Priti Bisario makes a strong case that the largest proportion of people using and gaining from analytics are not analysts but operational teams and users on the front line whether private or public sector. That is why Self-Service Analytics must span a range of personas from (1) the regular consumers of information with regular dashboards and reports, through (2) creators who need to query data and author reports according to priorities to (3) analysts that need to visually explore data, draw insights to test & validate with business users. All of these can be served from one secure, agile multi-tenant BI platform these days. Not just for large enterprises but SMEs as well.
One factor I need to emphasise, however, is that insights are one thing, better decision making another. But unless BI platforms enable better decision execution they are inferior. For this they need to be embedded in processes, applications with write-back to help initiate action.
More guidiance 2015 State of Self-Service BI Report
Most data science teams make up less than one percent of a company, but everyone in an organization needs to have the data gene to make informed decisions. Big data around pricing performance or customer traffic patterns can help anticipate client needs, improve on demand service offerings, and uncover new revenue streams. As all business users become their own data scientists through self-service predictive applications, everyone gains fact-based insights that help them make effective decisions.