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Big Data Posing Challenges? Data Governance Offers Solutions

Big Data is causing complexity for many organizations, not just because of the volume of data they’re collecting, but because of the variety of data they’re collecting.

Big Data often consists of unstructured data that streams into businesses from social media networks, internet-connected sensors, and more. But the data operations at many organizations were not designed to handle this flood of unstructured data.

Dealing with the volume, velocity and variety of Big Data is causing many organizations to re-think how they store and govern their data. A perfect example is the data warehouse. The people who built and manage the data warehouse at your organization built something that made sense to them at the time. They understood what data was stored where and why, as well how it was used by business units and applications.

The era of Big Data introduced inexpensive data lakes to some organizations’ data operations, but as vast amounts of data pour into these lakes, many IT departments found themselves managing a data swamp instead.

In a perfect world, your organization would treat Big Data like any other type of data. But, alas, the world is not perfect. In reality, practicality and human nature intervene. Many new technologies, when first adopted, are separated from the rest of the infrastructure.

“New technologies are often looked at in a vacuum, and then built in a silo,” says Danny Sandwell, director of product marketing for erwin, Inc.

That leaves many organizations with parallel collections of data: one for so-called “traditional” data and one for the Big Data.

There are a few problems with this outcome. For one, silos in IT have a long history of keeping organizations from understanding what they have, where it is, why they need it, and whether it’s of any value. They also have a tendency to increase costs because they don’t share common IT resources, leading to redundant infrastructure and complexity. Finally, silos usually mean increased risk.

But there’s another reason why parallel operations for Big Data and traditional data don’t make much sense: The users simply don’t care.

At the end of the day, your users want access to the data they need to do their jobs, and whether IT considers it Big Data, little data, or medium-sized data isn’t important. What’s most important is that the data is the right data – meaning it’s accurate, relevant and can be used to support or oppose a decision.

Reputation Management - What's Driving Data Governance

How Data Governance Turns Big Data into Just Plain Data

According to a November 2017 survey by erwin and UBM, 21 percent of respondents cited Big Data as a driver of their data governance initiatives.

In today’s data-driven world, data governance can help your business understand what data it has, how good it is, where it is, and how it’s used. The erwin/UBM survey found that 52 percent of respondents said data is critically important to their organization and they have a formal data governance strategy in place. But almost as many respondents (46 percent) said they recognize the value of data to their organization but don’t have a formal governance strategy.

A holistic approach to data governance includes thesekey components.

  • An enterprise architecture component is important because it aligns IT and the business, mapping a company’s applications and the associated technologies and data to the business functions they enable. By integrating data governance with enterprise architecture, businesses can define application capabilities and interdependencies within the context of their connection to enterprise strategy to prioritize technology investments so they align with business goals and strategies to produce the desired outcomes.
  • A business process and analysis component defines how the business operates and ensures employees understand and are accountable for carrying out the processes for which they are responsible. Enterprises can clearly define, map and analyze workflows and build models to drive process improvements, as well as identify business practices susceptible to the greatest security, compliance or other risks and where controls are most needed to mitigate exposures.
  • A data modeling component is the best way to design and deploy new databases with high-quality data sources and support application development. Being able to cost-effectively and efficiently discover, visualize and analyze “any data” from “anywhere” underpins large-scale data integration, master data management, Big Data and business intelligence/analytics with the ability to synthesize, standardize and store data sources from a single design, as well as reuse artifacts across projects.

When data governance is done right, and it’s woven into the structure and architecture of your business, it helps your organization accept new technologies and the new sources of data they provide as they come along. This makes it easier to see ROI and ROO from your Big Data initiatives by managing Big Data in the same manner your organization treats all of its data – by understanding its metadata, defining its relationships, and defining its quality.

Furthermore, businesses that apply sound data governance will find themselves with a template or roadmap they can use to integrate Big Data throughout their organizations.

