7 practical steps for improving your organization's spatial data governance
Not putting any kind of management structure around your data can have huge financial consequences—and spatial data is no exception. In this blog post, management consultant Allen Williams talks about why spatial data governance is important, then offers seven hands-on steps that organizations can take to maintain their data in a way that can save millions of dollars per year.
What’s spatial data governance, and why does it matter for my business?
According to research by Gartner, “the average financial impact of poor data quality on organizations is $9.7 million per year”. For example, poor data quality can affect organizations’ ability to respond to customer requests. This kind of poor data quality is often the result of a lack of governance. Organizations with inadequate data governance struggle with data ownership and often have no means of certifying data integrity, which over time can become a critical business problem. Lack of governance also impedes organizations’ ability to make data-driven decisions and affects long-range capital planning, making it difficult for organizations in this precarious position to understand the condition of assets and how best to optimize their investments.
In my work, I frequently deal with customers who are having trouble getting the most out of their spatial data. Spatial data is critical for understanding the locations of assets and business interests; detecting relationships, patterns and correlations; and predicting events. Another key finding from the Gartner study revealed that “inadequate data quality is a primary reason for 40 percent of all business initiatives failing to achieve their targeted benefits”. To translate that into the GIS context, ensuring that your organization’s spatial data is accurate, current and complete is essential for deriving any meaningful analysis from it.
Interestingly, the GIS teams I work with often struggle with understanding the integrity of their spatial data, network domains and tiers. While they usually support and maintain core spatial datasets, they aren’t experts in the data themselves. When I work with teams like this, I try to emphasize to them that data governance is a shared responsibility. Planners, engineers, field inspectors, construction crews and maintenance personnel are the subject matter experts (SMEs) and understand the data and its use, so GIS teams need to work collaboratively with these SMEs to apply governance controls, processes and measures. This helps them manage the organization’s spatial data over its entire lifecycle.
This is where spatial data governance comes in. Spatial data governance is an organizing framework that helps organizations establish strategies, objectives and policies for effectively managing and maintaining their data. This approach defines five fundamental elements that are essential for helping organizations establish and maintain decision making structures and governance processes when it comes to their spatial data.
The five elements are:
- Leadership recognizes the value, purpose and scope of spatial data governance.
- Spatial data governance roles and accountabilities are clearly defined.
- Internal procedures and decision making processes are described, understood and adopted.
- Required documentation, standards, policies and guidelines are in place.
- Quantitative and qualitative performance measures are established and actively being used.
It sounds simple, but it takes time and commitment to do it right. Every day, I help customers build practical approaches to implementing and maturing spatial data governance programs within their organizations. The following steps are high level, based on common patterns I’ve seen in my work—so they won’t fix everything, but they will move you in the right direction.
Step 1: Gain executive support
Okay, let’s be honest. For GIS managers, soliciting leadership support can be a daunting task, but it’s critical. Take the time to construct a compelling case for data governance by exposing high-profile problems. This helps to show the organization’s key gaps and data-related challenges. Focus on issues that will attract executive leaders’ attention and be sure to support your claims with facts and economics. The goal is to establish an active and engaged leadership group that understands GIS and is invested in your organization’s growing geospatial practices. Executive leaders need to provide support and commitment, which will be critical to advancing your spatial data governance program.
Step 2: Create an inventory of spatial data
While you’re working on obtaining executive support, build or refresh your organization’s inventory of spatial data. Use a template to capture the details and categorize your datasets by business area, intended use, format, geometry, update frequency, spatial coverage and currency. This will help you clearly define the scope and breadth of spatial data within the governance program. Then, prioritize the spatial inventory by business criticality and importance, asking yourself: what is the most critical spatial data for the organization and why?
Step 3: Identify spatial data domain owners
Once you’ve built an inventory of data, define the data owners. If you want to understand data domain ownership in your organization, you need clearly defined data governance roles. Each role should have a set of responsibilities and mechanisms for decision making and issue resolution. This approach involves up-front time defining and describing activities related to data acquisition, collection, hosting, usage, maintenance and archival.
Once you’ve defined the data governance responsibilities, identify the business groups and stakeholders that might be possible authorities or that procure, produce or consume the data. In collaboration with these groups, contact potential representatives who can act as internal authorities for specific datasets. The focus is to identify individuals from various departments who are SMEs on the organization’s assets, services and programs.
There are two key roles to fill for each critical spatial dataset. These roles are:
- Spatial data stewards, who are business SMEs for specific datasets and act as the authorities for addressing questions and resolving discrepancies for the rest of the organization. Data stewards actively monitor spatial data with the goal of meeting or exceeding agreed-upon quality standards. Stewards also provide oversight on crucial governance decisions concerning the geospatial data model, any changes under consideration for that model and what the impacts of these changes might be across the organization.
