Tag: data literacy

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erwin Expert Blog Metadata Management

7 Benefits of Metadata Management

Metadata management is key to wringing all the value possible from data assets.

However, most organizations don’t use all the data at their disposal to reach deeper conclusions about how to drive revenue, achieve regulatory compliance or accomplish other strategic objectives.

What Is Metadata?

Analyst firm Gartner defines metadata as “information that describes various facets of an information asset to improve its usability throughout its life cycle. It is metadata that turns information into an asset.”

Quite simply, metadata is data about data. It’s generated every time data is captured at a source, accessed by users, moved through an organization, integrated or augmented with other data from other sources, profiled, cleansed and analyzed.

It’s valuable because it provides information about the attributes of data elements that can be used to guide strategic and operational decision-making. Metadata management is the administration of data that describes other data, with an emphasis on associations and lineage. It involves establishing policies and processes to ensure information can be integrated, accessed, shared, linked, analyzed and maintained across an organization.

Metadata Answers Key Questions

A strong data management strategy and supporting technology enables the data quality the business requires, including data cataloging (integration of data sets from various sources), mapping, versioning, business rules and glossaries maintenance and metadata management (associations and lineage).

Metadata answers a lot of important questions:

  • What data do we have?
  • Where did it come from?
  • Where is it now?
  • How has it changed since it was originally created or captured?
  • Who is authorized to use it and how?
  • Is it sensitive or are there any risks associated with it?

Metadata also helps your organization to:

  • Discover data. Identify and interrogate metadata from various data management silos.
  • Harvest data. Automate the collection of metadata 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.

Metadata management

The Benefits of Metadata Management

1. Better data quality. With automation, data quality is systemically assured with the data pipeline seamlessly governed and operationalized to the benefit of all stakeholders. Data issues and inconsistencies within integrated data sources or targets are identified in real time to improve overall data quality by increasing time to insights and/or repair. 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.

2. Quicker project delivery. Automated enterprise metadata management provides greater accuracy and up to 70 percent acceleration in project delivery for data movement and/or deployment projects. It harvests metadata from various data sources and maps any data element from source to target and harmonize data integration across platforms. With this accurate picture of your metadata landscape, you can accelerate Big Data deployments, Data Vaults, data warehouse modernization, cloud migration, etc.

3. Faster speed to insights. High-paid knowledge workers like data scientists spend up to 80 percent of their time finding and understanding source data and resolving errors or inconsistencies, rather than analyzing it for real value. That equation can be reversed with stronger data operations and analytics leading to insights more quickly, with access/connectivity to underlying metadata and its lineage. Technical resources are free to concentrate on the highest-value projects, while business analysts, data architects, ETL developers, testers and project managers can collaborate more easily for faster decision-making.

4. Greater productivity & reduced costs. Being able to rely on automated and repeatable metadata management processes results in greater productivity. For example, one erwin DI customer has experienced a steep improvement in productivity – more than 85 percent – because manually intensive and complex coding efforts have been automated and 70+ percent because of seamless access to and visibility of all metadata, including end-to-end lineage. Significant data design and conversion savings, up to 50 percent and 70 percent respectively, also are possible with data mapping costs going down as much as 80 percent.

5. Regulatory compliance. Regulations such as the General Data Protection Regulation (GDPR), Health Insurance and Portability Accountability Act (HIPAA), Basel Committee on Banking Supervision (BCBS) and The California Consumer Privacy Act (CCPA) particularly affect sectors such as finance, retail, healthcare and pharmaceutical/life sciences. When key data isn’t discovered, harvested, cataloged, defined and standardized as part of integration processes, audits may be flawed. Sensitive data is automatically tagged, its lineage automatically documented, and its flows depicted so that it is easily found and its use across workflows easily traced.

6. Digital transformation. Knowing what data exists and its value potential promotes digital transformation by 1) improving digital experiences because you understand how the organization interacts with and supports customers, 2) enhancing digital operations because data preparation and analysis projects happen faster, 3) driving digital innovation because data can be used to deliver new products and services, and 4) building digital ecosystems because organizations need to establish platforms and partnerships to scale and grow.

