While big data has been lauded as the biggest business gamechanger of the century, the road to large-scale data collection, storage, and analysis has not always been smooth. As a result, many organizations face a complicated data situation, with assets distributed across multiple data layers, on-premises data warehouses, or diverse cloud services.
For these organizations, the ability to gain analytical insights into their business is hampered by the fragmented nature of their data storage. This can have a number of negative outcomes, such as:
- Time spent preparing and curating data can slow down querying processes
- Low data portability means that data held in cloud environments can be difficult and expensive to export or redeploy
- Requiring Extract – Transform – Load (ETL) processes to consolidate data in one warehouse
- Security and regulatory risks of copying and transporting data for analysis
- Risks introduced by third-party vendors, including loss of total access control and potentially being permanently locked out from their data in a bankruptcy or sale situation
Storing data assets across a number of on-premises, private cloud, or open cloud standard locations is not ideal but is a fact of life for many companies. Not only is complete data consolidation a very time-consuming process that requires considerable expertise, it can also be a financial impossibility due to huge capital or ongoing costs.
That’s why a lighter and more flexible approach to data storage and analysis is becoming increasingly popular: data interoperability.
What is data interoperability?
Data interoperability is defined as “the ability of systems and services that create, exchange and consume data to have clear, shared expectations for the contents, context, and meaning of that data.”
In other words, data interoperability allows an organization to use its data no matter how diverse its location or format. Data interoperability can overcome a number of the challenges faced by organizations in terms of improving their access and governance of their various data sources.
For example, the Intertrust Platform enables data interoperability by implementing a virtualized interoperable layer which sits on top of the data source, allowing for the querying and processing of the data. This breaks the data free from the shackles of cloud data frameworks as the data can be analyzed wherever it is, rather than having to be moved to a data lake or warehouse.
The advantages of data interoperability
For organizations which might not have the capacity to create a full on-premises data warehouse or the budget to invest in a full-scale private cloud environment, there are a number of different approaches to the storage and processing of an organization’s data. However, using virtualized datasets instead of something like data warehousing as a service delivers considerable advantages. Here are just a few:
- Data interoperability allows the integration of all existing data assets without having to go through a lengthy and expensive consolidation process.
- Virtualized datasets don’t require any copying or transferring of data or the usage of third-party vendors, eliminating significant attack surfaces.
- Data interoperability allows for single-source governance rules and security protocols to be implemented across all data assets, virtually eliminating the possibility of units within the same organization creating wrinkles in cyber defenses through varied processes.
- For application development, using data interoperability can bring together assets from a number of sources and collaborators. It also enables the creation of secure sandbox environments where code can be executed without threatening widespread system compromise.
- Virtualized datasets, such as those created by the Intertrust Platform, allow fine-grained access control, enabling safer collaboration while also ensuring data protection and regulatory compliance.
By its nature, data interoperability is about providing a flexible data operations system for organizations, free of proprietary software or frameworks and locked-in contracts. The use of open-source tools such as HDFS can be easily combined with existing cloud data architecture, giving organizations the flexibility to use their data as they need it and in whatever way suits them best.
Companies are seeking to modernize existing data warehouse assets with features like dynamic rights management, security, and multi-party data integration. They want complete control over all their data, no matter where it is and without the need for complex transfer or storage arrangements, which is why data interoperability has become such an attractive option.
Intertrust is proud to be an industry-leader in offering secure and flexible data interoperability which can transform businesses’ relationship with and usage of their data.
To find out more about how Intertrust Platform and virtualized datasets can deliver effective data interoperability for your business, download our solutions guide, Governed data analytics environments on virtualized datasets, or get in touch with our team.
About Shamik Mehta
Shamik Mehta is the Director of Product Marketing for Intertrust's Data Platform. Shamik has almost 25 years of experience in semiconductors, renewable energy, Industrial IoT and data management/data analytics software. Since getting an MSEE from San Jose State University, he’s held roles in chip design, pre-sales engineering and product and strategic marketing for technology products, including software solutions and platforms. He spent 6 years at SunEdison, once the world's largest renewable energy super-major, after spending 17 years in the semiconductor industry. Shamik has experience managing global product marketing, GTM activities, thought leadership content creation and sales enablement for software applications for the Smart Energy, Electrified Transportation and Manufacturing verticals. Shamik is a Silicon Valley native, having lived, studied and worked there since the early 90’s.