Welcome back to my final blog in the Data Quality for Everyone, Everywhere series. If you did not see my previous blogs, you’ll find them here: Data Quality for Everyone, Everywhere and Why Data Quality Does Not Extend To Everyone, Everywhere Today.
Given the financial and reputational impact of poor data quality, there is a clear incentive for your organization to expand data quality across all stakeholders, all data domains, all landscapes, all applications, and all geographies.
There are five key takeaways that you can help drive data quality throughout your organization:
To keep poor data quality from costing your company customers and competitive advantage, you need to identify, resolve, and prevent data quality problems—wherever they are.
The first step is to profile your data to uncover and understand its anomalies and hidden relationships, regardless of the complexity of the data itself or of the relationships between data sources. With a complete and completely accurate picture of your data’s content, quality, and structure, you can understand the impact of poor data quality on your business and quickly take corrective action.
But finding and fixing data quality problems is not a one-time project. You need to continuously measure and monitor data quality. Line-of-business managers, business analysts, and data stewards need appropriate tools to enable them to define data rules, track and monitor data quality trends, and publish and share data quality metrics themselves. By involving all the right people in understanding, measuring, monitoring, and ultimately improving data quality, your company can build long-term, sustainable data quality processes—so you can trust all your data.
Different users have different needs when working with the quality of their data. Business users frequently focus on analyzing data, while developers and architects need tools to help them define rules. But just as users need to collaborate, tools also need to work together to help that same team meet its goals.
Architects and developers need highly productive development tools. Data profiling, data cleansing, and data integration functionality should be unified, so they can rapidly develop, optimize, deploy, and manage centralized data quality services that can be reused across all applications and data management projects
Line-of-business managers, data stewards, and data analysts need self-service capabilities so they can quickly identify and resolve data quality issues without any additional IT coding or development. And as business users become technically savvy, they also need to specify, validate, and test re-usable data quality rules in a streamlined and collaborative environment.
Prebuilt rules for address cleansing and customer matching generate immediate, tangible value. These rules are possible because customer data formats and reference data that use postal address formats and lists of common derivations/abbreviations have become standardized around the world. The ability to extend these rules and processes for additional projects that benefit from the improvement in customer data is critical. This reuse results in greater consistency and faster time to value for new projects. But customer data is not enough.
For data domains where no global standards exist (e.g., product data, financial data, and asset data), you need an effective way to implement data quality using custom rules and company specific reference data. You need a platform that provides the configurability and flexibility necessary to build and maintain custom rules.
Historically, business applications include logic to support data quality (e.g., a customer name field expects a name, a date of birth field expects a date, a car registration number field expects a mix of alphanumeric characters). Because these rules are embedded into an application, they are often not documented, cannot be reconfigured, and as a result, cannot keep pace with changing business requirements. These factors make it nearly impossible to manage data quality or implement data governance across the organization.
The solution is to abstract the rules from the application, manage data quality rules centrally, and reuse the same rules across all applications. For this approach to work efficiently, rules need to be built independently of any one application. In this way, the same rules can be used for customer data in the marketing system or the billing system, or the planning system and the MDM application. Each business application can request domain-specific rules to be applied where they are needed (e.g., as data is being entered into a form or as a batch process). These reusable rules are called data quality services. These data quality services are made possible by leveraging capabilities unique to data integration technology: access to all sources, the ability to build and share rules and reference data independent of any physical source, and the ability to support multiple requests and guarantee the results within set response times. The most typical data quality services include profiling, cleansing, standardization, address validation, matching, and monitoring services.
The best and most cost-effective way to deliver data quality for everyone everywhere is to leverage an intelligent data management cloud platform. An intelligent data platform provides a single environment for data profiling and data cleansing, with one set of reusable rules and tools for managing data quality:
With an intelligent data platform, IT organizations can build, centrally manage, and rapidly deploy reusable data quality rules. These rules can be reused across all data integration projects to significantly reduce costs.
An intelligent data management cloud platform supplies a set of collaborative features and a common set of rules and metadata that can be shared across the enterprise. As a result, business and IT staff can work more efficiently together to design and implement the data rules necessary to meet the business’s needs, in days rather than months. An intelligent data management cloud platform that provides universal connectivity to all data sources—whether on premise, with partners, or in the cloud—and offers unified data profiling and cleansing capabilities is the ideal infrastructure for delivering pervasive data quality.
Informatica ensures that all key stakeholders in your organizations can work together effectively to identify bad data and fix it faster. With the Informatica Intelligent Data Management Cloud platform, your organization can:
Informatica leverages our many years of experience working with customers to identify and resolve their data quality problems. Informatica Cloud Data Quality, which runs on Informatica Intelligent Cloud Services, is part of Informatica Intelligent Data Platform so you can quickly identify and resolve data quality issues without any additional IT coding or development. As a result, you can leverage its security, reliability, and backup so you can focus on operational excellence instead of investing in additional infrastructure. Business users can readily specify, validate, and test re-usable data quality rules in a streamlined and collaborative environment.
You can manage the entire data quality process with Informatica Cloud Data Quality, no matter the size of your organization, the location of your operations, or the types and volumes of data. Whether your business initiative is centered on working with customer or third-party data, product or supplier data, transaction data, or data from IoT devices, Informatica Cloud Data Quality will ensure that you can trust the quality of your data.
Informatica Cloud Data Quality provides comprehensive and modular support for all data and all use cases, whether you’re focused on a small project or a complex, cross-enterprise initiative. You can deliver trusted customer, product, financial, or asset data to any data integration, master data management (MDM), or data governance project. It features standardization, matching, worldwide address cleansing, and versatile data quality management for all project types.
It’s time to find and fix the data quality problems—wherever they are—that cost your company millions. Data quality is not limited to a single department, project, application, domain, or geography. The responsibility for keeping data clean is everyone’s to share. Your company needs to extend data quality to all stakeholders, all projects, and all applications, so your company can trust all its data, for all its needs, all the time.
The Informatica Intelligent Data Platform can help your company:
Armed with data that you can trust, your company can attract and retain valuable customers, eliminate costly operational errors and delays, innovate faster, and make smarter business decisions.
Start finding and fixing data quality problems today with a Free 30-day Trial of Cloud Data Quality.