7. An enterprise data governance experience. Stakeholders include both IT and business users in collaborative relationships, so that makes data governance everyone’s business. Modern, strategic data governance must be an ongoing initiative, and it requires everyone from executives on down to rethink their data duties and assume new levels of cooperation and accountability. With business data stakeholders driving alignment between data governance and strategic enterprise goals and IT handling the technical mechanics of data management, the door opens to finding, trusting and using data to effectively meet any organizational objective.

An Automated Solution

When approached manually, metadata management is expensive, time-consuming, error-prone and can’t keep pace with a dynamic enterprise data management infrastructure.

And while integrating and automating data management and data governance is still a new concept for many organizations, its advantages are clear.

erwin’s metadata management offering, the erwin Data Intelligence Suite (erwin DI), includes data catalog, data literacy and automation capabilities for greater awareness of and access to data assets, guidance on their use, and guardrails to ensure data policies and best practices are followed. Its automated, metadata-driven framework gives organizations visibility and control over their disparate data streams – from harvesting to aggregation and integration, including transformation with complete upstream and downstream lineage and all the associated documentation.

erwin has been named a leader in the Gartner 2020 “Magic Quadrant for Metadata Management Solutions” for two consecutive years. Click here to download the full Gartner 2020 “Magic Quadrant for Metadata Management Solutions” report.

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erwin Expert Blog Data Intelligence

What Is Data Literacy?

Today, data literacy is more important than ever.

Data is now being used to support business decisions few executives thought they’d be making even six months ago.

With your employees connected and armed with data that paints a clear picture of the business, your organization is better prepared to turn its attention to whatever your strategic priority may be – i.e. digital transformation, customer experience, or withstanding this current (or future) crisis.

So, what is data literacy?

Data Literacy

Data Literacy Definition

Gartner defines data literacy as the ability to read, write and communicate data in context, including an understanding of data sources and constructs, analytical methods and techniques applied — and the ability to describe the use case, application and resulting value.

Organizations use data literacy tools to improve data literacy across the organization. A good data literacy tool will include functionality such as business glossary management and self-service data discovery. The end result is an organization that’s more data fluent and efficient in how they store, discover and use their data.

What Is Data Literacy For?

For years, we’ve been saying that “we’re all data people.” When all stakeholders in an organization can effectively “speak data” they can:

  • Better understand and identify the data they require
  • Be more self-sufficient in accessing and preparing the data
  • Better articulate the gaps that exist in the data landscape
  • Share their knowledge and experience with data with other consumers to contribute to the greater good
  • Collaborate more effectively with their partners in data (management and governance) for greater efficiency and higher quality outcomes

Why is Data Literacy Important?

Without good data, it’s difficult to make good decisions.

Data access, literacy and knowledge leads to sound decision-making and that’s key to data governance and any other data-driven effort.

Data literacy enables collaboration and innovation. To determine if your organization is data literate you need to ask two questions:  

  1. Can your employees use data to effectively communicate with each other?
  2. Can you develop and circulate ideas that will help the business move forward?

data literacy and data intelligence

The Data Literacy and Data Intelligence Connection

Businesses that invest in data intelligence and data literacy are better positioned to weather any storm and chart a path forward because they have accurate, trusted data at their disposal.

erwin helps customers turn their data from a burden into a benefit by fueling an accurate, real-time, high-quality data pipeline they can mine for insights that lead to smart decisions for operational excellence.

erwin Data Intelligence (erwin DI) combines data catalog and data literacy capabilities for greater awareness of and access to available data assets, guidance on their use, and guardrails to ensure data policies and best practices are followed.

erwin Data Literacy (DL) is founded on enriched business glossaries and socializing data so all stakeholders can view and understand it within the context of their roles.

It allows both IT and business users to discover the data available to them and understand what it means in common, standardized terms, and automates common data curation processes, such as name matching, categorization and association, to optimize governance of the data pipeline including preparation processes.

erwin DL provides self-service, role-based, contextual data views. It also provides a business glossary for the collaborative definition of enterprise data in business terms.

It also includes built-in accountability and workflows to enable data consumers to define and discover data relevant to their roles, facilitate the understanding and use of data within a business context, and ensure the organization is data literate.