- Data custodians, who can be information technology or GIS professionals or power business users, depending on their expertise, skill and capacity to edit and update their spatial data. Custodians work in accordance with established GIS data standards and expectations on process. People in this role spend time maintaining specific datasets related to their business domains. They also collaborate with stewards to investigate issues and make recommendations to address gaps.
These roles exist to help establish clear lines of accountability and reduce ambiguity about who’s responsible for what. Failure to assign these roles will limit your organization’s ability to establish accountability for the ownership and maintenance of spatial data, so make sure it’s part of your plan and be ready to engage in careful negotiation to get it.
Step 4: Establish a data stewards network
Once spatial data stewards have been identified and onboarded and their responsibilities clarified, the next step is to bring them together as a business network. The network’s function is to serve as a committee responsible for developing the organization’s data governance best practices, standards and processes. The stewards network acts as the final authority for data decisions and works to resolve cross-domain issues.
As your stewards network matures, its focus will turn to prioritizing new data inventories, understanding the level of integrity and risk associated with specific datasets, establishing data acquisition processes and creating procurement guidelines for data subscriptions and imagery. In terms of reporting, I also recommend to my customers that the data stewards network be responsible for publishing a quarterly or annual Stewards Report indicating data usage, quality against standards and associated risks. This way, you can have the report data in hand when making recommendations on your organization’s yearly budget for spatial data maintenance and related activities.
Step 5: Develop geospatial data policies, standards and guidelines
Typically, within a larger organization, a policy framework defines policy owners and describes a management mandate. You can apply this internal framework to the GIS function to illustrate the spectrum of policies or standards that need to be developed. The framework provides an anchor point for the GIS function to adopt, adapt or develop relevant policies focused on geospatial concerns.
For smaller companies, it’s a question of priority based on gaps and challenges. Focus on critical areas where you need documented approaches, processes and standards in order to improve.
All policies should follow a template and contain concise policy statements defining the principles that stakeholders must respect and follow. Policy statements need to be supported by:
- Detailed industry-related procedures that have been developed or adopted;
- Detailed standards that establish the minimum measures that must be achieved; and
- Stronger guidelines that provide the repeatable, proven steps and practices to be used.
Well-designed GIS policies control data usage, security, integration, access, integrity and ownership. With their help, your stakeholders will be able to work according to determined methods to build more trusted information products.
Step 6: Connect the data steward network with the business intelligence and analytics function
Next, you’ll need to plug your stewards into your corporate analytics function. By integrating the governance practices between your spatial data experts and your business analysts, you’ll improve your ability to track assets and equipment and derive insights from your data in real time. Doing so will also enhance your spatial data quality and increase analytical and decision making capability. And by forcing stewardship and corporate analysis into relationship with one another, data quality problems will be quickly exposed and investigated by spatial data stewards, thanks to regular reporting.
Step 7: Monitor governance effectiveness through performance measures
The last element of an effective data governance program is ensuring that the performance measures you’ve developed are established and actively used. To do this, establish and communicate clear performance indicators and plan to check on them semiannually. Your performance indicators need to clearly demonstrate whether governance controls are working. If they’re effective, they’ll help you track the degree of data quality improvement in your organization.
The spatial data governance measures you choose from could include:
- Percentage of geospatial data assets with defined business owners.
- Number of geospatial datasets with specified quality requirements.
- Percentage of geospatial datasets meeting or exceeding agreed-upon data quality standards.
- Level of compliance to data standards and policy.
- Number of tracked data changes related to compliance.
- Number of non-compliance issues and associated risk.
- Number of documented lifecycle management plans actively being used.
- Level of stakeholder satisfaction with the quality of geospatial data.
Your performance measures will depend on your organization, but take the time to devise a clear set of metrics. Collaboratively develop these with executive leaders, stewards, custodians and the GIS team. Then you can use these measures to internally benchmark and trend to demonstrate progress and data quality improvements.
Spatial data governance helps improve productivity and reduce costs
Remember that implementing spatial data governance is a long-term journey of continuous improvement. Focus on the high-priority areas first, avoid tackling everything at once and use a phased approach. And be patient—each of the steps on this list takes time to implement, but in the end you’ll be able to measure your progress in really visible ways.
In working with my clients, I’ve seen that when they take a systematic approach to establishing mature and well-functioning data governance programs, they realize tremendous benefits. For example, they’ve been able to more efficiently evaluate capital planning scenarios, maintain long-life assets in a state of good repair and reduce the risk of safety-related incidents. Their new spatial governance controls have helped improve productivity, avoid costs and maximize their spatial data’s full value potential.
Overall, implementing a spatial data governance program helps create a culture of accountability and system control over your geospatial data, unlocking the real power of location intelligence within your organization.
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