Tag: General Data Protection Regulation

Categories
erwin Expert Blog

erwin Automation Framework: Achieving Faster Time-to-Value in Data Preparation, Deployment and Governance

Data governance is more important to the enterprise than ever before. It ensures everyone in the organization can discover and analyze high-quality data to quickly deliver business value.

It assists in successfully meeting increasingly strict compliance requirements, such as those in the General Data Protection Regulation (GDPR). And it provides a clear gauge on business performance.

A mature and sustainable data governance initiative must include data integration.

This often requires reconciling two groups of individuals within the organization: 1) those who care about governance and the meaningful use of data and 2) those who care about getting and transforming the data from source to target for actionable insights.

Both ends of the data value chain are covered when governance is coupled programmatically with IT’s integration practices.

The tools and processes for this should automatically generate “pre-ETL” source-to-target mapping to minimize human errors that can occur while manually compiling and interpreting a multitude of Excel-based data mappings that exist across the organization.

In addition to reducing errors and improving data quality, the efficiencies gained through automation, including minimizing rework, can help cut system development lifecycle costs in half.

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.

Data Governance and the System Development Lifecycle

Boosting data governance maturity starts with a central metadata repository (data dictionary) for version-controlling metadata imported from a broad array of file and database types to inform data mappings. It can be used to automatically generate governed design mappings and code in the design phase of the system development lifecycle.

The right toolset – one that supports a unifying and underlying metadata model – will be a design and code-generation platform that introduces efficiency, visibility and governance principles while reducing the opportunity for human error.

Automatically generating ETL/ELT jobs for leading ETL tools based on best design practices accommodates those principles; it functions according to approved corporate and industry standards.

Automatically importing mappings from developers’ Excel sheets, flat files, access and ETL tools into a comprehensive mappings inventory, complete with automatically generated and meaningful documentation of the mappings, is a powerful way to support governance while providing real insight into data movement – for lineage and impact analysis – without interrupting system developers’ normal work methods.

GDPR compliance, for example, requires a business to discover source-to-target mappings with all accompanying transactions, such as what business rules in the repository are applied to it, to comply with audits.

THE REGULATORY RATIONALE FOR INTEGRATING DATA MANAGEMENT & DATA GOVERNANCE

When data movement has been tracked and version-controlled, it’s possible to conduct data archeology – that is, reverse-engineering code from existing XML within the ETL layer – to uncover what has happened in the past and incorporating it into a mapping manager for fast and accurate recovery.

This is one example of how to meet data governance demands with more agility and accuracy at high speed.

Faster Time-to-Value with the erwin Automation Framework

The erwin Automation Framework is a metadata-driven universal code generator that works hand in hand with erwin Mapping Manager (MM) 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

If you’d like to save time and money in preparing, deploying and governing you organization’s data, please join us for a demo of erwin MM.

Automate Data Mapping

Categories
erwin Expert Blog

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

Categories
erwin Expert Blog

Defining DG: What Can Data Governance Do for You?

Data governance (DG) is becoming more commonplace because of data-driven business, yet defining DG and putting into sound practice is still difficult for many organizations.

Defining DG

The absence of a standard approach to defining DG could be down to its history of missed expectations, false starts and negative perceptions about it being expensive, intrusive, impeding innovation and not delivering any value. Without success stories to point to, the best way of doing and defining DG wasn’t clear.

On the flip side, the absence of a standard approach to defining DG could be the reason for its history of lacklustre implementation efforts, because those responsible for overseeing it had different ideas about what should be done.

Therefore, it’s been difficult to fully fund a data governance initiative that is underpinned by an effective data management capability. And many organizations don’t distinguish between data governance and data management, using the terms interchangeably and so adding to the confusion.

Defining DG: The Data Governance Conundrum

While research indicates most view data governance as “critically important” or they recognize the value of data, the large percentage without a formal data governance strategy in place indicates there are still significant teething problems.

How Important is Data Governance

And that’s the data governance conundrum. It is essential but unwanted and/or painful.

It is a complex chore, so organizations have lacked the motivation to start and effectively sustain it. But faced with the General Data Protection Regulation (GDPR) and other compliance requirements, they have been doing the bare minimum to avoid the fines and reputational damage.

And arguably, herein lies the problem. Organizations look at data governance as something they have to do rather than seeing what it could do for them.

