6. Crafting the Message

Overview

This chapter covers the second of the three communications planning steps: crafting the content of the message to be delivered to your target audience. Message content should be based on the goals you want to achieve, but framed in a manner that resonates with the recipients.

Researching Your Target Audiences

If you don’t already have an established relationship with your target audience(s), it helps to do some research to understand how best to communicate with them. Methods for doing this are:

• Informal exploratory conversations
• Talking to colleagues who have worked with them
• Attending meetings where they are speaking
• Reviewing their prior presentations or email communications
• Conducting surveys or polls

The objective of this research is to find out:

• Their sphere of influence: What types of decisions can they make? What resources do they control?
• How much they know already: What background information will be needed?
• Their current level of buy-in: Will you be “preaching to the choir” or working to change their perspective?
• Their current initiatives, concerns, and pain points: What “hooks” can be used to connect with them and catch their attention?
• Their general orientation: Are they strategic or tactical thinkers? Are they focused internally or externally? Are they interested in breaking new ground or keeping to the status quo? What kinds of arguments will resonate for them? What questions are they likely to have?
• How busy are they: How much time will you have to deliver the message?

Messages for Data Governance

The following are some messages that respond to common questions people have about data governance. Some of these are suitable for including in general overview presentations, fact sheets, and web pages; others are intended to address specific concerns that may come up in one-on-one or small group conversations. Use the following links to navigate directly to the message(s) of interest:

What is data governance?

Data governance means managing data as an asset

Don’t we already govern our data within each program?

Isn’t data governance an IT responsibility?

What is involved in implementing data governance?

Why do we need data governance?

What outcomes should we expect from data governance?

Why is it important to do this now?

What are other public agencies doing?

We’d rather not focus on rules and processes that stifle creativity and create more work – we want to do things that more directly add business value

Can we do this without adding new positions?

Can we do this without leadership support?

Why do we need a data governance group? We already have so many management committees and steering teams

Why should we partner with the DOT on this? (External agencies)

What is data governance?

As a public agency, we already have a lot of governance in place to set the rules of the game for what we do and how. Our enabling legislation, position descriptions, and agency policies define who can make different types of decisions and who is responsible for different types of actions. Governance guides our work and reduces ambiguity about roles, helping us each to “keep in our lane”. We also have more granular governance for different aspects of what we do. For example:

• IT Governance sets up the processes we must follow to purchase or build new systems and defines who is involved in setting priorities and deciding what to build and how.
• Project Management Governance defines the various roles in a project team, as well as the steps, milestones, and deliverables in the project management process.

Data Governance is governance for all of the activities involved in managing data – including how we collect and update our data, how we document it, how we make it available to people, how we protect it, and how and when we delete it.

Data governance means managing data as an asset

If we are really serious about using data as the basis for making decisions, we need to treat it as an asset, not just as a byproduct of our work. We know how to manage our infrastructure assets – we should be doing the same for our data. This means:

• Being intentional about what data we collect (resource allocation and acquisition)
• Knowing what we data have (inventory)
• Tracking and improving data quality (condition)
• Maximizing data usability (functionality/access)
• Keeping data current through periodic updates (maintenance and rehabilitation)
• Managing access to data to avoid loss or damage (protection)

Data governance provides the oversight necessary to make sure that we are managing our data assets effectively throughout their life cycles to provide value.

Don’t we already govern our data within each program?

Yes, but there are different levels of data governance – agency-level, cross-divisional level and program-level. Both are important.

Program-level governance establishes procedures and responsibilities that are particular to the type of data being managed and the requirements of the program. For example, safety data governance makes sure that our crash data complies with applicable NHTSA and FHWA regulations and standards.

Cross-divisional governance focuses on establishing consistency and coordination across selected divisions in the agency where breaking down data silos is important to further business objectives and improve efficiencies – for example, divisions responsible for asset management, road inventory management and digital delivery.

Agency-level data governance covers the things that we want all of our important data programs to be doing in a consistent fashion:

• It defines best practices that all data programs should follow, while allowing flexibility for them to tailor what they do to meet their particular requirements
• It provides consistency needed to combine data from multiple business areas
• It makes it easier for our staff with valuable data skills to support multiple programs or to move from one to another

An agencywide approach to data governance also helps collaboration between our business units and our technology/IT function – which is so crucial to our efforts to modernize and streamline our processes.

Isn’t data governance an IT responsibility?

Some DOTs choose to locate data governance under IT; others put it under administration, planning, or engineering. Regardless of where it is located, data governance should be a collaborative effort between IT and the functional areas that collect, understand, and use the data.

