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Data Intelligence

Improve IT and business data literacy and knowledge, supporting enterprise data governance and business enablement

Know what data you have, where it is, where it’s been and how it transformed along the way, plus understand sensitivities and risks. With an automated, real-time, high-quality data pipeline, enterprise stakeholders can base strategic decisions on a full inventory of reliable information.

Organizations are flooded with data, so they’re scrambling to find ways to derive meaningful insights from it – and then act on them to improve the bottom line. But solving this data dilemma is easier said than done.

While data management drives the design, deployment and operation of systems that deliver operational and analytical data assets, data governance delivers these data assets within a business context, tracks their physical existence and lineage, and maximizes their security, quality and value. Both disciplines require a real-time, accurate picture of the metadata landscape.

But that’s a complex order because of numerous data types and data sources that were never designed to work together, data infrastructures that have been cobbled together over time with disparate technologies, poor documentation and little thought for downstream integration.

The lack of visibility and control over “data at rest” in databases, data lakes and data warehouses and “data in motion” as it is integrated with and used by key applications means organizations spend a lot more time searching for data rather than actually putting it to work.

In fact, data professionals spend 80 percent of their time looking for and preparing data and only 20 percent of their time on analysis, according to IDC.

So they need an automated, metadata-driven framework for:

  • Discovering data – Identify and interrogate metadata from various data management silos.
  • Harvesting data – Automate the collection of metadata from various data management silos and consolidate it into a single source.
  • Structuring and deploying data sources – Connect physical metadata to specific data models, business terms, definitions and reusable design standards.
  • Analyzing metadata – Understand how data relates to the business and what attributes it has.
  • Mapping data flows – Identify where to integrate data and track how it moves and transforms.
  • Governing data – Develop a governance model to manage standards, policies and best practices and associate them with physical assets.
  • Socializing data – Empower stakeholders to see data in one place and in the context of their roles.

erwin offers a data intelligence software suite combining the capabilities of erwin Data Catalog with erwin Data Literacy to fuel an automated, real-time, high-quality data pipeline. Then all enterprise stakeholders – data scientists, data stewards, ETL developers, enterprise architects, business analysts, compliance officers, CDOs and CEOs – can access data relevant to their roles for insights they can put into action.

Know what you need to know and then act on it. Make your enterprise a lot smarter and unlock the value of your data assets with the
erwin Data Intelligence Suite.