In many cases, existing systems store data records that are not yet being utilized for data analytics. Either analysis tools have not been taken advantage of, or simply do not exist. This is an important consideration when assessing pre-existing systems, because it is often low hanging fruit, as the physical infrastructure (sensors, etc.) is already in place.
Once we find out what data is currently available, the next step is to detect how and where it is stored. It is important to verify that data is stored in a manner that allows for accuracy, by removing data redundancies, inconsistencies and other impurities. It is also important to validate data relationships throughout the database, in order to guarantee that the correct data is being pulled for analytics. When there is a functional database structure, we can more easily add missing data fields as needed.
Data is essentially meaningless without applying data analytics. At the same time, data can be extremely valuable when data analytics are applied correctly. An examination into data sets can uncover hidden patterns, unknown correlations, market trends and customer preferences. Conclusions can be drawn based on such data modeling if the analytical backbone is present.
On the surface level, conclusions from data analytics can provide for decision making and data presentation. In the case of Tribal CRM, demographic data can be presented in an easy to display chart that is just a few clicks away. Audits can be answered quickly, because relevant data queries can be easily run at any time. The possibilities are endlessly customizable based on what an individual tribe may be trying to solve with their data analytics.
Going further, data analytics can provide the foundation for predictive data modeling. Taking proactive action towards data analysis findings can save costs and provide higher citizen satisfaction when compared to simply reacting from data. This is the difference between anticipating future trends in data and reacting to past or current trends. A real world application of this service would be a model that predicts a high school student’s eligibility for different college funding grants, and provides applications to the individual before graduating. This is just one of the many possible solutions that Tribal Point can provide for tribal organizations using data analytics.
During previous endeavors, Greg has helped implement data analysis tools aimed at increasing energy efficiency while working on the Energy and Environmental Portfolio team at Ford Motor Company. Additionally, throughout his experience at Dart Container, he utilized big data analytics for the purpose of increasing production tool efficiencies, while at the same time planning ahead with predictive analytics.
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