Tag: erwin EDGE

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Business Architecture and Process Modeling for Digital Transformation

At a fundamental level, digital transformation is about further synthesizing an organization’s operations and technology, so involving business architecture and process modeling is a best practice organizations cannot ignore.

This post outlines how business architecture and process modeling come together to facilitate efficient and successful digital transformation efforts.

Business Process Modeling: The First Step to Giving Customers What They Expect

Salesforce recently released the State of the Connected Customer report, with 75 percent of customers saying they expect companies to use new technologies to create better experiences. So the business and digital transformation playbook has to be updated.

These efforts must be carried out with continuous improvement in mind. Today’s constantly evolving business environment totally reinforces the old adage that change is the only constant.

Even historically reluctant-to-change banks now realize they need to innovate, adopting digital transformation to acquire and retain customers. Innovate or die is another adage that holds truer than ever before.

Fidelity International is an example of a successful digital transformation adopter and innovator. The company realized that different generations want different information and have distinct communication preferences.

For instance, millennials are adept at using digital channels, and they are the fastest-growing customer base for financial services companies. Fidelity knew it needed to understand customer needs and adapt its processes around key customer touch points and build centers of excellence to support them.

Business architecture and process modeling

Business Architecture and Process Modeling

Planning and working toward a flexible, responsive and adaptable future is no longer enough – the modern organization must be able to visualize not only the end state (the infamous and so-elusive “to-be”) but also perform detailed and comprehensive impact analysis on each scenario, often in real time. This analysis also needs to span multiple departments, extending beyond business and process architecture to IT, compliance and even HR and legal.

The ability of process owners to provide this information to management is central to ensuring the success of any transformation initiative. And new requirements and initiatives need to be managed in new ways. Digital and business transformation is about being able to do three things at the same time, all working toward the same goals:

  • Collect, document and analyze requirements
  • Establish all information layers impacted by the requirements
  • Develop and test the impact of multiple alternative scenarios

Comprehensive business process modeling underpins all of the above, providing the central information axis around which initiatives are scoped, evaluated, planned, implemented and ultimately managed.

Because of its central role, business process modeling must expand to modeling information from other layers within the organization, including:

  • System and application usage information
  • Supporting and reference documentation
  • Compliance, project and initiative information
  • Data usage

All these information layers must be captured and modeled at the appropriate levels, then connected to form a comprehensive information ecosystem that enables parts of the organization running transformation and other initiatives to instantly access and leverage it for decision-making, simulation and scenario evaluation, and planning, management and maintenance.

No Longer a Necessary Evil

Traditionally, digital and business transformation initiatives relied almost exclusively on human knowledge and experience regarding processes, procedures, how things worked, and how they fit together to provide a comprehensive and accurate framework. Today, technology can aggregate and manage all this information – and more – in a structured, organized and easily accessible way.

Business architecture extends beyond simple modeling; it also incorporates automation to reduce manual effort, remove potential for error, and guarantee effective data governance – with visibility from strategy all the way down to data entry and the ability to trace and manage data lineage. It requires robotics to cross-reference mass amounts of information, never before integrated to support effective decision-making.

The above are not options that are “nice to have,” but rather necessary gateways to taking business process management into the future. And the only way to leverage them is through systemic, organized and comprehensive business architecture modeling and analysis.

Therefore, business architecture and process modeling are no longer a necessary evil. They are critical success factors to any digital or business transformation journey.

A Competitive Weapon

Experts confirm the need to rethink and revise business processes to incorporate more digital automation. Forrester notes in its report, The Growing Importance of Process to Digital Transformation, that the changes in how business is conducted are driving the push “to reframe organizational operational processes around digital transformation efforts.” In a dramatic illustration of the need to move in this direction, the research firm writes that “business leaders are looking to use process as a competitive weapon.”

If a company hasn’t done a good job of documenting its processes, it can’t realize a future in which digital transformation is part of everyday operations. It’s never too late to start, though. In a fast-moving and pressure cooker business environment, companies need to implement business process models that make it possible to visually and analytically represent the steps that will add value to the company – either around internal operations or external ones, such as product or service delivery.

erwin BP, part of the erwin EDGE Platform, enables effective business architecture and process modeling. With it, any transformation initiative becomes a simple, streamlined exercise to support distributed information capture and management, object-oriented modeling, simulation and collaboration.

