DATA MANAGEMENT BASICS. July 19, 2017 September 8, 2017
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1 DATA MANAGEMENT BASICS July 19, 2017 September 8, 2017
2 OBJECTIVES Enthuse evaluative thinking Encourage performance measurement Build knowledge of concepts related to: o Data collection o Data analysis & interpretation o Data communication
3 BEST PRACTICES FOR EFFECTIVE PERFORMANCE MANAGEMENT PERFORMANCE MANAGEMENT
4 PERFORMANCE MEASUREMENT & MANAGEMENT Processes and systems to: GATHER MEANINGFUL DATA MONITOR IMPLEMENTATION & IMPACT
5 WHAT IS PERFORMANCE MANAGEMENT? What went well? What didn t go well? How do we improve? Repeat
6 PERFORMANCE WHAT IS PERFORMANCE MANAGEMENT MANAGEMENT? CONTINUUM Good Intentions Counting Outputs Measuring Outcomes Managing Performance
7 BEST PRACTICES DATA COLLECTION FOR EFFECTIVE PERFORMANCE MANAGEMENT
8 What is data?
9 Why collect data?
10 DATA LIFE CYCLE
11 What data should be collected?
12 OTHER CONSIDERATIONS Funder requirements Organizational goals Board requirements Auditor requirements
13 IDENTIFYING DATA TO COLLECT Consider specific about success in program How many units of service were delivered? Was program implemented with fidelity to program model? How many clients participated? What outcomes should clients achieve? o At what rate/target?
14 IDENTIFYING DATA OUTPUTS What you do OUTCOMES The change/ difference it makes
15 IDENTIFYING OUTCOMES DATA 1. Start with outcome 2. Write specific outcome indicator 3. Determine data that is needed
16 IDENTIFYING DATA OUTPUTS What you do PROCESS EVALUATION OUTCOMES The change/ difference it makes OUTCOMES EVALUATION
17 How should data be collected?
18 TYPES OF MEASUREMENT Quantitative (Numbers and Statistics) Pre/post survey Post only questionnaire Structured observation Qualitative (Words and Concepts) Survey/interview guide Focus group guide Administrative data Standardized instrument
19 STANDARDIZED MEASUREMENT TOOLS Proven validity & reliability Concerns o Expense o Training requirements o Length o Fit with outcomes Modified versions o May lose reliability & validity
20 DOING MEASUREMENT WELL Fidelity to intended use o Training o Staff credentials
21 SELECTING MEASUREMENT TOOLS Type of data needed & best tool o Existing tool available? Resources and feasibility o Financial and time cost o Administration requirements Client access & timeline
22 Tools & Tips
23 STRUCTURING A DATABASE Consider WHY data is being collected Identify what data is needed Prioritize data to include Structure for analysis Ideal data formats
24 IMPROVING DATA COLLECTION PROCESSES Streamline processes o Reduce entry steps Quantify and automate where possible o Check box vs. case notes o Observation checklist vs. summary
25 DATA COLLECTION PLANS Data collection and analysis roadmap Includes key outputs and outcomes Streamlines practices and processes Drives learning and evaluation culture
26 DATA DICTIONARIES METADATA Data about data File format When data was collected Who collected it File naming conventions Labeling Conventions DATA DICTIONARY Houses metadata Data is easier to interpret Eases staff transitions
