Although Data Governance has been a hot topic for a long time, companies are still struggling to get value from their governance programs. The astounding number of tools available on the market have potential, but organizations often have trouble leveraging those tools for the most value.
As data continues to become more complex and voluminous, “It’s really important that we re-energize the conversation around data and Data Governance right now, and that we focus on that value conversation,” said Kelle O’Neal, Founder and CEO of data consulting firm First San Francisco Partners. O’Neal spoke at DATAVERSITY® Enterprise Data Governance Online (EDGO) about how to maximize value by treating Data Governance as a service.
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Data Governance: Untapped Potential
In this “Golden Age of Data,” O’Neal said many organizations aspire to be data-driven, but find it out of reach, struggling to realize the value trapped in their data stores, despite developing data strategies and investing in technology. Data ownership costs and risks can outweigh anticipated benefits when they are unable to deliver relevant, timely, and efficient data for information and analysis.
Modern Governance in Action: An Agile Approach
O’Neal described early Data Governance as driven by a very top-down approach, focusing on the creation of Data Governance councils as a result of directives from the executives in the organization. Data Governance 2.0 broughtthe creation of the Data Governance office, which served as an organization accountable for delivering value expected from Data Governance.
The traditional vision of Data Governance has focused on risk mitigation, with little concern for the needs of staff who work with data, she said. A modern Agile Data Governance approach uses a bottom-up approach, empowering and providing support to staff who work with data. Governance is provided as a service, similar to how an HR department works.
Centralized vs. Agile Data Governance
Data privacy and security are still as important with Agile Data Governance as in a centralized approach. Executives still must provide resources and communicate overall strategy. Governance councils still have a place, but policies and processes are applied as close to the point of the data usage as possible and in a way that maximizes the user value of those policies, she said. “Think about governance as a capability that supports programs, processes, and projects.”
A Vision of Modern Data Governance as a Service
O’Neal presented a framework of concentric circles to illustrate the scope of modern Data Governance, with “Trusted Data” at the very center. Going outward, she outlined a series of core capabilities under the heading of “Collaborative Governance.” The next circle included areas where Governance plays a role, such as Metadata Management and Business Intelligence (BI). The outermost ring contains data-driven insight and digital transformation.
Trusted Data at the Center
The goal of Data Governance and Data Management is to provide “trusted data,” she said. Trusted data can be relied upon to originate from a verified source of high-quality data, and users can be confident that it has been used and protected appropriately. Trusted data is supported by architecture standards, and technology to ensure that it can properly be audited and measured, which assures efficiency and effectiveness of use.
Collaborative Governance is focused on supporting the goals of data democratization and providing access to as much data as possible, which requires an Agile Data Governance organization, she said. Enacting Collaborative Governance is a five-step process, encompassing Strategy, Organization, Directives, Measurement, and Organizational Change Management.
- Outlining a Strategy aligned with the goals, objectives, and measurable value that the organization is hoping to accomplish is the first step. “This doesn’t have to be a 50-page document. It could be a back-of-a-napkin sort of strategy,” she said. The important thing is to create that line of sight between business goals and the value that governance provides.
- Organization is the process of articulating the roles, responsibilities, and accountabilities for executing against that strategy. Organization outlines who is involved, why are they involved, and what is expected of them.
- Directives provide policy, processes, and standards that align with the organization’s strategy. Directives ensure that policy is put into practice and that the work done adds value.
- Measurement relates primarily to impact and value measurement, and includes progress metrics, as well. “If we’re not measuring the value that Data Governance provides to the organization, we’re going to have a very hard time sustaining it,” she said.
- Organizational Change Management is the communication, training, resistance management, and integration of governance into the enterprise to recognize and understand how governance supports the enterprise. Organizational change management is very important to call out as a separate, proactive component of governance because, she said, “If we don’t recognize that we’re asking people to do things differently, that we’re asking them to make a change, we might not be as successful as we want to be.”
A New Perspective on Data Governance
“In a perfect world, Data Governance is another capability in an organization, just like human resources, finance, or facilities,” she said. HR is looks after the “human capital,” the people within an organization, yet a human resources department doesn’t necessarily deliver every performance review. They do provide frameworks, tools, templates, policies, and processes so that people are supported and managed consistently across the organization.