To find out about how erwin can help in empowering your transformation initiatives, please click here.

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Four Use Cases Proving the Benefits of Metadata-Driven Automation

Organization’s cannot hope to make the most out of a data-driven strategy, without at least some degree of metadata-driven automation.

The volume and variety of data has snowballed, and so has its velocity. As such, traditional – and mostly manual – processes associated with data management and data governance have broken down. They are time-consuming and prone to human error, making compliance, innovation and transformation initiatives more complicated, which is less than ideal in the information age.

So it’s safe to say that organizations can’t reap the rewards of their data without automation.

Data scientists and other data professionals can spend up to 80 percent of their time bogged down trying to understand source data or addressing errors and inconsistencies.

That’s time needed and better used for data analysis.

By implementing metadata-driven automation, organizations across industry can unleash the talents of their highly skilled, well paid data pros to focus on finding the goods: actionable insights that will fuel the business.

Metadata-Driven Automation

Metadata-Driven Automation in the BFSI Industry

The banking, financial services and insurance industry typically deals with higher data velocity and tighter regulations than most. This bureaucracy is rife with data management bottlenecks.

These bottlenecks are only made worse when organizations attempt to get by with systems and tools that are not purpose-built.

For example, manually managing data mappings for the enterprise data warehouse via MS Excel spreadsheets had become cumbersome and unsustainable for one BSFI company.

After embracing metadata-driven automation and custom code automation templates, it saved hundreds of thousands of dollars in code generation and development costs and achieved more work in less time with fewer resources. ROI on the automation solutions was realized within the first year.

Metadata-Driven Automation in the Pharmaceutical Industry

Despite its shortcomings, the Excel spreadsheet method for managing data mappings is common within many industries.

But with the amount of data organizations need to process in today’s business climate, this manual approach makes change management and determining end-to-end lineage a significant and time-consuming challenge.

One global pharmaceutical giant headquartered in the United States experienced such issues until it adopted metadata-driven automation. Then the pharma company was able to scan in all source and target system metadata and maintain it within a single repository. Users now view end-to-end data lineage from the source layer to the reporting layer within seconds.

On the whole, the implementation resulted in extraordinary time savings and a total cost reduction of 60 percent.

Metadata-Driven Automation in the Insurance Industry

Insurance is another industry that has to cope with high data velocity and stringent data regulations. Plus many organizations in this sector find that they’ve outgrown their systems.

For example, an insurance company using a CDMA product to centralize data mappings is probably missing certain critical features, such as versioning, impact analysis and lineage, which adds to costs, times to market and errors.

By adopting metadata-driven automation, organizations can standardize the pre-ETL data mapping process and better manage data integration through the change and release process. As a result, both internal data mapping and cross functional teams now have easy and fast web-based access to data mappings and valuable information like impact analysis and lineage.

Here is the story of a business that adopted such an approach and achieved operational excellence and a delivery time reduction by 80 percent, as well as achieving ROI within 12 months.

Metadata-Driven Automation for a Non-Profit

Another common issue cited by organizations using manual data mapping is ballooning complexity and subsequent confusion.

Any organization expanding its data-driven focus without sufficiently maturing data management initiative(s) will experience this at some point.

One of the world’s largest humanitarian organizations, with millions of members and volunteers operating all over the world, was confronted with this exact issue.

It recognized the need for a solution to standardize the pre-ETL data mapping process to make data integration more efficient and cost-effective.

With metadata-driven automation, the organization would be able to scan and store metadata and data dictionaries in a central repository, as well as manage the business definitions and data dictionary for legacy systems contributing data to the enterprise data warehouse.

By adopting such an approach, the organization realized time savings across all IT development and cross-functional testing teams. Additionally, they were able to more easily manage mappings, code sets, reference data and data validation rules.

Again, ROI was achieved within a year.

A Universal Solution for Metadata-Driven Automation

Metadata-driven automation is a capability any organization can benefit from – regardless of industry, as demonstrated by the various real-world use cases chronicled here.

