The Four Pillars Of Data Integrity
Data integrity enables companies to make quick and safe decisions based on reliable data. Realizing that data is a strategic corporate asset for financial service providers is the first step in creating a clear framework.
The article provides answers to the following questions, among others:
- Why is data-driven decision-making beneficial for financial firms?
- To what extent can data form a strategic corporate value?
- What are the four pillars of data integrity, and how to implement them?
Covid-19 has accelerated digital transformation in the financial services sector at an unprecedented pace, forcing companies to adapt quickly and rely increasingly on digital services to remain competitive. To be at the forefront of the new world, financial institutions must deliver an excellent customer experience by delivering compelling products and services through strategically placed physical stores and digital channels. At the same time, financial institutions must adapt to new market needs and opportunities and ensure compliance with the regulatory framework that may constantly be changing.
In this challenging environment, it is critical that financial firms properly leverage first and third-party data and spatial analysis to improve their understanding of the market dynamics and customer behaviours impacting their business – ultimately leading to more innovative, data-driven decision-making support. However, a recent study of data integrity trends by Corinium Intelligence found that 50 per cent of financial services firms experienced “mixed” or “disappointing” results when attempting to implement essential data management and governance frameworks. The main reason is the lack of a solid data foundation to ensure the integrity of the data on which these frameworks are built.
Data integrity enables companies to make quick and confident decisions based on reliable data with the highest accuracy, consistency and context. For financial service providers, recognizing that data is a strategic business asset is the first step in creating a clear framework for implementing the four pillars of data integrity:
- data integration,
- Data Governance & Data Quality,
- location intelligence
- and data enrichment.
Table of Contents
Unlock The Potential Of Enterprise Data
Most complex businesses rely on multiple, often disjointed, applications to manage data about customers, prospects, suppliers, inventory, employees, and more. When these systems work in silos, creating a clear, unified view of the business is impossible. Financial institutions often have the added complexity of accessing critical customer data from mainframe applications and databases – traditional, highly reliable and secure systems whose complex data formats do not readily integrate with more modern data environments.
Building a holistic view requires linking multiple systems through mapping and translation. Integrating data across the enterprise, whether in mainframes, relational databases, or enterprise data warehouses, requires a carefully considered approach to bring the data together under one roof and in a way that best aligns with the organization’s strategic goals.
Regulatory Compliance Support
When an organization has broken down data silos, a common problem remains—that data quality. Data may be missing, inaccurate, inconsistent, or contain duplicates despite integrating multiple systems. Financial institutions are also under pressure from global regulations that dictate that they must know where data is coming from, demonstrate the accuracy and validity of data, and ensure its security. Consequently, data quality and security in the financial services space must be proactively maintained to meet standards for good data stewardship in the face of ever-changing and evolving regulations.
Good data quality practices require that business decision-makers work together to define clear outcomes. This also includes cross-functional collaboration across multiple departments. Because it’s impossible to regulate everything, subject matter experts from across the organization must work together to create a standard prioritized list of risk, compliance, financial, and marketing goals.
Robust data governance practices also require a solid strategy for deploying data quality automation technologies. This includes using tools to help organizations cleanse, validate, deduplicate, and standardize their critical data. Data quality tools can uncover issues that employees may not be aware of and provide dashboards and automated workflows that help employees quickly and easily identify and resolve data quality issues quickly and easily.
Optimal Decision Making
In the age of digital transformation, companies can hardly afford to ignore the added value of location intelligence. Location-based context improves business decision-making regarding people, assets, places, and opportunities. After all, virtually every data point in the world can be linked to a location in one way or another.
This could be as simple as standardizing and leveraging address information in a customer database so that the data can be understood and analyzed in an everyday context. For example, a single address can have a building number and an address name in Munich Schwanthalerstrasse 13, also known as the Deutsches Theater. This means that systems should understand that all business processes are in the same place.
Location intelligence can also add context to the data, allowing a better understanding of boundaries, movement, and the surroundings of customers, vendors, or locations. A typical application for financial services is for use in branch location analysis – for example, location to gain insights into which of the existing branches are used only occasionally by customers. Among other things, this has an impact on investments or modernization projects. Additionally, a local understanding of demand, the intensity of local competition, and current branch coverage are critical to identifying new locations with the best success.
Increasing The Competitive Advantage
Many companies also rely on data enrichment to gain a competitive advantage, the fourth pillar of data integrity. When accurate third-party data sets are available related to location, economics, climate or demographics and can be contextualized with existing company resources, the whole is greater than the sum of its parts. This can also include dynamic data sets, for example, weather or human mobility, which can be subject to great changes over time. The additional context that data enrichment provides helps financial institutions use more valuable insights to make smarter decisions – whether it’s choosing the most profitable branch locations.
Ultimately, the financial industry must adapt to rapidly changing markets and create a solid data foundation to support the success of these initiatives. For organizations 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 firms can be confident they are making more intelligent business decisions based on data they can trust.