Tag: automation framework

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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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erwin Expert Blog

What’s Business Process Modeling Got to Do with It? – Choosing A BPM Tool

With business process modeling (BPM) being a key component of data governance, choosing a BPM tool is part of a dilemma many businesses either have or will soon face.

Historically, BPM didn’t necessarily have to be tied to an organization’s data governance initiative.

However, data-driven business and the regulations that oversee it are becoming increasingly extensive, so the need to view data governance as a collective effort – in terms of personnel and the tools that make up the strategy – is becoming harder to ignore.

Data governance also relies on business process modeling and analysis to drive improvement, including identifying business practices susceptible to security, compliance or other risks and adding controls to mitigate exposures.

Choosing a BPM Tool: An Overview

As part of a data governance strategy, a BPM tool aids organizations in visualizing their business processes, system interactions and organizational hierarchies to ensure elements are aligned and core operations are optimized.

The right BPM tool also helps organizations increase productivity, reduce errors and mitigate risks to achieve strategic objectives.

With  insights from the BPM tool, you can clarify roles and responsibilities – which in turn should influence an organization’s policies about data ownership and make data lineage easier to manage.

Organizations also can use a BPM tool to identify the staff who function as “unofficial data repositories.” This has both a primary and secondary function:

1. Organizations can document employee processes to ensure vital information isn’t lost should an employee choose to leave.

2. It is easier to identify areas where expertise may need to be bolstered.

Organizations that adopt a BPM tool also enjoy greater process efficiency. This is through a combination of improving existing processes or designing new process flows, eliminating unnecessary or contradictory steps, and documenting results in a shareable format that is easy to understand so the organization is pulling in one direction.

Choosing a BPM Tool

Silo Buster

Understanding the typical use cases for business process modeling is the first step. As with any tech investment, it’s important to understand how the technology will work in the context of your organization/business.

For example, it’s counter-productive to invest in a solution that reduces informational silos only to introduce a new technological silo through a lack of integration.

Ideally, organizations want a BPM tool that works in conjunction with the wider data management platform and data governance initiative – not one that works against them.

That means it must support data imports and integrations from/with external sources, a solution that enables in-tool collaboration to reduce departmental silos, and most crucial, a solution that taps into a central metadata repository to ensure consistency across the whole data management and governance initiatives.

The lack of a central metadata repository is a far too common thorn in an organization’s side. Without it, they have to juggle multiple versions as changes to the underlying data aren’t automatically updated across the platform.

It also means organizations waste crucial time manually manufacturing and maintaining data quality, when an automation framework could achieve the same goal instantaneously, without human error and with greater consistency.

A central metadata repository ensures an organization can acknowledge and get behind a single source of truth. This has a wealth of favorable consequences including greater cohesion across the organization, better data quality and trust, and faster decision-making with less false starts due to plans based on misleading information.

Three Key Questions to Ask When Choosing a BPM Tool

Organizations in the market for a BPM tool should also consider the following:

1. Configurability: Does the tool support the ability to model and analyze business processes with links to data, applications and other aspects of your organization? And how easy is this to achieve?

2. Role-based views: Can the tool develop integrated business models for a single source of truth but with different views for different stakeholders based on their needs – making regulatory compliance more manageable? Does it enable cross-functional and enterprise collaboration through discussion threads, surveys and other social features?

3. Business and IT infrastructure interoperability: How well does the tool integrate with other key components of data governance including enterprise architecture, data modeling, data cataloging and data literacy? Can it aid in providing data intelligence to connect all the pieces of the data management and governance lifecycles?

For more information and to find out how such a solution can integrate with your organization and current data management and data governance initiatives, click here.

BPM Tool - erwin BP powered by Casewise

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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

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erwin Automation Framework: Achieving Faster Time-to-Value in Data Preparation, Deployment and Governance

Data governance is more important to the enterprise than ever before. It ensures everyone in the organization can discover and analyze high-quality data to quickly deliver business value.

It assists in successfully meeting increasingly strict compliance requirements, such as those in the General Data Protection Regulation (GDPR). And it provides a clear gauge on business performance.

A mature and sustainable data governance initiative must include data integration.

This often requires reconciling two groups of individuals within the organization: 1) those who care about governance and the meaningful use of data and 2) those who care about getting and transforming the data from source to target for actionable insights.

Both ends of the data value chain are covered when governance is coupled programmatically with IT’s integration practices.

The tools and processes for this should automatically generate “pre-ETL” source-to-target mapping to minimize human errors that can occur while manually compiling and interpreting a multitude of Excel-based data mappings that exist across the organization.

In addition to reducing errors and improving data quality, the efficiencies gained through automation, including minimizing rework, can help cut system development lifecycle costs in half.

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.

Data Governance and the System Development Lifecycle

Boosting data governance maturity starts with a central metadata repository (data dictionary) for version-controlling metadata imported from a broad array of file and database types to inform data mappings. It can be used to automatically generate governed design mappings and code in the design phase of the system development lifecycle.

The right toolset – one that supports a unifying and underlying metadata model – will be a design and code-generation platform that introduces efficiency, visibility and governance principles while reducing the opportunity for human error.

Automatically generating ETL/ELT jobs for leading ETL tools based on best design practices accommodates those principles; it functions according to approved corporate and industry standards.

Automatically importing mappings from developers’ Excel sheets, flat files, access and ETL tools into a comprehensive mappings inventory, complete with automatically generated and meaningful documentation of the mappings, is a powerful way to support governance while providing real insight into data movement – for lineage and impact analysis – without interrupting system developers’ normal work methods.

GDPR compliance, for example, requires a business to discover source-to-target mappings with all accompanying transactions, such as what business rules in the repository are applied to it, to comply with audits.

THE REGULATORY RATIONALE FOR INTEGRATING DATA MANAGEMENT & DATA GOVERNANCE

When data movement has been tracked and version-controlled, it’s possible to conduct data archeology – that is, reverse-engineering code from existing XML within the ETL layer – to uncover what has happened in the past and incorporating it into a mapping manager for fast and accurate recovery.

This is one example of how to meet data governance demands with more agility and accuracy at high speed.

Faster Time-to-Value with the erwin Automation Framework

The erwin Automation Framework is a metadata-driven universal code generator that works hand in hand with erwin Mapping Manager (MM) 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

If you’d like to save time and money in preparing, deploying and governing you organization’s data, please join us for a demo of erwin MM.

Automate Data Mapping