Data analysts are the lynchpins of data-driven organizations. They are voracious consumers of data; they deliver insights that spawn new business strategies, initiatives, and processes; and they increase data literacy through informal coaching with peers. Data-driven organizations invest considerable time and money in nurturing and cultivating their formal and informal networks of data analysts.
Unfortunately, many organizations bury data analysts in departments without oversight or opportunity for advancement. Analysts are often placed in the wrong position for their skills and are easy targets for recruiters. They use a hodgepodge of tools that undermine collaboration and reuse, and they spend most of their time fixing bad data rather than analyzing it. As a result, they are woefully inefficient, creating data bottlenecks inside the organization.
Collaborative intelligence addresses these issues. Its goal is to maximize the productivity, efficiency, and engagement of data analysts so they deliver large amounts of business value. Collaborative intelligence is based on four major tenets:
- Empower data analysts with the right tools, training, and support.
- Foster collaboration among analysts and data engineers with a shared platform that embeds integrated tools, social collaboration, business workflows, and architectural guardrails.
- Create data products that foster reuse and learning among data analysts, boost their efficiency and productivity, and ensure they execute accurate, consistent queries for standard metrics and data objects.
- Align the data analyst community through an analytics center of excellence (analytics CoE), an analytics council, communities of practice, and a data analyst career ladder.