Data governance has its roots in the structure of business terms and technical metadata, but it has tendrils and deep associations with many other components of a data management strategy and should serve as the foundation of that platform.

With data governance at the heart of data management, data can be discovered and made available throughout the organization for both IT and business stakeholders with approved access. This means enterprise architecture, business process, data modeling and data mapping all can draw from a central metadata repository for a single source of data truth, which improves data quality, trust and use to support organizational objectives.

But this “data nirvana” requires a change in approach to data governance. First, recognizing that Data Governance 1.0 was made for a different time when the volume, variety and velocity of the data an organization had to manage was far lower and when data governance’s reach only extended to cataloging data to support search and discovery. 

Data Governance Evolution

Modern data governance needs to meet the needs of data-driven business. We call this adaptation “Evolving DG.” It is the journey to a cost-effective, mature, repeatable process that permeates the whole organization.

The primary components of Evolving DG are:

  • Evaluate
  • Plan
  • Configure
  • Deliver
  • Feedback

The final step in such an evolution is the implementation of the erwin Enterprise Data Governance Experience (EDGE) platform.

The erwin EDGE places data governance at the heart of the larger data management suite. By unifying the data management suite at a fundamental level, an organization’s data is no longer marred by departmental and software silos. It brings together both IT and the business for data-driven insights, regulatory compliance, agile innovation and business transformation.

It allows every critical piece of the data management and data governance lifecycle to draw from a single source of data truth and ensure quality throughout the data pipeline, helping organizations achieve their strategic objectives including:

  • Operational efficiency
  • Revenue growth
  • Compliance, security and privacy
  • Increased customer satisfaction
  • Improved decision-making

To learn how you can evolve your data governance practice and get an EDGE on your competition, click here.

Solving the Enterprise Data Dilemma

Categories
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

Categories
erwin Expert Blog

Data Governance Tackles the Top Three Reasons for Bad Data

In modern, data-driven busienss, it’s integral that organizations understand the reasons for bad data and how best to address them. Data has revolutionized how organizations operate, from customer relationships to strategic decision-making and everything in between. And with more emphasis on automation and artificial intelligence, the need for data/digital trust also has risen. Even minor errors in an organization’s data can cause massive headaches because the inaccuracies don’t involve just one corrupt data unit.

Inaccurate or “bad” data also affects relationships to other units of data, making the business context difficult or impossible to determine. For example, are data units tagged according to their sensitivity [i.e., personally identifiable information subject to the General Data Protection Regulation (GDPR)], and is data ownership and lineage discernable (i.e., who has access, where did it originate)?

Relying on inaccurate data will hamper decisions, decrease productivity, and yield suboptimal results. Given these risks, organizations must increase their data’s integrity. But how?

Integrated Data Governance

Modern, data-driven organizations are essentially data production lines. And like physical production lines, their associated systems and processes must run smoothly to produce the desired results. Sound data governance provides the framework to address data quality at its source, ensuring any data recorded and stored is done so correctly, securely and in line with organizational requirements. But it needs to integrate all the data disciplines.

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 enables an organization to 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.

And 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.

Let’s look at some of the main reasons for bad data and how data governance helps confront these issues …

Reasons for Bad Data

Reasons for Bad Data: Data Entry

The concept of “garbage in, garbage out” explains the most common cause of inaccurate data: mistakes made at data entry. While this concept is easy to understand, totally eliminating errors isn’t feasible so organizations need standards and systems to limit the extent of their damage.

With the right data governance approach, organizations can ensure the right people aren’t left out of the cataloging process, so the right context is applied. Plus you can ensure critical fields are not left blank, so data is recorded with as much context as possible.

With the business process integration discussed above, you’ll also have a single metadata repository.

All of this ensures sensitive data doesn’t fall through the cracks.

Reasons for Bad Data: Data Migration

Data migration is another key reason for bad data. Modern organizations often juggle a plethora of data systems that process data from an abundance of disparate sources, creating a melting pot for potential issues as data moves through the pipeline, from tool to tool and system to system.

The solution is to introduce a predetermined standard of accuracy through a centralized metadata repository with data governance at the helm. In essence, metadata describes data about data, ensuring that no matter where data is in relation to the pipeline, it still has the necessary context to be deciphered, analyzed and then used strategically.

