
Key projects
- Advanced Analytics Foundation, implementing AI and machine learning (ML) capabilities to support risk scoring, triage and analysis of regulated entities’ transactions and relationships.
- Data Consumption Foundation, deploying a standard suite of business intelligence and analytics tools and training to support self-service reporting and visualisation.
- Data Integration Foundation, implementing AI and ML to facilitate the use of data and insights in ´óÏóÊÓƵbusiness applications workflows and processes, as well as efficient and secure data exchange capabilities with other entities, agencies and the public.
- Data management, movement and security foundations, introducing standards for data quality and access.
Key objectives
- Establish the architecture for storing, managing, exploring, and moving data.
- Introduce advanced analytics to optimise document analysis, indexing and triage, and integrate data analytics into standard ´óÏóÊÓƵbusiness processes.
- Implement a centralised data security and policy management solution.