Bridging the Data Quality Gap: Building a High Quality, Data-Driven Business Culture

Data is a critical asset for businesses. So why is data quality often treated as an afterthought? We’ve observed businesses struggle to adapt to our data-driven reality. Nonetheless, leadership are increasingly looking to use data to improve their business’s bottom line. To bridge the gap between data challenges and a data-driven future, businesses need to invest in data quality. This bridge rests on three main pillars: data governance, team engagement, and data tools. This is the key to unlocking data quality success.

Figure 1: The three pillars of data quality

Pillar 1: Governance

One of the greatest barriers to adopting a culture of data quality, and data governance more broadly, is the perceived inconvenience of yet another layer of governance in the business. Crucially, this perception often starts from the top: the C-suite needs to be onboard. When there’s a lack of governance structures, band-aid solution approaches tend to be applied to data quality. Simply addressing an issue at a single point-in-time does not prevent it from happening again. Data quality needs to become an embedded and ongoing concern, and to achieve this, appropriate governance structures are paramount.

This includes addressing the following:  

1. Who is ultimately responsible for the business’s data? Is there a senior leadership role (even a C-suite position) required to elevate its perceived importance in the business?  
2. Do you have enough resources with the right skills and experience to gather and maintain your data?  
3. What does data quality mean to your business? What does it look like?  
4. What’s the policy on data storage? How do you eliminate data-silos in your business?  
5. What’s the cadence for reviewing data?  
6. What’s the standard format for data? Does this standard translate across business systems?  
7. What’s the process for identifying and resolving data quality issues. 

Pillar 2: Team engagement

Without team buy-in, the adoption of a data-quality driven business culture can be undermined. That’s why it’s of the utmost importance that team engagement is a central part of your business’s data quality solution. This starts with education. It’s through understanding the defining elements of data quality that FSI businesses can avoid making common mistakes, such as:

1. Incorrect fee calculations.  
2. Interest crediting miscalculations and delays.   
3. Benefit eligibility miscalculations and delays. 
4. Critical information being incomplete or missing from the customer or policy. 
5. Systematic errors from technology changes over time. 

Improving the collective understanding of the essential characteristics of good quality data is one of the first steps towards combatting these kinds of issues (see Figure 2)

Figure 2: Essential Characteristics of good data quality

The importance of the human aspect of data quality cannot be understated. Building buy-in requires a few engagement tactics, including:

1. Clearly communicate expectations about the vision for the business’s data-driven culture. How will this culture feel? What does it look like in practical terms? What does it sound like? 
2. Embrace the “all in this together” sentiment by getting leadership to lead from the front, visibly and wholeheartedly. If employees see leadership begrudgingly participating in this change management process, they’re less likely to take it seriously and get involved.  
3. Ensure that employees have time allocated to maintain data quality. This time must be protected and endorsed by leadership.  
4. Creating a safe environment for employees to ask questions about the change in approach, to ask for help and to be actively involved in crafting the data-driven culture. 
5. Taking time to acknowledge and celebrate positive steps towards a data-driven culture. Positive reinforcement is critical for making people feel like they’re working towards something for a reason.

Pillar 3: Tools

In an industry that’s responsible for such extraordinary volumes of data, it makes sense to invest in the right data quality tools. We believe so strongly in having the right data quality tool, that we have our very own offering in this space, purpose-built for the superannuation and wealth management industries — Investigate DQ. When considering which product is right for your business, it’s important to consider the following:  

1. Scalability: with data volumes only ever going to increase, you should consider solutions that will be flexible enough to meet the current and future needs of your business.  
2. Usability: systems that are easy to use are more likely to be used 
3. Customisation: Make sure you invest in a system that can be tailored to fit your business. Very few out of the box solutions can “plug and play” in your business without some level of customisation. The idea here is to enable analyst-level users to configure data quality rules and dashboards that meet their specific needs. 
4. Dedicated Solution: invest in a system that is specifically crafted with your industry’s data-quality constraints and requirements in mind.  
5. Functionality: ensure that the product has all the necessary functionality and controls required to maintain and fully utilise data. Consider the management, preventative and detective controls, in particular.  

The bridge to a high quality, data-driven future

As businesses grapple with the transition to a data-driven era, the emphasis on data quality cannot be overstated. It is not merely an operational necessity but a strategic asset that can illuminate the path to informed decision-making and sustained business growth. This journey starts with implementing the right governance and tools and branches out into a range of other considerations and steps. The “9 steps to data quality” infographic below serves as a critical roadmap for organisations aiming to navigate the complexities of data quality. By adhering to these steps, businesses can systematically address the common pitfalls that undermine data integrity and reliability. In doing so, you not only secure your business’s bottom line, but position yourselves to lead in the increasingly data-centric landscape of the future. 

This article was produced as part of The Quarterly – Data and Technology in Superannuation, Q3 FY24

For more information about anything you’ve read here, or if you have a more general inquiry, please contact us.

Key Contributors:

Kevin Fernandez is General Manager, Market Strategy and Propositions at Novigi, and is based in the Melbourne office.

Sophie Bowen-James is an analyst in the Market Strategy and Propositions team at Novigi, and is based in the Sydney office.