Supporting financial services with data reporting

The caveat? Reporting doesn't happen in isolation. Instead, it requires a combination of processes, technology and people. Here's how banks and firms can make the move from information to insight and deliver effective data reporting.

More data, more problems? Challenges and opportunities for banks and financial services firms

According to a recent study, 80% of central banks now formally discuss the topics of big data and machine learning in their institution, up from just 30% in 2015. They're also taking action, with more than 75% of banks planning to increase technology spending this year. And 77% are worried that if they don't effectively leverage big data, they could lag behind.

The result is a big-data landscape with both challenges and opportunities. Consider rapidly increasing data volumes driven by internal digital transformation, the adoption of client-facing mobile applications and the growing number of online-first transactions. This data contains valuable insight for banks and firms about what customers want, how they prefer to interact with the institutions and what steps can be taken to boost client loyalty.

But making the most of this data presents challenges. The first is data format.

Along with structured data (e.g., spreadsheets, forms and transactional information), banks and firms are now dealing with massive amounts of unstructured data. This includes emails, images, videos, speech-to-text recordings of customer interactions and even sentiment analysis. Unlike structured data, this information has no consistent format, making it difficult for organizations to separate essential from extraneous data.

The next challenge is data silos. Information from different business units may be stored in secure, isolated databases tied to legacy systems. As a result, key datasets (e.g., customer accounts, loan processing, fraud analysis and revenue generation) sometimes aren’t easily visible to one another.

Effective data reporting demands the big picture in both cases: Firms need to leverage and derive value from both structured and unstructured data and must remove the barriers that prevent the juxtaposition of disparate datasets. By taking the time to set the stage with data-driven processes, banks and firms can position themselves for reporting success.

Processes: Setting the stage

While it's possible to use limited datasets with minimal context to create data visualizations (e.g., charts, graphs or tables), these outputs offer minimal value, in the sense that they’re merely descriptive and may not provide the full picture. The right processes, therefore, are essential for improved reporting outputs.

First is governance. Without a robust governance process in place, banks and firms run into significant problems, including erroneous metadata, accidental compliance infractions, data exposure or unauthorized access to secure information. Effective governance starts with an evaluation of what companies have: Who’s accountable for what data? Where’s the data located and who has access to what? What data is critical? For which use cases? What steps are required to protect this data?

Next is data strategy, which speaks to data quality, integrity, accessibility and security. Organizations must implement processes to test for overall data quality and implement processes to clean data, in turn reducing the risk of inaccurate, incomplete or duplicate information.

They must also evaluate optimal data management options, including warehousing and cloud strategy: What data should be stored in the cloud? What will stay on premises? How will these datasets interact? How will data be accessed, by whom, how often and for what purpose? Last but certainly not least, data security processes must be implemented in all areas.

Technology: Solving the puzzle

Technology comes next. For processes to be effective, they must be supported by the right technology enablers.

Key technologies include effective data management, such as MDM, along with advanced analytics tools such as machine learning (ML) and artificial intelligence (AI) tools. ML/AI algorithms offer a way for companies to derive value from large datasets, going from descriptive analytics to predictive and prescriptive analytics. Organizations can extract critical, relational insight by training these ML models to identify specific markers or patterns.

It's also worth noting that for many larger companies, IT and data environments are increasingly distinct. Running and supporting an IT infrastructure requires different skills and personnel than managing and leveraging enterprise data. As a result, organizations are increasingly building up their own dedicated data teams of analysts, engineers, architects and scientists that are managed by a company-wide chief data officer (CDO).

Therefore, when it comes to purchasing technology, businesses must manage not only how they perform specific data functions but also how they interoperate with IT environment solutions.

People: Sustaining the output

Processes establish the mechanisms required for data reporting. Technology offers the tools that enable these processes. People are the final, critical component.

This is because even the best processes and technology require human intervention and skills. Personnel must be trained and upskilled, so they become comfortable with and proficient in using newly implemented tools. Training existing staff is often complemented with the selective hiring of individuals with critically needed skill sets.

Ensuring adoption starts with stakeholder buy-in. This means bringing staff on board as early as possible in the process and providing an avenue for input at each stage of adoption. Future end users typically would ensure during the design stage that blueprinted solutions will largely alleviate their primary data pain points.

Getting people on board also requires ongoing culture change management. In practice, this means recognizing that new tools and technologies aren't fire-and-forget. Instead, companies must regularly "keep their finger on the pulse” to make sure that implemented solutions deliver value as expected and that the organization as a whole is successfully adopting a “data-driven” mindset.

Data reporting: Seeing it all come together

Data reporting is the end stage. It's more like the tip of an iceberg — visible above the water but supported by the massive body underneath. Banks and financial services firms need to start from the bottom up to turn growing datasets into actionable, visible outputs.

Processes provide the structure, guidelines and mechanisms; technology enables the efficient application of these processes; and people drive the deployment of solutions and capture the value they provide. Controlled, consistent progress on all three components lets banks and financial firms establish a reliable and repeatable reporting framework.

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The information provided here is for general guidance only, and does not constitute the provision of tax advice, accounting services, investment advice, legal advice, or professional consulting of any kind. The information provided herein should not be used as a substitute for consultation with professional tax, accounting, legal or other competent advisers.