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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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Four Use Cases Proving the Benefits of Metadata-Driven Automation

Organization’s cannot hope to make the most out of a data-driven strategy, without at least some degree of metadata-driven automation.

The volume and variety of data has snowballed, and so has its velocity. As such, traditional – and mostly manual – processes associated with data management and data governance have broken down. They are time-consuming and prone to human error, making compliance, innovation and transformation initiatives more complicated, which is less than ideal in the information age.

So it’s safe to say that organizations can’t reap the rewards of their data without automation.

Data scientists and other data professionals can spend up to 80 percent of their time bogged down trying to understand source data or addressing errors and inconsistencies.

That’s time needed and better used for data analysis.

By implementing metadata-driven automation, organizations across industry can unleash the talents of their highly skilled, well paid data pros to focus on finding the goods: actionable insights that will fuel the business.

Metadata-Driven Automation

Metadata-Driven Automation in the BFSI Industry

The banking, financial services and insurance industry typically deals with higher data velocity and tighter regulations than most. This bureaucracy is rife with data management bottlenecks.

These bottlenecks are only made worse when organizations attempt to get by with systems and tools that are not purpose-built.

For example, manually managing data mappings for the enterprise data warehouse via MS Excel spreadsheets had become cumbersome and unsustainable for one BSFI company.

After embracing metadata-driven automation and custom code automation templates, it saved hundreds of thousands of dollars in code generation and development costs and achieved more work in less time with fewer resources. ROI on the automation solutions was realized within the first year.

Metadata-Driven Automation in the Pharmaceutical Industry

Despite its shortcomings, the Excel spreadsheet method for managing data mappings is common within many industries.

But with the amount of data organizations need to process in today’s business climate, this manual approach makes change management and determining end-to-end lineage a significant and time-consuming challenge.

One global pharmaceutical giant headquartered in the United States experienced such issues until it adopted metadata-driven automation. Then the pharma company was able to scan in all source and target system metadata and maintain it within a single repository. Users now view end-to-end data lineage from the source layer to the reporting layer within seconds.

On the whole, the implementation resulted in extraordinary time savings and a total cost reduction of 60 percent.

Metadata-Driven Automation in the Insurance Industry

Insurance is another industry that has to cope with high data velocity and stringent data regulations. Plus many organizations in this sector find that they’ve outgrown their systems.

For example, an insurance company using a CDMA product to centralize data mappings is probably missing certain critical features, such as versioning, impact analysis and lineage, which adds to costs, times to market and errors.

By adopting metadata-driven automation, organizations can standardize the pre-ETL data mapping process and better manage data integration through the change and release process. As a result, both internal data mapping and cross functional teams now have easy and fast web-based access to data mappings and valuable information like impact analysis and lineage.

Here is the story of a business that adopted such an approach and achieved operational excellence and a delivery time reduction by 80 percent, as well as achieving ROI within 12 months.

Metadata-Driven Automation for a Non-Profit

Another common issue cited by organizations using manual data mapping is ballooning complexity and subsequent confusion.

Any organization expanding its data-driven focus without sufficiently maturing data management initiative(s) will experience this at some point.

One of the world’s largest humanitarian organizations, with millions of members and volunteers operating all over the world, was confronted with this exact issue.

It recognized the need for a solution to standardize the pre-ETL data mapping process to make data integration more efficient and cost-effective.

With metadata-driven automation, the organization would be able to scan and store metadata and data dictionaries in a central repository, as well as manage the business definitions and data dictionary for legacy systems contributing data to the enterprise data warehouse.

By adopting such an approach, the organization realized time savings across all IT development and cross-functional testing teams. Additionally, they were able to more easily manage mappings, code sets, reference data and data validation rules.

Again, ROI was achieved within a year.

A Universal Solution for Metadata-Driven Automation

Metadata-driven automation is a capability any organization can benefit from – regardless of industry, as demonstrated by the various real-world use cases chronicled here.

The erwin Automation Framework is a key component of the erwin EDGE platform for comprehensive data management and data governance.

With it, data professionals realize these industry-agnostic benefits:

  • Centralized and standardized code management with all automation templates stored in a governed repository
  • Better quality code and minimized rework
  • Business-driven data movement and transformation specifications
  • Superior data movement job designs based on best practices
  • Greater agility and faster time-to-value in data preparation, deployment and governance
  • Cross-platform support of scripting languages and data movement technologies

Learn more about metadata-driven automation as it relates to data preparation and enterprise data mapping.

Join one our weekly erwin Mapping Manager demos.

Automate Data Mapping

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Five Benefits of an Automation Framework for Data Governance

Organizations are responsible for governing more data than ever before, making a strong automation framework a necessity. But what exactly is an automation framework and why does it matter?

In most companies, an incredible amount of data flows from multiple sources in a variety of formats and is constantly being moved and federated across a changing system landscape.