27 METADATA & DATA DICTIONARIES
28 CONNECT TO PERFORMANCE MANAGEMENT Why is data collection important to performance management?
29 BEST PRACTICES FOR EFFECTIVE PERFORMANCE MANAGEMENT DATA ANALYSIS & INTERPRETATION
30 Why analyze?
31 FINDING MEANING IN DATA
32 UNDERSTANDING DATA
33 Is data ready for analysis?
34 DATA QUALITY & INTEGRITY ACCURATE & CONSISTENT DATA THAT IS FIT FOR ITS INTENDED USE
35 START WITH CLEAN DATA Complete data integrity audit Align entry formats o Jan 12, 2016 v. 1/2/16 v. 01/02/2016 Determine approach for abnormalities o Blanks
36 DATA INTEGRITY AUDIT Reviewing and cleaning data Spot check random sample Sort data o Missing values o Outliers (overly high/low values) o Check feasibility Discuss discrepancies
37 How to analyze data
38 WHAT DATA TO FOCUS ON
39 HOW TO SLICE & DICE DATA What question are you trying to answer? o Examine variables that will answer or influence that question o Consider key variable relationships o Identify appropriate calculations
40 QUANTITATIVE DATA ANALYSIS PROCESS Key Quantitative Areas Mean Median Mode Variability Frequency Distributions
41 QUANTITATIVE COMPARISONS COMPARE OUTCOMES change of benefit clients experience OVER TIME AGAINST TARGETS WITH BENCH- MARKS BY CLIENT GROUPS BY SERVICE Examine findings across all indicators
42 QUALITATIVE DATA ANALYSIS PROCESS Categorize/code responses o Predefined codes o Emergent codes o Can be numerical Identify trends and themes
43 How to interpret findings
44 INTERPRETING DATA
45
46 INTERPRETING TIPS Start with analysis of clean data Don t do it alone Try visualizations Allot time for interpretation
47 INTERPRETING DATA Interpretation is where meaning is found; consider o Patterns, themes & deviations o If results make sense o Surprising findings o Recommendations for improvement o Additional questions that arise
48 CAUSATION & CORRELATION Causation Correlation One factor leads to/causes another Mutual relationship between variables (positive or negative)
49 CONNECT TO PERFORMANCE MANAGEMENT Why are data analysis & interpretation important to performance management?
50 BEST PRACTICES DATA COMMUNICATION FOR EFFECTIVE PERFORMANCE MANAGEMENT
51 The ability to take data to be able to understand it, to process it, to extract value from it, to visualize it, to communicate it that s going to be a hugely important skill in the next decades Dr. Hal Varian, Google s Chief Economist
52 What findings to share
53 CONSIDER Purpose Audience Key points Available data Making data digestible
54 How to share findings
55 COMMUNICATING RESULTS Connect outputs and outcomes Use the data to show how you o Addressed a problem o Achieved a goal o Created an impact Connect data to the bigger picture Include quotes and personal stories
56 DATA STORYTELLING
57 VISUALLY COMMUNICATING RESULTS Line Graphs Bar Graphs
58 VISUALLY COMMUNICATING RESULTS Pie Charts Doughnut Charts Tables
59 VISUALLY COMMUNICATING RESULTS Scatter Plots Bubble Graphs Maps
60 VISUALLY COMMUNICATING RESULTS Wordles Word Clouds Videos
61 VISUALLY COMMUNICATING RESULTS Data Sculptures Installations
62 VISUALLY COMMUNICATING RESULTS Frequency Representations/ Pictorial Tree Maps
63 VISUALLY COMMUNICATING RESULTS Infographics Picktochart Canva Infogram Fiverr Microsoft BI
64 RESOURCES Dr. Edward Tufte s work Dr. Andrew Abela s Chart Selection diagram
65 VISUAL ASPECTS TO CONSIDER Simplify graphics o No 3-D, shading, background color, border, etc. o Reduce grid lines, tick marks, legend o Remove excess zeros, decimals, words Use titles, labels, and scales thoughtfully
66 BEWARE: THE LIE FACTOR Is there bias? Does graphic mislead viewers? Encourage objectivity o Avoid ambiguous labels o Align graphic and data dimensions o Use consistent units
67 EXERCISE For the Morning Reading Program, determine which visual best conveys findings listed Be ready to explain your choice
68 Communicating data to drive CQI
69 USE SCORECARDS & DASHBOARDS Assess performance in key areas Compare against goals Assess progress to date Set future goals
70 The Balanced Scorecard by Kaplan & Norton
71 DASHBOARD FOCUS KPIs for entire department One program/ priority area Community initiative
72
73 USE PROGRAM DESIGN TOOLS Assess resources Assess outputs Assess outcomes & impact
74 PDCA CYCLE 1. Identify possible changes that might help 2. Decide which changes to implement 3. Implement changes to the program 4. Measure again!
75 CONNECT TO PERFORMANCE MANAGEMENT Why is data communication important to performance management?
76 BEST PRACTICES REVIEW & FOR WRAP UP EFFECTIVE PERFORMANCE MANAGEMENT
77 DATA MANAGEMENT D R I V E S Good Intentions Counting Outputs Measuring Outcomes Managing Performance
78 BEST PRACTICES QUESTIONS? FOR EFFECTIVE PERFORMANCE MANAGEMENT
79 THANK YOU!!! Thank you for your time Please share feedback Other opportunities o Workshops & Program Impact Reviews Feel free to contact me Emily Uzzle emily.uzzle@stl.unitedway.org
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