3 Strategies for Creating Outcome-Driven Data Analytics Programs

3 Strategies for Creating Outcome-Driven Data Analytics Programs

With a renewed focus on evidence-based policymaking, government leaders are investing in modernization at an unprecedented level.  But while nearly 70% of government organizations are pursuing digital modernization, only 5% are actually achieving results, often because they don’t include a clear data strategy and the right business intelligence solutions. To create the scalable and secure platforms required to deliver digital services to constituents and advance evidence-based policies, agencies must begin to develop their analytical maturity by embracing the following strategies:

1. Institutionalize Data Sharing

Complex challenges, by their very nature, can’t be addressed by a single agency or organization, and sharing responsibility means sharing data. Setting up a cross-agency data sharing framework can seem daunting, but a well-structured data architecture, data trust, and data governance framework can eliminate some initial barriers to entry.

Elasticity is the new stability – Creating an enterprise architecture that is flexible and designed for change instead of merely capable of change will reduce risk and decrease time to value. Investing in secure cloud, automation, and modern data center operations can also dramatically reduce costs. Federal agencies have reportedly saved $1.1billion over the past two years by consolidating inefficient data center infrastructure and moving to cloud services.

Scale with a Data Trust – A Data Trust can eliminate both the legal and cultural roadblocks to cross-agency data sharing. It provides peace of mind to data owners who are concerned about how data will be used and eliminate ad-hoc, point-to-point data sharing agreements that stall progress.

Use a Data Governance Framework – A formal data governance framework touches every part of the data management process, including the technology, data architecture, and policies that govern how data is created, used, retained, and secured. The framework establishes measures for monitoring how data is managed and how regulatory compliance requirements are met.

2. Show Value Early and Often with Quick Wins

Eliminating waste, fraud, and abuse in government through data analytics can have returns as high 10 to 15 times their cost. Beyond the financial gains, data analytics can have a tremendous impact on employee productivity and morale, as well as help restore public faith in government. To achieve these cultural impacts, though, it’s important to demonstrate value early and often – we call this the Quick Win philosophy. A Quick Win will build confidence for the analytics program within your organization and ultimately drive user adoption.

Start by finding a high-value use case and working closely with business users to align expectations and begin a rapid prototyping process. In parallel, identify the next use case and create a roadmap for the next phase of the project.

3. Create Working Groups for Long-Term Success

A well-built Working Group will manage the critical success factors of a data analytics initiative and mitigate project risk. Composed of leaders from all agencies sharing data, the mission of the Working Group is to establish data sharing policies, assess and procure all relevant datasets, and provide subject matter expertise. Working Group members can also keep the leadership of participating agencies apprised of progress and secure buy-in for new developments.

Beyond data strategy and management, a key responsibility of the Working Group should be measuring project performance and marketing the success of modernization and digital transformation initiatives both internally and externally. In order to achieve that, though, agencies need the ability to effectively measure and report on relevant key metrics for digital transformation, and less than 5% of agencies have advanced capabilities in this area. Tracking KPIs must be baked in from the earliest stages of any modernization effort.