Tag: collaborative data governance

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erwin Expert Blog

Data Governance 2.0: The CIO’s Guide to Collaborative Data Governance

In the data-driven era, CIO’s need a solid understanding of data governance 2.0 …

Data governance (DG) is no longer about just compliance or relegated to the confines of IT. Today, data governance needs to be a ubiquitous part of your organization’s culture.

As the CIO, your stakeholders include both IT and business users in collaborative relationships, which means data governance is not only your business, it’s everyone’s business.

The ability to quickly collect vast amounts of data, analyze it and then use what you’ve learned to help foster better decision-making is the dream of business executives. But that vision is more difficult to execute than it might first appear.

While many organizations are aware of the need to implement a formal data governance initiative, many have faced obstacles getting started.

A lack of resources, difficulties in proving the business case, and challenges in getting senior management to see the importance of such an effort rank among the biggest obstacles facing DG initiatives, according to a recent survey by UBM.

Common Data Governance Challenges - Data Governance 2.0

Despite such hurdles, organizations are committed to trying to get data governance right. The same UBM study found that 98% of respondents considered data governance either important, or critically important to their organization.

And it’s unsurprising too. Considering that the unprecedented levels of digital transformation, with rapidly changing and evolving technology, mean data governance is not just an option, but rather a necessity.

Recognizing this, the IDC DX Awards recently resurfaced to give proper recognition and distinction to organizations who have successfully digitized their systems and business processes.

Creating a Culture of Data Governance

The right data of the right quality, regardless of where it is stored or what format it is stored in, must be available for use only by the right people for the right purpose. This is the promise of a formal data governance practice.

However, to create a culture of data governance requires buy-in from the top down, and the appropriate systems, tools and frameworks to ensure its continued success.

This take on data governance is often dubbed as Data Governance 2.0.

At erwin, we’ve identified what we believe to be the five pillars of data governance readiness:

  1. Initiative Sponsorship: Without executive sponsorship, you’ll have difficulty obtaining the funding, resources, support and alignment necessary for successful DG.
  2. Organizational Support: DG needs to be integrated into the data stewardship teams and wider culture. It also requires funding.
  3. Team Resources: Most successful organizations have established a formal data management group at the enterprise level. As a foundational component of enterprise data management, DG would reside in such a group.
  4. Enterprise Data Management Methodology: DG is foundational to enterprise data management. Without the other essential components (e.g., metadata management, enterprise data architecture, data quality management), DG will be a struggle.
  5. Delivery Capability: Successful and sustainable DG initiatives are supported by specialized tools, which are scoped as part of the DG initiative’s technical requirements.

Data Security

Data is becoming increasingly difficult to manage, control and secure as evidenced by the uptick in data breaches in almost every industry.

Therefore companies must work to secure intellectual property (IPs), client information and so much more.

So CIOs have to come up with appropriate plans to restrict certain people from accessing this information and allow only a small, relevant circle to view it when necessary.

However, this job isn’t as easy as you think it is. Organizations must walk the line between ease of access/data discoverability and security.

It’s the CIO’s responsibility to keep the balance, and data governance tools with role-based access can help maintain that balance.

Data Storage

The amount of data modern organizations have to manage means CIOs have to rethink data storage, as well as security.

This includes considerations as to what data should be stored and where, as well as understanding what data the organization – and the stakeholders within it – is responsible for.

This knowledge will enable better analysis, and the data used for such analysis more easily accessed when required and by approved parties. This is especially crucial for compliance with government regulations like the General Data Protection Regulation (GDPR), as well as other data regulations.

Defining the Right Audience

It’s a CIO’s responsibility to oversee the organization’s data governance systems. Of course, this means the implementation and upkeep of such systems, but it also includes creating the policies that will inform the data governance program itself.

Nowadays, lots of employees think they need access to all of an organization’s data to help them make better decisions for the company.

However, this can possibly expose company data to numerous threats and cyber attacks as well as intellectual property infringement.

So data governance that ensures only the right audience can access specific company information can come in handy, especially during a company’s brainstorming seasons, new products and services releases, and so much more.

Data governance is to be tailored by CIOs to meet their organizations’ specific needs (and wants). This is to ensure an efficient and effective way of utilizing data while also enabling employees to make better and wiser business decisions.

The Right Tools Help Solve the Enterprise Data Dilemma

What data do we have, where is it and what does it mean? This is the data dilemma that plagues most organizations.

The right tools can make or break your data governance initiatives. They encompass a number of different technologies, including data cataloging, data literacy, business process modeling, enterprise architecture and data modeling.

