Situation
Improving Data for Decision Making

Context

The agency has a longstanding practice of collecting and using data to report on our results. We have been using some of the data we collect to analyze our performance, especially for pavements and bridges. We need additional data to meet legislative requirements and ensure we are making wise investment decisions. Our current system limitations do not always provide adequate data validity and reliability, which makes it difficult to perform meaningful analysis and reporting. We want to determine what new data sources might be available to meet our needs in a cost-effective and efficient way. We also want to make sure that we minimize risk to the agency of using any external data sources. We want to leverage additional data science techniques enhance our ability to make investment decisions. Our long-term goal is to use leading performance measures and predictive analytics to become more adaptive in our programming and funding strategies to maximize our return on investment. We also want to put data in the hands of our staff, so they can have access across our IT systems to data they need to perform analyses and make operational decisions.

Change Forces

Technology Advancement. Today, our volume of data is much greater than our ability to use it. Our current systems need to be upgraded. Eventually, we want to move beyond having dedicated staff synthesizing data from separate, disconnected systems to creating an agency with integrated data that trained practitioners can use as a single source of truth to perform the analysis they need to do their jobs. In addition, we want to take advantage of machine learning and other technologies that are making it easier to collect data that can be used for predictive decision-making.
Legislation, Regulation, and Funding. Federal transportation performance management, performance-based planning and programming, and other requirements are drivers moving the agency toward linking data to investment decision-making, but our knowledge of how to align them is inadequate, and our current measurement system and local priorities are separate and not connected to the requirements. Legislation also creates opportunities to leverage data and analytics available from third parties and federal partners, especially in areas of national performance measurement.
Workforce Evolution. Advances in systems and reporting platforms should eventually make data more accessible to practitioners than they were in the past, but we still don’t have the specialized analysis and reporting skills we need to use data effectively for decision-making in all performance measurement areas. We want to create a set of standardized data elements that can easily be used across the agency for reporting. We want to train existing staff in data science skills and recruit new staff with business intelligence (BI), business analysis (BA), including forecasting and investment decision-making across performance areas.

What Capabilities Are Needed?

Aligning Skills to Needs. agencies need to identify specific skills needed to support data management, governance, analysis, and communication and put recruiting, training and mentoring processes in place to build these skillsets.
Attracting and Retaining. agencies must strengthen their capability to attract and retain employees who can support data management and analysis.
Transparency. agencies need to build an internal culture that values information sharing and data-informed decision making
Agility and Resilience. agencies need to improve their ability to change decision making processes to consider available data and incorporate scenario analysis.
Technology Adoption. agencies need to enhance their ability to identify and use evolving toolsets supporting data integration, reporting, visualization and analysis.

What can you do about it?

Organizational Management
  • Organizational Culture – to create a culture in which data sharing and data-informed decision making is expected and rewarded.
  • Change Management – to help the agency move towards more data-informed decision making processes.
Workforce Management
  • Strategic Workforce Planning – to proactively plan for needed skills for data management and analysis.
  • Recruitment and Retention – to develop targeted strategies for attracting talent and providing a rewarding work environment.
  • Employee engagement – to ensure two-way communication between employees and managers and ensure that employee concerns are addressed (as feasible).
  • Professional development –to build skills in needed areas and provide a growth path for employees.
Knowledge Management
  • Social and Learning Communities – to establish opportunities for learning and collaborative problem solving.
  • Knowledge Capture and Transfer – to facilitate onboarding of new staff and avoid loss of important knowledge about agency data sources when current staff change jobs or leave the agency.
  • Learning Organization – to promote a culture in which employees are challenged to improve use of data for decision making.
  • Mentoring – to provide opportunities for newer employees to learn from more experienced employees.
Information and Data Management
  • IT Strategic Planning – to ensure that planning for future investments considers needs to support data integration, analysis, reporting and commercially available data products.
  • Project and Services Portfolio Management – to provide a process for prioritizing new investments and services that is aligned with changing business priorities and with available agency resources.
  • IT and Data Governance – to establish clear decision making processes for new data-related products and services.
  • Enterprise Data Integration – to establish an agency-wide approach to curating, cleaning, integrating and delivering data from various information systems.
  • Business Intelligence and Analytics – to establish tools, processes and services for transforming data into information that can be used for decision making.

What resources will help?

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