michael_cook_dashboards-ifma presentation-3-5-2012 (2)
TRANSCRIPT
DASHBOARDS:
How to Identify, Track and Display Key Performance
Indicators
Michael Cook, Ph.D., CFM Director of Regional Building Operations
and Property Management,Kaiser Permanente – Southern California
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Meet Our Presenter:
Michael Cook, Ph.D., CFM
Director of Facilities Services, Kaiser Permanente - Southern California Region
Michael is currently responsible for facilities and property management for an 11M SF portfolio of leased and owned properties consisting of medical office buildings, administrative buildings, pharmacies, labs and warehouses. He has more than 16 years of experience in facilities management in various industries, including: Healthcare, Hospitality and Fitness, Entertainment and Government. Michael holds a Doctorate in Public Administration and an MBA. He has achieved the CFM certification, and is a Certified Six Sigma Black Belt. In addition to his work in facilities management, Michael also serves as an adjunct professor teaching Operations Management, and Quantitative Methods at the MBA level. He has been an active member in IFMA for more than 10 years and has given presentations on operations management and quality assessment.
Kaiser Permanente is an Integrated Health System
Kaiser Foundation Health Plan
Permanente Medical Groups
Kaiser Foundation Hospitals
Page 6
Page 7
Kaiser Permanente’s Reach
Recognized as one of America’s leading health care providers and not-for-profit health plans
8.9M members
37 hospitals611 medicaloffices
9 states and the District of Columbia
15,853 physicians167,178 employees
$47.9B operating revenue
DASHBOARDS:
How to Identify, Track, and Display Key Performance Indicators
This May Not Be For You If:
Not For You If:
No Excel Experience
Fear of Raw Data
Charts Make You
Dizzy
You’re Bored If:
You’re Bored If:
You’re an Excel Power User
You Program in Visual Basic
You’ve Produced 100’s of Dashboards
Just Right If:
Just Right If:
Moderate Excel Experience
Need to Understand Your Data Better
Focused on Making Decisions
Success Defined As:
Success Defined As:
Increased Knowledge of Key
Performance Indicators
Can Apply Techniques Today
Willing to Continue to Learn
IDENTIFYING KEY PERFORMANCE INDICATORS
(KPIs)
KPIs:Measures, Metrics, and Targets
“Winston Churchill observed ‘First we shape
our buildings; thereafter they shape us,’ and
the same is even more true of performance
metrics” (Meyer & Kirby, Harvard Business Review, Jan.-Feb.
2012, page 70.)
KPIs:Measures, Metrics, and Targets
Message:
* Behavior will be shaped by the metrics you select
You should:
* Select the “Critical Few” manageable metrics
And,
* Change your metrics if they are not useful
KPIs:Measures, Metrics, and Targets
What are Measures, Metrics, and Targets?
“Measures” = a key variable (temperature)
“Metrics” = a quantitative value in a unit of the measure (700F).
“Target” = a specific goal for the metric (maintain 70-74 temps 80 percent of the time)
KPIs:Measures, Metrics, and Targets
The Two Main Reasons for Measures and Metrics are:
Drive Change in Behavior
Drive Change in Processes
Critical To Quality KPIs:
Identifying the “Critical Few”
Identify and Interview Decision Makers
Research Trade Associations
Research Government Publications
Validate You have the Correct Indicators
Critical To Quality KPIs:Identifying the “Critical Few”
Identify and Interview Decision Makers
Research Trade Associations
Research Government Publications
Validate You have the Correct Indicators
Critical To Quality KPIs:Identifying the “Critical Few”
Identify and Interview Decision Makers
Research Trade Associations
Research Government Publications
Validate You have the Correct Indicators
Who is Responsible for Process Results?
Who Controls/Monitors the Process?
What Business Decisions are Most Critical?
What Behavior or Action do You Want to Impact?
Critical To Quality KPIs:Identifying the “Critical Few”
Identify and Interview Decision Makers
Research Trade Associations
Research Government Publications
Validate You have the Correct Indicators
What Data is Used to Make Each Decision?
What Triggers the Decision?
Who Has the Data?
How do You Know the Decision was Correct?
