Everyone knowingly or unknowingly misses out on major data opportunities. Determining the usefulness and costs of acquiring or ignoring data opportunities generally requires a multidisciplinerian team of individuals with diverse research background and connection to data systems and sources.

There are many academic, philanthropic, governmental, and professional sources of data that are available for free, at low costs as well as higher costs. Understanding the benefit, need, and fit for these alternate data sources will help determine which sources can be practical and profitable for your needs.

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.