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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.

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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.

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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.

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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 catalogingOrganization’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.