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FLEXIBLE INTEROPERABLE DATA PACKAGES

Give teams the power to define their own processes, use their own datatypes, and innovate as quickly as they want to. Gain the power of organizational wide data findability across platforms, while building reliable, trustable, version controlled data products.
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INFINITE POTENTIAL: THE DATA REVOLUTION

Today, however, the industry is changing. AI and Machine Learning are creating new opportunities to answer much broader questions than lab data was originally intended for. In fact, they require this data to train models that will rise above the pack. So leveraging data beyond its original scope is no longer just a nice to have - To be competitive in biotech, it’s increasingly an imperative.

A PARADIGM SHIFT

Biotechs of the past treated data as a resource that they used once and then threw away. They were effectively burning it like oil. The successful biotechs of the future will treat data as an asset that continues to compound value the longer they keep it. But like any asset, this can only happen if they invest in maintaining it.

DATA: REIMAGINED

Data bundled with metadata is doubly as valuable, allowing teams to trust, find and reuse data to create multiple successful outcomes. With metadata, organizations can make better, faster decisions, and rely on their data assets to lead the way.

If you can't find the data, you can't reproduce the analysis.

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Why build data packages?

Data is more powerful with context

Reliable, accessible, and well-documented data is far more valuable than unorganized data. Organizations that effectively package their data realize twice the value of those that don't. With AI and Machine Learning expanding the potential uses of data, utilizing it in varied contexts to create value is essential. For biotech competitiveness, leveraging data beyond its initial intent has become crucial.
Data in packages are
2X
More Valuable
Unfortunately,

Data without context is not reusable

LINKED DATA IS REUSABLE DATA

The final step in moving from data as an expendable resource to data as a reusable asset is to begin linking data to other datasets and resources that will allow teams to recreate and understand the original context, and understand how the data can be leveraged in new ways.
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What are Data Packages

Data packages are searchable, sharable, versioned, reproducible, and self contained. In essence, data packages are intelligent manifests that combine a list of pointers to data (for example objects in Amazon S3 or files in Sharepoint), with context about those data, including lineage, metadata, and revision comments.

Not only do data packages keep track of their own versions, they keep track of the versions of underlying data as well, giving teams the ability to review every version of every document contained in a data package. Teams can grab a version of a package and run it through pipelines to test repeatability, or can link a historical package to their colleague to ensure they’re iterating on the same data.
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Data Packages Defined

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Data with context

Data packages offer a streamlined approach to data management by storing both data and metadata together in object storage. This integrated method contrasts with traditional practices where metadata and versioning information are stored separately in databases, while the actual data resides in object stores. By consolidating data and its contextual information within the same storage unit, data packages enhance the integrity and coherence of data management, ensuring that context and content are always aligned and readily accessible.

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Deeply Versioned

One of the significant advantages of data packages is their support for deep versioning, which is facilitated by the use of SHA-256 checksums for each revision. This cryptographic hash function ensures the integrity of data by providing a unique identifier for every version, allowing users to track changes, verify data accuracy, and revert to previous versions if necessary. This robust versioning capability enhances data reliability and facilitates meticulous data management across different iterations.

Flexible Metadata

Data packages excel in accommodating diverse metadata needs through their flexible metadata schema. Unlike rigid systems that enforce a single, uniform metadata schema across all datasets, data packages allow teams to capture and manage metadata in a way that best suits their specific requirements. This flexibility ensures that relevant details are preserved and easily accessible without the need for a one-size-fits-all approach, thus supporting a wide range of data use cases and applications.

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Interoperable Data

The interoperability of data packages is a crucial feature, as it allows seamless integration and sharing of data across different platforms. By storing data in an open-source, customer-owned data storage system, data packages enable the attachment of data from various platforms (Platform A and Platform B) while maintaining compatibility. This open approach ensures that data can be effectively utilized across diverse systems and environments, fostering greater collaboration and data exchange.

Easily Accessed

Data packages are designed to be easily accessible, with features like SQL querying and faceted search to enhance data retrieval. These functionalities allow teams to efficiently locate and extract the data packages they need, regardless of the cloud environment they are using. The ability to perform advanced searches and queries ensures that users can quickly find relevant datasets and integrate them into their workflows, significantly improving data accessibility and usability.

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Data packages aren't theoretical

Build data packages with Quilt TODAY

Quilt is making it easier for teams to amass, oversee, and access datasets enriched with comprehensive, accurate context. Start building powerful data packages today with Quilt..
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AWS

Quilt is an Advanced AWS Technology Partner

Quilt Data is an AWS Advanced Technology Partner. Quilt brings seamless collaboration to Amazon S3 by connecting people, pipelines, and machines using visual, verifiable, versioned data packages. Amazon Web Services provides secure, cost-effective, and scalable big data services that can help you build a Data Lake to collect, store, and analyze massive volumes of heterogeneous data.