With erwin DL, your organization can build glossaries of terms in taxonomies with descriptions, synonyms, acronyms and their associations to data policies, rules and other critical governance artifacts. Other advantages are:

  • Data Visibility & Governance: Visualize and navigate any data from anywhere within a business-centric data asset framework that provides organizational alignment and robust, sustainable data governance.
  • Data Context & Enrichment: Put data in business context and enable stakeholders to share best practices and build communities by tagging/commenting on data assets, enriching the metadata.
  • Enterprise Collaboration & Empowerment: Break down IT and business silos to provide broad access to approved organizational information.
  • Greater Productivity: Reduce the time it takes to find data assets and therefore reliance on technical resources, plus streamline workflows for faster analysis and decision-making.
  • Accountability & Regulatory Peace of Mind: Create an integrated ecosystem of people, processes and technology to manage and protect data, mitigating a wide range of data-related risks and improving compliance.
  • Effective Change Management: Better manage change with the ability to identify data linkages, implications and impacts across the enterprise.
  • Data Literacy, Fluency & Knowledge: Enhance stakeholder discovery and understanding of and trust in data assets to underpin analysis leading to actionable insights.

Learn more about the importance of data literacy by requesting a free demo of erwin Data Intelligence.

erwin Data Intelligence

 

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Talk Data to Me: Why Employee Data Literacy Matters  

Organizations are flooded with data, so they’re scrambling to find ways to derive meaningful insights from it – and then act on them to improve the bottom line.

In today’s data-driven business, enabling employees to access and understand the data that’s relevant to their roles allows them to use data and put those insights into action. To do this, employees need to “talk data,” aka data literacy.

However, Gartner predicts that this year 50 percent of organizations will lack sufficient AI and data literacy skills to achieve business value. This requires organizations to invest in ensuring their employees are data literate.

Data Literacy & the Rise of the Citizen Analyst

According to Gartner, “data literacy is the ability to read, write and communicate data in context, including an understanding of data sources and constructs, analytical methods and techniques applied — and the ability to describe the use case, application and resulting value.”

Today, your employees are essentially data consumers. There are three technological advances driving this data consumption and, in turn, the ability for employees to leverage this data to deliver business value 1) exploding data production 2) scalable big data computation, and 3) the accessibility of advanced analytics, machine learning (ML) and artificial intelligence (AI).

The confluence of this data explosion has created a fertile environment for data innovation and transformation. As a result, we’re seeing the rise of the “citizen analyst,” who brings business knowledge and subject-matter expertise to data-driven insights.

Some examples of citizen analysts include the VP of finance who may be looking for opportunities to optimize the top- and bottom-line results for growth and profitability. Or the product line manager who wants to understand enterprise impact of pricing changes.

David Loshin explores this concept in an erwin-sponsored whitepaper, Data Intelligence: Empowering the Citizen Analyst with Democratized Data.

In the whitepaper he states, the priority of the citizen analyst is straightforward: find the right data to develop reports and analyses that support a larger business case. However, some practical data management issues contribute to a growing need for enterprise data governance, including:

  • Increasing data volumes that challenge the traditional enterprise’s ability to store, manage and ultimately find data
  • Increased data variety, balancing structured, semi-structured and unstructured data, as well as data originating from a widening array of external sources
  • Reducing the IT bottleneck that creates barriers to data accessibility
  • Desire for self-service to free the data consumers from strict predefined data transformations and organizations
  • Hybrid on-premises/cloud environments that complicate data integration and preparation
  • Privacy and data protection laws from many countries that influence the ways data assets may be accessed and used

Data Democratization Requires Data Intelligence

According to Loshin, organizations need to empower their citizen analysts. A fundamental component of data literacy involves data democratization, sharing data assets with a broad set of data consumer communities in a governed way.

  • The objectives of governed data democratization include:
  • Raising data awareness
  • Improving data literacy
  • Supporting observance of data policies to support regulatory compliance
  • Simplifying data accessibility and use

Effective data democratization requires data intelligence. This is dependent on accumulating, documenting and publishing information about the data assets used across the entire enterprise data landscape.

