- Woobler - Leaders in Spatial Finance

The Knowledge Graph
Revolution

Founded by leading Data Scientists from FactSet and Bridgewater

Open Data Set Mapping Relationships

18 Million

Location Attributes

645 Million

Locations

3.5 Million

Woobler is a data connectivity company focused on Spatial Finance. We’ve created
a multi-tier data map showing where physical economic activity takes place on a
global basis and have connected this to key risk factors and open data sets in the
form of a Knowledge Graph.

What is Woobler?

Founded by leading data science professionals at top tier firms in the financial sector (FactSet, Bridgewater). Woobler is a data connectivity company. We are creating an innovative global map of complex data relationships by connecting dozens of open and geo-spatial data sets into a highly precise and connected data structure known as a knowledge graph.

A Five Tier Spatial Finance Knowledge Graph

The Woobler Knowledge Graph is a nexus point of connectivity, bringing millions of Asset and Observational data points, across dozens of open data sets, around a unified taxonomy.

The Woobler Knowledge Graph

The First ‘AI Ready’ Knowledge Graph

Extracting Value

Extracting value out of the vast amounts of open data available today is difficult and expensive.

One of the biggest reasons is that open data sets do not connect or ‘talk’ to each other very well and if a data connection or ‘data conversation’ is possible it is on a strictly limited basis. The same restrictions will also hold true for other third party and proprietary data sets.

The Challenge

The overall burden and cost to connect data is a material consideration for any organization that wants to ‘play’ the data science and data analytics game. The challenge lies in the fact that most data sets employ different standards for Entity Level Identifiers, Industry, Classifications, Entity Names and Location Names/Codes. All these pieces of the ‘big data’ puzzle will never fit together unless consistent standards and methodologies are introduced and enforced. These consistent standards and methodologies are what Woobler calls ‘Data Governance.’

Data Governance

To connect datasets one needs to understand the various data identification and classification schemas used by the government agencies and industry bodies and figure out how to map or connect these differing schemas to the enterprise level data governance standards as enforced within the Woobler Knowledge Graph.

Data Conversation

Just as important, given the ever-changing nature of the world, maintaining data connectivity to ensure ‘data conversations’ continue to take place between data sources is a continuous effort of change monitoring and adjustments. These problems make the construction and maintaining of AI-ready databases expensive and time-consuming. It is estimated that data scientists typically spend up to 80% of their time on preparing data for analysis.

Woobler Knowledge Graph

Woobler Knowledge Graph enables location Intelligence and offers a powerful Data as a Service (DaaS) model that can accelerate and dramatically decrease the cost of AI-ready database construction by providing ONE SOURCE for accurate and consistently maintained inter-connects between physical asset locations and the information stored in the open, third party and proprietary data sets.

Enabling Cross Data Set Analytics

Leveraging Woobler’s massive Knowledge Graph (WKG) of pre-connected data points frees data scientists and analysts from tedious and time consuming tasks, and lets them focus on higher value analytical work. Woobler Knowledge Graph’s consistent classification schema, support for hierarchical relationships and bi-temporal (Point In Time) data increases transparency and confidence in analysis, and at the same time provides opportunities to uncover hidden data correlations as the Knowledge Graph structure greatly expands the absolute number of potential connected data points available for analysis.

What the WKG Can Do for You

Accelerate Research

Data must be high quality. ‘Dirty’ data will invalidate ML/AI results every time.

3.5 Million + Locations
615 Million+ Location Attributes
18 Million+ Open Data Set Mapping Relationships
Millions of Connected Data Points

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Accurate ML/AI

Strong x-walk intersects match dataset characteristics makes it possible to discover alpha by looking for causality in relationships between data sets.