Tag: impact analysis

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erwin Expert Blog

Business Process Can Make or Break Data Governance

Data governance isn’t a one-off project with a defined endpoint. It’s an on-going initiative that requires active engagement from executives and business leaders.

Data governance, today, comes back to the ability to understand critical enterprise data within a business context, track its physical existence and lineage, and maximize its value while ensuring quality and security.

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Historically, little attention has focused on what can literally make or break any data governance initiative — turning it from a launchpad for competitive advantage to a recipe for disaster. Data governance success hinges on business process modeling and enterprise architecture.

To put it even more bluntly, successful data governance* must start with business process modeling and analysis.

*See: Three Steps to Successful & Sustainable Data Governance Implementation

Business Process Data Governance

Passing the Data Governance Ball

For years, data governance was the volleyball passed back and forth over the net between IT and the business, with neither side truly owning it. However, once an organization understands that IT and the business are both responsible for data, it needs to develop a comprehensive, holistic strategy for data governance that is capable of four things:

  1. Reaching every stakeholder in the process
  2. Providing a platform for understanding and governing trusted data assets
  3. Delivering the greatest benefit from data wherever it lives, while minimizing risk
  4. Helping users understand the impact of changes made to a specific data element across the enterprise.

To accomplish this, a modern data governance strategy needs to be interdisciplinary to break down traditional silos. Enterprise architecture is important because it aligns IT and the business, mapping a company’s applications and the associated technologies and data to the business functions and value streams they enable.

Ovum Market Radar: Enterprise Architecture

The business process and analysis component is vital because it defines how the business operates and ensures employees understand and are accountable for carrying out the processes for which they are responsible. Enterprises can clearly define, map and analyze workflows and build models to drive process improvement, as well as identify business practices susceptible to the greatest security, compliance or other risks and where controls are most needed to mitigate exposures.

Slow Down, Ask Questions

In a rush to implement a data governance methodology and system, organizations can forget that a system must serve a process – and be governed/controlled by one.

To choose the correct system and implement it effectively and efficiently, you must know – in every detail – all the processes it will impact. You need to ask these important questions:

  1. How will it impact them?
  2. Who needs to be involved?
  3. When do they need to be involved?

These questions are the same ones we ask in data governance. They involve impact analysis, ownership and accountability, control and traceability – all of which effectively documented and managed business processes enable.

Data sets are not important in and of themselves. Data sets become important in terms of how they are used, who uses them and what their use is – and all this information is described in the processes that generate, manipulate and use them. So unless we know what those processes are, how can any data governance implementation be complete or successful?

Processes need to be open and shared in a concise, consistent way so all parts of the organization can investigate, ask questions, and then add their feedback and information layers. In other words, processes need to be alive and central to the organization because only then will the use of data and data governance be truly effective.

A Failure to Communicate

Consider this scenario: We’ve perfectly captured our data lineage, so we know what our data sets mean, how they’re connected, and who’s responsible for them – not a simple task but a massive win for any organization. Now a breach occurs. Will any of the above information tell us why it happened? Or where? No! It will tell us what else is affected and who can manage the data layer(s), but unless we find and address the process failure that led to the breach, it is guaranteed to happen again.

By knowing where data is used – the processes that use and manage it – we can quickly, even instantly, identify where a failure occurs. Starting with data lineage (meaning our forensic analysis starts from our data governance system), we can identify the source and destination processes and the associated impacts throughout the organization.

We can know which processes need to change and how. We can anticipate the pending disruptions to our operations and, more to the point, the costs involved in mitigating and/or addressing them.

But knowing all the above requires that our processes – our essential and operational business architecture – be accurately captured and modelled. Instituting data governance without processes is like building a castle on sand.

Rethinking Business Process Modeling and Analysis

Modern organizations need a business process modeling and analysis tool with easy access to all the operational layers across the organization – from high-level business architecture all the way down to data.