Data governance can’t succeed without strong business engagement and leadership. When we say that data governance is an IT responsibility, that often leads to business units abdicating their responsibilities for data. This can lead to tensions between business and IT due to faulty assumptions, overly restrictive standards, and reports that don’t provide value.

One way of understanding the respective roles of IT and business is to make an analogy with a utility - IT is responsible for the infrastructure (pipes and connectors); business is responsible for what flows through the pipes (data). Both need to work together on projects to add new capacity or replace existing infrastructure. But it is a business responsibility to define requirements and maintain knowledge about what is flowing through the pipes.

What is involved in implementing data governance?

We can tailor our data governance activities based on our priorities and our culture. There is no single standard approach to implementing data governance, but we need to set up ways to make sure the right people are involved in making decisions about what data we should collect and purchase, and how we manage that data so that it benefits all of us. Typical activities include:

• Designating a data governance lead (such as a Chief Data Officer or Chief Data Steward) and supporting staff

• Setting up one or more data governance groups to make sure we stay coordinated and avoid duplication
• Creating and maintaining an inventory of authoritative agency data
• Improving documentation of our data
• Improving the quality and accessibility of our data
• Defining and communicating expectations for staff who work with data

Why do we need data governance?

We spend a lot of money every year on collecting and managing data – but we aren’t getting full value from this investment. Data collected for a single purpose isn’t made available to others who might need it. It isn’t easy to find out what we have. And once someone finds data, it may be difficult to use, undocumented, or out of date.

Data governance sets up a way for us to make the best use of our investment in data – and be smart about how we collect, store, share and use data.

Having trustworthy and relevant data underpins the success of our programs – and data governance is the way to achieve this.

Our core agency data programs such as enterprise GIS, enterprise data warehouse/business intelligence, and digital project delivery (or BIM for Infrastructure) all rely on data governance processes to identify authoritative sources of data, improve data quality, and adopt data standards.

We standardize how we manage our construction projects, how we do procurement, and how we do corridor plans. We do this to provide consistency, repeatability, ensure coordination at the right points in time, and clarify roles and responsibilities. We have a central system for tracking our materials, so everyone knows what we have and understands how to order/get it. Why don’t we have this for data?

What outcomes should we expect from data governance?

Better data for decision making

• More relevant data that helps us plan, design, operate and manage the system
• More accurate, reliable, and usable data
• Improved ability to link data from different systems across districts/regions and divisions

Better access to data

• Better awareness of what data we have and what it means
• Faster and easier access to data
• More self-service data options

Improved efficiency

• Less data duplication
• Reduction in Redundant, Obsolete and Trivial (ROT) information on agency servers
• Employees able to devote more time to actual analysis rather than on finding, understanding, and cleaning data
• Reduced burden on our IT staff and data program staff to respond to individual data requests

Risk reduction

• Lowered risk that personal or sensitive information is inadvertently disclosed

Why is it important to do this now?

The amount of data we manage has been increasing – and this trend will continue given continued automation of our processes as well as advances in information capture from remote sensing, imagery, GPS devices and vehicle transponders. And there are more and more private vendors offering to license new datasets to us. We need to be smarter and more intentional about how we acquire and manage our data.

Data governance is critical to the success of our current data strategy – which includes:

• Breaking down our current data silos and creating a central repository of data that is ready to use for our analysts, field staff and managers.
• Setting up a new business intelligence/analytics group that will allow us to use advanced techniques such as machine learning to get more value from our data. This group will tap into our large datasets to improve efficiency and identify new ways to advance <safety/incident response/traffic operations/asset maintenance>.

We are planning several major IT projects <e.g., new Enterprise Resource Planning (ERP) system, new financial management system, new maintenance or asset management system, new roadway inventory system, new crash records system>. These projects provide a great opportunity to get our data house in order. Data governance implemented as an agency function rather than as an add-on to an IT project will set us up with good practices that will continue to serve us well in the future.

What are other public agencies doing?

Many of our peer agencies have implemented data governance. The American Association of State Highway and Transportation Officials (AASHTO) established core data principles that have been adopted by many DOTs. As of 2024, more than 10 state DOTs had established data governance functions and many more were planning or investigating data governance implementation. Leaders include California, Colorado, Florida, Ohio, Oregon, and Nebraska.

