# The four pillars of data integrity: what finance businesses need to know
Author:  Pal Sinha, Barnali 
Author URL: https://financedigest.com/author/pal-sinha-barnali
Published: 2021-11-22
Category: TECHNOLOGY
Category URL: https://financedigest.com/category/technology
Meta Title: Enhancing Financial Services with Data Integrity Strategies
Meta Description: Learn how to leverage first- and third-party data, spatial analytics, and data integration in financial services to make smarter, data-driven decisions and
URL: https://financedigest.com/the-four-pillars-of-data-integrity-what-finance-businesses-need-to-knowhtml

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_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](https://www.financedigest.com/financial-advice-for-starting-your-own-business.html "Financial advice for starting your own business") 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](https://www.precisely.com/resource-center/analystreports/data-integrity-trends) 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](https://www.financedigest.com/how-can-financial-institutions-make-the-most-of-data-for-their-business.html "HOW CAN FINANCIAL INSTITUTIONS MAKE THE MOST OF DATA FOR THEIR BUSINESS?   ") to make fast, confident decisions based on trusted data that has maximum accuracy, consistency, and context. For [financial services](https://www.financedigest.com/how-big-data-is-sending-shockwaves-through-the-financial-services-sector.html "How big data is sending shockwaves through the financial services sector"), 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](https://www.financedigest.com/how-green-are-your-investments-why-esg-data-integration-is-key-for-financial-services-firms.html "“How green are your investments?” – Why ESG data integration is key for financial services firms")_

Most complex enterprises rely on multiple, and often disjointed, applications to manage [data on customers](https://www.financedigest.com/should-insurers-buy-data-direct-from-their-customers.html "Should insurers buy data direct from their 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](https://www.financedigest.com/finalised-pan-european-data-protection-laws-set-the-new-standard-for-a-privacy-friendly-business-environment.html "Finalised Pan-European data protection laws set the new standard for a ‘privacy-friendly’ business environment").

[Building a holistic view requires](https://www.financedigest.com/whats-required-of-accounting-to-build-a-better-tomorrow.html "What’s required of Accounting to build a better tomorrow") 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](https://www.financedigest.com/how-to-bring-business-and-charities-together-through-events.html "How To Bring Business And Charities Together Through Events") under one roof, and in a way that is most aligned to the organisation’s strategic goals.

_Support regulatory [compliance with data](https://www.financedigest.com/britain-plans-new-data-rules-to-ease-compliance-burden.html "Britain plans new data rules to ease compliance burden") governance and quality_

Once an [organisation has managed to break down data](https://www.financedigest.com/avoiding-a-big-data-car-crash-how-to-organise-your-data-to-detect-fraudulent-claims.html "Avoiding a big data car crash: How to organise your data to detect fraudulent claims") 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](https://www.financedigest.com/the-financial-sector-must-act-to-tackle-internal-data-security.html "THE FINANCIAL SECTOR MUST ACT TO TACKLE INTERNAL DATA SECURITY"). As a result, [financial services data](https://www.financedigest.com/data-centres-and-the-changing-financial-trading-landscape.html "Data centres and the changing financial trading landscape") 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](https://www.financedigest.com/5-practical-ways-to-manage-your-business-expenses.html "5 Practical Ways To Manage Your Business Expenses") 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](https://www.financedigest.com/tech-awakening-needed-across-uk-workforce.html "Tech awakening needed across UK workforce") to work together to establish a shared list of priorities around risk, compliance, finance, and marketing objectives.

Robust data governance [practices](https://www.financedigest.com/5-simple-ways-to-prevent-a-data-breach-from-putting-your-accountancy-practice-out-of-business.html "5 Simple ways to prevent a data breach from putting your accountancy practice out of business") 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](https://www.financedigest.com/your-money-is-safe-but-your-data-might-not-be.html "YOUR MONEY IS SAFE, BUT YOUR DATA MIGHT NOT BE") 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](https://www.financedigest.com/redefining-the-human-touch-with-data-driven-digital-transformation.html "Redefining the human touch with data-driven 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](https://www.financedigest.com/what-is-a-virtual-data-room-unveil-the-essential-information-here.html "What is a Virtual Data Room? Unveil the Essential Information Here!") 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](https://www.financedigest.com/manchester-arts-centre-the-lowry-selects-logpoints-siem-technology-to-safeguard-customer-data.html "Manchester arts centre The Lowry selects LogPoint’s SIEM technology to safeguard customer 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](https://www.financedigest.com/5-reasons-why-modernising-is-the-most-important-ongoing-process-for-any-business.html "5 Reasons Why Modernising Is The Most Important Ongoing Process For Any Business").

Location intelligence can also add context to data, making it possible to better understand boundaries, movement, and the [environment surrounding customers](https://www.financedigest.com/retaining-insurance-customers-in-a-post-pandemic-environment.html "Retaining insurance customers in a post-pandemic environment"), vendors, or locations.  For [financial services](https://www.financedigest.com/data-fake-will-be-the-new-real-in-financial-services-in-2023.html "Data: Fake will be the new real in financial services in 2023"), 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](https://www.financedigest.com/turning-big-data-compliance-into-a-competitive-advantage.html "TURNING ‘BIG DATA’ COMPLIANCE INTO A COMPETITIVE ADVANTAGE") through data enrichment_

To fully build a competitive advantage, many [organisations are also looking to data](https://www.financedigest.com/auditing-in-cyber-how-organisations-can-keep-track-of-their-data.html "AUDITING IN CYBER: HOW ORGANISATIONS CAN KEEP TRACK OF THEIR 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](https://www.financedigest.com/making-your-accountant-an-invaluable-asset-to-your-business.html "MAKING YOUR ACCOUNTANT AN INVALUABLE ASSET TO YOUR BUSINESS"), 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](https://www.financedigest.com/banks-struggle-to-harness-big-data.html "BANKS STRUGGLE TO HARNESS BIG DATA") 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](https://www.financedigest.com/national-world-launches-new-ad-manager-platform-powered-by-danads-empowering-businesses-to-take-control-of-their-advertising.html "National World launches new ad manager platform, powered by DanAds, empowering businesses to take control of their advertising") to make faster, more confident decisions_

Ultimately, as the financial industry rapidly moves toward embracing digital transformation, it needs to ensure that [robust data](https://www.financedigest.com/dollar-buoyant-as-robust-u-s-data-keep-fed-hawks-in-control.html "Dollar buoyant as robust U.S. data keep Fed hawks in control") 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](https://www.financedigest.com/wage-data-dents-dollar-recovery-before-fed-rate-decision.html "Wage data dents dollar recovery before Fed rate decision") they can trust.


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