Tag: data intelligence

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erwin Expert Blog

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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Top 5 Data Catalog Benefits

A data catalog benefits organizations in a myriad of ways. With the right data catalog tool, organizations can automate enterprise metadata management – including data cataloging, data mapping, data quality and code generation for faster time to value and greater accuracy for data movement and/or deployment projects.

Data cataloging helps curate internal and external datasets for a range of content authors. Gartner says this doubles business benefits and ensures effective management and monetization of data assets in the long-term if linked to broader data governance, data quality and metadata management initiatives.

But even with this in mind, the importance of data cataloging is growing. In the regulated data world (GDPR, HIPAA etc) organizations need to have a good understanding of their data lineage – and the data catalog benefits to data lineage are substantial.

Data lineage is a core operational business component of data governance technology architecture, encompassing the processes and technology to provide full-spectrum visibility into the ways data flows across an enterprise.

There are a number of different approaches to data lineage. Here, I outline the common approach, and the approach incorporating data cataloging – including the top 5 data catalog benefits for understanding your organization’s data lineage.

Data Catalog Benefits

Data Lineage – The Common Approach

The most common approach for assembling a collection of data lineage mappings traces data flows in a reverse manner. The process begins with the target or data end-point, and then traversing the processes, applications, and ETL tasks in reverse from the target.

For example, to determine the mappings for the data pipelines populating a data warehouse, a data lineage tool might begin with the data warehouse and examine the ETL tasks that immediately proceed the loading of the data into the target warehouse.

The data sources that feed the ETL process are added to a “task list,” and the process is repeated for each of those sources. At each stage, the discovered pieces of lineage are documented. At the end of the sequence, the process will have reverse-mapped the pipelines for populating that warehouse.

While this approach does produce a collection of data lineage maps for selected target systems, there are some drawbacks.

  • First, this approach focuses only on assembling the data pipelines populating the selected target system but does not necessarily provide a comprehensive view of all the information flows and how they interact.
  • Second, this process produces the information that can be used for a static view of the data pipelines, but the process needs to be executed on a regular basis to account for changes to the environment or data sources.
  • Third, and probably most important, this process produces a technical view of the information flow, but it does not necessarily provide any deeper insights into the semantic lineage, or how the data assets map to the corresponding business usage models.

A Data Catalog Offers an Alternate Data Lineage Approach

An alternate approach to data lineage combines data discovery and the use of a data catalog that captures data asset metadata with a data mapping framework that documents connections between the data assets.

This data catalog approach also takes advantage of automation, but in a different way: using platform-specific data connectors, the tool scans the environment for storing each data asset and imports data asset metadata into the data catalog.

When data asset structures are similar, the tool can compare data element domains and value sets, and automatically create the data mapping.

In turn, the data catalog approach performs data discovery using the same data connectors to parse the code involved in data movement, such as major ETL environments and procedural code – basically any executable task that moves data.

The information collected through this process is reverse engineered to create mappings from source data sets to target data sets based on what was discovered.

For example, you can map the databases used for transaction processing, determine that subsets of the transaction processing database are extracted and moved to a staging area, and then parse the ETL code to infer the mappings.

These direct mappings also are documented in the data catalog. In cases where the mappings are not obvious, a tool can help a data steward manually map data assets into the catalog.

The result is a data catalog that incorporates the structural and semantic metadata associated with each data asset as well as the direct mappings for how that data set is populated.

Learn more about data cataloging.

Value of Data Intelligence IDC Report

And this is a powerful representative paradigm – instead of capturing a static view of specific data pipelines, it allows a data consumer to request a dynamically-assembled lineage from the documented mappings.

By interrogating the catalog, the current view of any specific data lineage can be rendered on the fly that shows all points of the data lineage: the origination points, the processing stages, the sequences of transformations, and the final destination.

Materializing the “current active lineage” dynamically reduces the risk of having an older version of the lineage that is no longer relevant or correct. When new information is added to the data catalog (such as a newly-added data source of a modification to the ETL code), dynamically-generated views of the lineage will be kept up-to-date automatically.

Top 5 Data Catalog Benefits for Understanding Data Lineage

A data catalog benefits data lineage in the following five distinct ways:

1. Accessibility

The data catalog approach allows the data consumer to query the tool to materialize specific data lineage mappings on demand.

2. Currency

The data lineage is rendered from the most current data in the data catalog.

3. Breadth

As the number of data assets documented in the data catalog increases, the scope of the materializable lineage expands accordingly. With all corporate data assets cataloged, any (or all!) data lineage mappings can be produced on demand.

4. Maintainability and Sustainability

Since the data lineage mappings are not managed as distinct artifacts, there are no additional requirements for maintenance. As long as the data catalog is kept up to date, the data lineage mappings can be materialized.

5. Semantic Visibility

In addition to visualizing the physical movement of data across the enterprise, the data catalog approach allows the data steward to associate business glossary terms, data element definitions, data models, and other semantic details with the different mappings. Additional visualization methods can demonstrate where business terms are used, how they are mapped to different data elements in different systems, and the relationships among these different usage points.

