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Hockeystick’s Taxonomy: The Need For Data Standardization

Hockeystick has been supporting financial reporting for private companies for many years. We aim to provide accurate data by drawing from a number of sources. In doing so, however, we often come across several data sources that describe the same metrics and entities, but use different terminologies.

Consequently, we have been forced to rethink how we handle data and what we can do to mitigate these data mapping issues. This is precisely why we, at Hockeystick, have diverted our attention to creating our own standard or, as we call it internally, the “Hockeystick taxonomy”.

What is a Taxonomy?

A data taxonomy is simply a hierarchical structure that inherently separates data into specific classes based on common characteristics.

The most notable example of a taxonomy is the Linnaean taxonomy, used for the categorization of organisms and binomial naming of organisms. This classification system was developed by Carl Linnaeus, a Swedish botanist who is considered to be the father of modern taxonomy.

 Carl_von_Linné.jpg

Portrait of Linnaeus 

Why are Taxonomies Necessary?

Classifying data aims to eliminate redundancy and promote the reuse of data in systems. More importantly, however, taxonomies frequently motivate the need for a standard that becomes the metadata framework against which information is reliably reported and consistently compared.

Adherence to standards ensures safety, reliability and interoperability. Moreover, standards are often cited by regulators and legislators when protecting user and business interests. Lastly, standardization encourages innovation, the development of new technologies and the enhancement of existing practices.

Why is Hockeystick Creating a Standard Taxonomy?

While current standards exist in our immediate domain of financial reporting, like the US Generally Accepted Accounting Principles (GAAP) and the International Financial Reporting Standards (IFRS), we at Hockeystick are looking at the bigger picture by attempting to standardize a taxonomy that supports our entire private market data network, in addition to the financial reporting and accounting component.

For example, which attributes define an “Accelerator”? What does it mean to be an “Investor”? How do we capture the different types of “Investments”? We are aiming to federate data from numerous data sources to provide us with richer entity profiles for our private market data network. The need for a taxonomy here is paramount as we need a standard to map different concepts to so we can have accurate comparisons, analysis and benchmarks.

What Does Our Hockeystick Taxonomy Contain So Far?

The Hockeystick taxonomy will serve to underpin the entities and data definitions involved in our private market data network. By standardizing such data, we will be firmly positioned to provide better analytics and more accurate data points across several domains.

Currently, our taxonomy supports the following definitions, out of the box: “balance sheet”, “income statement”, “financial ratios” and “securities”. In an effort to standardize all data that flows through Hockeystick, we have designed our taxonomy to support multiple accounting standards that our customers may use.

To reach our target customers’ markets, we have chosen to build our taxonomy on top of Canadian and US GAAP and other small business accounting software such as Quickbooks, Freshbooks and Xero.

This will allow us to create a more comprehensive tree that combines multiple standards, as shown below:

Property Plant Equipment

Accumulated Amortization, Depletion, Depreciation (Quickbooks)
Property Plant Equipment Cost (CA-GAAP)
                        Land, Buildings and Improvements (CA-GAAP)
                                                Land (CA-GAAP)
                                                Land Improvements (CA-GAAP)
                                                Buildings (CA-GAAP)
Vehicles (CA-GAAP)
Cars and Trucks (Freshbooks)
Construction in Progress (CA-GAAP, US-GAAP)
Leasehold Improvements (CA-GAAP, US-GAAP)
Capital Leased Assets (CA-GAAP, US-GAAP)
Depletable Assets (Quickbooks)
Equipment (Freshbooks)
Machinery and Equipment (CA-GAAP, US-GAAP)
Furniture and Fixtures (CA-GAAP, US-GAAP)
Computer Equipment (Xero)
Office Equipment (Xero)
Other Property, Plant and Equipment (CA-GAAP, US-GAAP)
Other Fixed Assets (Quickbooks)

In the example above, we have combined fields found in US and Canadian accounting standards with fields found in commonly used accounting software for small businesses. In doing so, we have created a new taxonomy tree section that supports companies reporting in any of these standards, while still rolling up to the standard “Property Plant Equipment” field.

Other supporting taxonomies, such as the “ratios” and “securities” taxonomies, will allow us to define a standard list of metrics and securities relating to the companies reporting on our data network. The financial ratio metrics that we provide will be calculated from the standardized fields in our taxonomy, providing confidence in the calculated figures:

Leverage Ratios

Debt to Total Assets (Total Liabilities / Total Assets)
                        Total Liabilities
                        Total Assets
Debt to Equity (Total Liabilities / Total Equity)
                        Total Liabilities
                        Total Equity
Debt Service Coverage (Total Operating Income / Debt Service)
                        Total Operating Income
                        Debt Service
Time Interest Earned (EBIT / Interest Expense)
                        EBIT
                        Interest Expense

 In the example above, the formulas defined next to the metric reference a standard field in the financial taxonomy, which are rollups from various reporting fields from different standards. In doing this, we are able to capture the correct values needed to calculate these metrics, regardless of how a company reports their data.

At Hockeystick, we have traversed the extra mile to model CRM fields that describe other entities that are relevant to our customers.Entities such as “Funds”, “Companies”, “Angel groups”, “Accelerators”, etc. are all modelled under a standard taxonomy. Having a standard taxonomy for describing these entities allows us to easily integrate their data alongside the rest of the financial data in our network.

For example, an “Accelerator” is modelled as the following:

Accelerator
                     Name
                     Year Founded
                     Contact Information
                     Industry
                     Industry Code
                     Industry Sector
                     Status
                     Programs
                                          Program Name
                                          Intake Options
                                          Start Date
                                          End Date
                                          Funding Support
                                          Equity
                                          Cost Type
                                          Development Stages
                                          Program Type
                                          Preferred Industries
                     Preferred Locations

With these fields, we are able to describe most, if not all, accelerators in existence and can easily report on their financial or business information to our end-users in a clean and organized manner.

What is Our Goal?

The aim in constructing a new taxonomy is to create a standard that allows a heterogeneous federation of data so we can provide richer information about entities in our domain.

Stay tuned for more details about Hockeystick’s taxonomy.

 

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