Data governance has also been widely adopted at the state and federal levels:

• Data governance was a key part of the 2020 Federal Data Strategy for “accelerating the use of data to deliver on mission, serve the public, and steward resources while protecting security, privacy, and confidentiality.”
• USDOT has had a Chief Data Officer since 2014, with responsibility for data strategy, data quality stewardship, data sharing and development of new data products.
• As of 2020, 28 states had established Chief Data Officer positions, over 80 percent of whom have responsibility for data governance.
• See reference [5] for an OECD reference providing an international perspective on data governance in public agencies

We’d rather not focus on rules and processes that stifle creativity and create more work – we want to do things that more directly add business value

Data governance that is done right will avoid unnecessary rules and processes. It will focus on solving real problems and providing the consistency and clarity we need to be more productive.

Some people may need to spend more time up front so that other people can spend less time later. For example, if we make sure that data is well documented, it will take some time to produce this documentation, but it will save time for the users of the data who are now spending time trying to figure out where the data comes from and what it means.

We may need to avoid falling into the trap of “we never have time to do it right, but we always have time to do it over.”

Can we do this without adding new positions?

Data governance won’t happen on its own and can’t be a volunteer activity – it requires dedicated resources. Several agencies have tried to implement data governance on a volunteer basis and have not been successful.

It is possible to get something started by designating a temporary lead and hiring consultants to set up the structure. After that, progress will be very slow unless there is a full time lead and 1-2 support staff. Some DOTs have been able to repurpose existing positions. Others have been able to get legislative approval for new positions based on the need to ensure data security, meet state and federal reporting requirements, or support state-level system implementation initiatives.

Can we do this without leadership support?

Data governance can start with a grass roots effort but at some point, the commitment of the leadership team is needed to stand behind and enforce data governance policies. Lack of leadership support is the reason why many data governance efforts fail. Many data governance initiatives requires people to take on new roles and responsibilities, change how they do things or allocate time to coordination and documentation activities. It will be very difficult to accomplish these initiatives without leadership support.

Early and continuous communication with agency leaders is a must to make data governance successful.

Why do we need a data governance group? We already have so many management committees and steering teams

Implementing agency-wide data governance requires buy-in across functional areas and districts/regions. A governance body is a good way to engage people from various parts of the agency, get them educated and make sure that data governance is solving real problems and has broad-based support.

But there are options:

• We can piggyback on an existing management team for strategic-level guidance and form a more tactically oriented data governance group. The existing management team might need to schedule some supplemental meetings or be prepared to periodically devote a meaningful block of time in existing meetings to data governance topics.
• Rather than forming standing committees for data governance, we can use temporary work groups to accomplish specific tasks and report to existing management committees for approvals.

Why should we partner with the DOT on this? (external agencies)

We want to make it as easy as possible for the traveling public to get timely and consistent information about transportation options, projects and services. Currently they have to navigate a confusing patchwork of information sources provided by different agencies.

Currently our process of sharing data is inefficient. You request data from us, then update it and publish it on your web page. We publish similar data on our data portal. People seeking data are confused about why there are two similar datasets that don’t quite match. We are all facing staffing shortages. If we work together, we can avoid duplicating work to gather, process and publish information – and collectively deliver a better set of products to our customers.

Tailoring Your Messages

Based on your research and what you already know, tailor your messages to each audience. it is important to make messages as relatable as possible, which means getting at how your target audience will be affected and why they should care. General tips to keep in mind are:

• Remind them of prior conversations - what you talked about, any commitments they made, and how you have heard and acted on any previous comments that they made.
• Include specific examples of data, projects, or events that will help them connect to what you are saying.
• Provide brief anecdotes or stories that illustrate your point.
• Use plain language – avoid technical jargon.
• Be as succinct as possible.

Table 5 provides ideas for highlighting the “what’s in it for me” or WIFM for data governance with individual in different DOT and partner agency roles. Keep in mind, however, that each individual may be very different.

Table 5. Data Governance What’s in it for Me (WIFM)

Summary

Crafting the content of data governance communications involves:

• Clarifying what you hope to accomplish through your communications activity
• Researching your target audience to determine their current level of awareness and buy-in and learn about their interests, activities, pain points, and overall perspectives
• Tailoring your messages for maximum impact – by using succinct, plain language and using specific examples to help your audience understand how the information you are providing will affect them.

This chapter has provided sample messages that address common questions and topics related to data governance implementation. It has also provide ideas for making messages relatable for a variety of different audiences within a DOT. These resources can be used as a starting point for developing impactful communications materials. The next chapter covers different ways that can be used to deliver these materials.