If your business isn’t capitalizing on the Big Data it’s collecting, then it’s throwing away dollars spent on data collection, storage and analysis. Just as bad, however, is a situation where all of that data and analysis is leading to the wrong decisions and poor business outcomes because the data isn’t properly governed.

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Data Plays Huge Role in Reputation Management

How much does your business invest in reputation management? It’s likely no one in the organization knows for sure because every interaction – in person, online or over the phone – can affect your firm’s reputation. The quality of the goods and services your organization provides, the training it gives employees, and the causes and initiatives it supports all can improve or worsen its reputation.

Reputation management has always been important to businesses, but because information flows so quickly and freely today, reputations are more fragile than ever. Bad news travels fast; often much faster than businesses can respond. It’s also incredibly hard to make bad news go away. Social media and search engines crushed the concept of the news cycle because they make it easy for information to circulate, even long after incidents have occurred.

One of the fastest ways to see your organization’s reputation suffer today is to lose or expose sensitive data. A study in the U.K. found that 86 percent of customers would not do business with a company that failed to protect its customers’ credit card data.

But data theft isn’t the only risk. Facebook may not have even violated its user agreement in the Cambridge Analytica scandal, but reputations have a funny way of rising and falling on perception, not just facts.

It’s estimated that Walmart, for example, spent $18 million in 2016 and 2017 on advertising for retrospective reputation management, after suffering from a perception the company was anti-worker, fixated on profits, and selling too many foreign-made products.

Perception is why companies publicize their efforts to be good corporate citizens, whether it means supporting charities or causes, or discussing sustainability initiatives that are aimed at protecting the environment.

When you are perceived as having a good reputation, a number of positive things happen. For starters, you can invest $18 million in your business and your customers, instead of spending it on ads you hope will change people’s perceptions of your company. But good reputation management also helps create happy, loyal customers who in turn become brand advocates spreading the word about your company.

Data permeates this entire process. Successful reputation management shows up in the data your business collects. Data also will help identify the brand ambassadors who are helping you sell your products and services.  When something goes wrong, the problem might first appear – and be resolved – thanks to data. But what data giveth, data can taketh away.

A big part of building and maintaining a good reputation today means avoiding missteps like those suffered by Facebook, Equifax, Uber, Yahoo, Wells Fargo and many others. Executives clearly grasp the importance of understanding and governing their organization’s data assets. More than three-quarters of the respondents to a November 2017 survey by erwin, Inc. and UBM said understanding and governing data assets is important or very important to their executives.

Reputation Management - How Important is DG

A strong data governance practice gives businesses the needed visibility into their data – what they’re collecting, why they’re collecting it, who can access it, where it’s stored, how it’s used, and more. This visibility can help protect reputations because knowing what you have, how it’s used, and where it is helps improve data protection.

Having visibility into your data also enables transparency, which works in two ways. Internally, transparency means being able to quickly and accurately answer questions posed by executives, auditors or regulators. Customer-facing transparency means businesses have a single view of their customers, so they can quickly solve problems, answer questions, and help align the products and services most relevant to customer needs.

Both types of transparency help manage an organization’s reputation. Businesses with a well-developed strategy for data governance are less likely to be caught off guard by a data breach months after the fact, and are better positioned to deliver the modern, personalized, omnichannel customer experience today’s consumers crave.

The connection between data governance and reputation is well understood. The erwin-UBM study found that 30 percent of organizations cite reputation management as the primary driver of their data governance initiative.

Reputation Management - What's Driving Data Governance

But data governance is more than protecting data (and by extension, your reputation). It is, when done well, a practice that permeates the organization. Integrating your data governance strategy with your enterprise architecture, for example, helps you define application capabilities and interdependencies within the context of your overall strategy. It also adds a layer of protection for data beyond your Level 1 security (the passwords, firewalls, etc., we know are vulnerable).

Data governance with a business process and analysis component helps enterprises clearly define, map and analyze their 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.