Each of these tools separately contribute to better data governance, however, increasingly, organizations are realizing the benefits of interconnectivity between them. This interconnectivity can be achieved through centralizing data-driven projects around metadata.

This means data professionals and their work benefits from a single source of truth, making analysis faster, more trustworthy and far easier to collaborate on.

With the erwin EDGE, an “enterprise data governance experience” is created to underpin Data Governance 2.0.

It unifies data and business architectures so all IT and business stakeholders can access relevant data in the context of their roles, supporting a culture committed to using data as a mission-critical asset and orchestrating the key mechanisms required to discover, fully understand, actively govern and effectively socialize and align data to the business.

You can learn more about data governance by reading our whitepaper: Examining the Data Trinity: Governance, Security and Privacy.

Examining the Data Trinity - Governance, Security and Privacy

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erwin Expert Blog

Business Process Can Make or Break Data Governance

Data governance isn’t a one-off project with a defined endpoint. It’s an on-going initiative that requires active engagement from executives and business leaders.

Data governance, today, comes back to the ability to understand critical enterprise data within a business context, track its physical existence and lineage, and maximize its value while ensuring quality and security.

Free Data Modeling Best Practice Guide

Historically, little attention has focused on what can literally make or break any data governance initiative — turning it from a launchpad for competitive advantage to a recipe for disaster. Data governance success hinges on business process modeling and enterprise architecture.

To put it even more bluntly, successful data governance* must start with business process modeling and analysis.

*See: Three Steps to Successful & Sustainable Data Governance Implementation

Business Process Data Governance

Passing the Data Governance Ball

For years, data governance was the volleyball passed back and forth over the net between IT and the business, with neither side truly owning it. However, once an organization understands that IT and the business are both responsible for data, it needs to develop a comprehensive, holistic strategy for data governance that is capable of four things:

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

To accomplish this, a modern data governance strategy needs to be interdisciplinary to break down traditional silos. 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 and value streams they enable.

Ovum Market Radar: Enterprise Architecture

The business process and analysis component is vital because 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.

Slow Down, Ask Questions

In a rush to implement a data governance methodology and system, organizations can forget that a system must serve a process – and be governed/controlled by one.

To choose the correct system and implement it effectively and efficiently, you must know – in every detail – all the processes it will impact. You need to ask these important questions:

  1. How will it impact them?
  2. Who needs to be involved?
  3. When do they need to be involved?

These questions are the same ones we ask in data governance. They involve impact analysis, ownership and accountability, control and traceability – all of which effectively documented and managed business processes enable.

Data sets are not important in and of themselves. Data sets become important in terms of how they are used, who uses them and what their use is – and all this information is described in the processes that generate, manipulate and use them. So unless we know what those processes are, how can any data governance implementation be complete or successful?

Processes need to be open and shared in a concise, consistent way so all parts of the organization can investigate, ask questions, and then add their feedback and information layers. In other words, processes need to be alive and central to the organization because only then will the use of data and data governance be truly effective.

A Failure to Communicate

Consider this scenario: We’ve perfectly captured our data lineage, so we know what our data sets mean, how they’re connected, and who’s responsible for them – not a simple task but a massive win for any organization. Now a breach occurs. Will any of the above information tell us why it happened? Or where? No! It will tell us what else is affected and who can manage the data layer(s), but unless we find and address the process failure that led to the breach, it is guaranteed to happen again.

By knowing where data is used – the processes that use and manage it – we can quickly, even instantly, identify where a failure occurs. Starting with data lineage (meaning our forensic analysis starts from our data governance system), we can identify the source and destination processes and the associated impacts throughout the organization.

We can know which processes need to change and how. We can anticipate the pending disruptions to our operations and, more to the point, the costs involved in mitigating and/or addressing them.

But knowing all the above requires that our processes – our essential and operational business architecture – be accurately captured and modelled. Instituting data governance without processes is like building a castle on sand.

Rethinking Business Process Modeling and Analysis

Modern organizations need a business process modeling and analysis tool with easy access to all the operational layers across the organization – from high-level business architecture all the way down to data.

Such a system should be flexible, adjustable, easy-to-use and capable of supporting multiple layers simultaneously, allowing users to start in their comfort zones and mature as they work toward their organization’s goals.

The erwin EDGE is one of the most comprehensive software platforms for managing an organization’s data governance and business process initiatives, as well as the whole data architecture. It allows natural, organic growth throughout the organization and the assimilation of data governance and business process management under the same platform provides a unique data governance experience because of its integrated, collaborative approach.

