What is data quality, part 2: useable information

Hypothetical: Your firm has added asset-based financing (ABF) for small- and medium-sized business (SMB) to its private credit line. Each loan servicer like BlueVine, Lending Club, and SoFi has a different operating model, file formats, and methods of sharing data, all within a daily NAV environment. An SMB lending pool comes with its own set of risk management dilemmas, like more limited borrower and loan details, tracking borrower attributes and credit worthiness, and time series requirements to see how loan health evolves. Along with this new investment vehicle comes new market data feeds, counterparties, and so on. Data quality matters.

Ingest it. Connectors are valves that siphon currents of new structured or unstructured data flows from data pipelines into models. Securitized investments like ABF lack standardized elements and have dynamic terms and cash flows, making analyzing bid tape pricing and rates over time a challenging task. With some tools, you can create custom data pipelines to ingest datasets from new sources. In this case, high volumes of disparate data from an array of alternative lending platforms, originators, and servicers, which if not properly consolidated, could threaten a precise, real-time view of portfolio health.

Normalize it: In private markets, data infrastructure must normalize multiple views of the same models and map existing data structures to a unified framework. The process of modeling instruments and loan tape cracking at scale using unstructured and inconsistent loan data is particularly intimidating. The process begins with identifying common data entities and their relationships across systems and then ensuring that data is stored in a consistent format, enabling seamless integration, analysis, and reporting across systems.

Our AI-powered unstructured data processing integrated into (link to Aquata subpage when published or similar page--->) Aquata’s data quality framework automates extraction and analysis from PDFs, research reports, and regulatory filings to unlocks insights from previously inaccessible unstructured investment research and compliance documents.

Data quality it: Data integrity tools power automated data cleansing that runs quality checks and automated validation processes, cross-checking incoming data against predefined quality standards to detect and resolve problems and prevent damaging errors.

Govern it: Data governance is your policymaking process that gives everyone in the firm the right access, permissions, and entitlements. This usually falls into the purview of data science/IT people in which they enact clear guidelines and procedures governing the acquisition, storage, sharing, and disposal of data.