Leveraging and Highlighting the Value of Data in Financial Services

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Leveraging Data to Power Business

It’s no secret, many financial institutions are undergoing digital transformation. While there are many motivations behind the need to do so, one of the most prevalent causes is the mounting pressures brought on by the tech sector. In recent years, Google, Apple, Facebook, and Amazon (GAFA), have created best-in-breed platforms that have introduced unprecedented levels of convenience, communication, and next best action offers, which have quickly reshaped customer expectations.  

For decades, the financial services sector has watched as time and time again technology has disrupted the industries around them. This is not to say the industry as a whole has simply sat idly by without answering the call for technological advances. Over the years, many financial institutions have created and introduced solutions that have made banking more convenient for consumers. However, in comparison to the disruptive technologies that have revolutionized entire industries the changes implemented by banks seem rudimentary. 

In response to this, big tech and fintech have jumped into action to do what banks couldn’t seem to do. Now, with the two steadily encroaching on financial services, banks have been left with little choice but to act themselves or risk becoming obsolete in the eyes of their customers. Some financial institutions have responded by creating internal innovation labs, while others have started acquiring fintech companies to propel internal innovation mandates. 

Yet despite the hope that these new technologies are the answer to their digitalization—or lack thereof—problem, many organizations are failing to capture the value they had envisioned the additions would bring. At the very root of this problem is the organization’s data. Not a lack of, but rather a lack of understanding the role data can play or even how to leverage it.


Barriers to leveraging data

In many cases, banks are unable to capitalize on their technology investment due to a lack of data readiness. What is meant by this is that not all the data needed to ensure new technologies can be leveraged optimally is accessible. As a result, budgets now go to solving this legacy IT issue and the envisioned time to market must now shift, all of which can put the impact of the technology at risk.

There are many reasons why banks find themselves in such a position, with the following being some of the most prominent:

  • Siloed data is perhaps one of the biggest culprits. Banks have spent decades adopting different software systems to serve a particular purpose at different junctures in time, for different departments. This resulted in having multiple systems that weren’t built to work together or store data in the same way and place.

    The challenge here is that these disparate systems make it difficult to gather proper insights, realize the full potential of data, and can hinder the technology being adopted. 

  • Another challenge siloed data raises is data inconsistencies. This is especially true of data collected between branches, entities, and group-level offices. To allow this data to work for banks, they must first assemble a data ecosystem that aggregates and normalizes these different sources of data. Doing so can be incredibly costly and time-consuming, which is often unforeseen and unaccounted for.

  • With the onslaught of technology and the multitude of ways in which data collection grows, the information that is returned to us is also more granular than ever before. This poses a challenge because the elements in one system can have completely different roles from another, which ultimately makes understanding data difficult. As such, time and effort now needs to be placed into structuring and formatting the data in a manner which can ensure clear and comprehensive views when it needs to be leveraged.

  • With customer interaction now spanning physical, online, social, and mobile channels, it is becoming increasingly difficult to assign a unique identifier to customers. This is because traditional identity management systems have been built around the least volatile types of user information, such as account and PIN numbers. Now, banks are in urgent need of new capabilities that will enable seamless, holistic, and robust identity over time, and across all encounters. Doing so will help banks differentiate their operations through improved customer service and experiences by creating more personalized 1:1 interaction.

Leveraging data to overcome barriers

In order for banks to take full advantage of the technology they take on, they must first have a strong data strategy that embeds analytics deep into their culture, decision-making processes, and business operations. Here are some steps financial institutions can take to stay one step ahead:

  • Roadmap analytics across the entire organization to ensure transparency and break down silos. For example, by aligning business strategies with the analytics needed to achieve goals brings a level of transparency and clarity the organization can get on board with. Doing so helps to streamline processes by identifying areas where analytics will do the most to enhance value.

     

  • Include analytics into workflows for data-driven decision-making. This will emphasize cross-organizationally the importance of analytics and how it complements established practices. This can be proven by illustrating the contributions of insights drawn from data in the decision-making process.
  • To place greater emphasis on the value of data, banks should fill talent gaps by investing in critical analytics roles. For example, having a core team who is responsible for analytics will increase the ability to overcome data challenges and centralize data. What’s more, by increasing this investment in analytics banks can expand the number of use cases they undertake to build out stronger customer journeys and experiences. Lastly, doing so will encourage banks to expand understanding, at all levels, of the role analytics plays in decision-making.
  • Aggregating data is perhaps the most crucial step in the journey to leveraging data. Disparate data makes it difficult for organizations to scale with the flow of technology. Bringing data together into an ecosystem makes it possible for financial institutions to leverage all the data they have on hand in order to execute on any use case. With this flexibility, financial institutions can experiment, analyze results, and iterate repeatedly knowing that all the required data is present.

    Assembling your data, of course, has many other benefits, including:

    • Unifying the view of data across the organization which will make data more accessible and consistent 
    • Allowing for a more complete picture of each customer
    • Allowing banks to interface with third-party vendors to leverage existing solutions, assets, and infrastructure
    • Unleashing the real potential of AI to find patterns and optimize product offerings on a person-by-person basis

As technology continues to advance and impact the world around us, financial institutions must answer the call of changing expectations by innovating their products and services. In order to do so, they must realize that data is at the core of executing innovations. After all, data has the power to fuel strategies and advance customer centricity. However, before all else, organizations must ensure their data is ready to eliminate key barriers and address current and future requirements.

Additionally, having a scalable and flexible ecosystem allows for ongoing experimentation and iteration at speed. This is achieved by supporting data quality and standardization through embedded processes acrosses the organization. Financial institutions that succeed in doing so will not only maintain relevance in the digital age but will also cut costs and boost revenue. 

To learn more on how you can assemble a scalable data ecosystem check out our latest ebook!

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