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Exploring Innovations in Data Integrity

Authors Photo Precisely Editor | May 31, 2023

To innovate, compete, and grow in the current macroeconomic environment, enterprises must approach data strategically. A sound data strategy doesn’t happen by accident; it’s built on a foundation of data integrity, including accuracy, consistency, and rich context.

Many organizations still struggle with data integrity. According to research performed at Drexel University’s LeBow College of Business, 76% of data practitioners are trying to improve their data-driven decision-making, and more than half don’t feel that they can fully trust their data.

When attempting to build a data strategy, the primary obstacle organizations face is a lack of resources. Teams are building complex, hybrid, multi-cloud environments, moving critical data workloads to the cloud, and addressing data quality challenges. To accomplish any of that with constrained resource, teams must more with less.

Woman looking intently at her laptop screen.

With the right enterprise-grade technology, organizations can find simple solutions to complex data integrity challenges at scale. In the same way that big cloud-platform providers offer simplified access to infrastructure, and data cloud providers like Databricks and Snowflake have vastly simplified access to data and analytics, modern data integrity tools must streamline and automate data integrity processes.

The act of bringing data into hybrid cloud and VPC environments doesn’t solve data integrity challenges by itself. It simply moves the same old problems into new environments, where they may even be amplified. Users cannot find the data they need, gain access in a timely fashion, rely on data being in a useable format, or understand its impact on the business – meaning that it is unusable to them. The end result is that users can’t trust their data because it lacks integrity.

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The Precisely Vision for Data Integrity

At Precisely, our vision for data integrity is built upon six key principles:

  1. Business and IT collaboration
  2. Real-time access to data
  3. Data integrity processes run where data lives
  4. Shared business and technical metadata
  5. AI-driven data integrity processes
  6. Flexibility and seamless interoperability

Business and IT Collaboration 

In the past, business and IT teams often disagreed about who should have access to data and when. Today, these two groups must be partners in management of data and its integrity. Business and IT teams need a simple, seamless data integrity solution that enables them to:

  • Collaborate on making data accessible
  • Ensure and maintain its quality
  • Enrich the data with third-party context

Real-time Access to Data

In the past, businesses struggled to get timely access to data for both analytical and operational use cases. Cumbersome batch ETL processes left users waiting for the information they needed. In many cases, data arrived too late to be useful. In today’s fast-paced business climate, data must be delivered when and where business users need it. In many cases, that means real-time availability.

Data Integrity Processes Run Where Data Lives

Traditional data management solutions have required that data be brought to where the tools run. As a result, organizations have had to bring copies of their data to the tools – one copy for data quality, one copy for data governance, and so on. In modern hybrid, multi-cloud environments, teams need tools that run where the data lives, whether that’s on-premises or in the cloud.

Shared Business and Technical Metadata

As IT landscapes grow larger and more complex, organizations struggle to gain a holistic understanding of where data comes from, how it’s used, and which data is most critical to the business. A data catalog of both business and technical metadata, shared by all data integrity services, enables everyone in the organization to understand both the technical and business context for their data.

AI-driven Data Integrity Processes

Searching for the root cause of data issues, creating data quality rules, and identifying datasets for enrichment can be cumbersome, time-consuming tasks. Automating data integrity processes with AI and large language models, including proactive detection of potential data issues and recommendations for data quality rules and enrichment data, can save hours, and potentially even days, of productivity.

Flexibility and seamless interoperability

Platforms that are difficult to deploy and maintain, or that require you to purchase more capabilities that you need, quickly become complicated, unwieldy, and expensive. Organizations today want the ability to choose the specific data integrity services they need, when they need them, and with seamless interoperability. That includes interoperability with other data integrity tools and with the rest of an organization’s business systems.

Data Integrity in the Modern Data Stack

These six tenets capture the Precisely vision for a new way to manage data integrity in modern data stacks. This vision underpins the Precisely Data Integrity Suite a SaaS solution that enables enterprises to quickly and easily build accuracy, consistency, and context in their data, with the scalability needed for a hybrid cloud environment.

The Suite’s Data Integration service allows users to replicate data from trusted sources, including complex sources like mainframe and IBM i, to modern platforms like Kafka, Snowflake, and Databricks. And support is now available for hundreds of new connectors for Data Integration – including popular applications, BI tools, and clouds.

The Data Integrity Suite’s Data Catalog of business and technical metadata connects all Suite services. This enables each service to build on the work of the others and provides easy searchability for users.

The Suite’s Data Governance service tightly integrates with the Data Catalog to enable business and IT to collaborate, share data insights, have greater visibility into the most critical data assets across the entire enterprise.

Colleagues having a discussion.

The Data Integrity Suite also offers a Data Observability service that allows enterprises to proactively monitor data trends and alert users to potential problems in the data before it impacts downstream systems. New alerts are frequently released, mostly recently including alerts on schema drift and data freshness, along with greater control over alerts and consolidated insights.

Announced in May, the Data Integrity Suite’s new Data Quality service reimages data quality to be more intelligently driven. It incorporates best-of-breed capabilities from Precisely’s Trillium, Spectrum, and Data360 products to cleanse, standardize, and verify data where it is. And the service offers a business-friendly interface, leveraging artificial intelligence to provide recommendations, so that anyone can build data quality pipelines in minutes. Address validation, Geo Addressing, and Data Enrichment are all available through the Suite’s data quality pipelines as well as through cloud-native APIs.

While all the Suite’s services can run independently to address specific challenges with data integrity, their value is exponentially multiplied when they interoperate within the context of the Data Integrity Suite and the broader Precisely portfolio of solutions.

The announcement of these exciting new suite capabilities enables new use cases for Precisely customers, giving them best-in-class data integration, data governance, data observability, data quality, geo addressing, and data enrichment.

The Precisely Data Integrity Suite offers a broad range of capabilities and can be tailored to suit the needs of any organization, from small businesses to large enterprises. To learn more about the new Data Integrity Suite capabilities, watch our demo and see firsthand how the Suite can help you cultivate confidence in your critical data asset.