Existing reviews and studies give access to best practices, market research information and infrastructure developments with limited cost in resources and time. It is incredibly valuable to understand which reviews/studies will be profitable and applicable to portions of your operations.

Combining offline and online data collection is normally straightforward provided that the collection mechanisms are compatible. Data is often collected in a CSV or other standard formatting procedure that can be exported to Excel or another software packaging system that can be cleaned and analyzed.

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.