Start your free, cloud-based trial of erwin Business Process and see how some of the world’s largest enterprises have benefited from its centralized repository and integrated, role-based views.

We’d also be happy to show you our data governance software, which includes data cataloging and data literacy capabilities.

Enterprise Architecture Business Process Trial

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Enterprise Architecture and Business Process: Common Goals Require Common Tools

For decades now, the professional world has put a great deal of energy into discussing the gulf that exists between business and IT teams within organizations.

They speak different languages, it’s been said, and work toward different goals. Technology plans don’t seem to account for the reality of the business, and business plans don’t account for the capabilities of the technology.

Data governance is one area where business and IT never seemed to establish ownership. Early attempts at data governance treated the idea as a game of volleyball, passing ownership back and forth, with one team responsible for storing data and running applications, and one responsible for using the data for business outcomes.

Today, we see ample evidence this gap is closing at many organizations. Consider:

  • Many technology platforms and software applications now are designed for business users. Business intelligence is a prime example; it’s rare today to see IT pros have to run reports for business users thanks to self-service.
  • Many workers, especially those that came of age surrounded by technology, have a better understanding of both the business and technology that runs their organizations. Education programs also have evolved to help students develop a background in both business and technology.
  • There’s more portability in roles, with technology minds moving to business leadership positions and vice versa.

“The business domain has always existed in enterprise architecture,” says Manuel Ponchaux, director of product management at erwin, Inc. “However, enterprise architecture has traditionally been an IT function with a prime focus on IT. We are now seeing a shift with a greater focus on business outcomes.”

You can see evidence of this blended focus in some of the titles, like “business architect,” being bestowed upon what was traditionally at IT function. These titles demonstrate an understanding that technology cannot exist in the modern organization for the sake of technology alone – technology needs to support the business and its customers. This concept is also a major focus of the digital transformation wave that’s washing over the business world, and thus we see it reflected in job titles that simply didn’t exist a decade ago.

Job titles aside, enterprise architecture (EA) and business process (BP) teams still have different goals, though at many organizations they now work more closely together than they did in the past. Today, both EA and BP teams recognize that their common goal is better business outcomes. Along the way to that goal, each team conducts a number of similar tasks.

Enterprise Architecture and Business Process: Better Together

One prominent example is modeling. Both enterprise architecture and business process teams do modeling, but they do it in different ways at different levels, and they often use different data and tools. This lack of coordination and communication makes it difficult to develop a true sense of a process from the IT and business sides of the equation. It can also lead to duplication of efforts, which is inefficient and likely to add further confusion when trying to understand outcomes.

Building better business outcomes is like following a plan at a construction site. If different teams are making their own decisions about the materials they’re going to use and following their own blueprints, you’re unlikely to see the building you expect to see at the end of the job.

And that’s essentially what is missing at many organizations: A common repository with role-based views, interfaces and dashboard so that enterprise architecture and business process can truly work together using the same blueprint. When enterprise architecture and business process can use common tools that both aid collaboration and help them understand the elements most important to their roles, the result is greater accuracy, increased efficiency and improved outcomes.

erwin’s enterprise architecture and business process tools provide the common repository and role-based views that help these teams work collaboratively toward their common goals. Finally, enterprise architecture and business process can be on the same page.

Business Process Modeling Use Cases

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erwin Expert Blog Data Governance

Data Governance Frameworks: The Key to Successful Data Governance Implementation

A strong data governance framework is central to successful data governance implementation in any data-driven organization because it ensures that data is properly maintained, protected and maximized.

But despite this fact, enterprises often face push back when implementing a new data governance initiative or trying to mature an existing one.

Let’s assume you have some form of informal data governance operation with some strengths to build on and some weaknesses to correct. Some parts of the organization are engaged and behind the initiative, while others are skeptical about its relevance or benefits.

Some other common data governance implementation obstacles include:

  • Questions about where to begin and how to prioritize which data streams to govern first
  • Issues regarding data quality and ownership
  • Concerns about data lineage
  • Competing project and resources (time, people and funding)

By using a data governance framework, organizations can formalize their data governance implementation and subsequent adherence to. This addressess common concerns including data quality and data lineage, and provides a clear path to successful data governance implementation.

In this blog, we will cover three key steps to successful data governance implementation. We will also look into how we can expand the scope and depth of a data governance framework to ensure data governance standards remain high.

Data Governance Implementation in 3 Steps

When maturing or implementing data governance and/or a data governance framework, an accurate assessment of the ‘here and now’ is key. Then you can rethink the path forward, identifying any current policies or business processes that should be incorporated, being careful to avoid making the same mistakes of prior iterations.

