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erwin Expert Blog Data Intelligence

Top 6 Benefits of Automating End-to-End Data Lineage

Replace manual and recurring tasks for fast, reliable data lineage and overall data governance

Benefits of Data Lineage

It’s paramount that organizations understand the benefits of automating end-to-end data lineage. Critically, it makes it easier to get a clear view of how information is created and flows into, across and outside an enterprise.

The importance of end-to-end data lineage is widely understood and ignoring it is risky business. But it’s also important to understand why and how automation plays a critical role.

Benjamin Franklin said, “Lost time is never found again.” According to erwin’s “2020 State of Data Governance and Automation” report, close to 70 percent of data professional respondents say they spend an average of 10 or more hours per week on data-related activities, and most of that time is spent searching for and preparing data.

Data automation reduces the loss of time in collecting, processing and storing large chunks of data because it replaces manual processes (and human errors) with intelligent processes, software and artificial intelligence (AI).

Automating end-to-end data lineage helps organizations further focus their available resources on more important and strategic tasks, which ultimately provides greater value.

For example, automatically importing mappings from developers’ Excel sheets, flat files, Access and ETL tools into a comprehensive mappings inventory, complete with auto generated and meaningful documentation of the mappings, is a powerful way to support overall data governance.

According to the erwin report, documenting complete data lineage is currently the data operation with the largest percentage spread between its current level of automation (25%) and being seen as the most valuable operation to automate (65%).

Doing Data Lineage Right

Eliminating manual tasks is not the only reason to adopt automated data lineage. Replacing recurring tasks that don’t rely on human intelligence for completion is where automation makes an even bigger difference. Here are six benefits of automating end-to-end data lineage:

  1. Reduced Errors and Operational Costs

Data quality is crucial to every organization. Automated data capture can significantly reduce errors when compared to manual entry. Company documents can be filled out, stored, retrieved, and used more accurately and this, in turn, can save organizations a significant amount of money.

The 1-10-100 rule, commonly used in business circles, states that preventing an error will cost an organization $1, correcting an error already made will cost $10, and allowing an error to stand will cost $100.

Ratios will vary depending on the magnitude of the mistake and the company involved, of course, but the point remains that adopting the most reliable means of preventing a mistake, is the best approach to take in the long run.

  1. Faster Business Turnaround

Speed and faster time to market is a driving force behind most organizations’ efforts with data lineage automation. More work can be done when you are not waiting on someone to manually process data or forms.

For example, when everything can be scanned using RFID technology, it can be documented and confirmed instantaneously, cutting hours of work down to seconds.

This opens opportunities for employees to train for more profitable roles, allowing organizations to reinvest in their employees. With complex data architectures and systems within so many organizations, tracking data in motion and data at rest is daunting to say the least.

Harvesting the data through automation seamlessly removes ambiguity and speeds up the processing time-to-market capabilities.

  1. Compliance and Auditability

Regulatory compliance places greater transparency demands on firms when it comes to tracing and auditing data.

For example, capital markets trading firms must implement data lineage to support risk management, data governance and reporting for various regulations such as the Basel Committee on Banking Supervision’s standard number 239 (BCBS 239) and Markets in Financial Instruments Directive (MiFID II).

Business terms and data policies should be implemented through standardized and documented business rules. Compliance with these business rules can be tracked through data lineage, incorporating auditability and validation controls across data transformations and pipelines to generate alerts when there are non-compliant data instances.

Also, different organizational stakeholders (customers, employees and auditors) need to understand and trust reported data. Automated data lineage ensures captured data is accurate and consistent across its trajectory.

  1. Consistency, Clarity and Greater Efficiency

Data lineage automation can help improve efficiency and ensure accuracy. The more streamlined your processes, the more efficient your business. The more efficient your business, the more money you save on daily operations.

For example, backing up your data effectively and routinely is important. Data is one of the most important assets for any business.

However, different types of data need to be treated differently. Some data needs to be backed up daily while some types of data demand weekly or monthly backups.

With automation in place, you just need to develop backup strategies for your data with a consistent scheduling process. The actual job of backing things up will be managed by the system processes you set up for consistency and clarity.