One can impose additional data governance controls with project management oversight, which allows you to designate data lineage mappings in terms of the project life cycle (such as development, test or production).

Aside from these data catalog benefits, this approach allows you to reduce the amount of manual effort for accumulating the information for data lineage and continually reviewing the data landscape to maintain consistency, thus providing a greater return on investment for your data intelligence budget.

Learn more about data cataloging.

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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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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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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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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?

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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.

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Digital Transformation In Retail: The Retail Apocalypse

Much like the hospitality industry, digital transformation in retail has been a huge driver of change.

One important fact is getting lost among all of the talk of “the retail apocalypse” and myriad stories about increasingly empty shopping malls: there’s a lot of money to be made in retail. In fact, the retail market was expected to grow by more than 3 percent in 2018, unemployment is low, and wages are at least stable.

In short, there’s money to be spent. Now, where are shoppers spending it?

Coming into 2019, consumers are in control when it comes to retail. Choices are abundant. According to Deloitte’s 2018 Retail, Wholesale and Distribution Industry Trends Outlook, “consumers have been conditioned to expect fast, convenient and effortless consumption.”

This is arguably the result of the degree of digital transformation in retail that we’ve seen in recent years.

If you want to survive in retail today, you need to make it easy on your customers. That means meeting their needs across channels, fulfilling orders quickly and accurately, offering competitive prices, and not sacrificing quality in the process.

Even in a world where Amazon has changed the retail game, Walmart just announced that it had its best holiday season in years. According to a recent Fortune article, “Walmart’s e-commerce sales rose 43 percent during the quarter, belying another myth: e-commerce and store sales are in competition with each other.”

Retail has always been a very fickle industry, with the right product mix and the right appeal to the right customers being crucial to success. But digital transformation in retail has seen the map change. You’re no longer competing with the store across the street; you’re competing with the store across the globe.

Digital Transformation In Retail

Retailers are putting every aspect of their businesses under scrutiny to help them remain relevant. Four areas in particular are getting a great deal of attention:

Customer experience: In today’s need-it-fast, need-it-now, need-it-right world, customers expect the ability to make purchases where they are, not where you are. That means via the Web, mobile devices or in a store. And all of the information about those orders needs to be tied together, so that if there is a problem, it can be resolved quickly via any channel.

Competitive differentiation: Appealing to retail customers used to mean appealing to all of your customers as one group or like-minded block. But customers are individuals, and today they can be targeted with personalized messaging and products that are likely to appeal to them, not to everyone.

Supply chain: Having the right products in the right place at the right time is part of the supply chain strategy. But moving them efficiently and cost effectively from any number of suppliers to warehouses and stores can make or break margins.

Partnerships: Among the smaller players in the retail space, partnerships with industry giants like Amazon can help reach a global audience that simply isn’t otherwise available and also reduce complexity. Larger players also recognize that partnerships can be mutually beneficial in the retail space.

Enabling each of these strategies is data – and lots of it. Data is the key to recognizing customers, personalizing experiences, making helpful recommendations, ensuring items are in stock, tracking deliveries and more. At its core, this is what digital transformation in retail seeks to achieve.

Digital Transformation in Retail – What’s the Risk?

But if data is the great enabler in retail, it’s also a huge risk – risk that the data is wrong, that it is old, and that it ends up in the hands of some person or entity that isn’t supposed to have it.

Danny Sandwell, director of product marketing for erwin, Inc., says retailers need to achieve a level of what he calls “data intelligence.” A little like business intelligence, Sandwell uses the term to mean that when someone in retail uses data to make a decision or power an experience or send a recommendation, they have the ability to find out anything they need about that data, including its source, age, who can access it, which applications use it, and more.

Given all of the data that flows into the modern retailer, this level of data intelligence requires a holistic, mature and well-planned data governance strategy. Data governance doesn’t just sit in the data warehouse, it’s woven into business processes and enterprise architecture to provide data visibility for fast, accurate decision-making, help keep data secure, identify problems early, and alert users to things that are working.

How important is clean, accurate, timely data in retail? Apply it to the four areas discussed above:

Customer experience:  If your data shows a lot of abandoned carts from mobile app users, then that’s an area to investigate, and good data will identify it.

Competitive differentiation: Are personalized offers increasing sales and creating customer loyalty? This is an important data point for marketing strategy.

Supply chain: Can a problem with quality be related to items shipping from a certain warehouse? Data will zero in on the location of the problem.

Partnerships: Are your partnerships helping grow other parts of your business and creating new customers? Or are your existing customers using partners in place of visiting your store? Data can tell you.

Try drawing these conclusions without data. You can’t. And even worse, try drawing them with inaccurate data and see what happens when a partnership that was creating customers is ended or mobile app purchases plummet after an ill-advised change to the experience.

If you want to focus on margins in retail, don’t forget this one: there is no margin for error.

Over the next few weeks, we’ll be looking closely at digital transformation examples in other sectors, including hospitality and government. Subscribe to to stay in the loop.

