• Jason Trueblood

Structured Data Vs. Unstructured Data

“Data”, one of the most popular words being thrown around technology offices in Silicon Valley and around the world.  Data is a relatively general term that could be organized into different silos from customer purchasing statistics and trends to product usage metrics and engagement data.  To leverage this information, companies are creating and relying upon new products that capture , filter, and analyze the data floating out in the world. As you can imagine, there are multiple ways to filter down this data into a digestible or organized format.

The two core methods of setting up organized databases are:

  1. Structured data - Data that can be organized into rows, columns and relational databases. Example: A list of portfolio companies, the securities you hold in them, and the total cost of those investments.

  2. Unstructured data - Data that cannot be structured into rows and columns and is therefore more difficult to manage. Example: a PDF document that contains financial data, with no way to automatically aggregate or filter any two data points.

At this point you may be thinking, “Why in the world do I care about different kinds of data organization?”  It actually matters a great deal when reporting on investment data. The core difference between structured and unstructured data is in how it can be reported. It’s difficult to report on unstructured data in ways that are dynamic as any one data point cannot be filtered out of the entire dataset.  Structured data, on the other hand, allows an application to easily filter and report on trends and metrics to draw correlations and study patterns.

Let’s dive into some real world examples powered by structured data:

Transaction Ledger Reports

A transaction ledger report is like an investment bank statement showing all outgoing and incoming transactions for a fund or firm.  The ledger displays data such as the name of the fund, portfolio company, transaction type, amount, equity class, etc. All this rich data is contained in closing documents in an unstructured format.  Only by structuring it into columns and rows can you build a solid transaction ledger report.

Ownership Dilution Reports:

Say a venture firm wants to know how their ownership percentage is being diluted over time as new financing rounds are being closed and the portfolio company is growing.   To evaluate dilution across multiple financing rounds, bringing information from each financing together is crucial to paint a clear and accurate picture. Structured data format allows this report to come to life while unstructured data prevents the data from being organized in a meaningful format.

So what do you do with unstructured data?

If you’re always referring to your deal documents to get answers to common questions, then you’re relying on unstructured data.  Aumni ingests those PDF documents, audits them for accuracy and extracts key financial and legal data in a structured format so it can be queried and reported on, saving you time and energy identifying critical investment data.

Portfolio intelligence data is changing every day and possibly every minute.  The flexibility of structured data is crucial to giving companies the ability to quickly pivot and evolve based on the ever changing needs of data.  Dynamic reporting functionality that can grow and develop over time is a truly scalable and long lasting system providing venture teams with highly detailed portfolio intelligence for years to come.

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