Critical To Quality KPIs:Identifying the “Critical Few”
Identify and Interview Decision Makers
Research Trade Associations
Research Government Publications
Validate You have the Correct Indicators
Critical To Quality KPIs:Identifying the “Critical Few”
Identify and Interview Decision Makers
Research Trade Associations
Research Government Publications
Validate You have the Correct Indicators
International Facility Management Association (IFMA)
Building Owners and Managers Association (BOMA)
American Society for Healthcare Engineering (ASHE)
American Institute of Architects (AIA)
Critical To Quality KPIs:Identifying the “Critical Few”
Identify and Interview Decision Makers
Research Trade Associations
Research Government Publications
Validate You have the Correct Indicators
(Released June 2009)
These survey data are from the 2008 Occupational Employment Statistics (OES) survey. The wages have all been updated to the first quarter of 2009 by applying the
US Department of Labor's Employment Cost Index to the 2008 wages. Occupations are classified using the Standard Occupational Classification (SOC) codes. For details of the methodology,
see the Overview of the OES Survey at http://www.labormarketinfo.edd.ca.gov.
Geography: Los Angeles-Long Beach-Glendale Metropolitan Division
Counties: Los Angeles
2009 - 1st Quarter Wages
MSA
Code Geographic Area Name
SOC
Code Occupational Title
May 2008
Employment
Estimates
Mean
Hourly
Wage
Mean
Annual
Wage
Mean
Relative
Standard
Error (1)
25th
Percentile
Hourly
Wage
50th
Percentile
(Median)
Hourly
Wage
75th
Percentile
Hourly
Wage
Occupational Employment (May 2008) & Wage (2009 - 1st Quarter) Data
Occupational Employment Statistics (OES) Survey Results
(Sorted by MSA code)
031084 Los Angeles-Long Beach-Glendale MD, CA 17-0000
Architecture and Engineering
Occupations 76,530 $40.85 $84,963 1.77 $29.33 $39.25 $51.04
031084 Los Angeles-Long Beach-Glendale MD, CA 17-1011 Architects, Except Landscape and Naval 3,010 $46.70 $97,134 4.86 $32.16 $40.67 $54.25
031084 Los Angeles-Long Beach-Glendale MD, CA 17-1012 Landscape Architects (3) $26.40 $54,904 5.43 $22.00 $23.99 $28.09
031084 Los Angeles-Long Beach-Glendale MD, CA 17-1021 Cartographers and Photogrammetrists 100 $30.05 $62,492 3.2 $24.63 $29.69 $34.35
031084 Los Angeles-Long Beach-Glendale MD, CA 17-1022 Surveyors 1,050 $38.29 $79,643 4.4 $33.16 $37.80 $42.99
031084 Los Angeles-Long Beach-Glendale MD, CA 17-2011 Aerospace Engineers 6,190 $53.34 $110,959 2.54 $43.60 $52.28 $63.31
031084 Los Angeles-Long Beach-Glendale MD, CA 17-2031 Biomedical Engineers 120 $46.42 $96,571 4.29 $37.09 $45.79 $53.99
031084 Los Angeles-Long Beach-Glendale MD, CA 17-2041 Chemical Engineers 240 $47.59 $98,983 8.73 $35.95 $44.15 $51.94
031084 Los Angeles-Long Beach-Glendale MD, CA 17-2051 Civil Engineers 7,900 $41.69 $86,716 1.58 $33.46 $41.04 $49.90