The potential fallout of using inaccurate data has become even more severe with the GDPR’s implementation. A simple case of tagging and subsequently storing personally identifiable information incorrectly could lead to a serious breach in compliance and significant fines.

Such fines must be considered along with the costs resulting from any PR fallout.

Reasons for Bad Data: Data Integration

The proliferation of data sources, types, and stores increases the challenge of combining data into meaningful, valuable information. While companies are investing heavily in initiatives to increase the amount of data at their disposal, most information workers are spending more time finding the data they need rather than putting it to work, according to Database Trends and Applications (DBTA). erwin is co-sponsoring a DBTA webinar on this topic on July 17. To register, click here.

The need for faster and smarter data integration capabilities is growing. At the same time, to deliver business value, people need information they can trust to act on, so balancing governance is absolutely critical, especially with new regulations.

Organizations often invest heavily in individual software development tools for managing projects, requirements, designs, development, testing, deployment, releases, etc. Tools lacking inter-operability often result in cumbersome manual processes and heavy time investments to synchronize data or processes between these disparate tools.

Data integration combines data from several various sources into a unified view, making it more actionable and valuable to those accessing it.

Getting the Data Governance “EDGE”

The benefits of integrated data governance discussed above won’t be realized if it is isolated within IT with no input from other stakeholders, the day-to-day data users – from sales and customer service to the C-suite. Every data citizen has DG roles and responsibilities to ensure data units have context, meaning they are labeled, cataloged and secured correctly so they can be analyzed and used properly. In other words, the data can be trusted.

Once an organization understands that IT and the business are both responsible for data, it can develop comprehensive, holistic data governance 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 reduce the risks of and tackle the reasons for bad data and realize larger organizational objectives, organizations must make data governance everyone’s business.

To learn more about the collaborative approach to data governance and how it helps compliance in addition to adding value and reducing costs, get the free e-book here.

Data governance is everyone's business

Categories
erwin Expert Blog

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.

Take the DG RediChek

Categories
erwin Expert Blog

GDPR, Compliance Concerns Driving Data Governance Strategies

There are many factors driving data governance adoption, as revealed in erwin’s State of Data Governance Report. Over the coming weeks, we’ll be exploring them in detail, starting with regulatory compliance.

By Michael Pastore

Almost every organization views data governance as important, so why don’t they all have it in place?

Modern organizations run on data. Whether from sensors monitoring equipment on a factory floor or a customer’s purchasing history, data enters modern businesses from every angle, gets stored in any number of places, and is used by many different people and applications.

Data governance refers to the practices that help businesses understand where their data comes from, where it resides, how accurate it is, who or what can access it, and how it can be used. The idea of data governance is not new, but putting data governance into practice and reaping the benefits remains a struggle for many organizations.

According to our November 2017 survey with UBM, nearly all (98 percent) respondents said their organizations view data governance as either important or critically important from a business perspective. Despite this, 46 percent of respondents indicated their organizations recognize the value of data, but lack a formal governance strategy.

One of the significant obstacles to data governance for many organizations is the idea of ownership. In many businesses, it’s safe to say that the IT organization has ownership over the network, just as it’s easy to say that the business oversees payroll.

Data is a bit more complicated. The business side of the organization often analyzes the data, but it’s the IT organization that stores and protects it. This data division of labor often leaves data governance in a sort of no-man’s land, with each side expecting the other to pick up the torch.

The results of the erwin-UBM survey indicate that businesses are increasingly treating data governance as an enterprise-wide imperative. At 57 percent of respondents’ organizations, both IT and the business are responsible for data governance. Just 34 percent of the organizations put IT solely in charge.

Strong data governance initiatives will overcome the issue of ownership thanks in part to a new organizational structure that considers the importance of data. The emergence of the chief data officer (CDO) is one sign that businesses recognize the vital role of their data.

Many of the first generation of CDOs reported to the CIO. Now, you’re more likely to see the CDO at forward-thinking organizations sit on the business side, perhaps in the finance department, or even marketing, which is a huge consumer of data in many businesses. Under the CDO, it’s increasingly likely to find a data protection officer (DPO) tasked with overseeing how the business safeguards its information.