With this in mind, here are three steps we recommend for implementing data governance and a data governance framework.

Data Governance Framework

Step 1: Shift the culture toward data governance

Data governance isn’t something to set and forget; it’s a strategic approach that needs to evolve over time in response to new opportunities and challenges. Therefore, a successful data governance framework has to become part of the organization’s culture but such a shift requires listening – and remembering that it’s about people, empowerment and accountability.

In most cases, a new data governance framework requires people – those in IT and across the business, including risk management and information security – to change how they work. Any concerns they raise or recommendations they make should be considered. You can encourage feedback through surveys, workshops and open dialog.

Once input has been discussed and plan agreed upon, it is critical to update roles and responsibilities, provide training and ensure ongoing communication. Many organizations now have internal certifications for different data governance roles who wear these badges with pride.

A top-down management approach will get a data governance initiative off the ground, but only bottom-up cultural adoption will carry it out.

Step 2: Refine the data governance framework

The right capabilities and tools are important for fueling an accurate, real-time data pipeline and governing it for maximum security, quality and value. For example:

Data cataloging – Organization’s implementing a data governance framework will benefit from automated metadata harvesting, data mapping, code generation and data lineage with reference data management, lifecycle management and data quality. With these capabilities, you can  efficiently integrate and activate enterprise data within a single, unified catalog in accordance with business requirements.

Data literacy – Being able to discover what data is available and understand what it means in common, standardized terms is important because data elements may mean different things to different parts of the organization. A business glossary answers this need, as does the ability for stakeholders to view data relevant to their roles and understand it within a business context through a role-based portal.

Such tools are further enhanced if they can be integrated across data and business architectures and when they promote self-service and collaboration, which also are important to the cultural shift.

 

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Step 3: Prioritize then scale the data governance framework

Because data governance is on-going, it’s important to prioritize the initial areas of focus and scale from there. Organizations that start with 30 to 50 data items are generally more successful than those that attempt more than 1,000 in the early stages.

Find some representative (familiar) data items and create examples for data ownership, quality, lineage and definition so stakeholders can see real examples of the data governance framework in action. For example:

  • Data ownership model showing a data item, its definition, producers, consumers, stewards and quality rules (for profiling)
  • Workflow showing the creation, enrichment and approval of the above data item to demonstrate collaboration

Whether your organization is just adopting data governance or the goal is to refine an existing data governance framework, the erwin DG RediChek will provide helpful insights to guide you in the journey.

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erwin Expert Blog

Top 7 Data Governance Blog Posts of 2018

The driving factors behind data governance adoption vary.

Whether implemented as preventative measures (risk management and regulation) or proactive endeavors (value creation and ROI), the benefits of a data governance initiative is becoming more apparent.

Historically most organizations have approached data governance in isolation and from the former category. But as data’s value to the enterprise has grown, so has the need for a holistic, collaborative means of discovering, understanding and governing data.

So with the impetus of the General Data Protection Regulation (GDPR) and the opportunities presented by data-driven transformation, many organizations are re-evaluating their data management and data governance practices.

With that in mind, we’ve compiled a list of the very best, best-practice blog posts from the erwin Experts in 2018.

Defining data governance: DG Drivers

Defining Data Governance

www.erwin.com/blog/defining-data-governance/

Data governance’s importance has become more widely understood. But for a long time, the discipline was marred with a poor reputation owed to consistent false starts, dogged implementations and underwhelming ROI.

The evolution from Data Governance 1.0 to Data Governance 2.0 has helped shake past perceptions, introducing a collaborative approach. But to ensure the collaborative take on data governance is implemented properly, an organization must settle on a common definition.

The Top 6 Benefits of Data Governance

www.erwin.com/blog/top-6-benefits-of-data-governance/

GDPR went into effect for businesses trading with the European Union, including hefty fines for noncompliance with its data collection, storage and usage standards.

But it’s important for organizations to understand that the benefits of data governance extend beyond just GDPR or compliance with any other internal or external regulations.

Data Governance Readiness: The Five Pillars

www.erwin.com/blog/data-governance-readiness/

GDPR had organizations scrambling to implement data governance initiatives by the effective date, but many still lag behind.

Enforcement and fines will increase in 2019, so an understanding of the five pillars of data governance readiness are essential: initiative sponsorship, organizational support, allocation of team resources, enterprise data management methodology and delivery capability.

Data Governance and GDPR: How the Most Comprehensive Data Regulation in the World Will Affect Your Business

www.erwin.com/blog/data-governance-and-gdpr/

Speaking of GDPR enforcement, this post breaks down how the regulation affects business.

