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4 steps to successful data governance programs for government

Getting started with data governance

Government agencies depend on data for all aspects of operations, from matching constituents with available resources and improving citizen services to streamlining day-to-day functions and capitalizing on funding opportunities. That’s why agencies on the path to data-driven digital transformation are exploring data governance solutions to:

  • Discover critical data across siloed systems and agencies
  • Understand data meaning, policies, and ownership
  • Provide visibility into data quality rules, metrics, and lineage
  • Monitor data usage, access, and changes

Unfortunately, when government agencies undertake traditional data governance initiatives, results frequently fall short of expectations. That’s because building data governance from the bottom up is difficult — particularly for agencies with siloed departments, outdated mainframe processes, and multiple incompatible databases.

The results too often are overly complex and focused too heavily on governance policies and enforcement. Government workers are asked to attend governance meetings, define terms, and change the way they work. Yet too often these workers don’t see the connection between data governance and how it makes an impact on meeting their own goals and responsibilities.

Data governance execution becomes bogged down in minutiae. It loses focus.

With no tangible improvement in insights, operations, costs, or constituent services — and no clear return on investment — leadership loses enthusiasm. Leadership may even consider defunding governance efforts, losing out on the benefits altogether.

 

4 steps to successful data governance programs for government

There is a better way: the stepwise, results-driven approach

Start your governance program with a stepwise, results-driven approach that engages employees, quickly delivers some high-value, low-effort wins, and establishes a foundation for better decisions. It’s been proven to generate between 2x and 7x greater return on investment over a more traditional approach, while delivering results up to 40% faster.

Stepwise, results-driven data governance promotes organizational adoption, lays the foundation for data integrity, and consistently delivers value over the long term. It increases the likelihood of agency-wide expansion of your data governance programs by as much as 75%.

Follow these four steps to realize a stepwise, results-driven approach for your agency:

  1. Link data governance assets to specific goals or objectives
  2. Prioritize data that is critical to impacting those goals
  3. Build stakeholder engagement across three tiers
  4. Clear the path for success

1. Link data governance to agency goals


A stepwise, results-driven governance strategy identifies agency-level goals and initiatives and builds governance capabilities around them.

How does this alignment play out? Suppose that, in an effort to improve constituent services, an agency wanted to increase the accuracy of matching citizens with services they could receive and increase the utilization of funding in two high-profile government programs. Organizational stakeholders include program operations and quality assurance. Operational goals include increasing approval ratings by 5% and increasing enrollment of qualified citizens in the two programs by 25%.

Governance objectives in support of these goals include establishing that elusive “golden” single constituent view across all divisions and branches.

Capabilities that governance teams can deploy to meet that objective include:

  • Establishing a master constituent data file
  • Building a user-friendly data catalog
  • Visualizing data lineage of upstream/downstream relationships
  • Automating auditable workflow rules to give appropriate users an easy, but secure path to access data

Identify potential data sources

 

 

Understand the use case for the data

Begin with clearly defined goals

A good data governance process begins with clearly defined goals. Government agencies often break these goals into three broad categories: minimize data risk, deliver better data insights, and improve operational processes and decisions.

  • Data-related risk minimization is driven by a clear understanding of potential gaps surrounding privacy, security, and compliance. This includes profiling risk exposure, managing the privacy and security of constituents and the workforce, internal IT controls, and more.
  • Analytical insights drive value in more targeted constituent outreach, program effectiveness, budget analytics, complaint management, and more. Analytics help stakeholders in program management to develop a clear 360° understanding of the agency’s constituents.
  • Operational excellence is about improving performance, citizen services, and data quality, while reducing costs in everything that the organization does. This includes program administration, citizen satisfaction, agency management, budget management, funding acquisition, data quality management, and more.

Many of the goals on your list will be driven by the same datasets. For instance, managing the privacy of program participant data is most likely the same data that helps strategic decision makers gain a 360° view of constituents. Those same datasets are likely to impact operational improvements around citizen satisfaction or workforce management. A critical part of the governance process consists of linking those data assets to your organization’s key goals and understanding those relationships to maximize value.

An overarching objective will be establishing a common view of trusted data assets. Specific data governance capabilities such as a data catalog, data lineage, approval workflows, and well-defined data integrity rules can then be identified as components that contribute to achieving that objective.

 

 

Focus on high priority governance projects for quick wins

By connecting the dots between high priority objectives and data governance capabilities, you’ll be laying the groundwork for success. But you can’t build your entire agency-wide program in your first governance iteration.

To increase the chances of success and to help win advocates for the program, start with low-effort, high-value initiatives that illustrate the value of data governance and produce quick wins.

You may want to use this prioritization framework to identify the right projects:

Reach: Will this new data governance initiative further an agency-wide initiative such as reducing program costs or establishing a 3600 view of constituents? Or will it just make life easier for a single branch or division?

Impact: What impact will the data governance initiative have on your agency or its constituents? Will it make it faster and easier if every constituent can access their own records online? Or will it enable a very small segment of program participants to get their benefits one day earlier?

Confidence: How sure are you that your governance initiative will help the agency meet a specific goal? Do you have metrics to back up your beliefs?

