Using Data for Informed Decision Making
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Using Data for Informed Decision Making






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Using Data for Informed Decision Making Using Data for Informed Decision Making Presentation Transcript

  • Using Data for Informed Decision Making: SAVI – A Resource for Central Indiana
  • What is SAVI? SAVI helps organizations and individuals make data-informed decisions. • Reliable Data • Actionable Information • Analysis and Visualization Tools • Capacity Building
  • SAVI’s Service Area •Boone •Brown •Hamilton •Hancock •Hendricks •Johnson •Madison •Marion •Morgan •Putnam •Shelby
  • What is SAVI? • A dynamic GIS-based community information system that provides: • • • • Data from over 30 data providers A Website to access and analyze data in charts, maps, reports, and tables User support Education program • SAVI = Social Assets and Vulnerabilities Indicators
  • SAVI Database • The SAVI Database can be divided into three components. • • • Social Assets Social Vulnerabilities Geographies (Boundaries & Service Areas) • These data can be used to understand and model communities. • • Formats: Maps, charts, and tables 20+ years of data: 1988 – 2013
  • SAVI Database • SAVI provides data in the following major categories: • • • • • • • • • • • • Arts, Culture, and Recreation Demographics Economy Education Environment Health Housing Political and Administrative Boundaries Public Assistance Public Safety Sites, Programs, and Agencies (Assets) Transportation and Mobility
  • Social Assets • An asset is something that can be a benefit to your community. • • • • • Place of Worship School Youth Program Hospital and more… • Assets are displayed as specific points on a map. • Some sites represent vulnerabilities such as hazardous waste sites. Community Centers
  • Social Vulnerabilities • Social vulnerabilities are data that reveal Social Vulnerabilities needs in a community. Crimes • • • • Crime Poverty Housing and more…
  • Social Vulnerabilities • SAVI vulnerability data are summarized by: • • • • • • • Counties Townships School Corporations Neighborhoods Census Tracts Census Block Groups and more…
  • Social Assets and Vulnerabilities
  • SAVI Geographies SAVI organizes data by geographic areas used by the Census Bureau in its data collection and tabulation operations: • • • • • • • States Metropolitan Statistical Areas (MSAs) Counties Cities Townships Census Tracts (~4,000 people or 1,500 housing units) Block Groups (~1,500 people or 550 housing units) Some data are also available using local geographies.
  • Families Living in Poverty By Townships By Counties By Census Tracts (Marion County) Source: SAVI Community Information System and 2000 Census By Block Groups (Marion County)
  • What Questions Can SAVI Help Answer? • Where are vulnerable populations? • What are the demographic and social issues in a particular neighborhood? • Where are existing resources? • How have things changed over time? • Where should resources be targeted?
  • Most Common Uses of SAVI • Community Assessment • • Identifying need for services Understanding community issues of concern • Grant Application Development • • • Statement of need Plan of work (identifying collaborating partners) Evaluation (measuring outcomes)
  • Most Common Uses of SAVI • Strategic Planning • • • • Membership/client mapping Understanding the communities where members/clients live Understanding need for services Communicating with stakeholders (visualizing the problem) • Research • • • Mapping where clients, patients, students live Understanding the neighborhood context in which these students live (demographics, educational attainment, environmental factors, infrastructure, economy, etc.) Understanding public health issues such as obesity, health disparities, STDs, etc.