From rules regarding active consent, data processing and the tricky “right to be forgotten” to required procedures for notifying afflicted parties of a data breach and documenting compliance, GDPR introduces a lot of complexity.

The Top Five Data Governance Use Cases and Drivers

www.erwin.com/blog/data-governance-use-cases/

An erwin-UBM study conducted in late 2017 sought to determine the biggest drivers for data governance.

In addition to compliance, top drivers turned out to be improving customer satisfaction, reputation management, analytics and Big Data.

Data Governance 2.0 for Financial Services

www.erwin.com/blog/data-governance-2-0-financial-services/

Organizations operating within the financial services industry were arguably the most prepared for GDPR, given its history. However, the huge Equifax data breach was a stark reminder that organizations still have work to do.

As well as an analysis of data governance for regulatory compliance in financial services, this article examines the value data governance can bring to these organizations – up to $30 billion could be on the table.

Understanding and Justifying Data Governance 2.0

www.erwin.com/blog/justifying-data-governance/

For some organizations, the biggest hurdle in implementing a new data governance initiative or strengthening an existing one is support from business leaders. Its value can be hard to demonstrate to those who don’t work directly with data and metadata on a daily basis.

This article examines this data governance roadblock and others in addition to advice on how to overcome them.

 

Automate Data Mapping

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Six Reasons Business Glossary Management Is Crucial to Data Governance

A business glossary is crucial to any data governance strategy, yet it is often overlooked.

Consider this – no one likes unpleasant surprises, especially in business. So when it comes to objectively understanding what’s happening from the top of the sales funnel to the bottom line of finance, everyone wants – and needs – to trust the data they have.

That’s why you can’t underestimate the importance of a business glossary. Sometimes the business folks say IT or marketing speaks a different language. Or in the case of mergers and acquisitions, different companies call the same thing something else.

A business glossary solves this complexity by creating a common business vocabulary. Regardless of the industry you’re in or the type of data initiative you’re undertaking, the ability for an organization to have a unified, common language is a key component of data governance, ensuring you can trust your data.

Are we speaking the same language?

How can two reports show different results for the same region? A quick analysis of invoices will likely reveal that some of the data fed into the report wasn’t based on a clear understanding of business terms.

Business Glossary Management is Crucial to Data Governance

In such embarrassing scenarios, a business glossary and its ongoing management has obvious significance. And with the complexity of today’s business environment, organizations need the right solution to make sense out of their data and govern it properly.

Here are six reasons a business glossary is vital to data governance:

  1. Bridging the gap between Business & IT

A sound data governance initiative bridges the gap between the business and IT. By understanding the underlying metadata associated with business terms and the associated data lineage, a business glossary helps bridge this gap to deliver greater value to the organization.

  1. Integrated search

The biggest appeal of business glossary management is that it helps establish relationships between business terms to drive data governance across the entire organization. A good business glossary should provide an integrated search feature that can find context-specific results, such as business terms, definitions, technical metadata, KPIs and process areas.

  1. The ability to capture business terms and all associated artifacts

What good is a business term if it can’t be associated with other business terms and KPIs? Capturing relationships between business terms as well as between technical and business entities is essential in today’s regulatory and compliance-conscious environment. A business glossary defines the relationship between the business terms and their underlying metadata for faster analysis and enhanced decision-making.

  1. Integrated project management and workflow

When the business and cross-functional teams operate in silos, users start defining business terms according to their own preferences rather than following standard policies and best practices. To be effective, a business glossary should enable a collaborative workflow management and approval process so stakeholders have visibility with established data governance roles and responsibilities. With this ability, business glossary users can provide input during the entire data definition process prior to publication.

  1. The ability to publish business terms

Successful businesses not only capture business terms and their definitions, they also publish them so that the business-at-large can access it. Business glossary users, who are typically members of the data governance team, should be assigned roles for creating, editing, approving and publishing business glossary content. A workflow feature will show which users are assigned which roles, including those with publishing permissions.

After initial publication, business glossary content can be revised and republished on an ongoing basis, based on the needs of your enterprise.

  1. End-to-end traceability

Capturing business terms and establishing relationships are key to glossary management. However, it is far from a complete solution without traceability. A good business glossary can help generate enterprise-level traceability in the form of mind maps or tabular reports to the business community once relationships have been established.

Business Glossary, the Heart of Data Governance

With a business glossary at the heart of your regulatory compliance and data governance initiatives, you can help break down organizational and technical silos for data visibility, context, control and collaboration across domains. It ensures that you can trust your data.