The erwin Automation Framework is a key component of the erwin EDGE platform for comprehensive data management and data governance.

With it, data professionals realize these industry-agnostic benefits:

  • Centralized and standardized code management with all automation templates stored in a governed repository
  • Better quality code and minimized rework
  • Business-driven data movement and transformation specifications
  • Superior data movement job designs based on best practices
  • Greater agility and faster time-to-value in data preparation, deployment and governance
  • Cross-platform support of scripting languages and data movement technologies

Learn more about metadata-driven automation as it relates to data preparation and enterprise data mapping.

Join one our weekly erwin Mapping Manager demos.

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The Unified Data Platform – Connecting Everything That Matters

Businesses stand to gain a lot from a unified data platform.

This decade has seen data-driven leaders dominate their respective markets and inspire other organizations across the board to use data to fuel their businesses, leveraging this strategic asset to create more value below the surface. It’s even been dubbed “the new oil,” but data is arguably more valuable than the analogy suggests.

Data governance (DG) is a key component of the data value chain because it connects people, processes and technology as they relate to the creation and use of data. It equips organizations to better deal with  increasing data volumes, the variety of data sources, and the speed in which data is processed.

But for an organization to realize and maximize its true data-driven potential, a unified data platform is required. Only then can all data assets be discovered, understood, governed and socialized to produce the desired business outcomes while also reducing data-related risks.

Benefits of a Unified Data Platform

Data governance can’t succeed in a bubble; it has to be connected to the rest of the enterprise. Whether strategic, such as risk and compliance management, or operational, like a centralized help desk, your data governance framework should span and support the entire enterprise and its objectives, which it can’t do from a silo.

Let’s look at some of the benefits of a unified data platform with data governance as the key connection point.

Understand current and future state architecture with business-focused outcomes:

A unified data platform with a single metadata repository connects data governance to the roles, goals strategies and KPIs of the enterprise. Through integrated enterprise architecture modeling, organizations can capture, analyze and incorporate the structure and priorities of the enterprise and related initiatives.

This capability allows you to plan, align, deploy and communicate a high-impact data governance framework and roadmap that sets manageable expectations and measures success with metrics important to the business.

Document capabilities and processes and understand critical paths:

A unified data platform connects data governance to what you do as a business and the details of how you do it. It enables organizations to document and integrate their business capabilities and operational processes with the critical data that serves them.

It also provides visibility and control by identifying the critical paths that will have the greatest impacts on the business.

Realize the value of your organization’s data:

A unified data platform connects data governance to specific business use cases. The value of data is realized by combining different elements to answer a business question or meet a specific requirement. Conceptual and logical schemas and models provide a much richer understanding of how data is related and combined to drive business value.

2020 Data Governance and Automation Report

Harmonize data governance and data management to drive high-quality deliverables:

A unified data platform connects data governance to the orchestration and preparation of data to drive the business, governing data throughout the entire lifecycle – from creation to consumption.

Governing the data management processes that make data available is of equal importance. By harmonizing the data governance and data management lifecycles, organizations can drive high-quality deliverables that are governed from day one.

Promote a business glossary for unanimous understanding of data terminology:

A unified data platform connects data governance to the language of the business when discussing and describing data. Understanding the terminology and semantic meaning of data from a business perspective is imperative, but most business consumers of data don’t have technical backgrounds.

A business glossary promotes data fluency across the organization and vital collaboration between different stakeholders within the data value chain, ensuring all data-related initiatives are aligned and business-driven.

Instill a culture of personal responsibility for data governance:

A unified data platform is inherently connected to the policies, procedures and business rules that inform and govern the data lifecycle. The centralized management and visibility afforded by linking policies and business rules at every level of the data lifecycle will improve data quality, reduce expensive re-work, and improve the ideation and consumption of data by the business.

Business users will know how to use (and how not to use) data, while technical practitioners will have a clear view of the controls and mechanisms required when building the infrastructure that serves up that data.

Better understand the impact of change:

Data governance should be connected to the use of data across roles, organizations, processes, capabilities, dashboards and applications. Proactive impact analysis is key to efficient and effective data strategy. However, most solutions don’t tell the whole story when it comes to data’s business impact.