Such a system should be flexible, adjustable, easy-to-use and capable of supporting multiple layers simultaneously, allowing users to start in their comfort zones and mature as they work toward their organization’s goals.

The erwin EDGE is one of the most comprehensive software platforms for managing an organization’s data governance and business process initiatives, as well as the whole data architecture. It allows natural, organic growth throughout the organization and the assimilation of data governance and business process management under the same platform provides a unique data governance experience because of its integrated, collaborative approach.

Start your free, cloud-based trial of erwin Business Process and see how some of the world’s largest enterprises have benefited from its centralized repository and integrated, role-based views.

We’d also be happy to show you our data governance software, which includes data cataloging and data literacy capabilities.

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erwin Expert Blog Data Intelligence

The Top 8 Benefits of Data Lineage

It’s important we recognize the benefits of data lineage.

As corporate data governance programs have matured, the inventory of agreed-to data policies has grown rapidly. These include guidelines for data quality assurance, regulatory compliance and data democratization, among other information utilization initiatives.

Organizations that are challenged by translating their defined data policies into implemented processes and procedures are starting to identify tools and technologies that can supplement the ways organizational data policies can be implemented and practiced.

One such technique, data lineage, is gaining prominence as a core operational business component of the data governance technology architecture. Data lineage encompasses processes and technology to provide full-spectrum visibility into the ways that data flow across the enterprise.

To data-driven businesses, the benefits of data lineage are significant. Data lineage tools are used to survey, document and enable data stewards to query and visualize the end-to-end flow of information units from their origination points through the series of transformation and processing stages to their final destination.

Benefits of Data Lineage

The Benefits of Data Lineage

Data stewards are attracted to data lineage because the benefits of data lineage help in a number of different governance practices, including:

1. Operational intelligence

At its core, data lineage captures the mappings of the rapidly growing number of data pipelines in the organization. Visualizing the information flow landscape provides insight into the “demographics” of data consumption and use, answering questions such as “what data sources feed the greatest number of downstream sources” or “which data analysts use data that is ingested from a specific data source.” Collecting this intelligence about the data landscape better positions the data stewards for enforcing governance policies.

2. Business terminology consistency

One of the most confounding data governance challenges is understanding the semantics of business terminology within data management contexts. Because application development was traditionally isolated within each business function, the same (or similar) terms are used in different data models, even though the designers did not take the time to align definitions and meanings. Data lineage allows the data stewards to find common business terms, review their definitions, and determine where there are inconsistencies in the ways the terms are used.

3. Data incident root cause analysis

It has long been asserted that when a data consumer finds a data error, the error most likely was introduced into the environment at an earlier stage of processing. Yet without a “roadmap” that indicates the processing stages through which the data were processed, it is difficult to speculate where the error was actually introduced. Using data lineage, though, a data steward can insert validation probes within the information flow to validate data values and determine the stage in the data pipeline where an error originated.

4. Data quality remediation assessment

Root cause analysis is just the first part of the data quality process. Once the data steward has determined where the data flaw was introduced, the next step is to determine why the error occurred. Again, using a data lineage mapping, the steward can trace backward through the information flow to examine the standardizations and transformations applied to the data, validate that transformations were correctly performed, or identify one (or more) performed incorrectly, resulting in the data flaw.

5. Impact analysis

The enterprise is always subject to changes; externally-imposed requirements (such as regulatory compliance) evolve, internal business directives may affect user expectations, and ingested data source models may change unexpectedly. 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.

6. Performance assessment

Not only does lineage provide a collection of mappings of data pipelines, it allows for the identification of potential performance bottlenecks. Data pipeline stages with many incoming paths are candidate bottlenecks. Using a set of data lineage mappings, the performance analyst can profile execution times across different pipelines and redistribute processing to eliminate bottlenecks.

7. Policy compliance

Data policies can be implemented through the specification of business rules. Compliance with these business rules can be facilitated using data lineage by embedding business rule validation controls across the data pipelines. These controls can generate alerts when there are noncompliant data instances.

