data governance culture Archives

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.

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.

Related content – Data Governance 2.0: Collaborative Data Governance

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

Initiative Sponsorship: Without executive sponsorship, you’ll have difficulty obtaining the funding, resources, support and alignment necessary for successful DG.
Organizational Support: DG needs to be integrated into the data stewardship teams and wider culture. It also requires funding.
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.
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.
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.

Related content: Data Governance Makes Data Security Less Scary

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.

White paper: Solving the Enterprise Data Dilemma

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.

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.

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.

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.

Creating a Culture of Data Governance

Related content – Data Governance 2.0: Collaborative Data Governance