  1. Improved Customer and Employee Satisfaction

Customer disengagement is a more severe problem than you might think. A recent study has shown that it costs U.S. businesses around $300 billion annually, nearly equal to the U.S. defense budget. When the employees are disengaged, they consistently give you their time but do not put the best of their efforts.

With data lineage automation, employers can automate such tasks and free up time for high-value work. According to a smartsheet report, 69% of employees thought that automation would reduce wasting time during their workday and 59% thought that they would have more than six spare hours per week if repetitive jobs were automated.

  1. Governance Enforcement

Data lineage automation is a great way to implement governance in any business. Any task that an automated process completes is always documented and has traceability.

For every task, you get clear logs that tell you what was done, who did it and when it was done. As stated before, automation plays a major role in reducing human errors and speeds up tasks that need to be performed repeatedly.

If you have not made the jump to digital yet, you are probably wading through high volumes of resources and manual processes daily. There is no denying the fact that automating business processes contributes immensely to an organization’s success. 

Automated Data Lineage in Action

Automated data lineage tools document the flow of data into and out of an organization’s systems. They capture end-to-end lineage and ensure proper impact analysis can be performed in the event of problems or changes to data assets as they move across pipelines.

erwin Data Intelligence (erwin DI) helps bind business terms to technical data assets with a complete data lineage of scanned metadata assets. Automating data capture frees up resources to focus on more strategic and useful tasks.

It automatically generates end-to-end data lineage, down to the column level and between repositories. You can view data flows from source systems to the reporting layers, including intermediate transformation and business logic.

Request your own demo of erwin DI to see metadata-driven, automated data lineage in action.

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erwin Expert Blog Data Intelligence

Why You Need End-to-End Data Lineage

Not Documenting End-to-End Data Lineage Is Risky Business – Understanding your data’s origins is key to successful data governance.

Not everyone understands what end-to-end data lineage is or why it is important. In a previous blog, I explained that data lineage is basically the history of data, including a data set’s origin, characteristics, quality and movement over time.

This information is critical to regulatory compliance, change management and data governance not to mention delivering an optimal customer experience. But given the volume, velocity and variety of data (the three Vs of data) we generate today, producing and keeping up with end-to-end data linage is complex and time-consuming.

Yet given this era of digital transformation and fierce competition, understanding what data you have, where it came from, how it’s changed since creation or acquisition, and whether it poses any risks is paramount to optimizing its value. Furthermore, faulty decision-making based on inconsistent analytics and inaccurate reporting can cost millions.

Data Lineage

Data Lineage Tells an Important Origin Story

End-to-end data lineage explains how information flows into, across and outside an organization. And knowing how information was created, its origin and quality may have greater value than a given data set’s current state.

For example, data lineage provides a way to determine which downstream applications and processes are affected by a change in data expectations and helps in planning for application updates.

As I mentioned above, the three Vs of data and the integration of systems makes it difficult to understand the resulting data web much less capture a simple visual of that flow. Yet a consistent view of data and how it flows is paramount to the success of enterprise data governance and any data-driven initiative.

Whether you need to drill down for a granular view of a particular data set or create a high-level summary to describe a particular system and the data it relies on, end-to-end data lineage must be documented and tracked, with an emphasis on the dynamics of data processing and movement as opposed to data structures. Data lineage helps answer questions about the origin of data in key performance indicator (KPI) reports, including:

  • How are the report tables and columns defined in the metadata?
  • Who are the data owners?
  • What are the transformation rules?

Five Consequences of Ignoring Data Lineage

Why do so many organizations struggle with end-to-end data lineage?

The struggle is real for a number of reasons. At the top of the list, organizations are dealing with more data than ever before using systems that weren’t designed to communicate effectively with one another.

Next, their IT and business stakeholders have a difficult time collaborating. And, for a lot of organizations, they’ve relied mostly on manual processes – if data lineage documentation has been attempted at all.

The risks of ignoring end-to-end data lineage are just too great. Let’s look at some of those consequences:

  1. Derailed Projects

Effectively managing business operations is a key factor to success– especially for organizations that are in the midst of digital transformation. Failures in business processes attributed to errors can be a big problem.

For example, in a typical business scenario where an incorrect data set is discovered within a report, the length of time (on average) that it takes a team to find the source of the error can take days or sometimes weeks – derailing the project and costing time and money.

