Analytics gives us the power to laud over large pools of data and form inferences which are fact based. Analytics take the opinion and hear-say out of the equation and replace it with conclusions and insights based on irrefutable underlying trends and patterns. This is the truth in our analytics!
Let us examine the premise above with a small example.
Your neighbourhood organises a week long Information Technology (IT) Literacy campaign drive with the sole objective to guide its members into use of suburbia state implemented digital applications and social network tools more regularly.
A loose and half-baked statement would appear as 'The campaign had a sombre response and failed to garner interest in its community members'.
An analyst on the other hand would pose very specific probing questions into the efficacy of the campaign like -
(a) How many members attended the overall campaign on each date from different impacting groups like 'age', 'gender', 'income levels', 'occupations', 'smart-phone users' etc and what was the expected numbers?
(b) What was the individual campaign program attendance from different impacting groups like 'age', 'gender', 'income levels', 'occupations' etc and what was the expected numbers?
(c) Who were the assigned ushers and volunteers for each campaign program and how many actually showed up?
(d) Which applications were set up for free download on participant smart-phones and how many were actually downloaded by different impacting groups like 'age', 'gender', 'income levels', 'occupations' etc?
The representative questions listed above can only be responded to through the truth of analytics.
The result of the representative questions will illuminate the organisers on 'What went right?', 'What went wrong?', 'Who failed?', 'What failed?' and more.
So lets try to use analytics today with the hope that we always ask and never assume.