Most people confuse these two things: data and information. Data are the hard, observable facts whereas information is (potentially) useful knowledge. This confusion can lead to abuse of statistical data by presenting it to look like information, whereas it may only be a distraction or, worse, misinformation.
We see this frequently in political advertising for example. "We've just added $15M to the budget of ABC" may be valid data, but it could be being used to obfuscate or misrepresent the relevant information - such as, that per capita spending (what people might really care about this situation) has decreased.
Getting useful information out of raw data is not always easy. In the field of ABA for example, it's particularly challenging. The data may tell us that a behaviour has increased in frequency but how useful is that information? Has the severity increased or decreased (and is that being measured?) Is it occurring more frequently within a specific context, that has come under increased scrutiny of observation? Has there been a recent change in the child's diet? Are there other stressors involved? A Behaviour Analyst undergoes years of training in order to understand what factors might need to be considered before making a decision as to what adjustments may or may not need to be made to an ABA program.
Modern computing solutions such as ours, offer a number of effective ways of providing analysts with the tools to more efficiently analyze the data collected by ABA teams and extract from its murk very useful information that might otherwise be overlooked by even the most skilled analysts.