  1. Policy Bloat and Unruly Rules

The business glossary environment must represent the actual environment, e.g., be refreshed and synched, otherwise it becomes obsolete. You need real collaboration.

Data dictionaries, glossaries and policies can’t live in different formats and in different places. It is common for these to be expressed in different ways, depending on the database and underlying storage technology, but this causes policy bloat and rules that no organization, team or employee will understand, let alone realistically manage.

Effective data governance requires that business glossaries, data dictionaries and data privacy policies live in one central location, so they can be easily tracked, monitored and updated over time.

  1. Major Inefficiencies

Successful data migration and upgrades rely on seamless integration of tools and processes with coordinated efforts of people/resources. A passive approach frequently relies on creating new copies of data, usually with sensitive identifiers removed or obscured.

Not only does this passive approach create inefficiencies between determining what data to copy, how to copy it, and where to store the copy, it also creates new volumes of data that become harder to track over time. Yet again, a passive approach to data cannot scale. Direct access to the same live data across the organization is required.

  1. Not Knowing Where Your Data Is

Metadata management and manual mapping are a challenge to most organizations. Data comes in all shapes, sizes and formats, and there is no way to know what type of data a project will need – or even where that data will sit.

Some data might be in the cloud, some on premise, and sometimes projects will require a hybrid approach. All data must be governed, regardless of where it is located.

  1. Privacy and Compliance Challenges

Privacy and compliance personnel know the rules that must be applied to data, but may not necessarily know the technology. Instead, automated data governance requires that anyone, with any level of expertise, can understand what rules (e.g. privacy policies) are applied to enterprise data.

Organizations with established data governance must empower both those with technical skill sets and those with privacy and compliance knowledge, so all teams can play a meaningful role controlling how data is used.

For more information on data lineage, get the free white paper, Tech Brief: Data Lineage.

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erwin Expert Blog Data Governance Data Intelligence

Do I Need a Data Catalog?

If you’re serious about a data-driven strategy, you’re going to need a data catalog.

Organizations need a data catalog because it enables them to create a seamless way for employees to access and consume data and business assets in an organized manner.

Given the value this sort of data-driven insight can provide, the reason organizations need a data catalog should become clearer.

It’s no surprise that most organizations’ data is often fragmented and siloed across numerous sources (e.g., legacy systems, data warehouses, flat files stored on individual desktops and laptops, and modern, cloud-based repositories.)

These fragmented data environments make data governance a challenge since business stakeholders, data analysts and other users are unable to discover data or run queries across an entire data set. This also diminishes the value of data as an asset.

Data catalogs combine physical system catalogs, critical data elements, and key performance measures with clearly defined product and sales goals in certain circumstances.

You also can manage the effectiveness of your business and ensure you understand what critical systems are for business continuity and measuring corporate performance.

The data catalog is a searchable asset that enables all data – including even formerly siloed tribal knowledge – to be cataloged and more quickly exposed to users for analysis.

Organizations with particularly deep data stores might need a data catalog with advanced capabilities, such as automated metadata harvesting to speed up the data preparation process.

For example, before users can effectively and meaningfully engage with robust business intelligence (BI) platforms, they must have a way to ensure that the most relevant, important and valuable data set are included in analysis.

The most optimal and streamlined way to achieve this is by using a data catalog, which can provide a first stop for users ahead of working in BI platforms.

As a collective intelligent asset, a data catalog should include capabilities for collecting and continually enriching or curating the metadata associated with each data asset to make them easier to identify, evaluate and use properly.

Data Catalog Benefits

Three Types of Metadata in a Data Catalog

A data catalog uses metadata, data that describes or summarizes data, to create an informative and searchable inventory of all data assets in an organization.

These assets can include but are not limited to structured data, unstructured data (including documents, web pages, email, social media content, mobile data, images, audio, video and reports) and query results, etc. The metadata provides information about the asset that makes it easier to locate, understand and evaluate.

For example, Amazon handles millions of different products, and yet we, as consumers, can find almost anything about everything very quickly.

Beyond Amazon’s advanced search capabilities, the company also provides detailed information about each product, the seller’s information, shipping times, reviews, and a list of companion products. Sales are measured down to a zip code territory level across product categories.

Another classic example is the online or card catalog at a library. Each card or listing contains information about a book or publication (e.g., title, author, subject, publication date, edition, location) that makes the publication easier for a reader to find and to evaluate.