Effort: How much governance effort will it take to reach a specific agency goal? How much time will it require? Does your staff have the skills needed in-house?

 

 

Separate “needs” from “wants” when selecting capabilities

Initial data governance initiatives should separate what capabilities the government agency truly needs to reach a specific goal from those capabilities it would simply like to have.

Group data governance components into “must haves” (needs) and “nice to haves” (wants). Anything that supports high-priority goals or is attached to multiple identified objectives is a “must have.” Anything else is a “nice to have” that can be deployed later.

If your goal is to increase program participation by qualified constituents, “must haves” may include a centralized collection of constituent data, a data lineage flow, and automated approval of workflows. “Nice to haves” may include machine learning capabilities that help a program determine whether certain data fields in each constituent profile are more trustworthy than others.

Monitoring for Deviations

 

2. Prioritize data that matters


The sheer volume of data maintained by government agencies today is indeed overwhelming. It includes both structured data such as constituent profiles and citizen service requests, and unstructured data, including emails, documents, PDFs, and images.

The good news is that you don’t have to govern all of it right away. Precisely estimates that as little as 5% percent of agency datasets drive 95% of results by delivering insights, minimizing risk, and improving performance. Reduce governance time-to-value by concentrating on just that critical data at first. This focus simplifies data governance significantly by limiting the amount of data to be governed in alignment with any single goal from perhaps thousands of elements to just a few dozen.

By understanding how each data element fits into the objectives established in step 1, data stewards can more easily set priorities by identifying which data points matter and can then focus on governing those that are most important.

Focus on the Data that Matters

As little as 5% of all data impacts critical KPI’s, goals, and objectives

3. Build stakeholder engagement across three tiers


The next step is to align stakeholders across the three levels of your organization: tactical (including data management, data science, and program management); operational (including things like quality assurance, policy development, HR, and information services); and strategic (executive management).

Each of these groups sees value through a different lens. That means they typically have different methods and metrics by which they determine success or failure. Building stakeholder engagement across all three levels of the organization requires the data governance process to address each group’s needs and concerns.

To do this, build meaningful KPIs measuring:

Current and future-state efficiencies at the tactical level. Data analysts, data scientists, data stewards, data engineers, and others want KPIs that measure improvement in data access and movement, data completeness, and improved curating.

Improved program processes, performance, and scalability at the operational level. Program leads, data governance leads, data management leads, information architects, and other professionals want KPIs measuring improvement in data quality, reduction in data errors, and reduced cycle times.

ROI and transformation efforts at the strategic level. Directors, CIOs, CDOs, data and analytics leads, and branch or program leads value KPIs measuring improvement in process enablement, constituent satisfaction, and funding, among other benefits

KPIs must link across organizational levels to tell the complete story of how governance supports a government agency’s key objectives. The complete data governance story may run something like this:

“We’ve identified the 12 critical demographic markers that potentially qualify a citizen as a candidate for our program and aligned on key definitions and rules for each evaluation data point. We’ve also acquired third-party data to ensure that we have access to accurate demographic data to complete our master constituent data records. As a result, we have identified 35% more underserved citizens out of our total population, increases program participation by 25%, and increased funding 25% to match participation levels.”

4. Clear the path for success


Finally, an effective data governance initiative will clear the path for success by removing friction from stakeholder processes, helping them to deliver on the desired outcomes. It will make it easy for teams to contribute to the data governance process, keeping teams engaged and wanting more. And it will create enthusiasm among employees by communicating value and celebrating success.

Governance initiatives depend on stakeholder participation to define key terms (how does a “program participant” differ from a “constituent” or an “account owner”?), identify baseline measurements, and more. Programs that include personalized governance onboarding, newsletters, and leadership endorsements will gain better traction than those which leave adoption to chance. Self-service solutions such as platform training videos, data integrity dashboards, and progress scorecards provide greater visibility to results and invite participation from a broad group of stakeholders.

Frequent communications and reinforcement from data governance ambassadors can further improve workforce engagement. Regular communications keep governance in the forefront of employees’ minds, consistently reminding them of the value that data governance brings to their work. Data governance ambassadors should regularly share insight, report issues, and quantify the impact of changes brought about through governance efforts.

tap the maximum potential of the data in your systems

Why Precisely?

Precisely is the global leader in data integrity, providing accuracy and consistency in data for 12,000 customers — including 99 of the Fortune 100 — in more than 100 countries.

Precisely’s data governance products establish strong, results-first frameworks to help you better find, understand, and manage data for improved operational outcomes.

The Data Governance Module is a data governance, catalog, and metadata management solution that improves the value, meaning, and trustworthiness of your data. It automates governance and stewardship tasks to help you answer essential questions about your data’s source, use, meaning, ownership, and quality.

With Precisely’s Data Governance Module, you can make better, faster data-management decisions, build collaboration across your entire organization, and empower users to get the answers they need, when they need them.

Linking goals to governed assets provides real-time views into how data supports organizational processes. Dashboards and reports personalize insights about your curated or transactional data with views that connect data to outcomes.

The Data Governance Module also provides insight into compliance events and metrics.

Deploy a data governance strategy that builds employee engagement to minimize risk, increase performance insight, and improve operations.

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