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.

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.

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.

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.

Tom Seel is the technology product manager at Investigate DQ and has been involved since its inception. He has worked in numerous complex data migration and data remediation projects and is passionate about all things data. 

In this Q and A, Tom discusses data quality software tool Investigate DQ and how its origins came from data remediation work. 

Organisations who have immediate access to rich and accurate data will advance faster and to a greater extent than their rivals. Investigate DQ's true value is realised in supporting data-driven strategic growth and business transformation.

There is a sea of customer data flowing into organisations every day, that leaves them susceptible to data quality issues. The underlying risk - reputational and financial damage. Investing in your data early is your gateway to a competitive edge.