8. Auditability of data pipelines

In many cases, regulatory compliance is a combination of enforcing a set of defined data policies along with a capability for demonstrating that the overall process is compliant. Data lineage provides visibility into the data pipelines and information flows that can be audited thereby supporting the compliance process.

Evaluating Enterprise Data Lineage Tools

While data lineage benefits are obvious, large organizations with complex data pipelines and data flows do face challenges in embracing the technology to document the enterprise data pipelines. These include:

  • Surveying the enterprise – Gathering information about the sources, flows and configurations of data pipelines.
  • Maintenance – Configuring a means to maintain an up-to-date view of the data pipelines.
  • Deliverability – Providing a way to give data consumers visibility to the lineage maps.
  • Sustainability – Ensuring sustainability of the processes for producing data lineage mappings.

Producing a collection of up-to-date data lineage mappings that are easily reviewed by different data consumers depends on addressing these challenges. When considering data lineage tools, keep these issues in mind when evaluating how well the tools can meet your data governance needs.

erwin Data Intelligence (erwin DI) helps organizations automate their data lineage initiatives. Learn more about data lineage with erwin DI.

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Constructing a Digital Transformation Strategy: Putting the Data in Digital Transformation

Having a clearly defined digital transformation strategy is an essential best practice for successful digital transformation. But what makes a digital transformation strategy viable?

Part Two of the Digital Transformation Journey …

In our last blog on driving digital transformation, we explored how business architecture and process (BP) modeling are pivotal factors in a viable digital transformation strategy.

EA and BP modeling squeeze risk out of the digital transformation process by helping organizations really understand their businesses as they are today. It gives them the ability to identify what challenges and opportunities exist, and provides a low-cost, low-risk environment to model new options and collaborate with key stakeholders to figure out what needs to change, what shouldn’t change, and what’s the most important changes are.

Once you’ve determined what part(s) of your business you’ll be innovating — the next step in a digital transformation strategy is using data to get there.

Digital Transformation Examples

Constructing a Digital Transformation Strategy: Data Enablement

Many organizations prioritize data collection as part of their digital transformation strategy. However, few organizations truly understand their data or know how to consistently maximize its value.

If your business is like most, you collect and analyze some data from a subset of sources to make product improvements, enhance customer service, reduce expenses and inform other, mostly tactical decisions.

The real question is: are you reaping all the value you can from all your data? Probably not.

Most organizations don’t use all the data they’re flooded with to reach deeper conclusions or make other strategic decisions. They don’t know exactly what data they have or even where some of it is, and they struggle to integrate known data in various formats and from numerous systems—especially if they don’t have a way to automate those processes.

How does your business become more adept at wringing all the value it can from its data?

The reality is there’s not enough time, people and money for true data management using manual processes. Therefore, an automation framework for data management has to be part of the foundations of a digital transformation strategy.

Your organization won’t be able to take complete advantage of analytics tools to become data-driven unless you establish a foundation for agile and complete data management.

You need automated data mapping and cataloging through the integration lifecycle process, inclusive of data at rest and data in motion.

An automated, metadata-driven framework for cataloging data assets and their flows across the business provides an efficient, agile and dynamic way to generate data lineage from operational source systems (databases, data models, file-based systems, unstructured files and more) across the information management architecture; construct business glossaries; assess what data aligns with specific business rules and policies; and inform how that data is transformed, integrated and federated throughout business processes—complete with full documentation.

Without this framework and the ability to automate many of its processes, business transformation will be stymied. Companies, especially large ones with thousands of systems, files and processes, will be particularly challenged by taking a manual approach. Outsourcing these data management efforts to professional services firms only delays schedules and increases costs.

With automation, data quality is systemically assured. The data pipeline is seamlessly governed and operationalized to the benefit of all stakeholders.

