By: Craig Wesley Carpenter and Rebekka Dudensing
Using data to guide local economic development efforts is essential to remaining competitive in a quickly evolving economy. Communities often gather this data through business retention and expansion (BR&E) programs or from other sources. Examining this data can help leaders identify changes, new opportunities, and emerging issues in order to make decisions that reflect the entire community. As more detailed data and sources emerge, the question becomes how to convert that data into actionable goals. The following focuses on various strategies for converting data into goals and on the importance of how the goals are written.
Have the right people at the table
Once you identify the industries that are the largest contributors to local employment and payroll, invite them or their industrial associations to participate in the goal writing. Their knowledge of the industry can be leveraged to set goals that are effective and reachable. Involving local businesses in the goal writing process also helps secure business owner buy-in within those industries. If you have difficulty involving an important business owner, inviting a competitor can be an effective way to encourage their participation.
Regardless of how well supported a set of goals may appear, failure to involve community members, civic groups, businesses, or industrial organizations early in the goal writing process can alienate them from the whole development effort. Alternatively, involving business owners and other stakeholders can strengthen collaboration and lead to better communication among businesses, leaders, and the community.
Integrating data into the discussion
Community members have different levels of experience and comfort with data. Some use spreadsheets and graphs daily, but bar charts and columns of numbers can intimidate others. It is important that participants understand that data is simply information that helps decision makers assess situations and alternatives objectively. Collecting data can be as simple as noting whether people seem warm or cold to determine the temperature of a room, or watching facial expressions to gain feedback about opinions of what was said in a meeting. Not all data is numerical (quantitative). Qualitative data, such as in the previous example, describes attributes in words and may be a result of questions like, “What are your biggest labor force challenges?” or “Why do you shop at local businesses?”
Most people can be comfortable with data when they are used as a starting point for a conversation that is of interest to them. For example, you might begin a conversation by saying, “The data indicate that our community has fewer adults with a college degree than our peer communities.” You could then ask, “Do you agree with that assessment? Why or why not?” Small group discussions or asking a quiet person for their opinion, particularly on a non-threatening issue, can make newcomers to data feel more comfortable and valued in the discussion.
It is important that the data you plan to use be appropriate for your work. Data should come from a reliable source and be presented in a way that is not misleading. When dealing with qualitative data, it is also important that you understand the meaning of variables and how the data are coded. The data you use should reflect your local area and should be reasonably up-to-date. Data from government sources often lag by several months to a year or more. Data may be also be available for multiple locations or time periods—this facilitates comparisons across time and place. Sources of demographic and economic data include the U.S. Census Bureau, the Bureau of Labor Statistics, and the Bureau of Economic Analysis.
Community members can be coached on how to examine data by asking questions related the following four facets.
- Conditions: What appears to be happening? Is unemployment high or low?
- Direction of change: How has the condition changed over time? Has unemployment increased or decreased? Have job numbers and labor force participation changed over time? Identify the period(s).
- Intensity of change: How dramatic are those shifts?
- Overall picture: How does this data fit with other information? From your observation of the community, is unemployment lower because people are getting jobs, families are moving away, or because the population is aging?
Keep in mind that not all questions apply across datasets. For example, there is often no baseline to measure the direction or intensity of change when your community has conducted its own survey. While governmental and nongovernmental data are often easy to obtain, they may not provide the data you need to answer a specific question. In those cases, it is best to reach out to the public for additional input via surveys, focus groups, SWOT, or other types of analysis.
SWOT analysis divides community data findings into strengths, weaknesses, opportunities, and threats. For survey results from a business retention and expansion program, for example, what appear to be the firms’ or the community’s primary strengths or weaknesses? Similarly, what appear to be the primary opportunities or threats to these businesses? If you are working with diverse group SWOT categories, creating a list under each one may be sufficient, though it helps to include the number of mentions for each strength, weakness, opportunity, or threat. Alternatively, a community can conduct SWOT analysis based simply on the interests of community members.