031084 Los Angeles-Long Beach-Glendale MD, CA 17-2061 Computer Hardware Engineers 1,350 $51.64 $107,410 3.9 $37.09 $49.62 $67.74
031084 Los Angeles-Long Beach-Glendale MD, CA 17-2071 Electrical Engineers 4,460 $43.45 $90,379 2.45 $33.04 $43.52 $53.06
031084 Los Angeles-Long Beach-Glendale MD, CA 17-2072 Electronics Engineers, Except Computer 6,010 $47.45 $98,705 1.79 $36.44 $46.43 $58.80
031084 Los Angeles-Long Beach-Glendale MD, CA 17-2081 Environmental Engineers 930 $40.81 $84,870 3.8 $32.20 $40.72 $48.58
031084 Los Angeles-Long Beach-Glendale MD, CA 37-0000
Building and Grounds Cleaning and
Maintenance Occupations 104,250 $12.81 $26,653 1.47 $9.43 $11.38 $14.85
031084 Los Angeles-Long Beach-Glendale MD, CA 37-1011
First-Line Supervisors/Managers of
Housekeeping and Janitorial Workers 3,840 $18.87 $39,267 1.54 $13.93 $17.48 $22.39
031084 Los Angeles-Long Beach-Glendale MD, CA 37-1012
First-Line Supervisors/Managers of
Landscaping, Lawn Service, and
Groundskeeping Workers 1,940 $23.13 $48,106 3.8 $16.15 $21.74 $28.95
031084 Los Angeles-Long Beach-Glendale MD, CA 37-2011
Janitors and Cleaners, Except Maids and
Housekeeping Cleaners 54,000 $12.11 $25,194 1.72 $9.19 $10.77 $13.80
031084 Los Angeles-Long Beach-Glendale MD, CA 37-2012 Maids and Housekeeping Cleaners 21,400 $11.09 $23,068 1.3 $9.08 $10.32 $12.73
031084 Los Angeles-Long Beach-Glendale MD, CA 37-2019 Building Cleaning Workers, All Other (3) $13.22 $27,500 7.56 $10.78 $13.79 $15.44
031084 Los Angeles-Long Beach-Glendale MD, CA 37-2021 Pest Control Workers 2,300 $15.63 $32,506 5.87 $12.01 $15.31 $18.31
031084 Los Angeles-Long Beach-Glendale MD, CA 37-3011
Landscaping and Groundskeeping
Workers 18,890 $13.79 $28,680 2.12 $10.25 $12.13 $16.42
031084 Los Angeles-Long Beach-Glendale MD, CA 17-0000
Architecture and Engineering
Occupations 76,530 $40.85 $84,963 1.77 $29.33 $39.25 $51.04
031084 Los Angeles-Long Beach-Glendale MD, CA 17-1011 Architects, Except Landscape and Naval 3,010 $46.70 $97,134 4.86 $32.16 $40.67 $54.25
031084 Los Angeles-Long Beach-Glendale MD, CA 17-1012 Landscape Architects (3) $26.40 $54,904 5.43 $22.00 $23.99 $28.09
031084 Los Angeles-Long Beach-Glendale MD, CA 17-1021 Cartographers and Photogrammetrists 100 $30.05 $62,492 3.2 $24.63 $29.69 $34.35
031084 Los Angeles-Long Beach-Glendale MD, CA 17-1022 Surveyors 1,050 $38.29 $79,643 4.4 $33.16 $37.80 $42.99
031084 Los Angeles-Long Beach-Glendale MD, CA 17-2011 Aerospace Engineers 6,190 $53.34 $110,959 2.54 $43.60 $52.28 $63.31
031084 Los Angeles-Long Beach-Glendale MD, CA 17-2031 Biomedical Engineers 120 $46.42 $96,571 4.29 $37.09 $45.79 $53.99
031084 Los Angeles-Long Beach-Glendale MD, CA 17-2041 Chemical Engineers 240 $47.59 $98,983 8.73 $35.95 $44.15 $51.94
031084 Los Angeles-Long Beach-Glendale MD, CA 17-2051 Civil Engineers 7,900 $41.69 $86,716 1.58 $33.46 $41.04 $49.90
