Data Visualization for Marketing Successes Dawn ColemanDirector, Center for Applied Research Central Piedmont Community College June 10, 2015 INAM conference Why should we care about data visualization? Hint: Its not about making things pretty Its because theres too much of this: And this: So much data, so little understanding Stephen Few 1 In order to truly make data-driven decisions we need to Get the right information To the right people In the right way You may have an excellent program but if you cant tell your programs story through data (so the decision-makers listen), you may jeopardize your programs future. And thats where a better understanding of data visualization comes in. Were big eyeball brains Stephanie Evergreen 2 Processing information: Three types of memory Iconic memory (preattentive processing) Immediate reaction to visual stimuli Detecting color, location, length/height Working memory (attentive processing) Visual info moves into working memory Only 3-4 chunks of info can be stored at one time Long-term memory We want to trigger preattentive processing and accommodate the limitations of working memory How do we do this? Choose fonts wisely Use color effectively Simplify tables Use more graphs (embrace minimalism) FONTS All about fonts SERIF or SANS SERIF? Times New Roman Arial Cambria Calibri Baskerville Verdana Georgia Trebuchet Palatino Gill Sans All about fonts Serif for long, narrative text that will be printed Sans serif for everything else (headings, tables, graphs, digital text) Avoid italics (they are generally harder to read) All about fonts Consider using a narrow/condensed font for graphs and tables (good for numbers) 1,234 Arial 1,234 TW Cen MT 1,234 Arial Narrow 1,234 TW Cen MT Condensed COLORCOLOR Using color Color should be used to enlighten not to entertain Using color Use color to emphasize a main point Use color consistently Use color sparingly Using color Think about how it will look printed in black and white Be aware of color-blindness (10% of men) A large share of ink on a graphic should present data-information Data-ink is the non- erasable core of a graphic, the non-redundant ink. Edward Tufte 3 Ink=Information (ideally) You want a higher data:ink ratio ink that represent information total amount of ink REMOVE EXTRANEOUS INK!!! Lets apply some data viz techniques to a table of data Simplifying a table Enrollment Summary Headcount by Gender Female15, %16, % 16, % 16, % 16, %16, % Male11, %11, % 12, % 12, % 12, %12, % Headcount by Ethnicity Am. Indian/Alaska Native % % % % % % Asian/Pacific Islander1, %1, % % % %1, % Black, non-Hispanic9, %9, % 9, % 10, % 9, %10, % Hispanic1, %1, % 1, % 2, % 2, %2, % White13, %14, % 14, % 14, % 13, %13, % Race Unknown/Other % % 1, % 2, % 2, %1, % Headcount by Age 51 years1, %1, % 1, % 1, % 1, %1, % Headcount by County Mecklenburg18, %19, % 20, % 20, % 20, %19, % Cabarrus % % 1, % 1, % 1, %1, % Catawba920.35%840.30% % % % % Gaston % % % % % % Iredell % % % % % % Lincoln % % % % % % Rowan % % % % % % Stanly990.37% % % % %890.31% Union2, %2, % 2, % 2, % 2, %2, % Other NC % % % % % % Out-of-State % % % % % % Int'l Students2, %2, % 2, % 2, % 2, %2, % Enrollment data Start removing ink that is not data Enrollment Summary Headcount by Gender Female 16, % 16, % 16, %16, % Male 12, % 12, % 12, %12, % Headcount by Ethnicity Am. Indian/Alaska Native % % % % Asian/Pacific Islander % % %1, % Black, non-Hispanic 9, % 10, % 9, %10, % Hispanic 1, % 2, % 2, %2, % White 14, % 14, % 13, %13, % Race Unknown/Other 1, % 2, % 2, %1, % Headcount by Age 51 years 1, % 1, % 1, %1, % Enrollment data Remove the shading Enrollment Summary Headcount by Gender Female 16, % 16, % 16, %16, % Male 12, % 12, % 12, %12, % Headcount by Ethnicity Am. Indian/Alaska Native % % % % Asian/Pacific Islander % % %1, % Black, non-Hispanic 9, % 10, % 9, %10, % Hispanic 1, % 2, % 2, %2, % White 14, % 14, % 13, %13, % Race Unknown/Other 1, % 2, % 2, %1, % Headcount by Age 51 years 1, % 1, % 1, %1, % Enrollment data Then the grids and borders Enrollment Summary Headcount by Gender Female 16, % 16, % 16, %16, % Male 12, % 12, % 12, %12, % Headcount by Ethnicity Am. Indian/Alaska Native % % % % Asian/Pacific Islander % % %1, % Black, non-Hispanic 9, % 10, % 9, %10, % Hispanic 1, % 2, % 2, %2, % White 14, % 14, % 13, %13, % Race Unknown/Other 1, % 2, % 2, %1, % Headcount by Age 51 years 1, % 1, % 1, %1, % Enrollment data Remove unnecessary decimal places Enrollment Summary Headcount by Gender Female 16,581 57% 16,680 57% 16,57457%16,36157% Male 12,702 43% 12,721 43% 12,63943%12,49743% Headcount by Ethnicity Am. Indian/Alaska Native 134