Often these enterprises are heavily regulated, so they need a well-defined data integration model that helps avoid data discrepancies and removes barriers to enterprise business intelligence and other meaningful use.

IT teams need the ability to smoothly generate hundreds of mappings and ETL jobs. They need their data mappings to fall under governance and audit controls, with instant access to dynamic impact analysis and lineage.

With an automation framework, data professionals can meet these needs at a fraction of the cost of the traditional manual way.

In data governance terms, an automation framework refers to a metadata-driven universal code generator that works hand in hand with enterprise data mapping for:

  • Pre-ETL enterprise data mapping
  • Governing metadata
  • Governing and versioning source-to-target mappings throughout the lifecycle
  • Data lineage, impact analysis and business rules repositories
  • Automated code generation

Such automation enables organizations to bypass bottlenecks, including human error and the time required to complete these tasks manually.

In fact, being able to rely on automated and repeatable processes can result in up to 50 percent in design savings, up to 70 percent conversion savings and up to 70 percent acceleration in total project delivery.

So without further ado, here are the five key benefits of an automation framework for data governance.

Automation Framework

Benefits of an Automation Framework for Data Governance

  1. Creates simplicity, reliability, consistency and customization for the integrated development environment.

Code automation templates (CATs) can be created – for virtually any process and any tech platform – using the SDK scripting language or the solution’s published libraries to completely automate common, manual data integration tasks.

CATs are designed and developed by senior automation experts to ensure they are compliant with industry or corporate standards as well as with an organization’s best practice and design standards.

The 100-percent metadata-driven approach is critical to creating reliable and consistent CATs.

It is possible to scan, pull in and configure metadata sources and targets using standard or custom adapters and connectors for databases, ERP, cloud environments, files, data modeling, BI reports and Big Data to document data catalogs, data mappings, ETL (XML code) and even SQL procedures of any type.

  1. Provides blueprints anyone in the organization can use.

Stage DDL from source metadata for the target DBMS; profile and test SQL for test automation of data integration projects; generate source-to-target mappings and ETL jobs for leading ETL tools, among other capabilities.

It also can populate and maintain Big Data sets by generating PIG, Scoop, MapReduce, Spark, Python scripts and more.

  1. Incorporates data governance into the system development process.

An organization can achieve a more comprehensive and sustainable data governance initiative than it ever could with a homegrown solution.

An automation framework’s ability to automatically create, version, manage and document source-to-target mappings greatly matters both to data governance maturity and a shorter-time-to-value.

This eliminates duplication that occurs when project teams are siloed, as well as prevents the loss of knowledge capital due to employee attrition.

Another value capability is coordination between data governance and SDLC, including automated metadata harvesting and cataloging from a wide array of sources for real-time metadata synchronization with core data governance capabilities and artifacts.

  1. Proves the value of data lineage and impact analysis for governance and risk assessment.

Automated reverse-engineering of ETL code into natural language enables a more intuitive lineage view for data governance.

With end-to-end lineage, it is possible to view data movement from source to stage, stage to EDW, and on to a federation of marts and reporting structures, providing a comprehensive and detailed view of data in motion.

The process includes leveraging existing mapping documentation and auto-documented mappings to quickly render graphical source-to-target lineage views including transformation logic that can be shared across the enterprise.

Similarly, impact analysis – which involves data mapping and lineage across tables, columns, systems, business rules, projects, mappings and ETL processes – provides insight into potential data risks and enables fast and thorough remediation when needed.

Impact analysis across the organization while meeting regulatory compliance with industry regulators requires detailed data mapping and lineage.

THE REGULATORY RATIONALE FOR INTEGRATING DATA MANAGEMENT & DATA GOVERNANCE

  1. Supports a wide spectrum of business needs.

Intelligent automation delivers enhanced capability, increased efficiency and effective collaboration to every stakeholder in the data value chain: data stewards, architects, scientists, analysts; business intelligence developers, IT professionals and business consumers.

It makes it easier for them to handle jobs such as data warehousing by leveraging source-to-target mapping and ETL code generation and job standardization.

It’s easier to map, move and test data for regular maintenance of existing structures, movement from legacy systems to new systems during a merger or acquisition, or a modernization effort.

erwin’s Approach to Automation for Data Governance: The erwin Automation Framework

Mature and sustainable data governance requires collaboration from both IT and the business, backed by a technology platform that accelerates the time to data intelligence.

Part of the erwin EDGE portfolio for an “enterprise data governance experience,” the erwin Automation Framework transforms enterprise data into accurate and actionable insights by connecting all the pieces of the data management and data governance lifecycle.

 As with all erwin solutions, it embraces any data from anywhere (Any2) with automation for relational, unstructured, on-premise and cloud-based data assets and data movement specifications harvested and coupled with CATs.

If your organization would like to realize all the benefits explained above – and gain an “edge” in how it approaches data governance, you can start by joining one of our weekly demos for erwin Mapping Manager.

Automate Data Mapping