Once the SWOT analysis is complete, there will likely be a long list—deciding which strength or opportunity to build on and which weakness or threat to address can be difficult. Evaluation based on the number of mentions is an objective way to gauge importance but is insufficient for effective economic development planning. Subjective analysis on the degree of importance requires multiple approaches. It could be that the SWOT analysis lists a particular threat only once, but though mentioned only once, the nature of that threat can indicate its importance. In a community forum or group meeting, voting on the issues often works well to create a list of the most important ones. Three methods that have worked well for prioritizing SWOT elements include the following.
- A vote by show of hands works well if there are a limited number of options and the subject is uncontroversial. This method, however, does not work well for complex or controversial subjects. A ballot can be used in those instances, but if ballots are not available immediately, the decision can be delayed. Ballots provide anonymity and can also be used with the 1-3-5 method (described below).
- When there are more options to prioritize, dot voting has proven effective. Options can be written on large notepads during small and large group discussions. Common or related themes and ideas may be combined before voting to make tallying easier. The sheets are posted around the room and every person is given a certain number of stickers (colored dots or stars work well) to place next to their priorities. The number of stickers each person receives and whether a person may cast all their votes for a single option may be based on capacity to take on more projects or the number of ideas on the sheets. Voting rules are at the discretion of the facilitators and participants.
- The 1-3-5 method provides stronger numerical indicators for prioritization. Participants create lists of options as in dot voting, but each person is allowed to place a 1, 3, and 5 beside their top three priorities. If 1-3-5 is used when many options are still on the table, each person may be able to give out a predetermined number of 1s, 3s, and 5s. Each option’s score is calculated as:
Option A score = (# of 5s × 5) + (# of 3s × 3) + (# of 1s × 1).
The options with the highest score are the priorities.
It is essential to present relevant data before voting. Specifically, it is vital to present data related to local industrial clusters, paying special attention to employment and payroll. This data informs voters about the relative significance of industries and issues. It can also highlight opportunities and threats which may be disproportionately important. By using data for context, groups can develop SMART goals to achieve the changes they want for their community.
SMART goals are specific, measurable, attainable, relevant, and time-framed. To write goals that have these 5 characteristics, you will need the right people at the table and use relevant data. Making sure that your goals are SMART, significantly improves your chances of achieving them. Further, the data collection and dissemination process you use will enhance each element of SMART as described below.
- Specific goals outline exactly what you hope to accomplish. Being specific helps you and others understand clearly what you plan to do and requires a detailed understanding of the data used for various local measures.
- Measurable goals include a metric against which to document progress. Data often provide a baseline and the progress or outcome measure for the goal.
- Attainable goals are ones that can be accomplished in a given social or political climate with the available resources. Attainable goals align with local assets and values. Knowing what is attainable requires people at the table who are knowledgeable about the community and regional data
- Relevant goals are important and will help you accomplish the desired outcome. A community can only ensure that a goal is relevant if they know the relative importance of various industries, as measured through employment, payroll, and other data.
- Time-Framed goals ensure accountability and keep progress on track by providing a deadline. Creating a realistic time-frame requires knowledge of regional data and data on similar communities.
Each of the SMART goal elements is included in the example below.
The number of children in poverty will decrease by 20 percent between 2018 and 2028.
The measure specifically relates to reducing the number of children in poverty. It is measured using Census Bureau data on the number of children in poverty in the 2018 base year and the 2028 deadline (time-frame). Whether the goal is attainable or relevant depend upon the community’s climate, assets, and overall vision.
Data enhances each element of the SMART goal system, and in turn, enhances the efficacy of efforts to improve your community. For more information on goal setting, see the Texas A&M AgriLife Extension Service’s publication: “Implementing Community Goals Successfully”
Some of this material draws from the United States Department of Agriculture Stronger Economies Together Phase VI coaches’ manual, as well as a series of booklets on Business Retention and Expansion written by George Morse, professor emeritus, University of Minnesota, and Scott Loveridge, Professor, Michigan State University. We are grateful to these authors, the Northeast Regional Center for Rural Development, and the Southern Rural Development Center for their support of Texas A&M AgriLife Extension Service’s adaptation presented here.
Download a printer-friendly version of this publication: Translating Data or Survey Results into SMART Goals
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