We are developing a 2017 Data Monetization Canvas (a workbook) to help organizations rapidly assess the potential of their Data Asset(s) and Data Product(s) as well as Operational, Commercial, Value-Driver and Data Maturity issues to create a Data Offering, for example:
- Data Asset(s)
- Understand the economic value of the entire Data Asset fleet or individual underlying Data Assets that produce the offering
- Are these assets considered Intangible Assets under IAS38 and treated accordingly ?
2. Data Product(s)
- What specific Dataset(s) are potentially worth and how to mount and deliver that data (extracts, batch, APIs, etc) as a product ?
- How to price the Data Product (per record, per API call, per set, etc) ?
- Can additional data (external, government, licensed, free, etc) be added to enrich the existing data, to create synergies and additional value ?
3. Data Maturity (ClearDQ helps automate the evidence for this section)
- What the damage bill is due to bad data, or poor quality data, or poor Data Maturity, and how to discover and recover that economic damage ?
- What level of Data Maturity is required to create and sustain the lifecycle and dependencies of the Data Asset(s) and Data Product(s) that produce the offering ?
- What scalable mechanism and tools will be used to collect and analyse customer usage evidence and customer feedback of the product to improve Access, Performance, Accuracy, Value and other Data Quality attributes or improvements ?
- What method will raw data be ingested (technically) and what commercial agreements are in place to support continued data ingestion ?
- What platform will be used to ingest, enrich and prepare the Data Product for monetization ?
- What are the costs associated with data acquisition, storage, refinement, distribution ?
- Can the Data Asset(s) be leased, rented, licensed or sold (at what price point) ? and when (at company sale, realtime, etc. ) ?
- What is the price point of the data offering and is this price sustainable over time ? and sustainable across different markets ?
- Are there any privacy or other regulatory considerations that create risks to manage ?
- Why will customers buy or pay or trade for this data offering ?
- What is so unique or valuable or advantageous that you are creating for your customer, and what are their alternatives ?
This is a living document. Would you like a copy of the 2017 Data Monetization Canvas ? Drop us an email for the latest version.