By adopting a unified data platform, organizations can extend impact analysis well beyond data stores and data lineage for true visibility into who, what, where and how the impact will be felt, breaking down organizational silos.

Getting the Competitive “EDGE”

The erwin EDGE delivers an “enterprise data governance experience” in which every component of the data value chain is connected.

Now with data mapping, it unifies data preparation, enterprise modeling and data governance to simplify the entire data management and governance lifecycle.

Both IT and the business have access to an accurate, high-quality and real-time data pipeline that fuels regulatory compliance, innovation and transformation initiatives with accurate and actionable insights.

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Massive Marriott Data Breach: Data Governance for Data Security

Organizations have been served yet another reminder of the value of data governance for data security.

Hotel and hospitality powerhouse Marriott recently revealed a massive data breach that led to the theft of personal data for an astonishing 500 million customers of its Starwood hotels. This is the second largest data breach in recent history, surpassed only by Yahoo’s breach of 3 billion accounts in 2013 for which it has agreed to pay a $50 million settlement to more than 200 million customers.

Now that Marriott has taken a major hit to its corporate reputation, it has two moves:

  1. Respond: Marriott’s response to its data breach so far has not received glowing reviews. But beyond how it communicates to effected customers, the company must examine how the breach occurred in the first place. This means understanding the context of its data – what assets exist and where, the relationship between them and enterprise systems and processes, and how and by what parties the data is used – to determine the specific vulnerability.
  2. Fix it: Marriott must fix the problem, and quickly, to ensure it doesn’t happen again. This step involves a lot of analysis. A data governance solution would make it a lot less painful by providing visibility into the full data landscape – linkages, processes, people and so on. Then more context-sensitive data security architectures can put in place to for corporate and consumer data privacy.

The GDPR Factor

It’s been six months since the General Data Protection Regulation (GDPR) took effect. While fines for noncompliance have been minimal to date, we anticipate them to dramatically increase in the coming year. Marriott’s bad situation could potentially worsen in this regard, without holistic data governance in place to identify whose and what data was taken.

Data management and data governance, together, play a vital role in compliance, including GDPR. It’s easier to protect sensitive data when you know what it is, where it’s stored and how it needs to be governed.

FREE GUIDE: THE REGULATORY RATIONALE FOR INTEGRATING DATA MANAGEMENT & DATA GOVERNANCE 

Truly understanding an organization’s data, including the data’s value and quality, requires a harmonized approach embedded in business processes and enterprise architecture. Such an integrated enterprise data governance experience helps organizations understand what data they have, where it is, where it came from, its value, its quality and how it’s used and accessed by people and applications.

Data Governance for Data Security

Data Governance for Data Security: Lessons Learned

Other companies should learn (like pronto) that they need to be prepared. At this point it’s not if, but when, a data breach will rear its ugly head. Preparation is your best bet for avoiding the entire fiasco – from the painstaking process of identifying what happened and why to notifying customers their data and trust in your organization have been compromised.

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

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

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

What’s key to remember is that these components act as links in the data governance chain by making it possible to understand what data serves the organization, its connection to the enterprise architecture, and all the business processes it touches.

THE EXPERT GUIDE TO DATA GOVERNANCE, SECURITY AND PRIVACY

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

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

As a consequence, the private information held within organizations will continue to be at risk. Organizations suffering data breaches will be deprived of the benefits they had hoped to realize from the money spent on security technologies and the time invested in developing data privacy classifications. They also may face heavy fines and other financial, not to mention PR, penalties.

Less Pain, More Gain

Most organizations don’t have enough time or money for data management using manual processes. And outsourcing is also expensive, with inevitable delays because these vendors are dependent on manual processes too. Furthermore, manual processes require manual analysis and auditing, which is always more expensive and time consuming.

So the more processes an organization can automate, the less risk of human error, which is actually the primary cause of most data breaches. And automated processes are much easier to analyze and audit because everything is captured, versioned and available for review in a log somewhere. You can read more about automation in our 10 Reasons to Automate Data Mapping and Data Preparation.