Here are the steps to effective data intelligence:

  • Reconnaissance: Understanding the data environment and the corresponding business contexts and collecting as much information as possible
  • Surveillance: Monitoring the environment for changes to data sources
  • Logistics and Planning: Mapping the collected information production flows and mapping how data moves across the enterprise
  • Impact Assessment: Using what you have learned to assess how external changes impact the environment
  • Synthesis: Empowering data consumers by providing a holistic perspective associated with specific business terms
  • Sustainability: Embracing automation to always provide up-to-date and correct intelligence
  • Auditability: Providing oversight and being able to explain what you have learned and why

Data Literacy: The Heart of Data-Driven Innovation

Data literacy is at the heart of successful data-driven innovation and accelerating the realization of actionable data-driven insights.

It can reduce data source discovery and analyses cycles, improve accuracy in results, reduce the reliance expensive technical resources, assure the “right” data is used the first time reducing deployed errors and the need for expensive re-work.

Ultimately, a successful data literacy program will empower your employees to:

  • Better understand and identify the data they require
  • Be more self-sufficient in accessing and preparing the data they require
  • Better articulate the gaps that exist in the data landscape when it comes to fulfilling their data needs
  • Share their knowledge and experience with data with other consumers to contribute to the greater good
  • Collaborate more effectively with their partners in data (management and governance) for greater efficiency and higher quality outcomes

erwin offers a data intelligence software suite combining the capabilities of erwin Data Catalog with erwin Data Literacy to fuel an automated, real-time, high-quality data pipeline.

Then all enterprise stakeholders – data scientists, data stewards, ETL developers, enterprise architects, business analysts, compliance officers, citizen analysts, CDOs and CEOs – can access data relevant to their roles for insights they can put into action.

Click here to request a demo of erwin Data Intelligence.

erwin Data Intelligence

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Automation Gives DevOps More Horsepower

Almost 70 percent of CEOs say they expect their companies to change their business models in the next three years, and 62 percent report they have management initiatives or transformation programs underway to make their businesses more digital, according to Gartner.

Wouldn’t it be advantageous for these organizations to accelerate these digital transformation efforts? They have that option with automation, shifting DevOps away from dependence on manual processes. Just like with cars, more horsepower in DevOps translates to greater speed.

DevOps Automation

Doing More with Less

We have clients looking to do more with existing resources, and others looking to reduce full-time employee count on their DevOps teams. With metadata-driven automation, many DevOps processes can be automated, adding more “horsepower” to increase their speed and accuracy. For example:

Auto-documentation of data mappings and lineage: By using data harvesting templates, organizations can eliminate time spent updating and maintaining data mappings, creating them directly from code written by the ETL staff. Such automation can save close to 100 percent of the time usually spent on this type of documentation.

  • Data lineage and impact analysis views for ‘data in motion’ also stay up to date with no additional effort.
  • Human errors are eliminated, leading to higher quality documentation and output.

Automatic updates/changes reflected throughout each release cycle: Updates can be picked up and the ETL job/package generated with 100-percent accuracy. An ETL developer is not required to ‘hand code’ mappings from a spreadsheet – greatly reducing the time spent on the ETL process, and perhaps the total number of resources required to manage that process month over month.

  • ETL skills are still necessary for validation and to compile and execute the automated jobs, but the overall quality of these jobs (machine-generated code) will be much higher, also eliminating churn and rework.

Auto-scanning of source and target data assets with synchronized mappings: This automation eliminates the need for a resource or several resources dealing with manual updates to the design mappings, creating additional time savings and cost reductions associated with data preparation.

  • A change in the source-column header may impact 1,500 design mappings. Managed manually, this process – opening the mapping document, making the change, saving the file with a new version, and placing it into a shared folder for development – could take an analyst several days. But synchronization instantly updates the mappings, correctly versioned, and can be picked up and packaged into an ETL job/package within the same hour. Whether using agile or classic waterfall development, these processes will see exponential improvement and time reduction. 

Data Intelligence: Speed and Quality Without Compromise

Our clients often understand that incredible DevOps improvements are possible, but they fear the “work” it will take to get there.

It really comes down to deciding to embrace change a la automation or continue down the same path. But isn’t the definition of insanity doing the same thing over and over, expecting but never realizing different results?

