The penalty for poor business decisions and deficient customer experiences is enormous, so most financial institutions are combining new external data streams to existing internal data sets, applying advanced analytics to find the foundation for faster decisions and better consumer insights. This has never been more important than in a pandemic-impacted marketplace, where change is happening faster than ever and customer expectations are rising exponentially.
With the emergence of new data streams, cloud-based storage and parallel processing, technology is capable of leveraging all data that a financial institution collects. But the key is to identify specific business problems, invest in the right technology for each phase of the data lifecycle, and deliver insights in real-time to the teams that can impact both the internal business challenges and the external customer experiences.
Part of the data maturity continuum is that trust is built along the way. If quality data is collected and validated, business-driven analytics are leveraged, and recommendations are provided that can improve results, stakeholders will increasingly accept the process that is many times new to them. The trust increases exponentially as KPIs are achieved.
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4 Stages of Data Maturity
Dell created a simplified data maturity model to track their own data maturity and to help their customers track theirs. The progression along the data maturity scale illustrates greater proficiency of both collecting and using data. It also assumes a foundation of quality data and a movement to a culture where data and analytics drive virtually all decisions and customer experiences.
Data Aware. At the first stage, the emphasis in on trying to standardize reporting across multiple silos of data and databases. In this phase, there is a lack of data quantity and integration across business lines. For the most part, the organization is doing ad-hoc reporting and using data to solve very narrowly scoped challenges. Trust in data across the organization is lacking.
Financial institutions in this phase of data maturity will want to focus on improving data quantity and quality, increase the level of modeling and expand the scope of how data and analytics will support broader business needs and decision making.
Data Proficient. At this stage, data quantity is increasing, but data quality is still questioned. There are still multiple databases and limited app integration. The ability to process data has improved, allowing the establishment of KPIs and the emergence of test data initiatives. Without executive level sponsorship, the demand of users will still outstrip the ability of the data providers to support initiatives.
At this stage, financial organizations must focus on improving data quality and app integration, while committing to faster access to insights. This requires executive level support and a master data management strategy. The goal to move to the next stage is to standardize reporting on a platform available across the firm.
Data Savvy. At this stage, financial institutions are comfortable using data and advanced analytics to drive critical business decisions and deploy data and insights to improve customer experiences. This is the stage where executive sponsorship emerges to break down organizational and data silos. Data is now viewed as a competitive differentiator, requiring the IT area to store quality data effectively and be able to deploy insights on demand in real-time.
According to Dell, at this stage, “The IT-business partnership should focus on building advanced capabilities such as a data lake, data integration with external data sources, text mining, data mining, statistical model building, and predictive analytics.”
Data Driven. At the final stage of data maturity, the objective is to scale the data strategy while continuing to take out costs. IT and business units are on the same page with the same overarching mission, using multiple quality data sources and apps to support an advanced analytics platform. Analytics are embedded into all decisions and departments.
The mission at this stage is to move up the analytics maturity curve from descriptive and predictive analytics into prescriptive analytics. This applies not only to decision-making within the organization, but the personalization and proactive recommendation process for customers. The external focus is on an improved UI/UX across sales, marketing, operations and HR, building machine learning, forecast modeling, and sentiment analysis capabilities.
According to Dell, “Reaching data maturity and becoming a data-driven organization involves enabling end users with the ability to perform their own analysis, without the need for IT, on a trusted and supported architecture.” The focus moves from functionality to real-time insights, speed and iterative analysis capabilities on demand.
Most Financial Institutions are at Lower Levels of Data Maturity
Data, by itself is not a valuable asset – it’s what you do with it. And this is where the whole idea of data maturity comes into play. Data maturity is essentially the extent to which organizations utilize their data to get the most out of it. The more highly data is esteemed and the more sophisticated the techniques to analyze the data are, generally, the more data-mature the organization is.
Likewise, an organization that does little with the data they produce is likely to only be in the very early stages of their data maturity journey.
In research by the Digital Banking Report, we found that most financial institutions did not rank themselves very highly regarding data maturity. While most organizations appeared to be comfortable with data quantity and the ability to store data, the level of maturity plummeted for all other major components, including accessibility (only 27% of organizations ranked themselves strong or very strong), quality (26%), deployment (19%), or the ability to analyze data (18%).
These measures of data maturity were reinforced when we asked about the availability of data to improve the customer experience or the availability of data from a single source. The reason why we measured the availability of data to improve CX is because moving data objectives from inside the organization to outside experiences is where the real power of data is achieved. In our analysis, only 12% of organizations believed they had the level of data and analytics needed to drive a positive experience.
Without a single source of data, it is difficult for an organization to establish the trust requisite for a strong data strategy. Only 6% of organizations surveyed stated they had a single source of insights. This illustrated that most organizations would fall into the “Data Aware” or “Data Proficient” stages of data maturity.
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Importance of Improving Data Maturity
As with many areas of digital banking transformation, financial institution executives understand the importance of progressing – quickly – to a higher level of data maturity. In fact, the level of importance placed on improving data quality and analytic capability produced some of the highest scores we have seen in a post-pandemic world.
As we have found in many of the research studies for the Digital Banking Report, however, the understanding of the importance of data maturity falls far short of the commitment needed to improve data maturity within an organization. There is a concern from our research team around the rather low level of importance placed on the ability to deploy insights across the entire organization. Until this is deemed of paramount importance, organizations will continue to lack data maturity.
To reinforce our concern around the paradox of understanding the importance of data maturity and the commitment to making it happen within the organization, we do not see a level of investment required to achieve these aspirations by most financial institutions. In fact, close to 60% of organizations expect less than a 25% increase in investment in 2020.
And, while it initially appears encouraging that 40% of the organizations expect to increase their investment more than 25% in 2020, these increases were mostly against very modest current investments, thereby making the investment change nominal.
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Data Maturity Impacts Key Business Drivers
As data maturity within an organization improves, other metrics move in tandem. For instance, when a financial institution improves their data maturity, the cost of business intelligence actually decreases significantly because there are fewer ad hoc requests or non-standardized reports. This results in the overall collection, use and deployment of data becoming standardized and scalable.
In addition, an increased data maturity improves predictive analytics capabilities, which positively impacts an organization’s ability to plan for the future. It also directly impacts the ability for a financial institution to provide personalized recommendations to customers on a contextual and proactive basis. In the future, personalized and proactive recommendations are the key to an exceptional customer experience.
Finally, revenues increase substantially because KPIs are based on quality data that is actionable and because the speed of moving from data to decisions goes from weeks to hours to even minutes and seconds. And, in a world where change is happening faster than ever before, agility of insights is a competitive advantage.