Constructing a Digital Transformation Strategy: Smarter Data

Ultimately, data is the foundation of the new digital business model. Companies that have the ability to harness, secure and leverage information effectively may be better equipped than others to promote digital transformation and gain a competitive advantage.

While data collection and storage continues to happen at a dramatic clip, organizations typically analyze and use less than 0.5 percent of the information they take in – that’s a huge loss of potential. Companies have to know what data they have and understand what it means in common, standardized terms so they can act on it to the benefit of the organization.

Unfortunately, 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.

The solution is data intelligence. It improves IT and business data literacy and knowledge, supporting enterprise data governance and business enablement.

It helps solve 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.

Organizations need a real-time, accurate picture of the metadata landscape to:

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

The Right Tools

When it comes to digital transformation (like most things), organizations want to do it right. Do it faster. Do it cheaper. And do it without the risk of breaking everything. To accomplish all of this, you need the right tools.

The erwin Data Intelligence (DI) Suite is the heart of the erwin EDGE platform for creating an “enterprise data governance experience.” erwin DI combines data cataloging and data literacy capabilities to provide greater awareness of and access to available data assets, guidance on how to use them, and guardrails to ensure data policies and best practices are followed.

erwin Data Catalog automates enterprise metadata management, data mapping, reference data management, code generation, data lineage and impact analysis. It efficiently integrates and activates data in a single, unified catalog in accordance with business requirements. With it, you can:

  • Schedule ongoing scans of metadata from the widest array of data sources.
  • Keep metadata current with full versioning and change management.
  • Easily map data elements from source to target, including data in motion, and harmonize data integration across platforms.

erwin Data Literacy provides self-service, role-based, contextual data views. It also provides a business glossary for the collaborative definition of enterprise data in business terms, complete with built-in accountability and workflows. With it, you can:

  • Enable data consumers to define and discover data relevant to their roles.
  • Facilitate the understanding and use of data within a business context.
  • Ensure the organization is fluent in the language of data.

With data governance and intelligence, enterprises can discover, understand, govern and socialize mission-critical information. And because many of the associated processes can be automated, you reduce errors and reliance on technical resources while increasing the speed and quality of your data pipeline to accomplish whatever your strategic objectives are, including digital transformation.

Check out our latest whitepaper, Data Intelligence: Empowering the Citizen Analyst with Democratized Data.

Data Intelligence: Empowering the Citizen Analyst with Democratized Data

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Data Mapping Tools: What Are the Key Differentiators

The need for data mapping tools in light of increasing volumes and varieties of data – as well as the velocity at which it must be processed – is growing.

It’s not difficult to see why either. Data mapping tools have always been a key asset for any organization looking to leverage data for insights.

Isolated units of data are essentially meaningless. By linking data and enabling its categorization in relation to other data units, data mapping provides the context vital for actionable information.

Now with the General Data Protection Regulation (GDPR) in effect, data mapping has become even more significant.

The scale of GDPR’s reach has set a new precedent and is the closest we’ve come to a global standard in terms of data regulations. The repercussions can be huge – just ask Google.

Data mapping tools are paramount in charting a path to compliance for said new, near-global standard and avoiding the hefty fines.

Because of GDPR, organizations that may not have fully leveraged data mapping for proactive data-driven initiatives (e.g., analysis) are now adopting data mapping tools with compliance in mind.

Arguably, GDPR’s implementation can be viewed as an opportunity – a catalyst for digital transformation.

Those organizations investing in data mapping tools with compliance as the main driver will definitely want to consider this opportunity and have it influence their decision as to which data mapping tool to adopt.

With that in mind, it’s important to understand the key differentiators in data mapping tools and the associated benefits.

Data Mapping Tools: erwin Mapping Manager

Data Mapping Tools: Automated or Manual?

In terms of differentiators for data mapping tools, perhaps the most distinct is automated data mapping versus data mapping via manual processes.

Data mapping tools that allow for automation mean organizations can benefit from in-depth, quality-assured data mapping, without the significant allocations of resources typically associated with such projects.

