Maximizing insights through effective data integration

• Integrating, cleaning, and preparing data involves complex technical challenges; not all technology providers address those challenges comprehensively or effectively.

• Effective integration is key to scaling an organization and gaining actionable data insights from an increasing number of software systems.

• Integration supports interoperability and can mitigate the impacts of vendor lock-in.

Institutions dealing with cluttered data, duplicative records, and inefficient information management face a common underlying problem: data fragmentation. We built Peregrine to solve that problem and provide our society’s most critical decision-makers with a foundation of trustworthy, actionable data.

While charts, network diagrams, and maps lend to powerful and visually appealing demos, they're not actually useful if the data isn’t correct. Peregrine focuses on getting the nitty gritty details of data integration right to back those visualization applications with accurate, timely, and complete information.

Few solutions follow the meticulous best practices required for effective integration. To understand what effective integration would look like for your organization, you’ll need to know what “good” integration looks like and how it addresses common technical challenges.

Problems solved through effective integration

Data fragmentation spurs a host of technical hurdles, including difficulty scaling, limited insights, and vendor-lock in. Below I explore those challenges and how integration can alleviate them.

Diseconomies of scale

As organizations add more and more data sources to their tech stacks, it becomes increasingly difficult to fully capitalize on each solution. Each new vendor brings new concepts and UIs to manage, which ultimately hinders adoption and an organization’s ability to make data-driven decisions.

Effective integration pulls data from each siloed system and maps it to a single cohesive schema, so a person in system A can be compared to a person in system B. By purposefully decoupling the schema from the data, integration standardizes the ecosystem and creates a common vocabulary for all users. When data is integrated adhering to a common schema, users can understand what each data point means regardless of the context in which they are working.

This mapping is key to preventing diseconomies of scale. By harmonizing and standardizing across an organization, a common schema promotes compounding value when new data is added and prevents ever-increasing complexity from new systems.

Limited insights from disparate systems

Analysts working in fractious ecosystems often find themselves assembling piece-meal insights from data stored across disconnected sources.

Integration automates this process by synthesizing information from different stores in a unified, organized space. In the public sector, for example, this might look like merging public safety and health services data to provide a comprehensive view of an individual based on records from several siloed sources.

However, effective integration solutions go beyond collating, storing, and processing data. They make the data more meaningful by optimizing it for aggregation, reporting, and search.

Vendor lock-in

Lock-in happens when a vendor restricts customers from openly sharing, moving, and manipulating the data they have stored in the vendor’s system. By “locking in” customers’ data, vendors attempt to stifle competition by keeping customers from using their data in third-party platforms.

Integration cannot completely eliminate vendor lock-in practices, but an interoperable integration platform provides a layer of protection for customers’ information. An integrated data asset serves as a vendor-agnostic store in case a provider ceases operations or refuses to cooperate when a customer switches platforms. Integration also streamlines processes such as switching vendors, sharing records, and leveraging data across other applications.

Public agencies use many different applications to store, organize, manage, and leverage their data. Peregrine's effective integration backbone enables agencies and other users to find and action their data seamlessly, even when working from multiple sources and systems.

Building blocks for effective integration

Integration is a highly technical and challenging process. Finding a product that meticulously addresses each challenge is key to a robust, durable data integration strategy.

Below I dig into a handful of essential — but often overlooked — components of effective integration. Getting these right involves painstaking attention to detail on the provider’s part, requiring a diligent, conscientious human element.

• No-downtime schema-changes. Effective integrations can adapt and change to underlying schema changes from source systems without the user experiencing downtime. Inevitably, source systems change — underlying tables get new columns, or they delete old ones — and integrations must gracefully handle these changes without causing disruption to the end product.

• Compatibility with multiple sources and data types. Good integration solutions can handle data from most source types and in all types of formats. For example, SQL is a common source and data format, but the long tail of data sources — such as email, GIS, PDFs, and Word docs — are equally as important. Effective integrations handle both structured and unstructured data, combining data types that may have been impossible to combine previously.

• Updating and deleting records. Effective integrations ensure that when source systems change or delete records, those changes and deletions are accurately reflected in the integrated data asset. This key requirement may seem simple, but it can be challenging depending on the source type involved, often requiring complex, layered strategies.

• Real-time ingestion. Effective integrations can ingest data in real time with low latency and minimal disruption to the user’s workflow.

• Access controls. The best integration platforms offer access controls at the row and property levels. This enables users to adjust access to specific properties and to restrict specific rows of a dataset. Users should also be able to adjust access controls within specific departments, across their organization, and for data sharing with outside organizations.

• Data lineage. Effective integrations enable users to inspect and understand data sources. Allowing users to source values back to their originating data sources is difficult but enhances users’ trust in the data and streamlines debugging.

• Monitoring and observability. Effective integrations apply appropriate monitoring and observability to their pipelines so users receive notifications when data pipelines have broken, sources have gone offline, or other unexpected inconsistencies occur.

Why high-quality data integration matters

“Search-your-data” tools are only as good as the data backing them. In industries where everyday interactions impact lives, it’s paramount to deliver the right data at the right time. At Peregrine, we’re devoted to perfecting data integration software and enabling society’s most important institutions to make critical decisions with confidence.

When it comes to integration, Peregrine dives into the messy technical work rather than glossing over it. Our mission is to create durable, reliable, interoperable integration technology for industries where vendors have repeatedly over-promised and under-delivered.

We invest in the intricate (and sometimes daunting) details that other providers often overlook. Our dedicated data scientists and engineers embed with each customer to ensure a successful, comprehensive integration process in which no stone is left unturned. The result is Peregrine: a powerful integration platform that’s efficient to deploy and intuitive to use.