For example, many businesses today are likely keeping too much data. A wave of accounting scandals in the early 2000s, most notably at Enron, led to regulations that included the need to preserve records and produce them in a timely manner. As a result, businesses started to store data like never before. Add to this new sources of data, like social media and sensors connected to the Internet of Things (IoT), and you have companies awash in data, paying (in some cases) more to store and protect it than it’s actually worth to their businesses.

When done well, data governance helps businesses make more informed decisions about data, such as whether the reward from the data they’re keeping is worth the risk and cost of storage.

“The further data gets from everyday use, it just sits on these little islands of risk,” says Danny Sandwell, director of product marketing for erwin.

All it takes is someone with bad intentions or improper training to airlift that data off the island and your firm’s reputation will crash and burn.

Alternatively, your organization can adopt data governance practices that will work to prevent data loss or misuse and enable faster remediation should a problem occur. Developing a reputation for “data responsibility” – from protecting data to transparency around its collection and use – is becoming a valuable differentiator. It’s entirely possible that as the number of data breaches and scandals continue to pile up, firms will start using their efforts toward data responsibility to enhance their reputation and appeal to customers, much in the way businesses talk about environmental sustainability initiatives.

A strong data governance foundation underpins data security and privacy. To learn more about how data governance will work for you, click here.

Examining the Data Trinity

 

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The Role of An Effective Data Governance Initiative in Customer Purchase Decisions

A data governance initiative will maximize the security, quality and value of data, all of which build customer trust.

Without data, modern business would cease to function. Data helps guide decisions about products and services, makes it easier to identify customers, and serves as the foundation for everything businesses do today. The problem for many organizations is that data enters from any number of angles and gets stored in different places by different people and different applications.

Getting the most out of your data requires that you know what you have, where you have it, and that you understand its quality and value to the organization. This is where data governance comes into play. You can’t optimize your data if it’s scattered across different silos and lurking in various applications.

For about 150 years, manufacturers relied on their machinery and its ability to run reliably, properly and safely, to keep customers happy and revenue flowing. A data governance initiative has a similar role today, except its aim is to maximize the security, quality and value of data instead of machinery.

Customers are increasingly concerned about the safety and privacy of their data. According to a survey by Research+Data Insights, 85 percent of respondents worry about technology compromising their personal privacy. In a survey of 2,000 U.S. adults in 2016, researchers from Vanson Bourne found that 76 percent of respondents said they would move away from companies with a high record of data breaches.

For years, buying decisions were driven mainly by cost and quality, says Danny Sandwell, director of product marketing at erwin, Inc. But today’s businesses must consider their reputations in terms of both cost/quality and how well they protect their customers’ data when trying to win business.

Once the reputation is tarnished because of a breach or misuse of data, customers will question those relationships.

Unfortunately for consumers, examples of companies failing to properly govern their data aren’t difficult to find. Look no further than Under Armour, which announced this spring that 150 million accounts at its MyFitnessPal diet and exercise tracking app were breached, and Facebook, where the data of millions of users was harvested by third parties hoping to influence the 2016 presidential election in the United States.

Customers Hate Breaches, But They Love Data

While consumers are quick to report concerns about data privacy, customers also yearn for (and increasingly expect) efficient, personalized and relevant experiences when they interact with businesses. These experiences are, of course, built on data.

In this area, customers and businesses are on the same page. Businesses want to collect data that helps them build the omnichannel, 360-degree customer views that make their customers happy.

These experiences allow businesses to connect with their customers and demonstrate how well they understand them and know their preferences, like and dislikes – essentially taking the personalized service of the neighborhood market to the internet.

The only way to manage that effectively at scale is to properly govern your data.

Delivering personalized service is also valuable to businesses because it helps turn customers into brand ambassadors, and it’s a fact that it’s much easier to build on existing customer relationships than to find new customers.

Here’s the upshot: If your organization is doing data governance right, it’s helping create happy, loyal customers, while at the same time avoiding the bad press and financial penalties associated with poor data practices.