There are many types of metadata, but a data catalog deals primarily with three: technical metadata, operational or “process” metadata, and business metadata.

Technical Metadata

Technical metadata describes how the data is organized, stored, its transformation and lineage. It is structural and describes data objects such as tables, columns, rows, indexes and connections.

This aspect of the metadata guides data experts on how to work with the data (e.g. for analysis and integration purposes).

Operational Metadata

Operational metadata describes systems that process data, the applications in those systems, and the rules in those applications. This is also called “process” metadata that describes the data asset’s creation, when, how and by whom it has been accessed, used, updated or changed.

Operational metadata provides information about the asset’s history and lineage, which can help an analyst decide if the asset is recent enough for the task at hand, if it comes from a reliable source, if it has been updated by trustworthy individuals, and so on.

As illustrated above, a data catalog is essential to business users because it synthesizes all the details about an organization’s data assets across multiple data sources. It organizes them into a simple, easy- to-digest format and then publishes them to data communities for knowledge-sharing and collaboration.

Business Metadata

Business metadata is sometimes referred to as external metadata attributed to the business aspects of a data asset. It defines the functionality of the data captured, definition of the data, definition of the elements, and definition of how the data is used within the business.

This is the area which binds all users together in terms of consistency and usage of catalogued data asset.

Tools should be provided that enable data experts to explore the data catalogs, curate and enrich the metadata with tags, associations, ratings, annotations, and any other information and context that helps users find data faster and use it with confidence.

Why You Need a Data Catalog – Three Business Benefits of Data Catalogs

When data professionals can help themselves to the data they need—without IT intervention and having to rely on finding experts or colleagues for advice, limiting themselves to only the assets they know about, and having to worry about governance and compliance—the entire organization benefits.

Catalog critical systems and data elements plus enable the calculation and evaluation of key performance measures. It is also important to understand data linage and be able to analyze the impacts to critical systems and essential business processes if a change occurs.

  1. Makes data accessible and usable, reducing operational costs while increasing time to value

Open your organization’s data door, making it easier to access, search and understand information assets. A data catalog is the core of data analysis for decision-making, so automating its curation and access with the associated business context will enable stakeholders to spend more time analyzing it for meaningful insights they can put into action.

Data asset need to be properly scanned, documented, tagged and annotated with their definitions, ownership, lineage and usage. Automating the cataloging of data assets saves initial development time and streamlines its ongoing maintenance and governance.

Automating the curation of data assets also accelerates the time to value for analytics/insights reporting and significantly reduces operational costs.

  1. Ensures regulatory compliance

Regulations like the California Consumer Privacy Act (CCPA ) and the European Union’s General Data Protection Regulation (GDPR) require organizations to know where all their customer, prospect and employee data resides to ensure its security and privacy.

A fine for noncompliance or reputational damage are the last things you need to worry about, so using a data catalog centralizes data management and the associated usage policies and guardrails.

See a Data Catalog in Action

The erwin Data Intelligence Suite (erwin DI) provides data catalog and data literacy capabilities with built-in automation so you can accomplish all the above and much more.

Request your own demo of erwin DI.

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erwin Expert Blog Data Governance

What is Data Lineage? Top 5 Benefits of Data Lineage

What is Data Lineage and Why is it Important?

Data lineage is the journey data takes from its creation through its transformations over time. It describes a certain dataset’s origin, movement, characteristics and quality.

Tracing the source of data is an arduous task.

Many large organizations, in their desire to modernize with technology, have acquired several different systems with various data entry points and transformation rules for data as it moves into and across the organization.

data lineage

These tools range from enterprise service bus (ESB) products, data integration tools; extract, transform and load (ETL) tools, procedural code, application program interfaces (API)s, file transfer protocol (FTP) processes, and even business intelligence (BI) reports that further aggregate and transform data.

With all these diverse data sources, and if systems are integrated, it is difficult to understand the complicated data web they form much less get a simple visual flow. This is why data’s lineage must be tracked and why its role is so vital to business operations, providing the ability to understand where data originates, how it is transformed, and how it moves into, across and outside a given organization.