What's Driving Data Governance

Driving Data Governance: Compliance Is Leading Organizations to Data Governance

Now is a good time for businesses to re-think their data structure and governance initiatives. Data is central to organizations’ compliance, privacy and security initiatives because it has value — value to the business; value to the customer; and, like anything of value, value to criminals who want to get their hands on it.

The need to protect data and reduce risk is an important factor in driving data governance at many organizations. In fact, our survey found that regulatory compliance, cited by 60 percent of respondents, was the most popular factor driving data governance.

There’s an increased sense of urgency regarding data governance and compliance because of the European Union’s General Data Protection Regulation (GDPR), which goes into effect this month. According to our research, only 6 percent of respondents said their organization was “completely prepared” for the regulation.

Not only does the GDPR protect EU citizens at home, but it extends protections to EU citizens wherever they do business. It really goes much farther than any other legislation ever has.

The GDPR essentially gives rights to the people the data represents, so businesses must:

  • Minimize identifiability in data
  • Report data breaches within 72 hours
  • Give consumers the ability to dispute data and demand data portability
  • Understand the GDPR’s expanded definition of personally identifiable information (PII)
  • Extend to consumers the right to be “forgotten”

And much, much more.

The maximum fine for organizations in breach of the GDPR is up to 4 percent of annual global turnover or €20 million, whichever is greater. And because the GDPR will apply to anyone doing business with EU citizens, and the internet transcends international borders, it’s likely the GDPR will become the standard organizations around the world will need to rise to meet.

The GDPR is a hot topic right now, but it’s not the only data-security regulation organizations have to honor. In addition to Payment Card Industry (PCI) standards for payment processors, industry-specific regulations exist in such areas as financial services, healthcare and education.

This web of regulations brings us back to data governance. Simply put, it’s easier to protect data and mitigate a breach if your organization knows where the data comes from, where it is stored, and what it includes.

Businesses stand to gain a number of advantages by implementing strong data governance. Regulatory compliance is sure to get the attention of C-level executives, the legal team and the board, but it means very little to consumers – until there’s a breach.

With new breaches being reported on a seemingly daily basis, businesses that practice strong data governance can help build a competitive advantage by better protecting their data and gaining a reputation as an organization that can be trusted in a way that firms suffering from high-profile breaches cannot. In this way, data governance helps contribute directly to the bottom line.

Still, compliance is the No. 1 factor driving data governance initiatives for a reason.

Using data governance to drive upside growth is great, but not if you’re going to lose money in fines.

In our next post in this series, we’ll explore how your organization can use data governance to build trust with your customers.

 

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

Learn more about how data governance can help with GDPR compliance by downloading the free white paper: GDPR and Your Business: A Call to Enhance Data Governance Expertise.

Data Governance and GDPR: GDPR and Your Business Whitepaper

Categories
erwin Expert Blog

Five Pillars of Data Governance Readiness: Delivery Capability

The five pillars of data governance readiness should be the starting point for implementing or revamping any DG initiative.

In a recent CSO Magazine article, “Why data governance should be corporate policy,” the author states: “Data is like water, and water is a fundamental resource for life, so data is an essential resource for the business. Data governance ensures this resource is protected and managed correctly enabling us to meet our customer’s expectations.”

Over the past few weeks, we’ve been exploring the five pillars of data governance (DG) readiness, and this week we turn our attention to the fifth and final pillar, delivery capability.

Together, the five pillars of data governance readiness work as a step-by-step guide to a successful DG implementation and ongoing initiative.

As a refresher, the first four pillars are:

  1. The starting point is garnering initiative sponsorship from executives, before fostering support from the wider organization.

 

  1. Organizations should then appoint a dedicated team to oversee and manage the initiative. Although DG is an organization-wide strategic initiative, it needs experience and leadership to guide it.

 

  1. Once the above pillars are accounted for, the next step is to understand how data governance fits with the wider data management suite so that all components of a data strategy work together for maximum benefits.

 

  1. And then enterprise data management methodology as a plan of action to assemble the necessary tools.

Once you’ve completed these steps, how do you go about picking the right solution for enterprise-wide data governance?

Five Pillars of Data Governance: Delivery Capability – What’s the Right Solution?

Many organizations don’t think about enterprise data governance technologies when they begin a data governance initiative. They believe that using some general-purpose tool suite like those from Microsoft can support their DG initiative. That’s simply not the case.

