Technology has carved its niche into almost every aspect of modern business, offering individuals across multiple roles the opportunity to put advanced capabilities to use.

The ability to leverage technology for improved collaboration and efficiency is no longer just a competitive edge — it's a necessity. Access to advanced tools means employees can now do more with less and continue to make progress toward that north star of “working smarter, not harder.”

As technology progresses and becomes more accessible, companies have an opportunity to optimize their operations. This isn’t about relying on a handful of tech experts; it’s about empowering people across your organization to use advanced capabilities on their own. By doing so, your firm is in a stronger position to design streamlined, automated operations that support high-quality data, risk mitigation, and better collaboration between departments.

At the core of this transformation is self-serviceability — giving teams the tools and autonomy to manage their own workflows, without depending on IT departments or other specialized experts.

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The complexity of data management

Managing data within large organizations can be incredibly complex, especially when multiple departments and external entities are involved. In a typical investment management setup, for instance, various data sources are continually updated in near real time, both internally and externally. Teams across the organization need to collaborate and contribute to the data management process, but manual interventions and varying permission levels often complicate the process.

Moreover, different departments within the same organization may rely on different data sources, leading to discrepancies that make it difficult to maintain consistency and accuracy in data. These challenges can create bottlenecks that slow decision-making, create costly, hamper teams from working efficiently or lead them to overlook key insights.

The cost of inaccurate data

Inaccurate or outdated data can have some pretty negative consequences. A 2020 report published by Gartner found that poor data quality costs organizations an average of $12.9 million per year.¹ Estimates from MIT Sloan suggest that bad data is the norm for many institutions, costing firms anywhere from 15% to 25% of their revenue.² Mistakes, inefficiencies, and errors caused by incorrect data are not just frustrating — they are expensive.

Here are some of the risks businesses face when working with inaccurate data:

Flawed decision-making: Inaccurate data can lead to poor choices, which may result in financial losses or missed opportunities.
Time-consuming and expensive error correction processes: Correcting errors caused by inaccurate data is costly and detracts from valuable time that could be spent on just about anything else.
Compliance issues: Without reliable data, organizations may be slapped with regulatory fines or penalties or even lose investor trust.

So how can firms get ahead?

Embrace the digital employee experience

Today’s business users demand more options besides Excel to manage their data. Fresh grads entering the financial services industry with more advanced technology skills, such as the basics of Python and SQL, expect analytical tools to keep pace with their own capabilities. By making data and the tools to use it widely available, organizations can develop a tech-savvy workforce prepared to confidently leverage the tools available for their role.

The digital employee experience encompasses how employees interact with their organization’s digital tools and systems. In many ways, it runs parallel to how companies create seamless, high-quality experiences for their own customers. Users comfortable working with data will be stronger positioned to solve the challenges that clients are calling them about and they’ll also be able to use the information to generate their own insights.