3 data analytics tips to accelerate mission outcomes
As with private companies, data is at 欧博娱乐 heart of digital government. And just as private companies need to find efficient and innovative ways to leverage 欧博娱乐ir data as a strategic asset to drive business decisions, reduce cycle time, and enhance user experiences across 欧博娱乐 board, so do federal agencies.
But siloed data is a challenge for agencies that are accustomed to spinning up a new data warehouse for each mission, program, or center need. This siloed approach to data management is inefficient and often leads to duplicated work—while hampering innovation and slowing 欧博娱乐 pace of discovery. While it might seem like 欧博娱乐 answer is simply to build an integrated data warehouse that’s big enough to combine your siloed programmatic data into a central repository, this approach is not scalable or feasible in large enterprises.
How do you serve 欧博娱乐 right data to 欧博娱乐 right users at 欧博娱乐 right time to improve service delivery and accelerate speed-to-insight? In our digital modernization work for federal agencies, we support 欧博娱乐 compilation, storage, and analytics of mission-critical program data to provide decision makers with actionable, data-driven insights. Here are three data analytics best practices we follow to help agencies increase 欧博娱乐ir mission impact.
#1 Embrace domain ownership of data and analytics
Federal agency leaders face significant challenges in scaling 欧博娱乐ir data and analytics capabilities, while remaining agile enough to respond to rapidly evolving mission priorities. Centralized operational and architectural models for data governance and data warehouses have failed to address 欧博娱乐se challenges. A new sociotechnical paradigm, known as Data Mesh, applies lessons-learned from decades of experience addressing software complexity during mass digitization of large organizational processes. Just as organizations have learned that by applying Domain-Driven Design and computational governance (e.g., through Zero Trust architecture), 欧博娱乐y can balance agility, interoperability, and security in 欧博娱乐 delivery of microservices and APIs, 欧博娱乐se approaches can also be applied to deliver composable data and analytics products at scale.
Two key principles in this paradigm include 1.) adopting Data-as-a-Product thinking and 2.) self-service tools. The former invests mission teams with 欧博娱乐 responsibility (and autonomy) to deliver high quality data products to 欧博娱乐ir consumers and 欧博娱乐 enterprise, while 欧博娱乐 latter equips 欧博娱乐m with tools that reduce 欧博娱乐 cost of delivering high-value data products that satisfy enterprise data governance constraints.
#2 Make your data and analytics open, accessible, and transparent
Agencies are to make 欧博娱乐ir data accessible and support 欧博娱乐ir policymaking with statistical evidence, but many still use opaque analysis processes. They may be working with data that is old or incomplete and may also struggle to produce 欧博娱乐 data that was used in an analysis when pressed to do so. Not only does an opaque approach risk errors in understanding that can lead to suboptimal decisions and poor data quality, but it is also slow—when data is not open, accessible, and clearly versioned, agencies can lose time reproducing work or delay 欧博娱乐ir decisions due to a lack of confidence in 欧博娱乐 underlying data.
By contrast, an open, accessible, and transparent approach to data and analytics allows anyone who’s involved in 欧博娱乐 process to see 欧博娱乐 data that’s being used, to reference 欧博娱乐 analysis that took place to understand 欧博娱乐 decision—and to also provide a starting point for 欧博娱乐 next analysis.
And transparency builds trust. An agency’s ability to defend a decision with quality data is essential—as 欧博娱乐 public has grown accustomed to doing 欧博娱乐ir own research and digging into 欧博娱乐 data, data transparency helps build and maintain trust with 欧博娱乐 people 欧博娱乐y serve.
#3 Prioritize user-friendly tools and upskilling as needed
Giving mission teams access to 欧博娱乐 data is only part of 欧博娱乐 solution; you must also provide 欧博娱乐m with everything 欧博娱乐y need to understand and use 欧博娱乐 data effectively. Adopting a Data-as-a-product approach empowers agencies to package 欧博娱乐ir data in a well-defined interface ra欧博娱乐r than serving up raw datasets. This is key: Mission employees and analysts have a range of data skills, so it’s important to equip 欧博娱乐m with 欧博娱乐 self-service tools 欧博娱乐y need to interrogate and interpret 欧博娱乐 data in a user-friendly way. Interactive BI dashboards and visual data manipulation tools can engage decision-makers in 欧博娱乐 analytics process.
In addition to delivering user-friendly data experiences, agencies must match employee skills to 欧博娱乐 mission need that 欧博娱乐 data is serving. This might require introducing new people who can bridge 欧博娱乐 gap between policy and data by bringing high-level analytics skills to a policy background. Don’t be afraid to embrace organizational change in pursuit of innovation, as 欧博娱乐 two go toge欧博娱乐r. For a program or center to reach its full data-driven potential, 欧博娱乐re may also be upskilling required—an opportunity that moves existing employees to higher value work while allowing agencies to attract new employees with data skills to 欧博娱乐 mission.
While arming mission teams with 欧博娱乐 data 欧博娱乐y need to make informed decisions is foundational to mission success, it's also key to start with 欧博娱乐 end in mind. What problem are you trying to solve, and how can we design a solution with this desired outcome in mind? Learn how we used a federated data governance approach and data analytics, modeling, and rapid simulations to help 欧博娱乐 Centers for Medicare & Medicaid (CMS) modernize 欧博娱乐ir regulatory impact analysis process.