Putting A Data Governance Initiative Into Action

The good news is that 76 percent of respondents to a November 2017 survey we conducted with UBM said understanding and governing the data assets in the organization was either important or very important to the executives in their organization. Nearly half (49 percent) of respondents said that customer trust/satisfaction was driving their data governance initiatives.

Importance of a data governance initiative

What stops organizations from creating an effective data governance initiative? At some businesses, it’s a cultural issue. Both the business and IT sides of the organization play important roles in data, with the IT side storing and protecting it, and the business side consuming data and analyzing it.

For years, however, data governance was the volleyball passed back and forth over the net between IT and the business, with neither side truly owning it. Our study found signs this is changing. More than half (57 percent) of the respondents said both and IT and the business/corporate teams were responsible for data in their organization.

Who's responsible for a data governance initiative

Once an organization understands that IT and the business are both responsible for data, it still needs to develop a comprehensive, holistic strategy for data governance that is capable of:

  • Reaching every stakeholder in the process
  • Providing a platform for understanding and governing trusted data assets
  • Delivering the greatest benefit from data wherever it lives, while minimizing risk
  • Helping users understand the impact of changes made to a specific data element across the enterprise.

To accomplish this, a modern data governance initiative needs to be interdisciplinary. It should include not only data governance, which is ongoing because organizations are constantly changing and transforming, but other disciples as well.

Enterprise architecture is important because it aligns IT and the business, mapping a company’s applications and the associated technologies and data to the business functions they enable.

By integrating data governance with enterprise architecture, businesses can define application capabilities and interdependencies within the context of their connection to enterprise strategy to prioritize technology investments so they align with business goals and strategies to produce the desired outcomes.

A business process and analysis component is also vital to modern data governance. It defines how the business operates and ensures employees understand and are accountable for carrying out the processes for which they are responsible.

Enterprises can clearly define, map and analyze workflows and build models to drive process improvement, as well as identify business practices susceptible to the greatest security, compliance or other risks and where controls are most needed to mitigate exposures.

Finally, data modeling remains the best way to design and deploy new relational databases with high-quality data sources and support application development.

Being able to cost-effectively and efficiently discover, visualize and analyze “any data” from “anywhere” underpins large-scale data integration, master data management, Big Data and business intelligence/analytics with the ability to synthesize, standardize and store data sources from a single design, as well as reuse artifacts across projects.

Michael Pastore is the Director, Content Services at QuinStreet B2B Tech. This content originally appeared as a sponsored post on http://www.eweek.com/.

Read the previous post on how compliance concerns and the EU’s GDPR are driving businesses to implement data governance.

Determine how effective your current data governance initiative is by taking our DG RediChek.

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Defining Data Governance: What Is Data Governance?

Data governance (DG) is one of the fastest growing disciplines, yet when it comes to defining data governance many organizations struggle.

Dataversity says DG is “the practices and processes which help to ensure the formal management of data assets within an organization.” These practices and processes can vary, depending on an organization’s needs. Therefore, when defining data governance for your organization, it’s important to consider the factors driving its adoption.

The General Data Protection Regulation (GDPR) has contributed significantly to data governance’s escalating prominence. In fact, erwin’s 2018 State of Data Governance Report found that 60% of organizations consider regulatory compliance to be their biggest driver of data governance.

Defining data governance: DG Drivers

Other significant drivers include improving customer trust/satisfaction and encouraging better decision-making, but they trail behind regulatory compliance at 49% and 45% respectively. Reputation management (30%), analytics (27%) and Big Data (21%) also are factors.

But data governance’s adoption is of little benefit without understanding how DG should be applied within these contexts. This is arguably one of the issues that’s held data governance back in the past.

With no set definition, and the historical practice of isolating data governance within IT, organizations often have had different ideas of what data governance is, even between departments. With this inter-departmental disconnect, it’s not hard to imagine why data governance has historically left a lot to be desired.

