Valuing data for commercialization

Why?

Data is a valuable resource – but it’s illiquid and hard to value

Unlock opportunity

Data on the balance sheet

Work data harder

Bring to front of business

Recognise importance

The biggest, most useful datasets are still held in islands

We need rails for data liquidity

Valuation is part of the rails

Clarity for buyers

Fungibility

Value an asset

But valuation is difficult

Knowledge gap

Unlimited inventory

Unprocessed material

So we addressing it

1. Data marketplace

2. Ontology for data

3. Dimensions of value

4. Parameters for dimensions

1. Data marketplace

Generate transactions

Observe deals

Monitor pricing

2. Ontology of data

Classify data for comparability

3. Dimensions that determine data value

Business Value

Uniqueness

Incisiveness

Granularity

Reliability

Freshness

Use and risk

Collection cost

Company size

Cost context

Commitment

3. Dimensions that determine data value

Business Value

Uniqueness

Incisiveness

Granularity

Reliability

Freshness

Use and risk

Collection cost

Company size

Cost context

Commitment

4. Use transactions to calibrate

Requests and prices set by data

owners

Model parameters to prices

Apply model to future pricing

Basic modelling exercise

Application to insights data

Insights are priced on outputs, not inputs

What is my market share in Australia?

8 billion input rows

1 output answer row

What is my market share in Australia

by postcode, demographics and week?

8 billion input rows

1,000,000 output answer rows

Number of output rows

Application to personalisation data

Lots of testing happening

Efficacy more easily proven

Efficiency improvements

Long review time

Data valuation is an unfinished journey

Starting to: Value data for each transaction Assess total market potential for commercialisation Make strong business cases for data exchange Consider this across the industry

White paper goes into much more detailFind it at datarepublic.comPlease give us feedback steve@datarepublic.com

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