And to learn more about how data governance underpins data security and privacy, click here.

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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

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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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Five Pillars of Data Governance Readiness: Initiative Sponsorship

“Facebook at the center of global reckoning on data governance.” This headline from a March 19 article in The Wall Street Journal sums up where we are. With only two months until the General Data Protection Regulation (GDPR) goes into effect, we’re going to see more headlines about improper data governance (DG) – leading to major fines and tarnished brands.

Since the news of the Facebook data scandal broke, the company’s stock has dropped and Nordea, the largest bank in the Nordic region, put a stop to Facebook investments for three months because “we see that the risks related to governance around data protection may have been severely compromised,” it said in a statement.

Last week, we began discussing the five pillars of data governance readiness to ensure the data management foundation is in place for mitigating risks, as well as accomplishing other organizational goals. There can be no doubt that data governance is central to an organization’s customer relationships, reputation and financial results.

So today, we’re going to explore the first pillar of DG readiness: initiative sponsorship. Without initiative sponsorship, organizations will struggle to obtain the funding, resources, support and alignment necessary for successful implementation and subsequent performance.

A Common Roadblock

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. But unfortunately, the 2018 State of Data Governance Report finds lack of executive support to be the most common roadblock to implementing DG.

This is historical baggage. Traditional DG has been an isolated program housed within IT, and thus, constrained within that department’s budget and resources. More significantly, managing DG solely within IT prevented those in the organization with the most knowledge of and investment in the data from participating in the process.

This silo created problems ranging from a lack of context in data cataloging to poor data quality and a sub-par understanding of the data’s associated risks. Data Governance 2.0 addresses these issues by opening data governance to the whole organization.

Its collaborative approach ensures that those with the most significant stake in an organization’s data are intrinsically involved in discovering, understanding, governing and socializing it to produce the desired outcomes. In this era of data-driven business, C-level executives and department leaders are key stakeholders.

But they must be able to trust it and then collaborate based on their role-specific insights to make informed decisions about strategy, identify new opportunities, address redundancies and improve processes.

So, it all comes back to modern data governance: 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.

Initiative Sponsorship: Encouraging Executive Involvement

This week’s headlines about Facebook have certainly gotten Mark Zuckerberg’s attention, as there are calls for the CEO to appear before the U.S. Congress and British Parliament to answer for his company’s data handling – or mishandling as it is alleged.

Public embarrassment, Federal Trade Commission and GDPR fines, erosion of customer trust/loyalty, revenue loss and company devaluation are real risks when it comes to poor data management and governance practices. Facebook may have just elevated your case for implementing DG 2.0 and involving your executives.

Initiative Sponsorship Data Governance GDPR

Business heads and their teams, after all, are the ones who have the knowledge about the data – what it is, what it means, who and what processes use it and why, and what rules and policies should apply to it. Without their perspective and participation in data governance, the enterprise’s ability to intelligently lock down risks and enable growth will be seriously compromised.

Appropriately implemented – with business data stakeholders driving alignment between DG and strategic enterprise goals and IT handling the technical mechanics of data management – the door opens to trusting data and using it effectively.

Also, a chief data officer (CDO) can serve as the bridge between IT and the business to remove silos in the drive toward DG and subsequent whole-of-business outcomes. He or she would be the ultimate sponsor, leading the charge for the necessary funding, resources, and support for a successful, ongoing initiative.

Initiative Sponsorship with an ‘EDGE’

Once key business leaders understand and buy into the vital role they play in a Data Governance 2.0 strategy, the work of building the infrastructure enabling the workforce and processes to support actively governing data assets and their alignment to the business begins.

To find it, map it, make sure it’s under control, and promote it to appropriate personnel requires a technology- and business-enabling platform that covers the entire data governance lifecycle across all data producer and consumer roles.

The erwin EDGE delivers an ‘enterprise data governance experience’ to unify critical DG domains, use 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 that are required to discover, fully understand, actively govern and effectively socialize and align data to the business.

To assess your organizations current data governance readiness, take the erwin DG RediChek.

To learn more about the erwin EDGE, reserve your seat for this webinar.

Take the DG RediChek