  • Data-Driven Decision Making Process 1. Framing the Issue 2. Question Development 3. Data Collection 4. Data Analysis 5. Data Interpretation 6. Decision Making 7. Communication Source: Measuring Success
  • 1. Framing the Issue • How do you know what concerns to study and understand better? • What do you want to accomplish? • What is your overall objective? Ask the following questions about your issue:    How did this issue come to my attention? What communities and/or populations are impacted? How does the issue relate to your organization’s mission? Source: Measuring Success
  • 2. Question Development • Question development is a key step that organizations often skip. This is a crucial step and if missed can complicate the remaining steps of the process and can make it difficult to draw clear conclusions. Keep these in mind when developing your question(s):        Be specific and measurable. Identify the need or the “state of the community.” Determine how it changes over time. Getting better or worse? Find out the extent of the problem. Who is impacted? What communities are impacted? What level of geographic detail is necessary? Consider community “assets,” not just “needs” (what resources already exist to address this problem?) Source: Measuring Success
  • Examples of “Good” vs. “Bad” Questions • Can you tell me everything there is to know about the demographics in the Mid-North Neighborhood? • How have the demographics of MidNorth changed over the past 5-10 years, and how does that impact my program’s service delivery? Is our service area boundary still appropriate given the changes?
  • 3. Data Collection • Use these tips to collect the right data:        Determine what data you need. Don’t just go after what’s easiest but create a plan to gather the data you truly need. Seek guidance from subject matter experts. Look for existing quantitative data then determine if additional collection methods (focus groups, surveys, etc.) are necessary. Be aware of data quality and limitations. Don’t forget the data already within your organization. Be aware of the timeliness of data. Historical data is needed when doing trend analysis. If the ideal data isn’t available, consider proxy data to stand in. Source: Measuring Success
  • 4. Data Analysis • You don’t need to be a statistician to successfully analyze and later interpret data. You just need to know how to:     Digest and assess statistical data provided Calculate summary statistics (average, median, standard deviation and distributions) Look for anomalies, trends, relationships, and patterns Use maps to look geographically at the issue • Contextualization is critical. Compare your data several ways    Longitudinally to see history and trends Demographically Against peers Source: Measuring Success
  • 5. Data Interpretation • This is where you make meaning out the data analysis to answer the “So what?” question.  What does the data reveal about the issue? Did it answer your questions? Did you identify any gaps or opportunities? • Translate into understanding the questions trying to answer Source: Measuring Success
  • 6. Decision Making • Data is there to inform your decision-making process, but often times, it leads to more questions than it does answer your original question • What were revealed in the data: Where there any surprises? If so, did it change your understanding of the issue? • What were the gaps and opportunities and how does that relate to your mission? • Use the data analysis results to develop options (e.g., choose 3 underserved neighborhoods where we might target our program), which can be the starting point for gathering stakeholder input • What else do you need to know to make a decision? Do you need to revisit your questions and do more data collection? • Examples   Strategic planning – engage community, assess mission and goals Grant Development – justify need Source: Measuring Success
  • 7. Communication • In the final stage of the process, you must focus on communicating the results of your analysis. Not everyone will be receptive to the information, especially if it highlights weaknesses or challenges anecdotal beliefs commonly held in the organization. For each stakeholder group:    Create a “story” that helps your audience understand what the data says. Consider using data visualizations (charts, maps, infograms, etc.) for communication. Use data to communicate more effectively. Source: Measuring Success
  • 1. Framing the Issue
  • 2. Question Development • Where are the assets in NEC? • Is crime getting worse? • Can education lead a turnaround? • Is unemployment in NEC worse than Marion County? • Where are the concentrations of poverty in NEC?
  • 3. Data Collection Data sources • IMPD • Decennial Census • American Community Survey • SAVI • Indiana Dept. of Education • U.S. Postal Service • MIBOR • Marion County Assessor • Others
  • 4. Data Analysis
  • 5. Data Interpretation • What did the data tell us?     Age Gender Crime Education
  • 6. Decision Making • • • • • • • • • Housing Culture and Livability Education Safety and Crime Jobs and Training Business Growth Youth Engagement Health and Wellness Connectivity Nine “Action Teams” were established based on what the data told us
  • 7. Communication • Share, Publish, Publicize • Public or Internal
  • Audience Participation • Pose a problem • Formulate the questions • Walk through the 7 step data-driven decision making process Source: Measuring Success
  • Contact Information Jay Colbert Tammy Robinson GIS Project Manager The Polis Center at IUPUI 317.278.9212 Managing Principal Engaging Solution, LLC 317.283.8300