However, with the mandate for DG within GDPR, organizations must work on defining data governance organization-wide to manage its successful implementation, or face GDPR’s penalties.

Defining Data Governance: Desired Outcomes

A great place to start when defining an organization-wide DG initiative is to consider the desired business outcomes. This approach ensures that all parties involved have a common goal.

Past examples of Data Governance 1.0 were mainly concerned with cataloging data to support search and discovery. The nature of this approach, coupled with the fact that DG initiatives were typically siloed within IT departments without input from the wider business, meant the practice often struggled to add value.

Without input from the wider business, the data cataloging process suffered from a lack of context. By neglecting to include the organization’s primary data citizens – those that manage and or leverage data on a day-to-day basis for analysis and insight – organizational data was often plagued by duplications, inconsistencies and poor quality.

The nature of modern data-driven business means that such data citizens are spread throughout the organization. Furthermore, many of the key data citizens (think value-adding approaches to data use such as data-driven marketing) aren’t actively involved with IT departments.

Because of this, Data Governance 1.0 initiatives fizzled out at discouraging frequencies.

This is, of course, problematic for organizations that identify regulatory compliance as a driver of data governance. Considering the nature of data-driven business – with new data being constantly captured, stored and leveraged – meeting compliance standards can’t be viewed as a one-time fix, so data governance can’t be de-prioritized and left to fizzle out.

Even those businesses that manage to maintain the level of input data governance needs on an indefinite basis, will find the Data Governance 1.0 approach wanting. In terms of regulatory compliance, the lack of context associated with data governance 1.0, and the inaccuracies it leads to mean that potentially serious data governance issues could go unfounded and result in repercussions for non-compliance.

We recommend organizations look beyond just data cataloging and compliance as desired outcomes when implementing DG. In the data-driven business landscape, data governance finds its true potential as a value-added initiative.

Organizations that identify the desired business outcome of data governance as a value-added initiative should also consider data governance 1.0’s shortcomings and any organizations that hasn’t identified value-adding as a business outcome, should ask themselves, “why?”

Many of the biggest market disruptors of the 21st Century have been digital savvy start-ups with robust data strategies – think Airbnb, Amazon and Netflix. Without high data governance standards, such companies would not have the level of trust in their data to confidently action such digital-first strategies, making them difficult to manage.

Therefore, in the data-driven business era, organizations should consider a Data Governance 2.0 strategy, with DG becoming an organization-wide, strategic initiative that de-silos the practice from the confines of IT.

This collaborative take on data governance intrinsically involves data’s biggest beneficiaries and users in the governance process, meaning functions like data cataloging benefit from greater context, accuracy and consistency.

It also means that organizations can have greater trust in their data and be more assured of meeting the standards set for regulatory compliance. It means that organizations can better respond to customer needs through more accurate methods of profiling and analysis, improving rates of satisfaction. And it means that organizations are less likely to suffer data breaches and their associated damages.

Defining Data Governance: The Enterprise Data Governance Experience (EDGE)

The EDGE is the erwin approach to Data Governance 2.0, empowering an organization to:

  • Manage any data, anywhere (Any2)
  • Instil a culture of collaboration and organizational empowerment
  • Introduce an integrated ecosystem for data management that draws from one central repository and ensures data (including real-time changes) is consistent throughout the organization
  • Have visibility across domains by breaking down silos between business and IT and introducing a common data vocabulary
  • Have regulatory peace of mind through mitigation of a wide range of risks, from GDPR to cybersecurity. 

To learn more about implementing data governance, click here.

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Data Governance & GDPR: How it Will Affect Your Business

If you’re a data professional, data governance and GDPR are likely at the top of your agenda right now.

Because if your organization exists within the European Union (EU) or trades with the EU, the General Data Protection Regulation (GDPR) will affect your operations.

