ecosystem in which • data is gathered, organized, and • exchanged by network of vendors • for the purpose of deriving value from the accumulated information • the gathered data is then passed to individuals or firms which typically take a fee. @Jarkko_Moilanen Data economy is
• Empowering the sales force with data • Data is collected and analyzed for product development purposes • Data is used to identify problems and bottlenecks in internal processes • Using data in marketing and advertising • Selling data to players up and down the industry value chain • Selling data to players outside your own industry • Selling insights to customers • Using data to increase company valuation @Jarkko_Moilanen
IBM, 90 percent of the data in the world today was created during the last two years alone. The data growth is most likely not decreasing since new devices, sensors and technologies emerge. 1. We are drowning into data…
analytics (BDA) increasingly provide value to firms for robust decision making and solving business problems. DBA per se does not provide other organisations any ability to utilize the data for own purposes. 2. Processed and analyzed… Platform of Trust is NOT a Big data analytics service
API Economy has become a concrete opportunity to go beyond the traditional development of vertical ICT solutions. APIs are used to provide abstracted access massive data and services to external developers through Open APIs 3. Access via APIs…
@Jarkko_Moilanen We can sum that 1. data is gathered more and more easily and automatically 2. we have efficient data management solutions and 3. We have technical solutions to enable access to data. Yet not too often productized. For data economy to emerge, it has to be monetised and commercialized. Problem is in creating economy around the data. We need easy to consume data products
the Problems @Jarkko_Moilanen • Data is collected in lakes if even that • Data is ”just data” – mindset problem • Data is hard to monetize outside own organisation • No understanding of productizement of data (or need of it) • Data offered as snapshots – datasets • Consumers want API access (streaming) • Consumers want to buy access to data, not the data itself (subscription) • APIs as pipeline to data are addons – not well documented, difficult to onboard Built environment context
Product value chain @Jarkko_Moilanen Business oriented tools to design data products Self-service tools to create data products Common data models and ontology Platform provides productized scalable access to consume data products AI, Automation, Analytics, Apps, data storage… Productized APIs & code libraries, SDKs and widgets Data Product Harmonized Data Data Payload in Hubs Data Points created by devices Higher Value Lower Value Customers only see the tip of the iceberg Device and data Management
”Swagger” of data products @Jarkko_Moilanen Pricing options Subscribe Unit based Data Stream options Current values History Prediction Trust 10 quality attributes Product information Versioning Open technical specification emerging in dataproduct.oftrust.net Machine-readable standard specification of data product schema Name Lifecycle status Conditions Singular Ontology
we can technically define a data product! Yeah! Rock! ….Why aren’t the data owners creating data products? Answer is that we we did not look BACK far enough in the process. Customers have no tools to design data products.
program from dataproductbusiness.com 1. Data Product Value Proposition 2. Data Product Canvas. 3. Data Product Lifecycle canvas Currently in beta testing in pharmaceutical industry and smart city development. Data Product Toolkit is a collection of Data Product canvases which will help you to design a data product while keeping business objective in mind.
we can technically define a data product and customers can design business driven data products ….Why aren’t the consumers using the data products? Answer is that you need modern access to data. Consumers want APIs and subscription driven data streams.
data products via API family • Platform Design Guide contains API design guide • APIs developed with Design-First principle • API Family which mut be unified • Clear versioning which is inline with data product ontology versioning (decoupled as much as possible) • Developer Guides • API Docs with tested code examples • API Management for mocking, analysis • Each API in Uptime tracking for developers • API Client packages Insomnia for V1 APIs, Postman for V2 APIs @Jarkko_Moilanen
Data Strategy Goals Discover Define Develop Deliver Evolve Productized data (streams) and access Data Product Toolkit Productized APIs Data Product Shared Ontology / Schemas Data Product Model Data Product Data product consumers Data Governance
Productized data (products) and access (APIs) @Jarkko_Moilanen The added value is in the data which is packaged into easy to understand, buy and consume data products. Consumers are not buying APIs. APIs provide modern access to discover, purchase and consume given data products.