031084 Los Angeles-Long Beach-Glendale MD, CA 17-2061 Computer Hardware Engineers 1,350 $51.64 $107,410 3.9 $37.09 $49.62 $67.74
031084 Los Angeles-Long Beach-Glendale MD, CA 17-2071 Electrical Engineers 4,460 $43.45 $90,379 2.45 $33.04 $43.52 $53.06
031084 Los Angeles-Long Beach-Glendale MD, CA 17-2072 Electronics Engineers, Except Computer 6,010 $47.45 $98,705 1.79 $36.44 $46.43 $58.80
031084 Los Angeles-Long Beach-Glendale MD, CA 17-2081 Environmental Engineers 930 $40.81 $84,870 3.8 $32.20 $40.72 $48.58
031084 Los Angeles-Long Beach-Glendale MD, CA 37-0000
Building and Grounds Cleaning and
Maintenance Occupations 104,250 $12.81 $26,653 1.47 $9.43 $11.38 $14.85
031084 Los Angeles-Long Beach-Glendale MD, CA 37-1011
First-Line Supervisors/Managers of
Housekeeping and Janitorial Workers 3,840 $18.87 $39,267 1.54 $13.93 $17.48 $22.39
031084 Los Angeles-Long Beach-Glendale MD, CA 37-1012
First-Line Supervisors/Managers of
Landscaping, Lawn Service, and
Groundskeeping Workers 1,940 $23.13 $48,106 3.8 $16.15 $21.74 $28.95
031084 Los Angeles-Long Beach-Glendale MD, CA 37-2011
Janitors and Cleaners, Except Maids and
Housekeeping Cleaners 54,000 $12.11 $25,194 1.72 $9.19 $10.77 $13.80
031084 Los Angeles-Long Beach-Glendale MD, CA 37-2012 Maids and Housekeeping Cleaners 21,400 $11.09 $23,068 1.3 $9.08 $10.32 $12.73
031084 Los Angeles-Long Beach-Glendale MD, CA 37-2019 Building Cleaning Workers, All Other (3) $13.22 $27,500 7.56 $10.78 $13.79 $15.44
031084 Los Angeles-Long Beach-Glendale MD, CA 37-2021 Pest Control Workers 2,300 $15.63 $32,506 5.87 $12.01 $15.31 $18.31
031084 Los Angeles-Long Beach-Glendale MD, CA 37-3011
Landscaping and Groundskeeping
Workers 18,890 $13.79 $28,680 2.12 $10.25 $12.13 $16.42
(Released June 2009)
These survey data are from the 2008 Occupational Employment Statistics (OES) survey. The wages have all been updated to the first quarter of 2009 by applying the
US Department of Labor's Employment Cost Index to the 2008 wages. Occupations are classified using the Standard Occupational Classification (SOC) codes. For details of the methodology,
see the Overview of the OES Survey at http://www.labormarketinfo.edd.ca.gov.
Geography: Los Angeles-Long Beach-Glendale Metropolitan Division
Counties: Los Angeles
2009 - 1st Quarter Wages
MSA
Code Geographic Area Name
SOC
Code Occupational Title
May 2008
Employment
Estimates
Mean
Hourly
Wage
Mean
Annual
Wage
Mean
Relative
Standard
Error (1)
25th
Percentile
Hourly
Wage
50th
Percentile
(Median)
Hourly
Wage
75th
Percentile
Hourly
Wage
Occupational Employment (May 2008) & Wage (2009 - 1st Quarter) Data
Occupational Employment Statistics (OES) Survey Results
(Sorted by MSA code)
Critical To Quality KPIs:Identifying the “Critical Few”
Identify and Interview Decision Makers
Research Trade Associations
Research Government Publications
Validate You have the Correct Indicators
Descriptive Statistics is a valuable way to begin analyzing the data.
