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.

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.

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.

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.

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.

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.

The quality of data is key to an organisation's strategic decision making, agility, productivity, and survival. For regulated organisations data quality is becoming more important because the consequences and risks of making incorrect decisions are now far greater.

Transformational change is the biggest challenge insurers are facing today. Change can be as simple as first addressing the data quality across your existing systems. Create that single source of truth and build from there.

QMV and Investigate DQ’s co founder believes reliable, accurate and timely information is critical to the industry’s future. So is minimising data errors which are costing the industry and its customers millions each year.