Selecting the proper data governance solution should be part of developing the data governance initiative’s technical requirements. However, the first thing to understand is that the “right” solution is subjective.

Data stewards work with metadata rather than data 80 percent of the time. As a result, successful and sustainable data governance initiatives are supported by a full-scale, enterprise-grade metadata management tool.

Additionally, many organizations haven’t implemented data quality products when they begin a DG initiative. Product selections, including those for data quality management, should be based on the organization’s business goals, its current state of data quality and enterprise data management, and best practices as promoted by the data quality management team.

If your organization doesn’t have an existing data quality management product, a data governance initiative can support the need for data quality and the eventual evaluation and selection of the proper data quality management product.

Enterprise data modeling is also important. A component of enterprise data architecture, it’s an enabling force in the performance of data management and successful data governance. Having the capability to manage data architecture and data modeling with the optimal products can have a positive effect on DG by providing the initiative architectural support for the policies, practices, standards and processes that data governance creates.

Finally, and perhaps most important, the lack of a formal data governance team/unit has been cited as a leading cause of DG failure. Having the capability to manage all data governance and data stewardship activities has a positive effect.

Shopping for Data Governance Technology

DG is part of a larger data puzzle. Although it’s a key enabler of data-driven business, it’s only effective in the context of the data management suite in which it belongs.

Therefore when shopping for a data governance solution, organizations should look for DG tools that unify critical data governance domains, leverage role-appropriate interfaces to bring together stakeholders and processes to support a culture committed to acknowledging data as the mission-critical asset that it is, and orchestrate the key mechanisms required to discover, fully understand, actively govern and effectively socialize and align data to the business.

Data Governance Readiness: Delivery Capability

Here’s an initial checklist of questions to ask in your evaluation of a DG solution. Does it support:

  • Relational, unstructured, on-premise and cloud data?
  • Business-friendly environment to build business glossaries with taxonomies of data standards?
  • Unified capabilities to integrate business glossaries, data dictionaries and reference data, data quality metrics, business rules and data usage policies?
  • Regulating data and managing data collaboration through assigned roles, business rules and responsibilities, and defined governance processes and workflows?
  • Viewing data dashboards, KPIs and more via configurable role-based interfaces?
  • Providing key integrations with enterprise architecture, business process modeling/management and data modeling?
  • A SaaS model for rapid deployment and low TCO?

To assess your data governance readiness, especially with the General Data Protection Regulation about to take effect, click here.

You also can try erwin DG for free. Click here to start your free trial.

Take the DG RediChek

Categories
erwin Expert Blog

Pillars of Data Governance Readiness: Enterprise Data Management Methodology

Facebook’s data woes continue to dominate the headlines and further highlight the importance of having an enterprise-wide view of data assets. The high-profile case is somewhat different than other prominent data scandals as it wasn’t a “breach,” per se. But questions of negligence persist, and in all cases, data governance is an issue.

This week, the Wall Street Journal ran a story titled “Companies Should Beware Public’s Rising Anxiety Over Data.” It discusses an IBM poll of 10,000 consumers in which 78% of U.S. respondents say a company’s ability to keep their data private is extremely important, yet only 20% completely trust organizations they interact with to maintain data privacy. In fact, 60% indicate they’re more concerned about cybersecurity than a potential war.

The piece concludes with a clear lesson for CIOs: “they must make data governance and compliance with regulations such as the EU’s General Data Protection Regulation [GDPR] an even greater priority, keeping track of data and making sure that the corporation has the ability to monitor its use, and should the need arise, delete it.”

With a more thorough data governance initiative and a better understanding of data assets, their lineage and useful shelf-life, and the privileges behind their access, Facebook likely could have gotten ahead of the problem and quelled it before it became an issue.  Sometimes erasure is the best approach if the reward from keeping data onboard is outweighed by the risk.

But perhaps Facebook is lucky the issue arose when it did. Once the GDPR goes into effect, this type of data snare would make the company non-compliant, as the regulation requires direct consent from the data owner (as well as notification within 72 hours if there is an actual breach).

Five Pillars of DG: Enterprise Data Management Methodology

Considering GDPR, as well as the gargantuan PR fallout and governmental inquiries Facebook faced, companies can’t afford such data governance mistakes.