Data Lineage Use Case: From Tracing COVID-19’s Origins to Data-Driven Business

A lot of theories have emerged about the origin of the coronavirus. A recent University of California San Francisco (UCSF) study conducted a genetic analysis of COVID-19 to determine how the virus was introduced specifically to California’s Bay Area.

It detected at least eight different viral lineages in 29 patients in February and early March, suggesting no regional patient zero but rather multiple independent introductions of the pathogen. The professor who directed the study said, “it’s like sparks entering California from various sources, causing multiple wildfires.”

Much like understanding viral lineage is key to stopping this and other potential pandemics, understanding the origin of data, is key to a successful data-driven business.

Top Five Data Lineage Benefits

From my perspective in working with customers of various sizes across multiple industries, I’d like to highlight five data lineage benefits:

1. Business Impact

Data is crucial to every organization’s survival. For that reason, businesses must think about the flow of data across multiple systems that fuel organizational decision-making.

For example, the marketing department uses demographics and customer behavior to forecast sales. The CEO also makes decisions based on performance and growth statistics. An understanding of the data’s origins and history helps answer questions about the origin of data in a Key Performance Indicator (KPI) reports, including:

  • How the report tables and columns are defined in the metadata?
  • Who are the data owners?
  • What are the transformation rules?

Without data lineage, these functions are irrelevant, so it makes sense for a business to have a clear understanding of where data comes from, who uses it, and how it transforms. Also, when there is a change to the environment, it is valuable to assess the impacts to the enterprise application landscape.

In the event of a change in data expectations, data lineage provides a way to determine which downstream applications and processes are affected by the change and helps in planning for application updates.

2. Compliance & Auditability

Business terms and data policies should be implemented through standardized and documented business rules. Compliance with these business rules can be tracked through data lineage, incorporating auditability and validation controls across data transformations and pipelines to generate alerts when there are non-compliant data instances.

Regulatory compliance places greater transparency demands on firms when it comes to tracing and auditing data. For example, capital markets trading firms must understand their data’s origins and history to support risk management, data governance and reporting for various regulations such as BCBS 239 and MiFID II.

Also, different organizational stakeholders (customers, employees and auditors) need to be able to understand and trust reported data. Data lineage offers proof that the data provided is reflected accurately.

3. Data Governance

An automated data lineage solution stitches together metadata for understanding and validating data usage, as well as mitigating the associated risks.

It can auto-document end-to-end upstream and downstream data lineage, revealing any changes that have been made, by whom and when.

This data ownership, accountability and traceability is foundational to a sound data governance program.

See: The Benefits of Data Governance

4. Collaboration

Analytics and reporting are data-dependent, making collaboration among different business groups and/or departments crucial.

The visualization of data lineage can help business users spot the inherent connections of data flows and thus provide greater transparency and auditability.

Seeing data pipelines and information flows further supports compliance efforts.

5. Data Quality

Data quality is affected by data’s movement, transformation, interpretation and selection through people, process and technology.

Root-cause analysis is the first step in repairing data quality. Once a data steward determines where a data flaw was introduced, the reason for the error can be determined.

With data lineage and mapping, the data steward can trace the information flow backward to examine the standardizations and transformations applied to confirm whether they were performed correctly.

See Data Lineage in Action

Data lineage tools document the flow of data into and out of an organization’s systems. They capture end-to-end lineage and ensure proper impact analysis can be performed in the event of problems or changes to data assets as they move across pipelines.

The erwin Data Intelligence Suite (erwin DI) automatically generates end-to-end data lineage, down to the column level and between repositories. You can view data flows from source systems to the reporting layers, including intermediate transformation and business logic.

Join us for the next live demo of erwin Data Intelligence (DI) to see metadata-driven, automated data lineage in action.

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What is a Data Catalog?

The easiest way to understand a data catalog is to look at how libraries catalog books and manuals in a hierarchical structure, making it easy for anyone to find exactly what they need.

Similarly, a data catalog enables businesses to create a seamless way for employees to access and consume data and business assets in an organized manner.

By combining physical system catalogs, critical data elements, and key performance measures with clearly defined product and sales goals, you can manage the effectiveness of your business and ensure you understand what critical systems are for business continuity and measuring corporate performance.

As illustrated above, a data catalog is essential to business users because it synthesizes all the details about an organization’s data assets across multiple data sources. It organizes them into a simple, easy- to-digest format and then publishes them to data communities for knowledge-sharing and collaboration.