Eighty percent of data scientists’ and other data professionals’ time is spent on manual data maintenance. That’s anything and everything from addressing errors and inconsistencies and trying to understand source data or track its lineage. This doesn’t even account for the time lost due to missed errors that contribute to inherently flawed endeavors.

Automated data mapping tools render such issues and concerns void. In turn, data professionals’ time can be put to much better, proactive use, rather than them being bogged down with reactive, house-keeping tasks.

FOUR INDUSTRY FOCUSSED CASE STUDIES FOR AUTOMATED METADATA-DRIVEN AUTOMATION 
(BFSI, PHARMA, INSURANCE AND NON-PROFIT) 

 

As well as introducing greater efficiency to the data governance process, automated data mapping tools enable data to be auto-documented from XML that builds mappings for the target repository or reporting structure.

Additionally, a tool that leverages and draws from a single metadata repository means that mappings are dynamically linked with underlying metadata to render automated lineage views, including full transformation logic in real time.

Therefore, changes (e.g., in the data catalog) will be reflected across data governance domains (business process, enterprise architecture and data modeling) as and when they’re made – no more juggling and maintaining multiple, out-of-date versions.

It also enables automatic impact analysis at the table and column level – even for business/transformation rules.

For organizations looking to free themselves from the burden of juggling multiple versions, siloed business processes and a disconnect between interdepartmental collaboration, this feature is a key benefit to consider.

Data Mapping Tools: Other Differentiators

In light of the aforementioned changes to data regulations, many organizations will need to consider the extent of a data mapping tool’s data lineage capabilities.

The ability to reverse-engineer and document the business logic from your reporting structures for true source-to-report lineage is key because it makes analysis (and the trust in said analysis) easier. And should a data breach occur, affected data/persons can be more quickly identified in accordance with GDPR.

Article 33 of GDPR requires organizations to notify the appropriate supervisory authority “without undue delay and, where, feasible, not later than 72 hours” after discovering a breach.

As stated above, a data governance platform that draws from a single metadata source is even more advantageous here.

Mappings can be synchronized with metadata so that source or target metadata changes can be automatically pushed into the mappings – so your mappings stay up to date with little or no effort.

The Data Mapping Tool For Data-Driven Businesses

Nobody likes manual documentation. It’s arduous, error-prone and a waste of resources. Quite frankly, it’s dated.

Any organization looking to invest in data mapping, data preparation and/or data cataloging needs to make automation a priority.

With automated data mapping, organizations can achieve “true data intelligence,”. That being the ability to tell the story of how data enters the organization and changes throughout the entire lifecycle to the consumption/reporting layer.  If you’re working harder than your tool, you have the wrong tool.

The manual tools of old do not have auto documentation capabilities, cannot produce outbound code for multiple ETL or script types, and are a liability in terms of accuracy and GDPR.

Automated data mapping is the only path to true GDPR compliance, and erwin Mapping Manager can get you there in a matter of weeks thanks to our robust reverse-engineering technology. 

Learn more about erwin’s automation framework for data governance here.

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Data Governance Stock Check: Using Data Governance to Take Stock of Your Data Assets

For regulatory compliance (e.g., GDPR) and to ensure peak business performance, organizations often bring consultants on board to help take stock of their data assets. This sort of data governance “stock check” is important but can be arduous without the right approach and technology. That’s where data governance comes in …

While most companies hold the lion’s share of operational data within relational databases, it also can live in many other places and various other formats. Therefore, organizations need the ability to manage any data from anywhere, what we call our “any-squared” (Any2) approach to data governance.

Any2 first requires an understanding of the ‘3Vs’ of data – volume, variety and velocity – especially in context of the data lifecycle, as well as knowing how to leverage the key  capabilities of data governance – data cataloging, data literacy, business process, enterprise architecture and data modeling – that enable data to be leveraged at different stages for optimum security, quality and value.

Following are two examples that illustrate the data governance stock check, including the Any2 approach in action, based on real consulting engagements.