During the past few weeks, we’ve been exploring each of the five pillars of data governance readiness in detail and how they come together to provide a full view of an organization’s data assets. In this blog, we’ll look at enterprise data management methodology as the fourth key pillar.

Enterprise Data Management in Four Steps

Enterprise data management methodology addresses the need for data governance within the wider data management suite, with all components and solutions working together for maximum benefits.

A successful data governance initiative should both improve a business’ understanding of data lineage/history and install a working system of permissions to prevent access by the wrong people. On the flip side, successful data governance makes data more discoverable, with better context so the right people can make better use of it.

This is the nature of Data Governance 2.0 – helping organizations better understand their data assets and making them easier to manage and capitalize on – and it succeeds where Data Governance 1.0 stumbled.

Enterprise Data Management: So where do you start?

  1. Metadata management provides the organization with the contextual information concerning its data assets. Without it, data governance essentially runs blind.

The value of metadata management is the ability to govern common and reference data used across the organization with cross-departmental standards and definitions, allowing data sharing and reuse, reducing data redundancy and storage, avoiding data errors due to incorrect choices or duplications, and supporting data quality and analytics capabilities.

  1. Your organization also needs to understand enterprise data architecture and enterprise data modeling. Without it, enterprise data governance will be hard to support

Enterprise data architecture supports data governance through concepts such as data movement, data transformation and data integration – since data governance develops policies and standards for these activities.

Data modeling, a vital component of data architecture, is also critical to data governance. By providing insights into the use cases satisfied by the data, organizations can do a better job of proactively analyzing the required shelf-life and better measure the risk/reward of keeping that data around.

Data stewards serve as SMEs in the development and refinement of data models and assist in the creation of data standards that are represented by data models. These artifacts allow your organization to achieve its business goals using enterprise data architecture.

  1. Let’s face it, most organizations implement data governance because they want high quality data. Enterprise data governance is foundational for the success of data quality management.

Data governance supports data quality efforts through the development of standard policies, practices, data standards, common definitions, etc. Data stewards implement these data standards and policies, supporting the data quality professionals.

These standards, policies, and practices lead to effective and sustainable data governance.

  1. Finally, without business intelligence (BI) and analytics, data governance will not add any value. The value of data governance to BI and analytics is the ability to govern data from its sources to destinations in warehouses/marts, define standards for data across those stages, and promote common algorithms and calculations where appropriate. These benefits allow the organization to achieve its business goals with BI and analytics.

Gaining an EDGE on the Competition

Old-school data governance is one-sided, mainly concerned with cataloging data to support search and discovery. The lack of short-term value here often caused executive support to dwindle, so the task of DG was siloed within IT.

These issues are circumvented by using the collaborative Data Governance 2.0 approach, spreading the responsibility of DG among those who use the data. This means that data assets are recorded with more context and are of greater use to an organization.

It also means executive-level employees are more aware of data governance working as they’re involved in it, as well as seeing the extra revenue potential in optimizing data analysis streams and the resulting improvements to times to market.

We refer to this enterprise-wide, collaborative, 2.0 take on data governance as the enterprise data governance experience (EDGE). But organizational collaboration aside, the real EDGE is arguably the collaboration it facilitates between solutions. The EDGE platform recognizes the fundamental reliance data governance has on the enterprise data management methodology suite and unifies them.

By existing on one platform, and sharing one repository, organizations can guarantee their data is uniform across the organization, regardless of department.

Additionally, it drastically improves workflows by allowing for real-time updates across the platform. For example, a change to a term in the data dictionary (data governance) will be automatically reflected in all connected data models (data modeling).

Further, the EDGE integrates enterprise architecture to define application capabilities and interdependencies within the context of their connection to enterprise strategy, enabling technology investments to be prioritized in line with business goals.

Business process also is included so 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.

Essentially, it’s the approach data governance needs to become a value-adding strategic initiative instead of an isolated effort that peters out.

To learn more about enterprise data management and getting an EDGE on GDPR and the competition, click here.

To assess your data governance readiness ahead of the GDPR, click here.

Take the DG RediChek

Categories
erwin Expert Blog

Five Pillars of Data Governance Readiness: Team Resources

The Facebook scandal has highlighted the need for organizations to understand and apply the five pillars of data governance readiness.

All eyes were on Mark Zuckerberg this week as he testified before the U.S. Senate and Congress on Facebook’s recent data drama.