With traditional means, you may improve speed but sacrifice quality. On the flipside, you may improve quality but sacrifice speed.

However, erwin’s technology shifts this paradigm. You can have both speed and quality.

The erwin Data Intelligence Suite (erwin DI) combines the capabilities of erwin Data Catalog with erwin Data Literacy to fuel an automated, real-time, high-quality data pipeline.

Then all enterprise stakeholders – data scientists, data stewards, ETL developers, enterprise architects, business analysts, compliance officers, CDOs and CEOs – can access data relevant to their roles for insights they can put into action.

It creates the fastest path to value, with an automation framework and metadata connectors configured by our team to deliver the data harvesting and preparation features that make capturing enterprise data assets fast and accurate.

Click here to request a free demo of erwin DI.

erwin Data Intelligence

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Very Meta … Unlocking Data’s Potential with Metadata Management Solutions

Untapped data, if mined, represents tremendous potential for your organization. While there has been a lot of talk about big data over the years, the real hero in unlocking the value of enterprise data is metadata, or the data about the data.

However, most organizations don’t use all the data they’re flooded with to reach deeper conclusions about how to drive revenue, achieve regulatory compliance or make other strategic decisions. They don’t know exactly what data they have or even where some of it is.

Quite honestly, knowing what data you have and where it lives is complicated. And to truly understand it, you need to be able to create and sustain an enterprise-wide view of and easy access to underlying metadata.

This isn’t an easy task. Organizations are dealing with numerous data types and data sources that were never designed to work together and data infrastructures that have been cobbled together over time with disparate technologies, poor documentation and with little thought for downstream integration.

As a result, the applications and initiatives that depend on a solid data infrastructure may be compromised, leading to faulty analysis and insights.

Metadata Is the Heart of Data Intelligence

A recent IDC Innovators: Data Intelligence Report says that getting answers to such questions as “where is my data, where has it been, and who has access to it” requires harnessing the power of metadata.

Metadata is generated every time data is captured at a source, accessed by users, moves through an organization, and then is profiled, cleansed, aggregated, augmented and used for analytics to guide operational or strategic decision-making.

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.

To flip this 80/20 rule, they need an automated metadata management solution for:

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

Addressing the Complexities of Metadata Management

The complexities of metadata management can be addressed with a strong data management strategy coupled with metadata management software to enable the data quality the business requires.

This encompasses data cataloging (integration of data sets from various sources), mapping, versioning, business rules and glossary maintenance, and metadata management (associations and lineage).

erwin has developed the only data intelligence platform that provides organizations with a complete and contextual depiction of the entire metadata landscape.

It is the only solution that can automatically harvest, transform and feed metadata from operational processes, business applications and data models into a central data catalog and then made accessible and understandable within the context of role-based views.

erwin’s ability to integrate and continuously refresh metadata from an organization’s entire data ecosystem, including business processes, enterprise architecture and data architecture, forms the foundation for enterprise-wide data discovery, literacy, governance and strategic usage.

Organizations then can take a data-driven approach to business transformation, speed to insights, and risk management.
With erwin, organizations can:

1. Deliver a trusted metadata foundation through automated metadata harvesting and cataloging
2. Standardize data management processes through a metadata-driven approach
3. Centralize data-driven projects around centralized metadata for planning and visibility
4. Accelerate data preparation and delivery through metadata-driven automation
5. Master data management platforms through metadata abstraction
6. Accelerate data literacy through contextual metadata enrichment and integration
7. Leverage a metadata repository to derive lineage, impact analysis and enable audit/oversight ability

With erwin Data Intelligence as part of the erwin EDGE platform, you know what data you have, where it is, where it’s been and how it transformed along the way, plus you can understand sensitivities and risks.

With an automated, real-time, high-quality data pipeline, enterprise stakeholders can base strategic decisions on a full inventory of reliable information.

Many of our customers are hard at work addressing metadata management challenges, and that’s why erwin was Named a Leader in Gartner’s “2019 Magic Quadrant for Metadata Management Solutions.”

Gartner Magic Quadrant Metadata Management

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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.

Free Data Modeling Best Practice Guide

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.