Another foundational purpose of a data catalog is to streamline, organize and process the thousands, if not millions, of an organization’s data assets to help consumers/users search for specific datasets and understand metadata, ownership, data lineage and usage.

Look at Amazon and how it handles millions of different products, and yet we, as consumers, can find almost anything about everything very quickly.

Beyond Amazon’s advanced search capabilities, they also give detailed information about each product, the seller’s information, shipping times, reviews and a list of companion products. The company measure sales down to a zip-code territory level across product categories.

Data Catalog Use Case Example: Crisis Proof Your Business

One of the biggest lessons we’re learning from the global COVID-19 pandemic is the importance of data, specifically using a data catalog to comply, collaborate and innovate to crisis-proof our businesses.

As COVID-19 continues to spread, organizations are evaluating and adjusting their operations in terms of both risk management and business continuity. Data is critical to these decisions, such as how to ramp up and support remote employees, re-engineer processes, change entire business models, and adjust supply chains.

Think about the pandemic itself and the numerous global entities involved in identifying it, tracking its trajectory, and providing guidance to governments, healthcare systems and the general public. One example is the European Union (EU) Open Data Portal, which is used to document, catalog and govern EU data related to the pandemic. This information has helped:

  • Provide daily updates
  • Give guidance to governments, health professionals and the public
  • Support the development and approval of treatments and vaccines
  • Help with crisis coordination, including repatriation and humanitarian aid
  • Put border controls in place
  • Assist with supply chain control and consular coordination

So one of the biggest lessons we’re learning from COVID-19 is the need for data collection, management and governance. What’s the best way to organize data and ensure it is supported by business policies and well-defined, governed systems, data elements and performance measures?

According to Gartner, “organizations that offer a curated catalog of internal and external data to diverse users will realize twice the business value from their data and analytics investments than those that do not.”

Data Catalog Benefits

5 Advantages of Using a Data Catalog for Crisis Preparedness & Business Continuity

The World Bank has been able to provide an array of real-time data, statistical indicators, and other types of data relevant to the coronavirus pandemic through its authoritative data catalogs. The World Bank data catalogs contain datasets, policies, critical data elements and measures useful for analysis and modeling the virus’ trajectory to help organizations measure the impact.

What can your organization learn from this example when it comes to crisis preparedness and business continuity? By developing and maintaining a data catalog as part of a larger data governance program supported by stakeholders across the organization, you can:

  1. Catalog and Share Information Assets

Catalog critical systems and data elements, plus enable the calculation and evaluation of key performance measures. It’s also important to understand data linage and be able to analyze the impacts to critical systems and essential business processes if a change occurs.

  1. Clearly Document Data Policies and Rules

Managing a remote workforce creates new challenges and risks. Do employees have remote access to essential systems? Do they know what the company’s work-from-home policies are? Do employees understand how to handle sensitive data? Are they equipped to maintain data security and privacy? A data catalog with self-service access serves up the correct policies and procedures.

  1. Reduce Operational Costs While Increasing Time to Value

Datasets need to be properly scanned, documented, tagged and annotated with their definitions, ownership, lineage and usage. Automating the cataloging of data assets saves initial development time and streamlines its ongoing maintenance and governance. Automating the curation of data assets also accelerates the time to value for analytics/insights reporting significantly reduce operational costs.

  1. Make Data Accessible & Usable

Open your organization’s data door, making it easier to access, search and understand information assets. A data catalog is the core of data analysis for decision-making, so automating its curation and access with the associated business context will enable stakeholders to spend more time analyzing it for meaningful insights they can put into action.

  1. Ensure Regulatory Compliance

Regulations like the California Consumer Privacy Act (CCPA) and the European Union’s General Data Protection Regulation (GDPR) require organizations to know where all their customer, prospect and employee data resides to ensure its security and privacy.

A fine for noncompliance is the last thing you need on top of everything else your organization is dealing with, so using a data catalog centralizes data management and the associated usage policies and guardrails.

See a Data Catalog in Action

The erwin Data Intelligence Suite (erwin DI) provides data catalog and data literacy capabilities with built-in automation so you can accomplish all of the above and more.

Join us for the next live demo of erwin DI.

Data Intelligence for Data Automation