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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.

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.

Digital Transformation in Municipal Government: The Hidden Force Powering Smart Cities

Digital Transformation in Municipal Government: Being “Smart” About It

The ability to serve constituents easily and efficiently is of increasing importance and a key objective of digital transformation in municipal government. It’s a direct result of the data-driven customer experiences that are increasingly the norm in the private sector.

Residents want the ability to pay their taxes online, report a pothole from their phone, and generally make it easier to interact with their local officials and services. This can be accomplished with dashboards and constituent portals.

The smart cities movement refers to the broad effort of municipal governments to incorporate sensors, data collection and analysis to improve responses to everything from rush-hour traffic to air quality to crime prevention. When the McKinsey Global Institute examined smart technologies that could be deployed by cities, it found that the public sector would be the natural owner of 70 percent of the applications it reviewed.

“Cities are getting in on the data game,” says Danny Sandwell, product marketing director at erwin, Inc. And with information serving as the lifeblood of many of these projects, the effectiveness of the services offered, the return on the investments in hardware and software, and the happiness of the users all depend on timely, accurate and effective data.

These initiatives present a pretty radical departure from the way cities have traditionally been managed.

A constituent portal, for example, requires that users can be identified, authenticated and then have access to information that resides in various departments, such as the tax collector to view and pay taxes, the building department to view a building permit, and the parking authority to manage public parking permits.

For many municipalities, this is uncharted territory.

Data Governance: The Force Powering Smart Cities

The efficiencies offered by smart city technologies only exist if the data leads to a proper allocation of resources.

If you can identify an increase in crime in a certain neighborhood, for example, you can increase police patrols in response. But if the data is inaccurate, those patrols are wasted while other neighborhoods experience a rise in crime.

Now that they’re in the data game, it’s time for municipal governments to understand data governance – the driving force behind any successful data-driven operation. When you have the ability to understand all of the information related to a piece of data, you have more confidence in how it is analyzed, used and protected.

Data governance doesn’t take place at a single application or in the data warehouse. It needs to be woven into the enterprise architecture and processes of the municipality to ensure data is accurate, timely and accessible to those who need it (and inaccessible to everyone else).

When this all comes together – good data, solid analytics and improved services for residents – the results can be quite striking. New efficiencies will make municipal governments better stewards of tax dollars. An improved quality of life can lift tax revenue by making the city more appealing to citizens and developers.

There’s a lot for cities to gain if they get in the data game. And truly smart cities will make sure they play the game right with effective data governance.