Enterprise Architecture Business Process Trial

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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

Using Strategic Data Governance to Manage GDPR/CCPA Complexity

In light of recent, high-profile data breaches, it’s past-time we re-examined strategic data governance and its role in managing regulatory requirements.

News broke earlier this week of British Airways being fined 183 million pounds – or $228 million – by the U.K. for alleged violations of the European Union’s General Data Protection Regulation (GDPR). While not the first, it is the largest penalty levied since the GDPR went into effect in May 2018.

Given this, Oppenheimer & Co. cautions:

“European regulators could accelerate the crackdown on GDPR violators, which in turn could accelerate demand for GDPR readiness. Although the CCPA [California Consumer Privacy Act, the U.S. equivalent of GDPR] will not become effective until 2020, we believe that new developments in GDPR enforcement may influence the regulatory framework of the still fluid CCPA.”

With all the advance notice and significant chatter for GDPR/CCPA,  why aren’t organizations more prepared to deal with data regulations?

In a word? Complexity.

The complexity of regulatory requirements in and of themselves is aggravated by the complexity of the business and data landscapes within most enterprises.

So it’s important to understand how to use strategic data governance to manage the complexity of regulatory compliance and other business objectives …

Designing and Operationalizing Regulatory Compliance Strategy

It’s not easy to design and deploy compliance in an environment that’s not well understood and difficult in which to maneuver. First you need to analyze and design your compliance strategy and tactics, and then you need to operationalize them.

Modern, strategic data governance, which involves both IT and the business, enables organizations to plan and document how they will discover and understand their data within context, track its physical existence and lineage, and maximize its security, quality and value. It also helps enterprises put these strategic capabilities into action by:

  • Understanding their business, technology and data architectures and their inter-relationships, aligning them with their goals and defining the people, processes and technologies required to achieve compliance.
  • Creating and automating a curated enterprise data catalog, complete with physical assets, data models, data movement, data quality and on-demand lineage.
  • Activating their metadata to drive agile data preparation and governance through integrated data glossaries and dictionaries that associate policies to enable stakeholder data literacy.

Strategic Data Governance for GDPR/CCPA

Five Steps to GDPR/CCPA Compliance

With the right technology, GDPR/CCPA compliance can be automated and accelerated in these five steps:

  1. Catalog systems

Harvest, enrich/transform and catalog data from a wide array of sources to enable any stakeholder to see the interrelationships of data assets across the organization.

  1. Govern PII “at rest”

Classify, flag and socialize the use and governance of personally identifiable information regardless of where it is stored.

  1. Govern PII “in motion”

Scan, catalog and map personally identifiable information to understand how it moves inside and outside the organization and how it changes along the way.

  1. Manage policies and rules

Govern business terminology in addition to data policies and rules, depicting relationships to physical data catalogs and the applications that use them with lineage and impact analysis views.

  1. Strengthen data security

Identify regulatory risks and guide the fortification of network and encryption security standards and policies by understanding where all personally identifiable information is stored, processed and used.

How erwin Can Help

erwin is the only software provider with a complete, metadata-driven approach to data governance through our integrated enterprise modeling and data intelligence suites. We help customers overcome their data governance challenges, with risk management and regulatory compliance being primary concerns.

However, the erwin EDGE also delivers an “enterprise data governance experience” in terms of agile innovation and business transformation – from creating new products and services to keeping customers happy to generating more revenue.

Whatever your organization’s key drivers are, a strategic data governance approach – through  business process, enterprise architecture and data modeling combined with data cataloging and data literacy – is key to success in our modern, digital world.

If you’d like to get a handle on handling your data, you can sign up for a free, one-on-one demo of erwin Data Intelligence.

For more information on GDPR/CCPA, we’ve also published a white paper on the Regulatory Rationale for Integrating Data Management and Data Governance.

GDPR White Paper

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erwin Expert Blog

Democratizing Data and the Rise of the Citizen Analyst

Data innovation is flourishing, driven by the confluence of exploding data production, a lowered barrier to entry for big data, as well as advanced analytics, artificial intelligence and machine learning.