Plus, you can unify the people, processes and systems that manage and protect data through consistent exchange, understanding and processing to increase quality and trust.

By building a glossary of business terms in taxonomies with synonyms, acronyms and relationships, and publishing approved standards and prioritizing them, you can map data in all its forms to the central catalog of data elements.

That answers the vital question of “where is our data?” Then you can understand who and what is using your data to ensure adherence to usage standards and rules.

Value of Data Intelligence IDC Report

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Healthy Co-Dependency: Data Management and Data Governance

Data management and data governance are now more important than ever before. The hyper competitive nature of data-driven business means organizations need to get more out of their data than ever before – and fast.

A few data-driven exemplars have led the way, turning data into actionable insights that influence everything from corporate structure to new products and pricing. “Few” being the operative word.

It’s true, data-driven business is big business. Huge actually. But it’s dominated by a handful of organizations that realized early on what a powerful and disruptive force data can be.

The benefits of such data-driven strategies speak for themselves: Netflix has replaced Blockbuster, and Uber continues to shake up the taxi business. Organizations indiscriminate of industry are following suit, fighting to become the next big, disruptive players.

But in many cases, these attempts have failed or are on the verge of doing so.

Now with the General Data Protection Regulation (GDPR) in effect, data that is unaccounted for is a potential data disaster waiting to happen.

So organizations need to understand that getting more out of their data isn’t necessarily about collecting more data. It’s about unlocking the value of the data they already have.

Data Management and Data Governance Co-Dependency

The Enterprise Data Dilemma

However, most organizations don’t know exactly what data they have or even where some of it is. And some of the data they can account for is going to waste because they don’t have the means to process it. This is especially true of unstructured data types, which organizations are collecting more frequently.

Considering that 73 percent of company data goes unused, it’s safe to assume your organization is dealing with some if not all of these issues.

Big picture, this means your enterprise is missing out on thousands, perhaps millions in revenue.

The smaller picture? You’re struggling to establish a single source of data truth, which contributes to a host of problems:

  • Inaccurate analysis and discrepancies in departmental reporting
  • Inability to manage the amount and variety of data your organization collects
  • Duplications and redundancies in processes
  • Issues determining data ownership, lineage and access
  • Achieving and sustaining compliance

To avoid such circumstances and get more value out of data, organizations need to harmonize their approach to data management and data governance, using a platform of established tools that work in tandem while also enabling collaboration across the enterprise.

Data management drives the design, deployment and operation of systems that deliver operational data assets for analytics purposes.

Data governance delivers these data assets within a business context, tracking their physical existence and lineage, and maximizing their security, quality and value.

Although these two disciplines approach data from different perspectives (IT-driven and business-oriented), they depend on each other. And this co-dependency helps an organization make the most of its data.

The P-M-G Hub

Together, data management and data governance form a critical hub for data preparation, modeling and data governance. How?

It starts with a real-time, accurate picture of the data landscape, including “data at rest” in databases, data warehouses and data lakes and “data in motion” as it is integrated with and used by key applications. That landscape also must be controlled to facilitate collaboration and limit risk.

But knowing what data you have and where it lives is complicated, so you need to create and sustain an enterprise-wide view of and easy access to underlying metadata. That’s a tall order with numerous data types and data sources that were never designed to work together and data infrastructures that have been cobbled together over time with disparate technologies, poor documentation and little thought for downstream integration. So the applications and initiatives that depend on a solid data infrastructure may be compromised, and data analysis based on faulty insights.

However, these issues can be addressed with a strong data management strategy and technology to enable the data quality required by the business, which encompasses data cataloging (integration of data sets from various sources), mapping, versioning, business rules and glossaries maintenance and metadata management (associations and lineage).

Being able to pinpoint what data exists and where must be accompanied by an agreed-upon business understanding of what it all means in common terms that are adopted across the enterprise. Having that consistency is the only way to assure that insights generated by analyses are useful and actionable, regardless of business department or user exploring a question. Additionally, policies, processes and tools that define and control access to data by roles and across workflows are critical for security purposes.

These issues can be addressed with a comprehensive data governance strategy and technology to determine master data sets, discover the impact of potential glossary changes across the enterprise, audit and score adherence to rules, discover risks, and appropriately and cost-effectively apply security to data flows, as well as publish data to people/roles in ways that are meaningful to them.

Data Management and Data Governance: Play Together, Stay Together

When data management and data governance work in concert empowered by the right technology, they inform, guide and optimize each other. The result for an organization that takes such a harmonized approach is automated, real-time, high-quality data pipeline.