Data Governance Stock Check

Data Governance “Stock Check” Case 1: The Data Broker

This client trades in information. Therefore, the organization needed to catalog the data it acquires from suppliers, ensure its quality, classify it, and then sell it to customers. The company wanted to assemble the data in a data warehouse and then provide controlled access to it.

The first step in helping this client involved taking stock of its existing data. We set up a portal so data assets could be registered via a form with basic questions, and then a central team received the registrations, reviewed and prioritized them. Entitlement attributes also were set up to identify and profile high-priority assets.

A number of best practices and technology solutions were used to establish the data required for managing the registration and classification of data feeds:

1. The underlying metadata is harvested followed by an initial quality check. Then the metadata is classified against a semantic model held in a business glossary.

2. After this classification, a second data quality check is performed based on the best-practice rules associated with the semantic model.

3. Profiled assets are loaded into a historical data store within the warehouse, with data governance tools generating its structure and data movement operations for data loading.

4. We developed a change management program to make all staff aware of the information brokerage portal and the importance of using it. It uses a catalog of data assets, all classified against a semantic model with data quality metrics to easily understand where data assets are located within the data warehouse.

5. Adopting this portal, where data is registered and classified against an ontology, enables the client’s customers to shop for data by asset or by meaning (e.g., “what data do you have on X topic?”) and then drill down through the taxonomy or across an ontology. Next, they raise a request to purchase the desired data.

This consulting engagement and technology implementation increased data accessibility and capitalization. Information is registered within a central portal through an approved workflow, and then customers shop for data either from a list of physical assets or by information content, with purchase requests also going through an approval workflow. This, among other safeguards, ensures data quality.

Benefits of Data Governance

Data Governance “Stock Check” Case 2: Tracking Rogue Data

This client has a geographically-dispersed organization that stored many of its key processes in Microsoft Excel TM spreadsheets. They were planning to move to Office 365TM and were concerned about regulatory compliance, including GDPR mandates.

Knowing that electronic documents are heavily used in key business processes and distributed across the organization, this company needed to replace risky manual processes with centralized, automated systems.

A key part of the consulting engagement was to understand what data assets were in circulation and how they were used by the organization. Then process chains could be prioritized to automate and outline specifications for the system to replace them.

This organization also adopted a central portal that allowed employees to register data assets. The associated change management program raised awareness of data governance across the organization and the importance of data registration.

For each asset, information was captured and reviewed as part of a workflow. Prioritized assets were then chosen for profiling, enabling metadata to be reverse-engineered before being classified against the business glossary.

Additionally, assets that were part of a process chain were gathered and modeled with enterprise architecture (EA) and business process (BP) modeling tools for impact analysis.

High-level requirements for new systems then could be defined again in the EA/BP tools and prioritized on a project list. For the others, decisions could be made on whether they could safely be placed in the cloud and whether macros would be required.

In this case, the adoption of purpose-built data governance solutions helped build an understanding of the data assets in play, including information about their usage and content to aid in decision-making.

This client then had a good handle of the “what” and “where” in terms of sensitive data stored in their systems. They also better understood how this sensitive data was being used and by whom, helping reduce regulatory risks like those associated with GDPR.

In both scenarios, we cataloged data assets and mapped them to a business glossary. It acts as a classification scheme to help govern data and located data, making it both more accessible and valuable. This governance framework reduces risk and protects its most valuable or sensitive data assets.

Focused on producing meaningful business outcomes, the erwin EDGE platform was pivotal in achieving these two clients’ data governance goals – including the infrastructure to undertake a data governance stock check. They used it to create an “enterprise data governance experience” not just for cataloging data and other foundational tasks, but also for a competitive “EDGE” in maximizing the value of their data while reducing data-related risks.

To learn more about the erwin EDGE data governance platform and how it aids in undertaking a data governance stock check, register for our free, 30-minute demonstration here.

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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.

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