Eight thousand currencies circulated in the United States in the 1840s. Small town banks, credit unions, and even railroad companies printed their own cash. When you travelled your money lost value or even got refused because people in Virginia might not trust your Baltimore and Ohio Railroad bucks. Bartenders and hotels referred to giant logbooks of monthly exchange rates so for individual purchases. The resulting transaction costs were a drag on the overall national economy.
As cumbersome as this sounds, it’s a lot like the data currently circulating through your company. Whether you know it or not, your firm is running an internal data economy hiding in plain sight. And it’s probably underperforming.
Data gets minted by thousands of apps, devices, and sensors across the enterprise—each in its particular format and structure. Think of this as the data supply side. Demand for that data comes from thousands of analytics, AI, and even other apps that want to use those observations but in different formats and structures, or in combination with other datasets.
But the solution the United States settled on, a single currency, won’t work for the data economy inside your company. Creating diverse data and using it in novel ways is inseparable from innovation in apps, analytics, and AI.
But the digital world offers a possible solution the physical world can’t—shapeshifting data. For example, you may want to write data as JSON objects and then query them with SQL as if the data had originated in relational tables. This is an increasingly common need as enterprises realize they want to run analytics on data created by Web and mobile apps.
Similarly, a bank writing transactions in a relational model may want to query that data as a network, or graph, of payments and transfers to better spot patterns that indicate money laundering.
This insight is one of the main motivations behind Oracle’s converged database. This multi-model, multi-tenant, multi-workload database brings together a unique combination of abilities to deliver shapeshifting data at scale. Oracle’s converged database (Oracle Database 19c and later) is:
◉ Multi-model. Native support for multiple types of data models and access methods includes cross-model methods like SQL and model-specific ones like RESTful APIs for JSON objects or PGQL (property graph query language) for property graphs.
◉ Multi-tenant. A database container architecture provides database consolidation, isolation, and agility.
◉ Multi-workload. A wide array of software optimizations work alone or in conjunction to deliver exceptional price-for-performance across all types of database jobs.
Oracle’s converged database helps companies increase the value of their hidden data economies in a number of ways. First, it lets companies have their data cake and eat it too by giving developers, data scientists, and analysts what they want—APIs with the access methods their current project requires—while CTOs and IT operations people get what they want—a fleet of engines configured to their particular job, but with the same architecture, tooling, security methods, patching schedules, and upgrade path.
Second, by reducing the time, cost, effort, and risk of repurposing a given dataset, Oracle’s converged database encourages firms to use their data in a wider variety of processes and decision points across the enterprise, thereby creating more value from that one data asset.
Third, Oracle’s converged database can be deployed in public cloud, local cloud, and on-premises, allowing firms to create a unified data tier across these diverse operating environments.
As companies become more focused on bringing their hidden data economies into the light and increasing their return on data capital, they will increasingly look for technologies that reduce the time, cost, effort, and risk of creating proprietary data assets and using them in new ways. Oracle’s converged database is one of the most important technologies in making this possible.
Source: oracle.com
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