Then all stakeholders — data scientists, data stewards, ETL developers, enterprise architects, business analysts, compliance officers, CDOs and CEOs – can access the data they’re authorized to use and base strategic decisions on what is now a full inventory of reliable information.

The erwin EDGE creates an “enterprise data governance experience” through integrated data mapping, business process modeling, enterprise architecture modeling, data modeling and data governance. No other software platform on the market touches every aspect of the data management and data governance lifecycle to automate and accelerate the speed to actionable business insights.

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erwin Expert Blog

Solving the Enterprise Data Dilemma

Due to the adoption of data-driven business, organizations across the board are facing their own enterprise data dilemmas.

This week erwin announced its acquisition of metadata management and data governance provider AnalytiX DS. The combined company touches every piece of the data management and governance lifecycle, enabling enterprises to fuel automated, high-quality data pipelines for faster speed to accurate, actionable insights.

Why Is This a Big Deal?

From digital transformation to AI, and everything in between, organizations are flooded with data. So, companies are investing heavily in initiatives to use all the data at their disposal, but they face some challenges. Chiefly, deriving meaningful insights from their data – and turning them into actions that improve the bottom line.

This enterprise data dilemma stems from three important but difficult questions to answer: What data do we have? Where is it? And how do we get value from it?

Large enterprises use thousands of unharvested, undocumented databases, applications, ETL processes and procedural code that make it difficult to gather business intelligence, conduct IT audits, and ensure regulatory compliance – not to mention accomplish other objectives around customer satisfaction, revenue growth and overall efficiency and decision-making.

The lack of visibility and control around “data at rest” combined with “data in motion”, as well as difficulties with legacy architectures, means these organizations spend more time finding the data they need rather than using it to produce meaningful business outcomes.

To remedy this, enterprises need smarter and faster data management and data governance capabilities, including the ability to efficiently catalog and document their systems, processes and the associated data without errors. In addition, business and IT must collaborate outside their traditional operational silos.

But this coveted state of data nirvana isn’t possible without the right approach and technology platform.

Enterprise Data: Making the Data Management-Data Governance Love Connection

Enterprise Data: Making the Data Management-Data Governance Love Connection

Bringing together data management and data governance delivers greater efficiencies to technical users and better analytics to business users. It’s like two sides of the same coin:

  • Data management drives the design, deployment and operation of systems that deliver operational and analytical data assets.
  • Data governance delivers these data assets within a business context, tracks their physical existence and lineage, and maximizes their security, quality and value.

Although these disciplines approach data from different perspectives and are used to produce different outcomes, they have a lot in common. Both require a real-time, accurate picture of an organization’s data landscape, including data at rest in data warehouses and data lakes and data in motion as it is integrated with and used by key applications.

However, creating and maintaining this metadata landscape is challenging because this data in its various forms and from numerous sources was never designed to work in concert. Data infrastructures have been cobbled together over time with disparate technologies, poor documentation and little thought for downstream integration, so the applications and initiatives that depend on data infrastructure are often out-of-date and inaccurate, rendering faulty insights and analyses.

Organizations need to know what data they have and where it’s located, where it came from and how it got there, what it means in common business terms [or standardized business terms] and be able to transform it into useful information they can act on – all while controlling its access.

To support the total enterprise data management and governance lifecycle, they 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 with reliable information on which to base strategic decisions.

Enterprise Data: Creating Your “EDGE”

At the end of the day, all industries are in the data business and all employees are data people. The success of an organization is not measured by how much data it has, but by how well it’s used.

Data governance enables organizations to use their data to fuel compliance, innovation and transformation initiatives with greater agility, efficiency and cost-effectiveness.

Organizations need to understand their data from different perspectives, identify how it flows through and impacts the business, aligns this business view with a technical view of the data management infrastructure, and synchronizes efforts across both disciplines for accuracy, agility and efficiency in building a data capability that impacts the business in a meaningful and sustainable fashion.

The persona-based 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.

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. 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.

With the erwin EDGE, data management and data governance are unified and mutually supportive, with one hand aware and informed by the efforts of the other 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.

An integrated solution with data preparation, modeling and governance helps businesses reach data governance maturity – which equals a role-based, collaborative data governance system that serves both IT and business users equally. Such maturity may not happen overnight, but it will ultimately deliver the accurate and actionable insights your organization needs to compete and win.

Your journey to data nirvana begins with a demo of the enhanced erwin Data Governance solution. Register now.

