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The four pillars of data integrity: what finance businesses need to know

The four pillars of data integrity: what finance businesses need to know 41

By Amy O’Connor, chief data and information officer at Precisely

COVID-19 accelerated digital transformation across financial services at a previously unseen pace, forcing organisations to adapt quickly and become increasingly reliant on digital services to remain competitive. To stay ahead in the new world, financial institutions need to be able to provide superior customer experience by delivering compelling product and services through a mix of strategically placed branch locations and digital channels – all whilst staying responsive to new market needs and opportunities and ensuring compliance against a backdrop of ever-changing regulations.

This challenging environment makes it critical that financial businesses make proper use of first- and third-party data, as well as spatial analytics, to improve their understanding of the market dynamics and customer behaviours impacting their business – ultimately supporting smarter, data-driven decision making.

However, a recent Corinium Intelligence study on data integrity trends revealed that 50% of financial services organisations report attempts to put core data management and governance frameworks in place as yielding “mixed” or “disappointing” results, with the primary reason being a lack of robust data foundations being put in place to ensure the integrity of the data that these frameworks are built upon.

Data integrity empowers businesses to make fast, confident decisions based on trusted data that has maximum accuracy, consistency, and context. For financial services, knowing that data is a strategic corporate asset is the first step to establishing clear frameworks for implementing the four pillars of data integrity: data integration, data governance and quality, location intelligence and data enrichment.

Unlock the power of enterprise data through data integration

Most complex enterprises rely on multiple, and often disjointed, applications to manage data on customers, prospects, vendors, inventory, employees and more – and when these systems operate in silos it becomes impossible to create a clear, unified view of the business. Financial institutions often have the additional complexity of needing to access key customer data from mainframe applications  ̶  traditional systems which are highly reliable and secure but whose complex data formats are not easily integrated into more modern data environments.

Building a holistic view requires tying multiple systems together through mapping and translation. Integration of data across the enterprise, whether in mainframes, relational databases, or enterprise data warehouses, requires a carefully considered approach to bringing the data together under one roof, and in a way that is most aligned to the organisation’s strategic goals.

Support regulatory compliance with data governance and quality

Once an organisation has managed to break down data silos, a common problem remains – one of data quality. Despite integrating multiple systems, data may be missing, inaccurate, inconsistent, or may contain duplicates. For financial institutions, there is also the pressure of global regulations which mandate an understanding of where that data has come from, as well as being able to prove accuracy and validity of the data and ensure its security. As a result, financial services data quality and security must be proactively maintained to comply with good data governance standards for which regulations are constantly evolving.

Good data quality practices dictate that business leaders work together to define clear outcomes. That includes cross-functional collaboration across multiple departments. It is impossible to govern everything, so subject matter experts from across the organisation need to work together to establish a shared list of priorities around risk, compliance, finance, and marketing objectives.

Robust data governance practices also imply a sound strategy for using technology to automate data quality. That includes the use of tools that help companies to cleanse, validate, de-duplicate, and standardise their critical data. Data quality tools can detect problems of which personnel might not be aware, and then provide dashboards and automated workflows that help staff members to identify and resolve data quality problems quickly and easily.

Supercharge decision making with location intelligence

In the era of digital transformation, companies cannot afford to ignore the value of location intelligence.

Adding location-based context elevates business decision-making in relation to people, assets, places, and opportunities. After all, virtually every data point in the world can be associated with location in one way or another.

This could be as simple as standardising and leveraging address information across a customer database so the data can be understood and analysed within a common context. A single address may have a building number as well as an address name, e.g., “20 Tudor Road” also being known as “The Pinnacle Building”. It means systems should be capable of understanding that they are, in fact, the same location across all business processes.

Location intelligence can also add context to data, making it possible to better understand boundaries, movement, and the environment surrounding customers, vendors, or locations.  For financial services, a common application is its use in branch rationalisation – leveraging location to gain insights into which existing branches to close, invest in, or renovate, as well as understanding local market demand, the intensity of the surrounding competition, and current branch coverage to identify new locations that have the best opportunity for success.

Increase competitive advantage through data enrichment

To fully build a competitive advantage, many organisations are also looking to data enrichment, the fourth pillar of data integrity. When accurate third-party datasets related to location, business, climate, or demographics are combined with existing business assets, the whole adds up to more than the sum of its parts. This can also include dynamic datasets, such as for weather or human mobility, that track variations over time. The additional context that data enrichment provides helps financial institutions harness more valuable insights for smarter decision marking – whether it’s choosing the most profitable branch locations, forecasting demand, or targeting marketing programs to make the biggest impact.

Data integrity empowers businesses to make faster, more confident decisions

Ultimately, as the financial industry rapidly moves toward embracing digital transformation, it needs to ensure that robust data foundations are being put in place to support the success of these initiatives. For those seeking competitive advantage, data integrity is a non-negotiable requirement. By building a meaningful strategy around data integration, data governance and quality, location intelligence, and data enrichment, financial services organisations can be confident that they are making smarter business decisions based on data they can trust.

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