December 20, 2017 - Paul Lashmet | Industry Solutions

Monetize Data Assets for the Alternative Data Economy

This post was written in collaboration with Pitney Bowes.

Summary

The alternative data economy provides an opportunity for organizations to create new revenue streams by repurposing their data assets for the financial services industry.  This post is geared to organizations that are evaluating their place in the alternative data ecosystem as originators of data.  It describes how to position data assets in a way that enables prospective users to access it, assess its quality and put it to use, creating new value.

Welcome to the Alternative Data Economy

According to a recent article in the Economist, “Data are to this century what oil was to the last one: a driver of growth and change.”  This flow of data has created new economies and the “wildcatters” of this new data economy are the startups that “prospect for digital oil, extract it and turn it into clever new services”.

The alternative data economy is slightly different.  Instead of prospecting to discover new data wells, organizations instead repurpose the data that they already generate in the course of normal business operations.  These data are then consumed by financial services companies to create new value by enriching investment strategies, spotting new trading opportunities, or identifying signals that foreshadow risk.

Alternative data in financial services are data that are not commonly thought of as financial in nature but can be repurposed to enrich conventional analyses with actionable insight.

Repurposed Data:  A Lighting Design Example

Repurposing has its challenges.  For example, bottles can be turned into lighting fixtures but depending on the skill of the maker, it could be an awesome project for all to see or simply an amateur craft project that ends up hidden in the attic.

In the successful example, the designer is a subject matter expert, perhaps an electrician or lighting designer.  As such, they may combine bottles with bulbs, electric cords, sockets, dimmers, a power source, and other materials to create a highly designed and functional element that works perfectly for the right room.  The expert designer understands the nuances of light and electric current but probably knows very little about how the raw materials of silica sand, soda ash, and other chemicals were heated to create it.  However, with the right tools and connectors, bottles are used to enrich the design.

Steps to Repurposing Data Assets

The first step in repurposing your data assets is getting them in front of skilled subject matter experts.  An example in financial services would be the portfolio manager.

The diagram at the right describes an ecosystem that is comprised of four parts:  

  1. The beneficiaries of alternative data strategies;
  2. The originators of the data;
  3. The providers of modern big data platforms that ingest, store, and process the data and;
  4. The subject matter experts that put it to use.

The challenge for the data originators is to motivate subject matter experts to incorporate data (the bottles) into their investment strategies (the design).

Motivation Through Understanding

The subject matter expert needs to understand how an unfamiliar data set fits within the context of their strategy in order to be motivated to use it.  Location provides this context.

Location is common to just about any data asset and can provide the proximity relationships between a new data source and other, supplemental data.  For example, location provides context to energy consumption across regions, pedestrian traffic around stores, and the intended and actual delivery of goods.

Location intelligence is the ability to derive actionable information from understanding the proximity relationship of location-based data. Linking multiple data sources by location, enables the subject matter expert to compare, contrast, and juxtapose disparate data sets without needing to understand the intricacies of any one of them. Going back to the lighting design analogy, how does it get worked into the design and plugged in?  The subject matter expert needs to be able to work with it and assess the quality and value it brings to the equation.

Accessibility and Workability

In the alternative data ecosystem diagram above, notice that the red arrow passes through a third component, “Providers of Modern Big Data Platforms.”  Big data platforms ingest, store, and process these data.

It is crucial that the architecture of the platform enables the subject matter expert to work with the data on their own terms by integrating with tools that enable both performance and flexibility.  Software Development Kits (SDKs) and visual analytics that run natively on the modern big data platform provide that option and enables your prospective data consumer to explore more data faster and more accurately than if the data needed to be moved to specialized analytics servers.

Data Quality Assessment

Geoenrichment, a means of augmenting data with geospatial attributes improves utility and value.  Geoenrichment can also add a persistent and unique identifier for each location and supplements authoritative and descriptive attributes, each providing additional levels of accuracy. For example, a ground floor address or a third floor address matters in flooding situations. Demographic differences between a current resident and the person who was living at an apartment last month is relevant to understanding localized pricing and market trends.  

Accuracy at the level described above provides the subject matter expert with confidence in the assessment of the data’s quality and the value it brings.  Easy access and workability with the data enables a fit for purpose result.

Summary

To successfully repurpose data assets for financial services in the alternative data economy, the addition of location-based data can significantly improve the ability to spot trends and exploit competitive positions. Providing context, accessibility, and workability is a significant advantage for today’s data scientists and other knowledge workers.

For more information, please see the following links.

View this video that demonstrates how to enhance investment decisions with alternative data.


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