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The Challenge of Data Integration in a Multiple Data Source World

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By Inesa Smigola, Head of Presales, EMEA and APAC at Xceptor.

Financial institutions have a growing data challenge – ever increasing data volumes, much of it unstructured, multiple data sources, and hugely varied data formats and structures.

Across this is the additional challenge of inconsistent data quality according to data source and format– an Excel document, may contain higher quality data than an email, but that also assumes the underlying data is accurate, and there’s a good chance it’s not.

Building a foundation of data integrity that supports all subsequent analytics, insights, and business decisions ensures FIs can operate on solid ground. Improving data integration helps firms as they seek to improve their processes, meet their regulatory needs and build additional customer value-add services. Doing so demands a sophisticated and flexible approach to data integration that safeguards and enhances the integrity and utility of the consolidated data, converting it into an asset that is trusted and usable. It’s unlikely that firms can do this in isolation – it’s a complex process, fraught with potential pitfalls, which requires the right approach and the right partners.

Data integration is built on data quality and consistency

Within a diverse universe of data sources and types, ensuring that data is high quality to meet data integration requirements, can be challenging, and inconsistencies may seem inevitable.

In tackling these inconsistencies, the importance of implementing robust data validation and cleansing mechanisms is key. Left unchecked, inaccurate or incomplete data will skew analyses and lead to flawed decision-making.

Data validation ensures that incoming data meets stringent quality standards and is accurate and consistent. Cleansing processes add to the mix by removing inaccuracies, correcting errors and aligning disparate data sets into a coherent whole – an essential contribution for ensuring the sustainability of financial reporting and analysis trustworthiness.

A lack of robust data validation and cleansing can lead to faulty analytics and decision-making. Both of which produce results for the FI and its customers that are at best unsatisfactory, and at worst calamitous.

Adopting a strategic approach to data management is vital. By establishing protocols and systems that ensure uniformity and reliability, the integration process is streamlined. Perhaps more importantly, the data used for critical financial decisions becomes significantly more dependable and accurate.

Technological and system limitations

Outdated systems and incompatible technology also pose considerable barriers to effective data integration. Legacy systems, often characterised by limited flexibility and outdated architectures, struggle to accommodate modern financial data’s diverse and dynamic nature. These constraints manifest in an inability to process various data efficiently and lead to the kind of data flow bottlenecks and low integration that hobble effective decision-making and regulatory compliance.

This makes the adoption of modern, agile systems critical for FIs. Advanced systems are designed to handle multiple data formats, and to facilitate a streamlined integration process that allows FIs to extract and transform data from any source, delivering it downstream for processing.

Data automation for data integration

Modern data automation platforms have emerged as an effective solution to address data integration challenges, giving FIs that do this right a notable strategic edge. The best platforms supercharge operational efficiency and data accuracy by automating the integration of multiple data sources. Data is then validated, cleansed, normalised, and enriched before it is delivered to the relevant workflows as trusted data.

Specifically designed to manage the inherent complexities of multiple data sources and types, a data automation platform transforms the way firms can handle their data, reducing the need for manual processes that are both time consuming and error prone. Complex and fragmented data is converted for coherent, actionable insights, which is crucial for firms relying on accurate, real-time data for process automation and efficiency, decision-making, and strategic planning.

Effective management of data integration through a data automation platform also unlocks compelling possibilities: FIs are empowered to harness the full potential of their data assets to leverage new opportunities and improve customer experiences.

Data integration fuels a changing world

As data has taken on new forms and significance in recent years, its influence has been extended far beyond traditional operational and IT functions with front-office stakeholders also recognising its pivotal role in almost every business process and customer experience, and in the implementation of efficient automation.

This shift has brought change in how data projects are evaluated moving away from a focus on cost-efficiency or resource reduction towards a growing demand for an understanding and assessment of their impact on areas ranging from innovation and trading strategies to customer management.

This should be the ultimate aim of any integration initiative – fostering a customer-centric approach that aligns with the core competencies and strategic objectives by bringing high quality data to where it’s needed in the right format.

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