TIBCO’s All-New Data Quality 5.1 Solution: A Critical Step to Building Your Agile Data Fabric

Blog: The Tibco Blog

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As enterprises invest more and more into their data and analytics, their responsibility for managing that data increases too. They have all of these data streams but are they accurate, accessible, and easily understood? Many enterprises struggle even to spend time analyzing and finding insights from their data because they are too occupied trying to sort and translate the cluster of data coming in. With TIBCO^® Data Quality (DQ) software, we address the barrier of untrustworthy data that prevent you from achieving an agile data fabric and give you the solution to make the best decisions possible.

Why You Need a Data Quality Solution

According to TechCrunch, “A data scientist spends 80 percent of their time at work cleaning up messy data as opposed to doing actual analysis or generating insights.” TIBCO^® DQ software cleanses raw data, detects outliers and duplicates, and establishes a quality dataset for augmented intelligence and machine learning processes so more time can be spent using the data-driven insights instead of trying to understand them. Data quality solutions give agility, freedom, and power back to developers and citizen users with a rich set of tools and services to allow them to build and implement customized features to help produce cross-platform, reusable assets both within or outside the solution landscape.

TIBCO^® DQ software is built for citizen users, so data analysts and data scientists aren’t wasting time on manual and repetitive data validation tasks—being truly enterprise and industry ready. Regardless of technical expertise, you can access all the features to evaluate, validate, and cleanse data. With TIBCO^® DQ technology, your business can gain data transparency and make the best business decisions—avoiding tech silos, poor data quality, or untrustworthy data. Last but not least, TIBCO^® DQ software provides its users and consumers with increased ease when it comes to accessibility by connecting “data people” with measurable, reportable, and actionable facts about their data.

The Evolution of TIBCO’s Data Quality Solutions

To fully understand how data quality solutions can change your business, you must first understand the big picture—building an agile data fabric. Think of this as a data architecture that includes shared data assets and optimized data pipelines that can be used to address today’s data challenges in a unified way. A key element to achieving an agile data fabric is having enterprise data you can trust to help inform the business decisions you will make later today, next month, or next year. Over the years, TIBCO^® DQ software has been designed to make data quality more accessible to citizen users by providing them with simple and harmonized interfaces to profile data and run rules-based quality analyses.

Trusted data is not only more available to more people with data quality solutions, but it’s available in more places wherever they need it, both on-premises or in the cloud. With TIBCO’s extensive set of prepackages and “ready to use” rules in an enterprise data quality framework, businesses across different industries have successfully implemented complex data quality solutions with less effort, faster turnaround time, and increased efficiency. With an open architecture and a robust orchestration framework, TIBCO^® DQ software establishes the foundation for delivering automated features and rich user experiences that will evolve with insights gained from semi-supervised and unsupervised learning methods on active metadata.

Solutions similar to TIBCO^® DQ software, such as the TIBCO^® Omni-Gen integration framework, are a great introduction to clean, accessible, and streamlined data. TIBCO® DQ software builds on the data standards, governance, and migration features of the TIBCO Omni-Gen integration framework and takes it to the next level. With TIBCO^® DQ technology, you can maintain the quality of your data and the systems that support it at all times.

What’s New with TIBCO^® DQ 5.1

Scalability, intelligence, and integration—these three words continually define TIBCO^® DQ solutions. More specifically, the latest release of TIBCO^® DQ 5.1 software connects its citizen users with measurable, reportable, and actionable facts about their data. These features automate data quality by restoring “trust” in data and by working hard to maintain that trust—detecting, preventing, and quickly correcting data quality problems. TIBCO^® DQ technology brings transparency and accountability to data quality initiatives by generating reportable metrics—including profiling, stats, and scores that add observability to data quality.

See further details on the most recent release of TIBCO^® DQ 5.1 software below:

Refreshed user interfaces with valuable insights on data profiles and quality scores
Advanced profiling capabilities to augment data exploration for data science initiatives
New user interfaces that enable the creation of custom data classification and custom data analysis rules
Expanded business rules that analyze, cleanse, validate, and enrich your data
Revamped REST API services to support new integration patterns
New observability dashboard to track and monitor DQ metrics as well as send alerts when they fail to meet desirable thresholds
Containerized distribution for scalable and hybrid deployment.

Create a Unified Data Pipeline with TIBCO

Creating trusted and unified data pipelines is not an inherently easy task, but with the right data quality tools, implementation is simple. Before you know it, you will be using the data insights provided from your agile data fabric to make informed business decisions that will help you scale. You will break free of restrictions on who and where data can be accessed and analyzed and unlock the true capabilities of your master data intelligence.

Want to be the first to know about other data quality and management solutions that can completely alter the way your business uses analytics to make decisions? Download our Complete Guide to Data Quality Management.

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