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Data Governance Makes Data Security Less Scary

Happy Halloween!

Do you know where your data is? What data you have? Who has had access to it?

These can be frightening questions for an organization to answer.

Add to the mix the potential for a data breach followed by non-compliance, reputational damage and financial penalties and a real horror story could unfold.

In fact, we’ve seen some frightening ones play out already:

  1. Google’s record GDPR fine – France’s data privacy enforcement agency hit the tech giant with a $57 million penalty in early 2019 – more than 80 times the steepest fine the U.K.’s Information Commissioner’s Office had levied against both Facebook and Equifax for their data breaches.
  2. In July 2019, British Airways received the biggest GDPR fine to date ($229 million) because the data of more than 500,000 customers was compromised.
  3. Marriot International was fined $123 million, or 1.5 percent of its global annual revenue, because 330 million hotel guests were affected by a breach in 2018.

Now, as Cybersecurity Awareness Month comes to a close – and ghosts and goblins roam the streets – we thought it a good time to resurrect some guidance on how data governance can make data security less scary.

We don’t want you to be caught off guard when it comes to protecting sensitive data and staying compliant with data regulations.

Data Governance Makes Data Security Less Scary

Don’t Scream; You Can Protect Your Sensitive Data

It’s easier to protect sensitive data when you know what it is, where it’s stored and how it needs to be governed.

Data security incidents may be the result of not having a true data governance foundation that makes it possible to understand the context of data – what assets exist and where, the relationship between them and enterprise systems and processes, and how and by what authorized parties data is used.

That knowledge is critical to supporting efforts to keep relevant data secure and private.

Without data governance, organizations don’t have visibility of the full data landscape – linkages, processes, people and so on – to propel more context-sensitive security architectures that can better assure expectations around user and corporate data privacy. In sum, they lack the ability to connect the dots across governance, security and privacy – and to act accordingly.

This addresses these fundamental questions:

  1. What private data do we store and how is it used?
  2. Who has access and permissions to the data?
  3. What data do we have and where is it?

Where Are the Skeletons?

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 your organization is 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: Comprehensively defining 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.

No Hocus Pocus

Truly understanding an organization’s data, including its 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.

An ounce of prevention is worth a pound of cure  – from the painstaking process of identifying what happened and why to notifying customers their data and thus their trust in your organization has been compromised.

A well-formed security architecture that is driven by and aligned by data intelligence is your best defense. However, if there is nefarious intent, a hacker will find a way. So being prepared means you can minimize your risk exposure and the damage to your reputation.

Multiple components must be considered to effectively support a data governance, security and privacy trinity. They are:

  1. Data models
  2. Enterprise architecture
  3. Business process models

Creating policies for data handling and accountability and driving culture change so people understand how to properly work with data are two important components of a data governance initiative, as is the technology for proactively managing data assets.

Without the ability to harvest metadata schemas and business terms; analyze data attributes and relationships; impose structure on definitions; and view all data in one place according to each user’s role within the enterprise, businesses will be hard pressed to stay in step with governance standards and best practices around security and privacy.

As a consequence, the private information held within organizations will continue to be at risk.

Organizations suffering data breaches will be deprived of the benefits they had hoped to realize from the money spent on security technologies and the time invested in developing data privacy classifications.

They also may face heavy fines and other financial, not to mention PR, penalties.

Gartner Magic Quadrant 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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erwin Expert Blog

The Data Governance (R)Evolution

Data governance continues to evolve – and quickly.

Historically, Data Governance 1.0 was siloed within IT and mainly concerned with cataloging data to support search and discovery. However, it fell short in adding value because it neglected the meaning of data assets and their relationships within the wider data landscape.

Then the push for digital transformation and Big Data created the need for DG to come out of IT’s shadows – Data Governance 2.0 was ushered in with principles designed for  modern, data-driven business. This approach acknowledged the demand for collaborative data governance, the tearing down of organizational silos, and spreading responsibilities across more roles.

But this past year we all 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 Aetna. 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 where are we today?

Simply put, data governance needs to be a ubiquitous part of your company’s culture. Your stakeholders encompass both IT and business users in collaborative relationships, so that makes data governance everyone’s business.

Data Governance is Everyone's Business

Data governance underpins data privacy, security and compliance. Additionally, most organizations don’t use all the data they’re flooded with to reach deeper conclusions about how to grow revenue, achieve regulatory compliance, or make strategic decisions. They face a data dilemma: not knowing what data they have or where some of it is—plus integrating known data in various formats from numerous systems without a way to automate that process.

To accelerate the transformation of business-critical information into accurate and actionable insights, organizations need an automated, real-time, high-quality data pipeline. Then every stakeholder—data scientist, ETL developer, enterprise architect, business analyst, compliance officer, CDO and CEO—can fuel the desired outcomes based on reliable information.

Connecting Data Governance to Your Organization

  1. Data Mapping & Data Governance

The automated generation of the physical embodiment of data lineage—the creation, movement and transformation of transactional and operational data for harmonization and aggregation—provides the best route for enabling stakeholders to understand their data, trust it as a well-governed asset and use it effectively. Being able to quickly document lineage for a standardized, non-technical environment brings business alignment and agility to the task of building and maintaining analytics platforms.

  1. Data Modeling & Data Governance

Data modeling discovers and harvests data schema, and analyzes, represents and communicates data requirements. It synthesizes and standardizes data sources for clarity and consistency to back up governance requirements to use only controlled data. It benefits from the ability to automatically map integrated and cataloged data to and from models, where they can be stored in a central repository for re-use across the organization.

  1. Business Process Modeling & Data Governance

Business process modeling reveals the workflows, business capabilities and applications that use particular data elements. That requires that these assets be appropriately governed components of an integrated data pipeline that rests on automated data lineage and business glossary creation.

  1. Enterprise Architecture & Data Governance

Data flows and architectural diagrams within enterprise architecture benefit from the ability to automatically assess and document the current data architecture. Automatically providing and continuously maintaining business glossary ontologies and integrated data catalogs inform a key part of the governance process.

The EDGE Revolution

 By bringing together enterprise architecture, business process, data mapping and data modeling, erwin’s approach to data governance enables organizations to get a handle on how they handle their data and realize its maximum value. With the broadest set of metadata connectors and automated code generation, data mapping and cataloging tools, the erwin EDGE Platform simplifies the total data management and data governance lifecycle.

This single, integrated solution makes it possible to gather business intelligence, conduct IT audits, ensure regulatory compliance and accomplish any other organizational objective by fueling an automated, high-quality and real-time data pipeline.

The erwin EDGE creates an “enterprise data governance experience” that facilitates collaboration between both IT and the business to discover, understand and unlock the value of data both at rest and in motion.

With the erwin EDGE, data management and data governance are unified and mutually supportive of business stakeholders and IT to:

  • Discover data: Identify and integrate 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 stakeholders to see data in one place and in the context of their roles.

If you’ve enjoyed this latest blog series, then you’ll want to request a copy of Solving the Enterprise Data Dilemma, our new e-book that highlights how to answer the three most important data management and data governance questions: What data do we have? Where is it? And how do we get value from it?

Solving the Enterprise Data Dilemma