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Keeping Up with New Data Protection Regulations

Keeping up with new data protection regulations can be difficult, and the latest – the General Data Protection Regulation (GDPR) – isn’t the only new data protection regulation organizations should be aware of.

California recently passed a law that gives residents the right to control the data companies collect about them. Some suggest the California Consumer Privacy Act (CCPA), which takes effect January 1, 2020, sets a precedent other states will follow by empowering consumers to set limits on how companies can use their personal information.

In fact, organizations should expect increasing pressure on lawmakers to introduce new data protection regulations. A number of high-profile data breaches and scandals have increased public awareness of the issue.

Facebook was in the news again last week for another major problem around the transparency of its user data, and the tech-giant also is reportedly facing 10 GDPR investigations in Ireland – along with Apple, LinkedIn and Twitter.

Some industries, such as healthcare and financial services, have been subject to stringent data regulations for years: GDPR now joins the Health Insurance Portability and Accountability Act (HIPAA), the Payment Card Industry Data Security Standard (PCI DSS) and the Basel Committee on Banking Supervision (BCBS).

Due to these pre-existing regulations, organizations operating within these sectors, as well as insurance, had some of the GDPR compliance bases covered in advance.

Other industries had their own levels of preparedness, based on the nature of their operations. For example, many retailers have robust, data-driven e-commerce operations that are international. Such businesses are bound to comply with varying local standards, especially when dealing with personally identifiable information (PII).

Smaller, more brick-and-mortar-focussed retailers may have had to start from scratch.

But starting position aside, every data-driven organization should strive for a better standard of data management — and not just for compliance sake. After all, organizations are now realizing that data is one of their most valuable assets.

New Data Protection Regulations – Always Be Prepared

When it comes to new data protection regulations in the face of constant data-driven change, it’s a matter of when, not if.

As they say, the best defense is a good offense. Fortunately, whenever the time comes, the first point of call will always be data governance, so organizations can prepare.

Effective compliance with new data protection regulations requires a robust understanding of the “what, where and who” in terms of data and the stakeholders with access to it (i.e., employees).

The Regulatory Rationale for Integrating Data Management & Data Governance

This is also true for existing data regulations. Compliance is an on-going requirement, so efforts to become compliant should not be treated as static events.

Less than four months before GDPR came into effect, only 6 percent of enterprises claimed they were prepared for it. Many of these organizations will recall a number of stressful weeks – or even months – tidying up their databases and their data management processes and policies.

This time and money was spent reactionarily, at the behest of proactive efforts to grow the business.

The implementation and subsequent observation of a strong data governance initiative ensures organizations won’t be put on the spot going forward. Should an audit come up, current projects aren’t suddenly derailed as they reenact pre-GDPR panic.

New Data Regulations

Data Governance: The Foundation for Compliance

The first step to compliance with new – or old – data protection regulations is data governance.

A robust and effective data governance initiative ensures an organization understands where security should be focussed.

By adopting a data governance platform that enables you to automatically tag sensitive data and track its lineage, you can ensure nothing falls through the cracks.

Your chosen data governance solution should enable you to automate the scanning, detection and tagging of sensitive data by:

  • Monitoring and controlling sensitive data – Gain better visibility and control across the enterprise to identify data security threats and reduce associated risks.
  • Enriching business data elements for sensitive data discovery – By leveraging a comprehensive mechanism to define business data elements for PII, PHI and PCI across database systems, cloud and Big Data stores, you can easily identify sensitive data based on a set of algorithms and data patterns.
  • Providing metadata and value-based analysis – Simplify the 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 and PCI.

With these precautionary steps, organizations are primed to respond if a data breach occurs. Having a well governed data ecosystem with data lineage capabilities means issues can be quickly identified.

Additionally, if any follow-up is necessary –  such as with GDPR’s data breach reporting time requirements – it can be handles swiftly and in accordance with regulations.

It’s also important to understand that the benefits of data governance don’t stop with regulatory compliance.

A better understanding of what data you have, where it’s stored and the history of its use and access isn’t only beneficial in fending off non-compliance repercussions. In fact, such an understanding is arguably better put to use proactively.

Data governance improves data quality standards, it enables better decision-making and ensures businesses can have more confidence in the data informing those decisions.

The same mechanisms that protect data by controlling its access also can be leveraged to make data more easily discoverable to approved parties – improving operational efficiency.

All in all, the cumulative result of data governance’s influence on data-driven businesses both drives revenue (through greater efficiency) and reduces costs (less errors, false starts, etc.).

To learn more about data governance and the regulatory rationale for its implementation, get our free guide here.

DG RediChek

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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 architecturebusiness processdata 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