Tag: automated metadata management

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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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Compliance First: How to Protect Sensitive Data

The ability to more efficiently govern, discover and protect sensitive data is something that all prospering data-driven organizations are constantly striving for.

It’s been almost four months since the European Union’s General Data Protection Regulation (GDPR) took effect. While no fines have been issued yet, the Information Commissioner’s Office has received upwards of 500 calls per week since the May 25 effective date.

However, the fine-free streak may be ending soon with British Airways (BA) as the first large company to pay a GDPR penalty because of a data breach. The hack at BA in August and early September lasted for more than two weeks, with intruders getting away with account numbers and personal information of customers making reservations on the carrier’s website and mobile app. If regulators conclude that BA failed to take measures to prevent the incident— a significant fine may follow.

Additionally, complaints against Google in the EU have started. For example, internet browser provider Brave claims that Google and other advertising companies expose user data during a process called “bid request.” A data breach occurs because a bid request fails to protect sensitive data against unauthorized access, which is unlawful under the GDPR.

Per Brave’s announcement, bid request data can include the following personal data:

  • What you are reading or watching
  • Your location
  • Description of your device
  • Unique tracking IDs or a “cookie match,” which allows advertising technology companies to try to identify you the next time you are seen, so that a long-term profile can be built or consolidated with offline data about you
  • Your IP address,depending on the version of “real-time bidding” system
  • Data broker segment ID, if available, which could denote things like your income bracket, age and gender, habits, social media influence, ethnicity, sexual orientation, religion, political leaning, etc., depending on the version of bidding system

Obviously, GDPR isn’t the only regulation that organizations need to comply with. From HIPAA in healthcare to FINRA, PII and BCBS in financial services to the upcoming California Consumer Privacy Act (CCPA) taking effect January 1, 2020, regulatory compliance is part of running – and staying in business.

The common denominator in compliance across all industry sectors is the ability to protect sensitive data. But if organizations are struggling to understand what data they have and where it’s located, how do they protect it? Where do they begin?

Protect sensitive data

Discover and Protect Sensitive Data

Data is a critical asset used to operate, manage and grow a business. While sometimes at rest in databases, data lakes and data warehouses; a large percentage is federated and integrated across the enterprise, introducing governance, manageability and risk issues that must be managed.

Knowing where sensitive data is located and properly governing it with policy rules, impact analysis and lineage views is critical for risk management, data audits and regulatory compliance.

However, when key data isn’t discovered, harvested, cataloged, defined and standardized as part of integration processes, audits may be flawed and therefore putting your organization at risk.

Sensitive data – at rest or in motion – that exists in various forms across multiple systems must be automatically tagged, its lineage automatically documented, and its flows depicted so that it is easily found and its usage across workflows easily traced.

Thankfully, tools are available to help automate the scanning, detection and tagging of sensitive data by:

  • Monitoring and controlling sensitive data: Better visibility and control across the enterprise to identify data security threats and reduce associated risks
  • Enriching business data elements for sensitive data discovery: Comprehensive mechanism to define business data element for PII, PHI and PCI across database systems, cloud and Big Data stores to easily identify sensitive data based on a set of algorithms and data patterns
  • Providing metadata and value-based analysis: Discovery and classification of sensitive data based on metadata and data value patterns and algorithms. Organizations can define business data elements and rules to identify and locate sensitive data including PII, PHI, PCI and other sensitive information.


A Regulatory Rationale for Integrating Data Management and Data Governance

Data management and data governance, together, play a vital role in compliance. It’s easier to protect sensitive data when you know where it’s stored, what it is, and how it needs to be governed.

Truly understanding an organization’s data, including the data’s value and quality, requires a harmonized approach embedded in business processes and enterprise architecture. Such an integrated enterprise data governance experience helps organizations understand what data they have, where it is, where it came from, its value, its quality and how it’s used and accessed by people and applications.

But how is all this possible? Again, it comes back to the right technology for IT and business collaboration that will enable you 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
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Automated Data Management: Stop Drowning in Your Data 

Due to the wealth of data data-driven organizations are tasked with handling, organizations are increasingly adopting automated data management.

There are 2.5 quintillion bytes of data being created every day, and that figure is increasing in tandem with the production of and demand for Internet of Things (IoT) devices. However, Forrester reports that between 60 and 73 percent of all data within an enterprise goes unused.

Collecting all that data is pointless if it’s not going to be used to deliver accurate and actionable insights.

But the reality is there’s not enough time, people and/or money for effective data management using manual processes. Organizations won’t be able to take advantage of analytics tools to become data-driven unless they establish a foundation for agile and complete data management. And organizations that don’t employ automated data management risk being left behind.

In addition to taking the burden off already stretched internal teams, automated data management’s most obvious benefit is that it’s a key enabler of data-driven business. Without it, a truly data-driven approach to business is either ineffective, or impossible, depending on the scale of data you’re working with.

This is because there’s either too much data left unaccounted for and too much potential revenue left on the table for the strategy to be considered effective. Or it’s because there’s so much disparity in the data sources and silos in where data is stored that data quality suffers to an insurmountable degree, rendering any analysis fundamentally flawed.

But simply enabling the strategy isn’t the most compelling use case, or organizations across the board would have implemented it already.

The Case for Automated Data Management

Business leaders and decision-makers want a business case for automated data management.

So here it is …

Without automation, 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. And outsourcing these data management efforts to professional services firms only delays schedules and increases cost.

By automating data cataloging and data mapping inclusive of data at rest and data in motion through the integration lifecycle process, organizations will benefit from:

  • A metadata-driven automated framework for cataloging data assets and their flows across the business
  • An efficient, agile and dynamic way to generate data lineage from operational systems (databases, data models, file-based systems, unstructured files and more) across the information management architecture
  • Easy access to what data aligns with specific business rules and policies
  • The ability to inform how data is transformed, integrated and federated throughout business processes – complete with full documentation
  • Faster project delivery and lower costs because data is managed internally, without the need to outsource data management efforts
  • Assurance of data quality, so analysis is reliable and new initiatives aren’t beleaguered with false starts
  • A seamlessly governed data pipeline, operationalized to the benefit of all stakeholders

erwin Data Intelligence