If data is the proverbial “lifeblood” of investment decisions, the sheer volume and complexity of information is draining the energy from investment professionals. Enter machine learning (ML) and artificial intelligence (AI). These powerful technologies are revolutionizing how firms ingest, normalize, and understand data, and simplifying how teams work with data.

As data grows in size and complexity, an enterprise may need to handle hundreds — or even thousands — of diverse sources. Whether firms are using structured, semi-structured, or unstructured information via real-time feeds, flat files, or streaming sources, they must prepare to evaluate and potentially incorporate new information.

ML models are helping firms intelligently manage mounting volumes of data and quickly access ready-to-use information. At a high level, ML delivers algorithms to ingest data, identify patterns, and make decisions. ML thrives on new information and repetition to learn and improve predictability and performance. As a result, higher data quality drives more accurate output.

From data to action

The use of data science to generate insights and optimize operations is growing in lockstep with the industry’s explosion in sources and uses of data from both traditional and alternative sources. By applying machine learning across the ingestion lifecycle, firms achieve efficiency, eliminate redundant tasks, and better manage their data pipelines.

Take market vendor integrations as an example. Firms often build adapters as a common integration point to source data from financial feeds and support complex transactions. To automate manual, time-consuming processes, natural language processing (NLP) models use standard catalogs defined by market vendors to read input sources and auto-detect schemas and mappings. These advanced solutions can source corporate actions data, internal modeling of securities, and more. Similar solutions apply to semi-structured datasets, such as schema definitions for XML feeds. Using metadata-driven standardizations, NLP tools enable firms to maintain high data standards and quickly deliver information to end users.

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Moving beyond declarative data checks

Data from counterparties and vendor sources, such as pricing, can be valuable in helping firms analyze industry sectors, geographic regions, credit ratings, and risk. Consider the benefits of enabling your technology to evaluate the ingestion and transformation process:

Creates mapping logic: Challenges often arise in manually mapping counterparty feeds due to various sources and disparate systems. As a machine performs more transformations, it studies and recommends conversions based on how it historically ingested information. In addition to quicker insights, a machine can suggest mappings for counterparty, vendor, and administrator files based on how it maps, cleanses, formats, and prepares similar flat files for ingestion. Instead of manually identifying and updating each normalization, analysts can focus on reviewing the machine’s recommendations and higher-value activities. As your firm scales across counterparties and requires additional broker mappings, the consistent flow of information will improve the model’s accuracy.
Performs transformations: Procedural, or rules-based transformations, which follow the traditional extract, transform, and load approach, enable quicker analysis. During the transformation process, your system will convert raw data into a usable format by extracting data from the source, perform the transformations, and store transformed data in the proper dataset.
Ensures data quality: Data that is accessible, secure, and usable will allow your teams to use data in a variety of ways. ML models help streamline operations with suggestions that allow analysts to implement changes faster and reduce turnaround time for data ingestion and transformation.

Adding structure to unstructured data

Whether firms are invested in the public or private markets — or a convergence of both — managing unstructured data can be a major challenge. Hedge funds and institutional investment firms increasingly rely on unstructured data from social media and new stories, and some have even gone as granular as looking at satellite imagery of parking lots at large department stores, to get a pulse on trends. Specific to the private markets, deal information in lengthy, complex legal documents, quarterly reports, and capital account statements is loaded with bespoke terms.

It’s widely estimated that 80% to 90% of the world’s data is unstructured.¹ While ingesting low-volume data can be done manually, large volumes of information become more complicated.

Sophisticated techniques, like optical character recognition (OCR), commonly help digitize sources, such as PDFs and textual data. Firms also use OCR in processes such as machine translation, speech synthesizers commonly known as extracted text-to-speech, and text mining to systematically identify patterns and commonly sourced data points. These tools allow organizations to automate how they capture, extract, and validate unstructured workflow before storing data in a warehouse. The tools’ flexible nature allows users to add new sources and data points or make changes to existing workflows. A solution that can automatically scan a landing area, ingest new sources, and adapt to schema changes is a must-have.

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Attributing mappings for market data feeds

For years, a common industry challenge has been the time spent on mapping market data and integrating counterparty feeds. Tools that extract, transform, and load data, commonly called ETL tools, allow firms to use advanced technologies for data ingress and feed downstream services – freeing up their teams to focus on other priorities. With the application of ML, mapping data becomes more automated through continuous feedback and improvements to the model.

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Rahul Bighane