Data Quality

Data matching and entity resolution solutions

Achieve the highest quality data with accurate data matching and entity resolution for deeper, trusted insights, effective governance and compliance to support your strategic initiatives

Entity resolution and data matching

Large-scale data matching is critical to ensure accurate, trusted results and insights. This is true for achieving a successful 360-degree customer view, fraud detection, anti-money laundering, or supporting new big data and data science initiatives with AI and machine learning.

These use cases typically require massive volumes of disparate data stored in a data lake or across a cluster. Distinguishing matches that indicate a single specific entity requires the ability to test multiple passes of data in multiple permutations with sophisticated multi-field matching algorithms – and still deliver meaningful results that are understandable by business users.

Apply multiple matching algorithms including fuzzy matching across multiple fields with consistent scoring for different data segments to achieve accurate outcomes.

You need to achieve better, faster matching accuracy at lower costs. Accurate, efficient entity resolution is essential to data quality. Now you can simplify, streamline, and automate by combining machine learning with human expertise, all with less reliance on IT. You can also accelerate the matching process and shorten the match-tuning exercise from weeks to days.

Present business users with data in the form they understand best. With Spectrum Smart Data Quality, they can verify if the machine learning model is suggesting the right match decisions with just a few clicks.

Boost your entity resolution process and put your business users in control with:

  • Predictive machine learning algorithms that automatically adjust via expert inputs so they always improve;
  • Fast time-to-value: Instantly reduce tuning iterations;
  • Limited IT involvement with automatic match key and match rule generation;
  • Continuous Model Training with Spectrum Smart Data Quality now linked with Spectrum Stewardship provides access for data stewards to evolve existing match rules with a push of a button.

Discover our business-user-friendly interface and our intuitive, four-step process:

  • Select source: Upload your sample data.
  • Select columns: Choose columns from your data required for finding duplicates.
  • Tag records: Analyze record pairs and categorize them as Match, Non-Match, or Unsure per your business use case
  • Analyze results: Based on the selections made in previous steps, match rule and potential match keys are generated

Learn more about Spectrum Smart Data Quality. 

As organizations look to achieve higher levels of data accuracy to drive business decisions, they need to apply consistent, effective data cleansing and enrichment to produce the consistent, high quality entity data your business desires.

Use cases such as detecting fraud, predictive analytics, omni-channel marketing, risk assessment, or better B2B relationships often require additional data such as demographics, firmographics and location intelligence to be matched and linked to existing entities to identify and minimize risk, improve customer engagement, reduce costs and optimize processing.

Your business success depends on accurate data for the right insights, whether for a single customer view, fraud detection, or predictive analytics. But as data volumes grow, it becomes a significant challenge to understand, measure, match and resolve the entities within that data, including ensuring its quality and fitness-for-purpose.

Inaccurate, incomplete and missing data diminishes your ability to achieve the precise and accurate data matching needed to achieve high quality customer experiences, operational efficiency, or to detect and predict fraud.

Precisely Spectrum Quality solutions help you address and resolve key data matching challenges. Data profiling and business rules provide upfront understanding of data content and quality, including which data might be used for match keys that segment the data for each match pass, and which data should be compared and scored to identify common entities.

Additionally, business rules can be built to continue assessing the quality of these key fields over time. As issues are identified, capabilities to cleanse, enrich, match, and consolidate your data help you improve data quality and provide accurate, effective data for multiple use cases.

Case Study: Solving Serious Data-Quality Problems is Key to AML Compliance for Global Bank

Your business success depends on accurate data for the right insights whether for a single customer view, fraud detection or predictive analytics. But as data volumes grow, it becomes a significant challenge to understand, measure, match and resolve the entities within that data, including ensuring its quality and fitness-for-purpose. Inaccurate, incomplete and missing data diminishes your ability to achieve the precise and accurate data matching needed to achieve high quality customer experiences, operational efficiency, or to detect and predict fraud.

Precisely data quality solutions help you address and resolve key data-matching challenges. Data profiling and business rules provide upfront understanding of data content and quality, including which data might be used for match keys that segment the data for each match pass and which data should be compared and scored to identify common entities. Additionally, business rules can be built to continue assessing the quality of these key fields over time. As issues are identified, capabilities to cleanse, enrich, match and consolidate data help you improve data quality and provide accurate, effective data for multiple use cases.