A statement from Facebook indicates that the data snare was created due to permission settings leveraged by the Facebook-linked third-party app ‘thisisyourdigitallife.’

Although the method used by Cambridge Analytica to amass personal data from 87 million Facebook users didn’t constitute a “data breach,” it’s still a major data governance (DG) issue that is now creating more than a headache for the company.

The #DeleteFacebook movement is gaining momentum, not to mention the company’s stock dip.

With Facebook’s DG woes a mainstay in global news cycles, and the General Data Protection Regulation’s (GDPR) implementation just around the corner, organizations need to get DG-ready.

During the past few weeks, the erwin Expert Blog has been exploring the five pillars of data governance readiness. So far, we’ve covered initiative sponsorship and organizational support. Today, we talk team resources.

Facebook and the Data Governance Awakening

Most organizations lack the enterprise-level experience required to advance a data governance initiative.

This function may be called by another name (e.g., data management, information management, enterprise data management, etc.), a successful organization recognizes the need for managing data as an enterprise asset.

Data governance, as a foundational component of enterprise data management, would reside within such a group.

You would think an organization like Facebook would have this covered. However, it doesn’t appear that they did.

The reason Facebook is in hot water is because the platform allowed ‘thisisyourdigitallife’ to capture personal data from the Facebook friends of those who used the app, increasing the scope of the data snare by an order of magnitude.

Pillars of Data Governance; Facebook

For context, it took only 53 Australian ‘thisisyourdigitallife’ users to capture 310,000 Australian citizens’ data.

Facebook’s permission settings essentially enabled ‘thisisyourdigitallife’ users to consent on behalf of their friends. Had GDPR been in effect, Facebook would have been non-compliant.

Even so, the extent of the PR fallout demonstrates that regulatory compliance shouldn’t be the only driver for implementing data governance.

Understanding who has access to data and what that data can be used for is a key use case for data governance. This considered, it’s not difficult to imagine how a more robust DG program could have covered Facebook’s back.

Data governance is concerned with units of data – what are they used for, what are the associated risks, and what value do they have to the business? In addition, DG asks who is responsible for the data – who has access? And what is the data lineage?

It acts as the filter that makes data more discoverable to those who need it, while shutting out those without the required permissions.

The Five Pillars of Data Governance: #3 Team Resources

Data governance can’t be executed as a short-term fix. It must be an on-going, strategic initiative that the entire organization supports and is part of. But ideally, a fixed and formal data management group needs to oversee it.

As such, we consider team resources one of the key pillars of data governance readiness.

Data governance requires leadership with experience to ensure the initiative is a value-adding success, not the stifled, siloed programs associated with data governance of old (Data Governance 1.0).

Without experienced leadership, different arms of the organization will likely pull in different directions, undermining the uniformity of data that DG aims to introduce. If such experience doesn’t exist within the organization, then outside consultants should be tapped for their expertise.

As the main technical enabler of the practice, IT should be a key DG participant and even house the afore-mentioned data management group to oversee it. The key word here is “participant,” as the inclination to leave data governance to IT and IT alone has been a common reason for Data Governance 1.0’s struggles.

With good leadership, organizations can implement Data Governance 2.0: the collaborative, outcome-driven approach more suited to the data-driven business landscape. DG 2.0 avoids the pitfalls of its predecessor by expanding the practice beyond IT and traditional data stewards to make it an enterprise-wide responsibility.

By approaching data governance in this manner, organizations ensure those with a stake in data quality (e.g., anyone who uses data) are involved in its discovery, understanding, governance and socialization.

This leads to data with greater context, accuracy and trust. It also hastens decision-making and times to market, resulting in fewer bottlenecks in data analysis.

We refer to this collaborative approach to data governance as the enterprise data governance experience (EDGE).

Back to Facebook. If they had a more robust data governance program, the company could have discovered the data snare exploited by Cambridge Analytica and circumvented the entire scandal (and all its consequences).

But for data governance to be successful, organizations must consider team resources as well as enterprise data management methodology and delivery capability (we’ll cover the latter two in the coming weeks).

To determine your organization’s current state of data governance readiness, take the erwin DG RediChek.

To learn more about how to leverage data governance for GDPR compliance and an EDGE on the competition, click here.

Take the DG RediChek