Despite this fact, only 6% of organizations say they are “completely prepared” ahead of the mandate’s May 25 effective date, according to the 2018 State of Data Governance Report.

Perhaps some solace can be found in that 39% of those surveyed for the report indicate they are “somewhat prepared,” with 27% starting preparations.

But 11% indicate they are “not prepared at all,” and the most damning of revelations is that 17% of organizations believe GDPR does not affect them.

I’m afraid these folks and their organizations are misguided because any company in any industry is within GDPR’s reach. Even if only one EU citizen’s data is included within an organization’s database(s), compliance is mandatory.

So it’s important for organizations to understand exactly what they need to do before the deadline and the potential fines of up to €20 million or 4% of annual turnover, whichever is greater.

How Does GDPR Affect My Business

With the advent of any new regulation, it’s crucial that organizations know which elements of their organization are affected and what they need to do to stay compliant. Regarding the latter, the GDPR requires organizations to have a comprehensive and effective data governance strategy. In terms of the areas affected, organizations need to be aware of the following:

Personally Identifiable Information (PII)

GDPR introduces tighter regulations around the storage, management and transfer of PII. According to the GDPR, personal data is any information related to a person such as a name, a photo, an email address, bank details, updates on social networking websites, location details, medical information, or a computer IP address.

Personal data also comes in many forms and extends to the combination of different data elements that individually are not PII but contribute to PII status when consolidated.

Data governance allows organizations to more easily identify and classify PII and in turn, introduce appropriate measures to keep it safe.

Therefore, a good data governance solution should enable organizations to add and manage metadata – the data about data – regarding a unit of data’s sensitivity. It should also have strong data discoverability capabilities, and the ability to control access to data through user-based permissions.

Active Consent, Data Processing and the Right to Be Forgotten

GDPR also strengthens the conditions for consent, which must be clear and distinguishable from other matters and provided in an intelligible and easily accessible form, using clear and plain language. It must be as easy to withdraw consent as it is to give it.​

Data subjects also have the right to obtain confirmation as to whether their personal data is being processed, where and for what purpose. The data controller must provide a copy of said personal data in an electronic format – free of charge. This change is a dramatic shift in data transparency and consumer empowerment.

The right to be forgotten entitles the data subject to have the data controller erase his/her personal data, cease further dissemination of the data, and potentially have third parties halt processing of the data.

The information and processes required to address these restrictions can be found in the metadata and managed via metadata management tools – a key facet of data governance. Better management of such metadata is key to optimizing an organization’s data processing capabilities. Without such optimization, compliance with the GDPR-granted “right to be forgotten” can become too complex to effictively manage.

Gartner Magic Quadrant

Documenting Compliance and Data Breaches

GDPR also looks to curb data breaches that have become more extensive and frequent in recent years. Data’s value has sky-rocketed, making data-driven businesses targets of cyber threats.

Organizations must document what data they have, where it resides, the controls in place to protect it, and the measures that will be taken to address mistakes/breaches. In fact, data breach notification is mandatory within 72 hours if that breach is likely to “result in risk for the rights and freedoms of individuals.”

A comprehensive data governance strategy encompasses and enables the documentation process outlined above. However, a data governance strategy decreases the likelihood of such breaches occurring as it provides organizations with greater insight as to which data should be more closely guarded.

Data Governance and GDPR Compliance

Based on the results of the State of DG Report referenced at the beginning of this post, organizations aren’t as GDPR-ready as they should be. But there’s still time to act.

Data governance and GDPR go hand in hand. A strong data governance program is critical to the data visibility and categorization needed for GDPR compliance. And it will help in assessing and prioritizing data risks and enable easier verification of compliance with GDPR auditors.

Data governance enables an organization to discover, understand, govern and socialize its data assets – not just within IT but across the entire organization. Not only does it encompass data’s current iteration but also its entire lineage and connections through the data ecosystem.