Our solutions are designed to implement data-matching processes that scale to your business needs quickly, easily, and with collaboration between IT and business teams. Through innovative software, global address validation and data enrichment, robust data matching with a broad array of configurable match algorithms, and a proven methodology, you can rapidly design and deploy entity resolution solutions in batch or real-time throughout your enterprise as part of your applications and data integration pipelines.

Deploy entity resolution and data matching when and where needed with the scalability offered in Trillium’s big data solutions to deliver high-value entity resolution faster, helping meet critical Service Level Agreements.

Take full advantage of the business value of big data with industry-leading data profiling and data quality, with the scalability and performance you need for your largest data volumes, to deliver trusted business applications.

For over two decades, Precisely has been recognized as a leader in the data quality market. Our innovative technology and unmatched subject matter expertise enable us to help customers solve their most complex data challenges. Our pragmatic approach focuses on delivering business value in the short term and ensuring sustained value in the long term.

Learn how Trillium DQ for Big Data addresses the challenge of data matching at scale.

Improve data quality with machine learning

It’s time for expert machine learning.

In today’s competitive landscape, data quality matters more than ever. That’s why data users across every industry need to take a more active role in data quality. Yet, most machine learning applications aren’t designed to build on the expertise of these users. Data cleansing and entity resolution platforms typically require IT expertise. Design is technical and time-consuming. And the real data experts are a step or more removed from the process.

But now, new innovations combine leading-edge machine learning with intuitive tools business users can use to review and interact with data presented in familiar formats. Machine learning occurs directly as a result of users’ actions, so entity resolution accelerates, and efficiencies increase. Find out how, with this smart approach, organizations can improve data quality with less effort and better results.

Download the white paper “Data Quality Gets Smart” to learn more.

Financial institutions present one of governments’ best lines of defense in the battle against terrorism and financial crimes. The challenge for banks is to avoid spending valuable resources on counterparty investigations that have no chance of bearing fruit. That means banks need to minimize the number of unnecessary alerts coming out of their screening systems. Find out how this U.S. based retail bank minimizes AML compliance risks while improving investigator efficiency.

Read our case study for this US retail bank.

 

The perfect match

Matching data for any type of entity:

It’s not just customer or personal data that organizations need to resolve, it’s data for any type of entity, whether that be a party, household, business, asset, product, part, location, or something else. Because data is a shared asset, unreliable data permeates across organizations, derailing important IT initiatives such as enterprise resource planning (ERP), supply chain management (SCM), master data management (MDM), as well as downstream initiatives such as predictive analytics.

Commonly, this data originates from many different and varied sources (including mergers and acquisitions). So the integration rules for the data are often complex to deduce and volatile over time, and issues propagate and procreate across systems and business processes. The configurability of our solutions means that organizations can apply relevant data-matching rules to create a single, comprehensive, correct record for each unique entity, whether household, asset, product, part, location, or otherwise.

Building a competitive advantage through data maturity.

Detecting fraud and addressing anti-money laundering:

Fraudulent transactions and money laundering often rely on subtle variations of data passing through organizational systems undetected. New machine learning technologies can identify the underlying patterns to detect fraud, but the necessary data is often too large and diverse to effectively analyze. Data matching and entity resolution are critical to provide consolidated, clean, verified data for these use cases. We provide data verification, enrichment and demanding multi-field entity resolution to deliver on these requirements.

Learn more about Precisely financial service data solutions. 

Producing an accurate 360-degree customer view

Customer engagement is key for a successful business. Achieving that requires an organization to not only have a comprehensive understanding of their customers, but also be prepared to protect their customers through effective fraud detection. Building an accurate 360-degree view of customers is a complicated undertaking, particularly for global operations. Often, key customer information is limited, flawed, out-of-date, or held in different systems and formats. Consequently, putting data quality processes in place to standardize, cleanse, and enrich that data is critical to correctly match and resolve the entity data for any given customer and generate a trusted single customer view.

Read more: Customer 360 solutions