Additionally, the ability to access and analyze all of this information has given rise to the “citizen analyst” – a business-oriented problem-solver with enough technical knowledge to understand how to apply analytical techniques to collections of massive data sets to identify business opportunities.

Empowering the citizen analyst relies on, or rather demands, data democratization – making shared enterprise assets available to a set of data consumer communities in a governed way.

This idea of democratizing data has become increasingly popular as more organizations realize that data is everyone’s business in a data-driven organization. Those that embrace digital transformation, regardless of industry, experience new levels of relevance and success.

Securing the Asset

Consumers and businesses alike have started to view data as an asset they must take steps to secure. It’s both a lucrative target for cyber criminals and a combustible spark for PR fires.

However, siloing data can be just as costly.

For some perspective, we can draw parallels between a data pipeline and a factory production line.

In the latter example, not being able to get the right parts to the right people at the right time leads to bottlenecks that stall both production and potential profits.

The exact same logic can be applied to data. To ensure efficient processes, organizations need to make the right data available to the right people at the right time.

In essence, this is data democratization. And the importance of democratized data governance cannot be stressed enough. Data security is imperative, so organizations need both technology and personnel to achieve it.

And in regard to the human element, organizations need to ensure the relevant parties understand what particular data assets can be used and for what. Assuming that employees know when, what and how to use data can make otherwise extremely valuable data resources useless due to not understanding its potential.

The objectives of governed data democratization include:

  • Raising data awareness among the different data consumer communities to increase awareness of the data assets that can be used for reporting and analysis,
  • Improving data literacy so that individuals will understand how the different data assets can be used,
  • Supporting observance of data policies to support regulatory compliance, and
  • Simplifying data accessibility and use to support citizen analysts’ needs.

Democratizing Data: Introducing Democratized Data

To successfully introduce and oversee the idea of democratized data, organizations must ensure that information about data assets is accumulated, documented and published for context-rich use across the organization.

This knowledge and understanding are a huge part of data intelligence.

Data intelligence is produced by coordinated processes to survey the data landscape to collect, collate and publish critical information, namely:

  • Reconnaissance: Understanding the data environment and the corresponding business contexts and collecting as much information as possible;
  • Surveillance: Monitoring the environment for changes to data sources;
  • Logistics and Planning: Mapping the collected information production flows and mapping how data moves across the enterprise
  • Impact Assessment: Using what you have learned to assess how external changes impact the environment
  • Synthesis: Empowering data consumers by providing a holistic perspective associated with specific business terms
  • Sustainability: Embracing automation to always provide up-to-date and correct intelligence; and
  • Auditability: Providing oversight and being able to explain what you have learned and why

erwin recently sponsored a white paper about data intelligence and democratizing data.

Written by David Loshin of Knowledge Integrity, Inc., it take a deep dive into this topic and includes crucial advice on how organizations should evaluate data intelligence software prior to investment.

Data Intelligence: Democratizing Data

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erwin Expert Blog

A Guide to CCPA Compliance and How the California Consumer Privacy Act Compares to GDPR

California Consumer Privacy Act (CCPA) compliance shares many of the same requirements in the European Unions’ General Data Protection Regulation (GDPR).

While the CCPA has been signed into law, organizations have until Jan. 1, 2020, to enact its mandates. Luckily, many organizations have already laid the regulatory groundwork for it because of their efforts to comply with GDPR.

However, there are some key differences that we’ll explore in the Q&A below.

Data governance, thankfully, provides a framework for compliance with either or both – in addition to other regulatory mandates your organization may be subject to.

CCPA Compliance Requirements vs. GDPR FAQ

Does CCPA apply to not-for-profit organizations? 

No, CCPA compliance only applies to for-profit organizations. GDPR compliance is required for any organization, public or private (including not-for-profit).

What for-profit businesses does CCPA apply to?

The mandate for CCPA compliance only applies if a for-profit organization:

  • Has an annual gross revenue exceeding $25 million
  • Collects, sells or shares the personal data of 50,000 or more consumers, households or devices
  • Earns 50% of more of its annual revenue by selling consumers’ personal information

Does the CCPA apply outside of California?

As the name suggests, the legislation is designed to protect the personal data of consumers who reside in the state of California.