Identify and Interview Decision Makers
Research Trade Associations
Research Government Publications
Validate You have the Correct Indicators
Statistic Sq Ft $hour/lbr traffic hrs sick flr care
Mean 68540.93 10.19 661.74 46.33 11.49
StD Error 6256.97 0.24 6.34 7.36 2.29
Median 44264.00 9.66 664.48 21.00 0.00
Mode #N/A #N/A #N/A 0.00 0.00
StD Dev 61624.01 2.31 62.46 72.47 22.52
Sample Var 3797518244.34 5.36 3900.93 5252.56 506.93
Kurtosis 2.21 -1.15 0.40 7.19 14.45
Skewness 1.70 0.10 -0.35 2.49 3.47
Range 267992.00 7.85 300.15 412.02 140.85
Minimum 8877.00 6.48 484.88 0.00 0.00
Maximum 276869.00 14.33 785.03 412.02 140.85
Sum 6648470.00 988.39 64188.61 4494.05 1114.40
Count 97.00 97.00 97.00 97.00 97.00
Largest(1) 276869.00 14.33 785.03 412.02 140.85
Smallest(1) 8877.00 6.48 484.88 0.00 0.00
CI (95%) 12419.99 0.47 12.59 14.61 4.54
Multilple Regression analysis is useful way to scientifically validate indicator relationships
SUMMARY OUTPUT
Regression Statistics
Multiple R 0.710
R Square 0.504
Adj R Sq 0.482
Std Error 44346.893
Obs 97
ANOVA
df SS MS F Sig F
Regression 4 183630238388.053 45907559597.013 23.343 0.000
Residual 92 180931513068.442 1966646881.179
Total 96 364561751456.495
Coefficients Standard Error t Stat P-value
Intercept 146822.154 52180.686 2.814 0.006
$hour/lbr -18370.431 2164.424 -8.487 0.000
traffic 159.405 76.002 2.097 0.039
hrs sick 59.595 69.335 0.860 0.392
flr care 57.410 219.459 0.262 0.794
-100000
0
100000
200000
0 5 10 15 20
Re
sid
ual
s
$hour/lbr
$hour/lbr Residual Plot
Identify and Interview Decision Makers
Research Trade Associations
Research Government Publications
Validate You have the Correct Indicators
Data VisualizationHow to Display Data Graphically
“The ability to visualize and communicate
data is critical, because even with good data,
the results will not convince if poorly
visualized” (Mena Doshi, “Statistics, thy name is Flexibility,”
AMSTATNEWS, Jan. 2012, issue 415).
Data Visualization:
How to Display Data Graphically
Common Graphical Presentation Methods:
Data Table:
Bar Chart:
Scatter Graph:
Time Series:
Box Plot:
Bullet Graph:
Data VisualizationHow to Display Data Graphically
Common Graphical Presentation Methods:
Data TableBar Chart:
Scatter Graph:
Time Series:
Box Plot:
Bullet Graph:
Categories 28
Property: ANA Date: 2/15/2012 Unsat (X) 4
Location Code: XXXFF Property Manager:
Building Type: MOB Contractor/Supplier: IN-HOUSE JANITORIAL
Service Area: O.C. SCORE: 86%
ID S U ID S U
STAIRWAYS
4 0 3 0
4 0 2 X
5 0 4 0
3 0 5 0
4 0 5 0
3 0 4 0
4 0 4 0
3 0 5 0
4 0 3 0
4 0
RESTROOMS SAFETY/CODE COMPLIANCE
4 0 5 0
2 X 5 0
5 0 4 0
4 0 3 0
3 0 4 0
2 X 5 0
5 0
4 0 2 X
5 0 4 0
4 0 3 0
3 0 4 0
planters
BUILDING INSPECTION
Dust
Cleanliness
FLOORS
Surface Surface
Litter
Steps
Doors
Fixtures/lights/mirrors
Walls
Wash Basin
Floor
Seating
WAITING ROOMS
Light Fixtures
LOBBY
Seating
Walls
fire extinguiser
Biohazard(logs, clean, etc)
Landings
electrical rooms
Floors
Cobwebs
Floor
Commode/Urinals
shrubs/color
Curbing/striping/signage
Doors
Spots
exit signs
sprinkler systems
Gloss
Trip Hazards
Counter
LAND/HARDSCAPE
Windows/Treatments
bldg entrance
Partitions/trashcans/doors
EXAM ROOMS
Floor
Walls
Doors
Counter Tops
Walls
safety lighting
Categories 28
Property: ANA Date: 2/15/2012 Unsat (X) 4
Location Code: XXXFF Property Manager:
Building Type: MOB Contractor/Supplier: IN-HOUSE JANITORIAL
Service Area: O.C. SCORE: 86%
ID S U ID S U
STAIRWAYS
4 0 3 0
4 0 2 X
5 0 4 0
3 0 5 0
4 0 5 0
3 0 4 0
4 0 4 0
3 0 5 0
4 0 3 0
4 0
RESTROOMS SAFETY/CODE COMPLIANCE
4 0 5 0
2 X 5 0
5 0 4 0
4 0 3 0
3 0 4 0
2 X 5 0
5 0
4 0 2 X
5 0 4 0
4 0 3 0
3 0 4 0
planters
BUILDING INSPECTION
Dust
Cleanliness
FLOORS
Surface Surface
Litter
Steps
Doors
Fixtures/lights/mirrors
Walls
Wash Basin
Floor
Seating
WAITING ROOMS
Light Fixtures
LOBBY
Seating
Walls
fire extinguiser
Biohazard(logs, clean, etc)
Landings
electrical rooms
Floors
Cobwebs
Floor
Commode/Urinals
shrubs/color
Curbing/striping/signage
Doors
Spots
exit signs
sprinkler systems
Gloss
Trip Hazards
Counter
LAND/HARDSCAPE
Windows/Treatments
bldg entrance
Partitions/trashcans/doors
EXAM ROOMS
Floor
Walls
Doors
Counter Tops
Walls
safety lighting
Data VisualizationHow to Display Data Graphically
Box Plot:
Visualize Data Distributions by Meaningful Groups
1 3 5
Lobby
Exam Room
Waiting Rooms
Restrooms
Floors
Landscape
Safety/Code
Rating (1 to 5)Categories 28
Property: ANA Date: 2/15/2012 Unsat (X) 4
Location Code: XXXFF Property Manager:
Building Type: MOB Contractor/Supplier: IN-HOUSE JANITORIAL
Service Area: O.C. SCORE: 86%
ID S U ID S U
STAIRWAYS
4 0 3 0
4 0 2 X
5 0 4 0
3 0 5 0
4 0 5 0
3 0 4 0
4 0 4 0
3 0 5 0
4 0 3 0
4 0
RESTROOMS SAFETY/CODE COMPLIANCE
4 0 5 0
2 X 5 0
5 0 4 0
4 0 3 0
3 0 4 0
2 X 5 0
5 0
4 0 2 X
5 0 4 0
4 0 3 0
3 0 4 0
planters
BUILDING INSPECTION
Dust
Cleanliness
FLOORS
Surface Surface
Litter
Steps
Doors
Fixtures/lights/mirrors
Walls
Wash Basin
Floor
Seating
WAITING ROOMS
Light Fixtures
LOBBY
Seating
Walls
fire extinguiser
Biohazard(logs, clean, etc)
Landings
electrical rooms
Floors
Cobwebs
Floor
Commode/Urinals
shrubs/color
Curbing/striping/signage
Doors
Spots
exit signs
sprinkler systems
Gloss
Trip Hazards
Counter
LAND/HARDSCAPE
Windows/Treatments
bldg entrance
Partitions/trashcans/doors
EXAM ROOMS
Floor
Walls
Doors
Counter Tops
Walls
safety lighting
Data VisualizationHow to Display Data Graphically
Common Graphical Presentation Methods:
Data Table:
Bar Chart: Scatter Graph:
Time Series:
Box Plot:
Bullet Graph: 0
500
1000
1500
2000
2500
apr may jun jul aug sep
Series1
BAR GRAPH
BENEFITS:
Graph
Distribution
Great for Time Series
Easy to Interpret
0
500
1000
1500
2000
2500
apr may jun jul aug sep
Series1
Data VisualizationHow to Display Data Graphically
Common Graphical Presentation Methods:
Data Table:
Bar Chart:
Scatter Graph:
Time Series:Box Plot:
Bullet Graph: 65
70
75
80
85
90
95
8/18 8/25 9/1 9/8 9/15
Time Series -Example
Temperature Scenario:
Complaint:
It’s TOO HOT!
Action:
Installed Data Loggers
Logged Over 650 Data Points in 3 Weeks
Date Time Int Temp (F)
8/18/11 12:41 PM 80.25
8/18/11 1:41 PM 71.01
8/18/11 2:41 PM 69.43
8/18/11 3:41 PM 69.18
8/18/11 4:41 PM 68.57
8/18/11 5:41 PM 67.79
8/18/11 6:41 PM 67.42
8/18/11 7:41 PM 70.09
8/18/11 8:41 PM 73.04
8/18/11 9:41 PM 74.32
8/18/11 10:41 PM 74.8
8/18/11 11:41 PM 74.54
8/19/11 12:41 AM 73.7
8/19/11 1:41 AM 73.17
8/19/11 2:41 AM 72.82
8/19/11 3:41 AM 72.47
Time Series
BENEFITS:
Shows Trends Over Time
Great For Near-Term Forecasting
Identify and Explain Peaks and Valleys
65
70
75
80
85
90
95
8/18 8/25 9/1 9/8 9/15
Time Series -Example
Temperature Scenario:
Complaint:
It’s TOO HOT!
