Unlocking the Power of Complex Structured Data: Streamlining Data Transformation and Integration

In fast-paced financial markets, the ability to manage and analyse complex structured data is crucial for maintaining a competitive edge. However, the diverse nature of this data—where the fundamentals may be consistent, but the structure varies widely—presents significant challenges. These challenges become especially evident when attempting to store data in a standardised way for analysis or when trying to use this data in downstream processes, such as configuring risk, processing customer requests for quotes, or managing structured order books. 

The Challenge: Diverse Structures, One Common Goal 

Financial institutions deal with a plethora of data types daily. Even when the core data remains the same—such as price, volume, or timestamp—the format in which this data is presented from your client can differ greatly across firms and even individuals. This lack of uniformity poses a substantial barrier to efficient data management. Without a standardised structure, firms often find themselves grappling with time-consuming manual processes to reformat and transform data before it can be used effectively. 

For instance, consider the process of managing lists of orders or prices submitted in Excel format or pasted into chat. Each trading participant may present data in a different format, requiring significant manual intervention to consolidate and analyse this information. Similarly, when integrating with an API to configure risk parameters, discrepancies in data formatting can lead to errors, delays, and increased operational costs. 

The Solution: Daisy-Chained Workflows and Data Transformation Modules 

At ipushpull, we recognise the importance of seamless data integration and the need to eliminate the inefficiencies caused by non-standardised data formats. Our solution lies in the use of daisy-chained workflows combined with powerful data transformation modules. 

These modules work together to handle non-standard datasets, analysing their structure and converting them into any required format. They can also export the transformed data through a variety of omnichannel delivery routes that ipushpull supports, including chat platforms, REST APIs, or our Web Portal. This approach significantly reduces the burden of manual data transformation, minimises risks, and frees your team to focus on higher-value tasks instead of repetitive reformatting. A short demo below shows how this works in practice.

How It Works: A Closer Look 

Imagine a broking firm that receives quote data from multiple sources, each with its own format. With ipushpull's data transformation modules, this firm can set up a workflow that automatically ingests the data, applies the necessary transformations, and outputs it in a standardised format ready for immediate use—whether for risk management, order book management, or servicing client requests. 

This not only speeds up the entire process but also significantly reduces the potential for errors. Moreover, the standardised data can then be stored in a unified way, making it easier to analyse and draw insights from, ultimately leading to better decision-making and enhanced operational efficiency. 

The Future of Data Transformation 

As financial markets continue to evolve, the need for efficient data management will only grow. By leveraging ipushpull's workflow automation and data transformation capabilities, firms can overcome the challenges posed by complex structured data, ensuring they remain agile, compliant, and ready to capitalise on new opportunities. 

With ipushpull, the days of manually transforming data are over. Our platform empowers you to unlock the full potential of your data, no matter how complex its structure may be. If you would like to know more about this topic contact us.