Leveraging Governance for Value
Governance is really all about making sure that the data within an organization is leveraged for its highest value. Like HR, the governance organization provides templates, tools, policies, and processes to support how data is created, managed, used, and shared within — and outside of — the organization. One of those key processes is within projects, and as an example, she next illustrated how Data Governance with a service mindset can support projects.
Data-Centric Development Life Cycle
O’Neal showed a methodology for using data and information within a project to make it as meaningful as functional or technical requirements. Use those information requirements to help create a design of the data that is needed to support that project, she said, “And in that way, you can most accurately create the data design that is needed to support that project.” The important thing about pulling forward information requirements and treating data as a unique component of a project, she said, is to make sure that data issues are identified early in a project, instead of waiting until the QA process.
Aligning Data Governance as a Service for Data Acquisition
O’Neal next used data acquisition as an example of a process that can be aligned with Data Governance as a service. Data acquisition is defined as the process for bringing data created by a source outside the organization into the organization, for production use. The idea of acquiring data from an outside source is becoming more and more common within organizations as data is becoming more and more voluminous.
Common Issues with Data Acquisition
When companies buy data, it is usually treated as a “purchase” rather than an “acquisition.” Because it’s treated as a purchase, it’s done in a siloed way, where each organization buys or just downloads their own data, she said, which creates inconsistencies across the rest of the business. People accessing the data don’t understand what’s in that dataset, how it should be used, and what constraints might be on it. When companies acquire data without a framework in mind, they’re not capturing the metadata and not truly getting the value from it.
Data acquisition involves multiple players and competing viewpoints, so the solution, she said, is to create a Governance process that provides accountability, and includes all of the different players involved in the data acquisition process.
Adopting a Strategic Data Acquisition Framework
The same way that Data Governance is applied across a project methodology, governance can be applied across a data acquisition process. An acquisition framework starts at the point when it’s determined that data is needed and Data Governance can participate, providing structure to the data acquisition process.
Having a framework ensures that information and metadata from the process is added to the body of knowledge around the organization’s data as well as ensuring that requirements for that data are followed throughout the entire process. This helps to mitigate privacy risk and ensures that by the time for onboarding, all requirements for safe, legal data use have been met throughout the process.
Establishing Roles and Responsibilities to Support a Service Mindset
Next, O’Neal talked about roles and responsibilities, and how the people in an Agile Data Governance program can support a service mindset. She shared an operating model for a “customer” decision-making framework, with a customer data executive team, an enterprise customer data working group and a data stewards’ group.
The framework is a typical operating model with a significant difference: The model includes a customer data team to the side, charged with treating customer data as a capability providing services and value back to the organization.
Establishing Service Capability
Data Governance services provided include setting up tools, templates, and artifacts to serve decisions that need to be made as part of a typical operating model. The team helps manage issues and concerns around data through a standard intake and triage process, and also provides content support through metadata and Data Quality metrics and measurements.
In addition, the team provides needed support to help the organization through changes inherent in implementing Data Governance as a capability and a practice, she said. “So, they’re actually acting as a service line to support the decisions of an enterprise Data Governance model.” Although this model is focused on customer, it can be used anywhere across the organization.
Identifying a Data Governance Lead
O’Neal recommends looking for someone with a service mindset when identifying a Data Governance lead for the organization. She purposefully recommends looking first for someone with strong collaboration skills who is a change agent, rather than someone with Data Management skills, because it’s easier to learn data skills than to become an influencer.
Other important characteristics include perseverance, the ability to understand and articulate the vision for the data program, and the ability to provide visible support and help people overcome resistance to change. Strong communication skills are key, as is a willingness to hold leaders and teams accountable and an ability to keep people engaged.
Start with Privacy
Addressing privacy is a great place to galvanize the organization around governance. “When trying to determine where to start with a Data Governance Program, starting with the data that’s regulated makes a lot of sense,” she said.
The Data Governance area is the best-placed organizational unit to coordinate interdisciplinary collaboration and should partner with the privacy team to provide the framework for the processes, policies, organization, and technologies required to manage and execute a data privacy program.
“Governance should drive the data strategy,” O’Neal said. Governance can unify the organization by linking business goals, objectives, and priorities to data requirements. Without a way to galvanize around data and link it to business value, she said, “Your strategy just becomes something on a shelf in your SharePoint site.”
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Here is the video of the Enterprise Data Governance Online Presentation:
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