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Data Governance Helps Build a Solid Foundation for Analytics

If your business is like many, it’s heavily invested in analytics. We’re living in a data-driven world. Data drives the recommendations we get from retailers, the coupons we get from grocers, and the decisions behind the products and services we’ll build and support at work.

None of the insights we draw from data are possible without analytics. We routinely slice, dice, measure and (try to) predict almost everything today because data is available to be analyzed. In theory, all this analysis should be helping the business. It should ensure we’re creating the right products and services, marketing them to the right people, and charging the right price. It should build a loyal base of customers who become brand ambassadors, amplifying existing marketing efforts to fuel more sales.

We hope all these things happen because all this analysis is expensive. It’s not just the cost of software licenses for the analytics software, but it’s also the people. Estimates for the average salary of data scientists, for example, can be upwards of $118,000 (Glassdoor) to $131,000 (Indeed). Many businesses also are exploring or already use next-generation analytics technology like predictive analytics or analytics supported by artificial intelligence or machine learning, which require even more investment.

If the underlying data your business is analyzing is bad, you’re throwing all this investment away. There’s a saying that scares everyone involved in analytics today: “Garbage in, garbage out.” When bad data is used to drive your strategic and operational decisions, your bad data suddenly becomes a huge problem for the business.

The goal, when it comes to the data you feed your analytics platforms, is what’s often referred to as the “single source of truth,” otherwise known as the data you can trust to analyze and create conclusions that drive your business forward.

“One source of truth means serving up consistent, high-quality data,” says Danny Sandwell, director of product marketing at erwin, Inc.

Despite all of the talk in the industry about data and analytics in recent years, many businesses still fail to reap the rewards of their analytics investments. In fact, Gartner reports that more than 60 percent of data and analytics projects fail. As with any software deployment, there are a number of reasons these projects don’t turn out the way they were planned. Among analytics, however, bad data can turn even a smooth deployment on the technology side into a disaster for the business.

What is bad data? It’s data that isn’t helping your business make the right decisions because it is:

  • Poor quality
  • Misunderstood
  • Incomplete
  • Misused

How Data Governance Helps Organizations Improve Their Analytics

More than one-quarter of the respondents to a November 2017 survey by erwin Inc. and UBM said analytics was one of the factors driving their data governance initiatives.

Reputation Management - What's Driving Data Governance

Data governance helps businesses understand what data they have, how good it is, where it is, and how it’s used. A lot of people are talking about data governance today, and some are putting that talk into action. The erwin-UBM survey found that 52 percent of respondents say data is critically important to their organization and they have a formal data governance strategy in place. But almost as many respondents (46 percent) say they recognize the value of data to their organizations but don’t have a formal governance strategy.

Data-driven Analytics: How Important is Data Governance

When data governance helps your organization develop high-quality data with demonstrated value, your IT organizations can build better analytics platforms for the business. Data governance helps enable self-service, which is an important part of analytics for many businesses today because it puts the power of data and analysis into the hands of the people who use the data on a daily basis. A well-functioning data governance program creates that single version of the truth by helping IT organizations identify and present the right data to users and eliminate confusion about the source or quality of the data.

Data governance also enables a system of best practices, subject matter experts, and collaboration that are the hallmarks of today’s analytics-driven businesses.

Like analytics, many early attempts at instituting data governance failed to deliver the expected results. They were narrowly focused, and their advocates often had difficulty articulating the value of data governance to the organization, which made it difficult to secure budget. Some organizations even viewed data governance as part of data security, securing their data to the point where the people who wanted to use it had trouble getting access.

Issues of ownership also hurt early data governance efforts, as IT and the business couldn’t agree on which side was responsible for a process that affects both on a regular basis. Today, organizations are better equipped to resolve these issues of ownership because many are adopting a new corporate structure that recognizes how important data is to modern businesses. Roles like chief data officer (CDO), which increasingly sits on the business side, and the data protection officer (DPO), are more common than they were a few years ago.

A modern data governance strategy weaves itself into the business and its infrastructure. It is present in the enterprise architecture, the business processes, and it helps organizations better understand the relationships between data assets using techniques like visualization. Perhaps most important, a modern approach to data governance is ongoing because organizations and their data are constantly changing and transforming, so their approach to data governance needs to adjust as they go.

When it comes to analytics, data governance is the best way to ensure you’re using the right data to drive your strategic and operational decisions. It’s easier said than done, especially when you consider all the data that’s flowing into a modern organization and how you’re going to sort through it all to find the good, the bad, and the ugly. But once you do, you’re on the way to using analytics to draw conclusions you can trust.

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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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