Understanding data lineage is absolutely necessary in the context of GDPR. Take the right to be forgotten, for example. Such compliance requires an organization to locate all an individual’s PII and any information that can be cross-referenced with other data points to become PII.

With the right data governance approach and supporting technology, organizations can ensure GDPR compliance with their current, as-is architecture and data assets – and ensure new data sources and/or changes to the to-be architecture incorporate the appropriate controls.

Stakeholders across the enterprise need to be GDPR aware and enabled so that compliance is built in at a cultural level.

For more information about increasing your expertise in relation to data governance and GDPR, download our guide to managing GDPR with data governance.

Data Governance, GDPR and Your Business

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State of DG: Shocking Number of Organizations Unprepared for GDPR, Is Yours?

The General Data Protection Regulation (GDPR) goes into effect in May, but a new study reveals that most organizations are overwhelmingly unprepared.

The State of Data Governance Report finds that only 6% of respondents consider themselves completely prepared for GDPR. That means a shocking 94% of the organizations surveyed are not ready for what is one of the most important data privacy and security regulations passed in recent years.

Failure to implement data governance (DG) to comply with GDPR will leave these organizations liable for fines of up to €20 million or 4% annual global turnover – whichever is greater.

But the news isn’t all bad; promising signs can be found. Although 46% of those surveyed indicate having “no formal strategy” in place for DG, 42% describe their data governance initiatives as a “work in progress.”

State of DG: Regulatory Compliance Driving Data Governance

Historically, data governance has left a lot to be desired. The value and ROI were insignificant to non-existent, and so executive buy-in and funding also has been low.

Business leaders usually left DG to their IT departments, but that created silos that cut off DG from it’s day to day “data owners” and “data stakeholders,” – in essence, everybody that uses data to drive business. With poor data discovery, lineage and context, data governance was largely abandoned or at least out of sight, out of mind.

Forty-two percent of the organizations participating in the State of DG Report survey indicate that lack of executive support is still a roadblock. But GDPR is spurring new interest in DG because companies must articulate what their data is, where it resides, what controls are in place to protect it, and the measures they will use to address mistakes/breaches.

An effective data governance initiative is critical for the data visibility and categorization needed to comply with GDPR. It also will help assess and prioritize data risks and enable easier verification of GDPR compliance to auditors.

Perhaps this is why 66% of those surveyed for the State of DG Report say understanding and governing enterprise assets has become more important or very important for their executives. And regulatory compliance is in fact the No. 1 driver for data governance.

State of DG: Implementing Data Governance for GDPR

It’s safe to say that organizations should be much further along with GDPR than they are.

The biggest challenge is to establish compliance with their current data architectures and then to build GDPR compliance into the processes for designing and deploying new data sources.

This requires visibility into the strategic roadmap and well-defined processes to govern new data deployments so that constant GDPR retrofits aren’t required.

Thankfully data governance has evolved from a siloed, IT-owned program primarily for data cataloging to support search and discovery. It has given way to proactive, enterprise-wide data governance to support regulatory compliance in addition to data-driven insights for achieving other organizational objectives.

Data Governance 2.0 understands that CTOs, CMOs and other C-level executives and business leaders across the enterprise are involved in data creation, management and use on a day-to-day basis. And GDPR compliance requires that all stakeholders be aware and empowered so that data governance is built in, and part of the culture.

By integrating data governance with enterprise architecture, business process and data modeling, you’ll have a GDPR compliance framework to:

  • Discover and harvest data assets
  • Classify data and create a GDPR inventory
  • Perform GDPR risk analysis
  • Define GDPR controls and standard operating procedures
  • Socialize and apply GDPR requirements across the organization
  • Implement GDPR controls into IT and business roadmaps for “compliance by design”
  • Prove compliance/respond to audits

Is your organization GDPR-ready?

Click here to get your State of DG Report to see how your organization compares to those we surveyed.

Of if you’d like to discuss how to improve your GDPR readiness with one of our solution specialists, click here.

State of DG: Get the full report