But like GDPR, CCPA compliance has impacts outside the area of origin. This means businesses located outside of California, but selling to (or collecting the data of) California residents must also comply.

Does the CCPA exclude anything that GDPR doesn’t? 

GDPR encompasses all categories of “personal data,” with no distinctions.

CCPA does make distinctions, particularly when other regulations may overlap. These include:

  • Medical information covered by the Confidentiality of Medical Information Act (CMIA) and the Health Insurance Portability and Accountability Act (HIPAA)
  • Personal information covered by the Gramm-Leach-Bliley Act (GLBA)
  • Personal information covered by the Driver’s Privacy Protection Act (DPPA)
  • Clinical trial data
  • Information sold to or by consumer reporting agencies
  • Publicly available personal information (federal, state and local government records)

What about access requests? 

Under the GDPR, organizations must make any personal data collected from an EU citizen available upon request.

CCPA compliance only requires data collected within the last 12 months to be shared upon request.

Does the CCPA include the right to opt out?

CCPA, like GDPR, empowers gives consumers/citizens the right to opt out in regard to the processing of their personal data.

However, CCPA compliance only requires an organization to observe an opt-out request when it comes to the sale of personal data. GDPR does not make any distinctions between “selling” personal data and any other kind of data processing.

To meet CCPA compliance opt-out standards, organizations must provide a “Do Not Sell My Personal Information” link on their home pages.

Does the CCPA require individuals to willingly opt in?

No. Whereas the GDPR requires informed consent before an organization sells an individual’s information, organizations under the scope of the CCPA can still assume consent. The only exception involves the personal information of children (under 16). Children over 13 can consent themselves, but if the consumer is a child under 13, a parent or guardian must authorize the sale of said child’s personal data.

What about fines for CCPA non-compliance? 

In theory, fines for CCPA non-compliance are potentially more far reaching than those of GDPR because there is no ceiling for CCPA penalties. Under GDPR, penalties have a ceiling of 4% of global annual revenue or €20 million, whichever is greater. GDPR recently resulted in a record fine for Google.

Organizations outside of CCPA compliance can only be fined up to $7,500 per violation, but there is no upper ceiling.

CCPA compliance is a data governance issue

Data Governance for Regulatory Compliance

While CCPA has a more narrow geography and focus than GDPR, compliance is still a serious effort for organizations under its scope. And as data-driven business continues to expand, so too will the pressure on lawmakers to regulate how organizations process data. Remember the Facebook hearings and now inquiries into Google and Twitter, for example?

Regulatory compliance remains a key driver for data governance. After all, to understand how to meet data regulations, an organization must first understand its data.

An effective data governance initiative should enable just that, by giving an organization the tools to:

  • Discover data: Identify and interrogate metadata from various data management silos
  • Harvest data: Automate the collection of metadata from various data management silos and consolidate it into a single source
  • Structure data: Connect physical metadata to specific business terms and definitions and reusable design standards
  • Analyze data: 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 and policies and set best practices
  • Socialize data: Enable all stakeholders to see data in one place in their own context

A Regulatory EDGE

The erwin EDGE software platform creates an “enterprise data governance experience” to transform how all stakeholders discover, understand, govern and socialize data assets. It includes enterprise modeling, data cataloging and data literacy capabilities, giving organizations visibility and control over their disparate architectures and all the supporting data.

Both IT and business stakeholders have role-based, self-service access to the information they need to collaborate in making strategic decisions. And because many of the associated processes can be automated, you reduce errors and increase the speed and quality of your data pipeline. This data intelligence unlocks knowledge and value.

The erwin EDGE provides the most agile, efficient and cost-effective means of launching and sustaining a strategic and comprehensive data governance initiative, whether you wish to deploy on premise or in the cloud. But you don’t have to implement every component of the erwin EDGE all at once to see strategic value.

Because of the platform’s federated design, you can address your organization’s most urgent needs, such as regulatory compliance, first. Then you can proactively address other organization objectives, such as operational efficiency, revenue growth, increasing customer satisfaction and improving overall decision-making.

You can learn more about leveraging data governance to navigate the changing tide of data regulations here.

Are you compliant with data regulations?