Creating Value From Data
Enhance current business | Enter adjacent businesses | Develop new businesses |
Leverage enhanced data for core business | Generate new insights | White-label capabilities & infrastructure | Create new data | Create new offerings |
–Seek opportunities to enrich existing service through new data sources –Develop and leverage new platforms –Deliver enhanced services (e.g., in real time) |
–Understand deep client insights –Enhance marketing campaign ROI and conversion |
–Monetise existing analytics capabilities via white labelling to clients and other partners across the value chain –Commercialize infrastructure to sell platforms as a service |
–Partner with adjacent players across the business value chain –Identify new sources of data (e.g., unstructured) to join with existingdata sets –Monetise new sets of data |
–Develop new sets of analytics and data products (e.g., benchmarks,tools) –Develop new products that benefit from enhanced data and analytics (e.g., realtime net asset value, active non-disclosed exchange traded funds) |
Review your data assets |
Data Monetisation Approach | Key Considerations | ||
Stock taking of data assets |
“What” What data sources, assets, capabilities do we have today? |
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Considerations for Data Valuation Framework |
“Who” Who are the right target customers and strategic partners? |
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“How” How do we build the right capabilities and business model? |
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Loyalty Programs |
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Risk Based Management & Pricing |
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Data Services |
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Intangible Assets/ Liabilities |
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Information Exchanges |
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Entertainment |
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Develop realistic aspirations for monetisation
EXAMPLE: Data Management Capability Framework |
Commercial
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Key Capabilities Required
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Data source | Data consolidation and storage | Data processing and export | |
Description |
• Data are generated and retrieved from internal or external sources. | • Data are compiled and converted into a readable format, then loaded into a long-term or intermediate storage system. | • Raw data are processed using a variety of tools to derive insights. Insights are exported to relevant stakeholders or sharing partners. |
Processes |
• Data retrieval |
• ETL (Extract-Transform-Load), ELT (Extract-Load-Transform) • Data virtualisation • Data governance, metadata management, materials management |
• Data visualisation • Data analytics: o Statistical analysis o Data mining o Predictive analysis • Machine-learning algorithms |
Technical Infrastructure |
• Internal sources: o Databases o Sensors o File-based o Data providers or organisers • External sources: o Data sharing agreements o Open data sources |
• Data warehouse (DW) o Traditional o Cloud • Operational data store (ODS) • Data mart (DM) |
• Technical delivery mechanisms: o File transfer o API o Platform |
Talent |
• Source system application expert |
• Data architect • Data governance SME • Security SME |
• Business analyst • Data engineer |