It is important to adopt a comprehensive approach to data migration, including a thorough analysis of the data and systems involved, careful planning and testing of the migration process and ongoing monitoring and review to ensure the accuracy and completeness of the data when it is implemented in the new system.

Data quality is pivotal for the efficacy and dependability of AI and ML models. In sectors where precision is paramount, like healthcare and finance, the calibre of data can make or break the predictive power of these technologies.

Introducing the latest milestone in our ongoing dedication to conquering data quality challenges: InvestigateDQ's newest major version is here. This release is driven by three core principles deeply ingrained in our mission:

1. Empowering users with user-friendly technology, ensuring that everyone can participate seamlessly.

2. Tackling even the most intricate and demanding data quality issues.

3. Catering to the ever-evolving real-world use cases we encounter today.

While AI and ML are transformative, they are not a replacement for established data quality best practices and tools. It is crucial to understand their capabilities, potential, and limitations, and to approach them as complementary tools rather than replacements. Using AI/ML as the only solution for data quality is akin to patching a boat's leak with tape – it helps but will not solve everything.

For regulated organisations in financial services sectors such as superannuation, banking, insurance and wealth management, poor data can result in breaches, potential fines, and reputation loss. Here’s how to ensure data quality success in financial institutions.

As a first step, our client was looking to streamline their operations and accelerate efficiencies. This called for a purpose-built solution that could be automated and customised to meet their organisations data quality needs. Another requirement was the ability to have a real-time, dashboard view of all the data discrepancies across multiple systems and data points within the organisation.

Our client would then use the rich data to inform organisational growth through data analytics, strategic decision making, digital transformation initiatives, automation and investment in artificial intelligence and machine learning.

As part of AUSTRAC Customer identification and verification obligations, Financial Institutions must identify any customers present on several, centralised-Government managed and issued customer listings. ​

Historically, organisations would meet this obligation by providing a significant volume of customer-sensitive data to an external party, who would assess the data and provide potential findings back to the organisation. ​

This presented not only a significant data security risk to organisations but also, an inability to keep pace with a rapidly changing political climate, such as the sanctions that were imposed due to the conflict in Ukraine.

Heavily regulated industries are required to monitor for financial crime and other related activities as a part of Anti-Money Laundering (AML) and Counter-Terrorism Financing (CTF). This includes activities such as transaction monitoring, fraudulent behaviour and screening of customers on a regular basis. Our client, a large superannuation fund, wanted a way to securely manage their fraud prevention and transaction monitoring.

InvestigateDQ was integrated into everyday BAU processes across multiple teams with 160 staff members in the production environment using it daily and the intention to push it out across the entire company. These are staff members from all aspects of the business, using it for everything from day-today work queues to report generation and risk management. Before transitioning, the InvestigateDQ team were able to train a few staff members, who in turn trained everyone else; a testament to the usability of the system

2023 has seen several trends emerge in the realm of data quality and management that organisations who rely on accurate data, such as the financial industry need to know.

While some organisations are increasingly focused on data privacy and the adoption of machine learning and AI, regulators are placing greater emphasis on data governance.

Here are the five key data management practices you need embed in your organisation.

Using Investigate DQ, the project team were able to connect to each data location and easily apply rules that could identify data discrepancies across members, attributes and financial data. Customised reporting and intuitive dashboards allowed the team to track and report on the number of issues identified at each stage of the migration. This provided clarity and transparency to the stakeholders involved on the project to understand shifting priorities and target areas of concern.

2023 has seen several trends emerge in the realm of data quality and management that organisations who rely on accurate data, such as the financial industry need to know.

While some organisations are increasingly focused on data privacy and the adoption of machine learning and AI, regulators are placing greater emphasis on data governance.

Here are the five key data management practices you need embed in your organisation.

Using Investigate DQ, the project team were able to connect to each data location and easily apply rules that could identify data discrepancies across members, attributes and financial data. Customised reporting and intuitive dashboards allowed the team to track and report on the number of issues identified at each stage of the migration. This provided clarity and transparency to the stakeholders involved on the project to understand shifting priorities and target areas of concern.

Navigating changing technological and regulatory environment, while meeting customer needs and maintaining a reputation can prove to be complex and difficult to tackle without robust data quality management.

Highly effective reporting and data quality management continues to be a hot topic in the industry. We believe that robust data quality management practices not only increase member engagement but also help solidify members’ trust in the company.

Your organisation may be collecting large volumes of raw data, but it is only useful if it is of high quality.

Can you imagine the consequences of investing millions of dollars to train your AI and ML models, but your data was unreliable?

Building trust in the quality of your data allows for optimal service outcomes and provides benefits that extend far beyond increased data quality.  However, the first step in this process is investing in automating your data validation.

Anyone that has tried appreciates that achieving good data across an organisation is not easy. For many organisations, getting to the point where data quality is seen as a “non-negotiable” will take commitment, effort, and investment.

For regulated organisations in financial services sectors such as superannuation, banking, insurance and wealth management, poor data can also result in breaches, potential fines, and reputation loss

For many organisations, getting to the point where data quality is seen as a “non-negotiable” will take commitment, effort, and investment. Here are 5 key strategies that will help overcome challenges in Data Quality.

There has never been more reliance and importance on data to provide administration of consumer products effectively and efficiently and in turn, organisations need to have robust Governance and security in place to safeguard information. The recent Optus data breach has demonstrated how far reaching and significant an impact compromised data can have to consumers.

Highly effective reporting and data quality management continues to be a hot topic in the industry. We believe that robust data quality management practices not only increase member engagement but also help solidify members’ trust in the company.