Data reviews will generally save time and money that reasonably and affordably replicate research that is helpful, necessary, or enlightening in areas of a program or project that will impede, expedite, or ignore critical components of whatever you are trying to do.

Predictive analytics and machine learning take information that is readily available to you and make predictions about future events based upon your data, existing theories, algorithms, and probability. These approaches may help identify future opportunities and threats in ways that allow you to have as much control and time to react to imporant events as possible.

Good research design is well balanced, usually incorporates a mixed method approach combining quantitative and qualitative processes and is fully informed by known assumptions and limitations given constraints, targeted outcomes, and informed input by requisite stakeholders.

Data processes benefit from a multidisciplinarian approach across qualitative and quantitative skillsets. Assessing the research design, data collection, data cleaning, analysis, results and reporting steps are all important components of robust data processes. Sometimes a fresh perspective or third party consultant or coach can help shore up potential weaknesses in process.

There is a nearly endless supply of data opportunities. Awareness of potentially untapped resources often requires a research background beyond the specific field in question.