Data Management and Data Governance: Solving the Enterprise Data Dilemma

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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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Top 10 Data Governance Predictions for 2019

This past year witnessed a data governance awakening – or as the Wall Street Journal called it, a “global data governance reckoning.” There was tremendous data drama and resulting trauma – from Facebook to Equifax and from Yahoo to Marriott. The list goes on and on. And then, the European Union’s General Data Protection Regulation (GDPR) took effect, with many organizations scrambling to become compliant.

So what’s on the horizon for data governance in the year ahead? We’re making the following data governance predictions for 2019:

Data Governance Predictions

Top 10 Data Governance Predictions for 2019

1. GDPR-esque regulation for the United States:

GDPR has set the bar and will become the de facto standard across geographies. Look at California as an example with California Consumer Privacy Act (CCPA) going into effect in 2020. Even big technology companies like Apple, Google, Amazon and Twitter are encouraging more regulations in part because they realize that companies that don’t put data privacy at the forefront will feel the wrath from both the government and the consumer.

2. GDPR fines are coming and they will be massive:

Perhaps one of the safest data governance predictions for 2019 is the coming clamp down on GDPR enforcement. The regulations weren’t brought in for show and so it’s likely the fine-free streak for GDPR will be ending … and soon. The headlines will resemble data breaches or hospitals with Health Information Portability Privacy Act (HIPAA) violations in the U.S. healthcare sector. Lots of companies will have an “oh crap” moment and realize they have a lot more to do to get their compliance house in order.

3. Data policies as a consumer buying criteria:

The threat of “data trauma” will continue to drive visibility for enterprise data in the C-suite. How they respond will be the key to their long-term success in transforming data into a true enterprise asset. We will start to see a clear delineation between organizations that maintain a reactive and defensive stance (pain avoidance) versus those that leverage this negative driver as an impetus to increase overall data visibility and fluency across the enterprise with a focus on opportunity enablement. The latter will drive the emergence of true data-driven entities versus those that continue to try to plug the holes in the boat.

4. CDOs will rise, better defined role within the organization:

We will see the chief data officer (CDO) role elevated from being a lieutenant of the CIO to taking a proper seat at the table beside the CIO, CMO and CFO.  This will give them the juice needed to create a sustainable vision and roadmap for data. So far, there’s been a profound lack of consensus on the nature of the role and responsibilities, mandate and background that qualifies a CDO. As data becomes increasingly more vital to an organization’s success from a compliance and business perspective, the role of the CDO will become more defined.

5. Data operations (DataOps) gains traction/will be fully optimized:

Much like how DevOps has taken hold over the past decade, 2019 will see a similar push for DataOps. Data is no longer just an IT issue. As organizations become data-driven and awash in an overwhelming amount of data from multiple data sources (AI, IOT, ML, etc.), organizations will need to get a better handle on data quality and focus on data management processes and practices. DataOps will enable organizations to better democratize their data and ensure that all business stakeholders work together to deliver quality, data-driven insights.

Data Management and Data Governance

6. Business process will move from back office to center stage:

Business process management will make its way out of the back office and emerge as a key component to digital transformation. The ability for an organization to model, build and test automated business processes is a gamechanger. 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.

7. Turning bad AI/ML data good:

Artificial Intelligence (AI) and Machine Learning (ML) are consumers of data. The risk of training AI and ML applications with bad data will initially drive the need for data governance to properly govern the training data sets. Once trained, the data they produce should be well defined, consistent and of high quality. The data needs to be continuously governed for assurance purposes.

8. Managing data from going over the edge:

Edge computing will continue to take hold. And while speed of data is driving its adoption, organizations will also need to view, manage and secure this data and bring it into an automated pipeline. The internet of things (IoT) is all about new data sources (device data) that often have opaque data structures. This data is often integrated and aggregated with other enterprise data sources and needs to be governed like any other data. The challenge is documenting all the different device management information bases (MIBS) and mapping them into the data lake or integration hub.

9. Organizations that don’t have good data harvesting are doomed to fail:

Research shows that data scientists and analysts spend 80 percent of their time preparing data for use and only 20 percent of their time actually analyzing it for business value. Without automated data harvesting and ingesting data from all enterprise sources (not just those that are convenient to access), data moving through the pipeline won’t be the highest quality and the “freshest” it can be. The result will be faulty intelligence driving potentially disastrous decisions for the business.

10. Data governance evolves to data intelligence:

Regulations like GDPR are driving most large enterprises to address their data challenges. But data governance is more than compliance. “Best-in-breed” enterprises are looking at how their data can be used as a competitive advantage. These organizations are evolving their data governance practices to data intelligence – connecting all of the pieces of their data management and data governance lifecycles to create actionable insights. Data intelligence can help improve the customer experiences and enable innovation of products and services.

The erwin Expert Blog will continue to follow data governance trends and provide best practice advice in the New Year so you can see how our data governance predictions pan out for yourself. To stay up to date, click here to subscribe.

Data Management and Data Governance: Solving the Enterprise Data Dilemma