Action:
Plotted Data
Peak Temps Above 90 Degrees
65
70
75
80
85
90
95
8/18 8/25 9/1 9/8 9/15
Time Series -Example
Temperature Scenario:
Shaded Weekends
65
70
75
80
85
90
95
8/18 8/25 9/1 9/8 9/15
Time Series -Example
Temperature Scenario:
Shaded Weekends
Added High and Low Limits
65
70
75
80
85
90
95
8/18 8/25 9/1 9/8 9/15
Time Series -Example
Temperature Scenario:
Shaded Weekends
Added High and Low Limits
Added Legend
65
70
75
80
85
90
95
8/18 8/25 9/1 9/8 9/15
weekend
Int Temp (F)
low temp
high temp
3 Full Weeks of Data Only Working Hours
65
70
75
80
85
90
95
8/18 8/25 9/1 9/8 9/15
Time Series - Example
65
70
75
80
8/18 8/25 9/1 9/8 9/15
3 Full Weeks of Data Only Working Hours
65
70
75
80
85
90
95
8/18 8/25 9/1 9/8 9/15
Time Series - Example
65
70
75
80
8/18 8/25 9/1 9/8 9/15
San Diego Power Outage
COMBINATION
BENEFITS:
Show Several Series at Once
Provides Context
Easy to Interpret
65
70
75
80
8/17 8/24 8/31
weekend
Int Temp (F)
high temp
low temp
Data VisualizationHow to Display Data Graphically
Common Graphical Presentation Methods:
Data Table:Bar Chart:
Scatter Graph:
Time Series:
Box Plot:Bullet Graph:
1
3
5
Lobby Exam Room WaitingRooms
Restrooms Floors Landscape Safety/Code
Rati
ng
(1 t
o 5
)
Key Inspection Areas
Property Management Building Inspection –Cleaning
BOX PLOT DATA LAYOUT
Data VisualizationHow to Display Data Graphically
Statistic Lobby Exam Room Waiting Rooms Restrooms Floors Landscape Safety/Code
1st Qrtile 3 2.75 4 2.25 3.75 4 4
min 3 2 3 2 3 3 3
median 4 3.5 4 3.5 4 4 4.5
max 4 4 5 5 5 5 5
3rd Qrtile 4 4 5 4 4.25 4.75 5
BOX PLOT
BENEFITS:
Graph
Descriptive
Inter-Quartile Range
Median 1
3
5
Lobby Exam Room WaitingRooms
Restrooms Floors Landscape Safety/Code
Rati
ng
(1 t
o 5
)
Key Inspection Areas
Property Management Building Inspection – Cleaning
Data VisualizationHow to Display Data Graphically
Common Graphical Presentation Methods:
Data Table:
Bar Chart:
Scatter Graph:
Time Series:
Box Plot:
Bullet Graph:0
25
50
75
100
Bed-STAT Bed-NEXT HCAHPS AVATAR GLOWGERM - OR
GLOWGERM -
PAT CARE
Excellent
Good
Fair
Poor
Actual
Target
BULLET GRAPH DATA LAYOUT
Data VisualizationHow to Display Data Graphically
Bed-STAT Bed-NEXT HCAHPS AVATAR GLOW GERM - OR GLOW GERM - PAT CARE
Poor 40 40 25 25 40 40
Fair 30 30 25 25 30 30
Good 15 15 25 25 15 15
Excellent 15 15 25 25 15 15
Actual 59.04 90.00 65.00 82.00 95.00 91.00
Target 95 90 70 80 93 90
BULLET GRAPH
BENEFITS:
Target vs. Actual
Qualitative Range
Adjustable by Category
0
25
50
75
100
Bed-STAT Bed-NEXT HCAHPS AVATAR GLOWGERM - OR
GLOWGERM - PAT
CARE
Excellent
Good
Fair
Poor
Actual
Target
Final Cover of Dashboard
BENEFITS:
Combine Several Elements
Provide Overall Perspective
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