What is the importance of understanding patterns and trends in data?
Importance of Understanding Patterns and Trends in Data
Patterns in data refer to a way of collecting data, which can be structured, semi-structured, or indexed, and made accessible for searching. The fundamental function of developing data patterns is to identify areas that need to be taken out of the data collected (Phinyomark & Scheme, 2018). Trends in data connote upward or downward change in data set over time. Trends in data enable organizations to anticipate what can occur in the future (CDC, 2017).
Understanding patterns and trends in data is important in helping the health care settings to analyze the data and create a forecast. It also helps healthcare organizations to plan for approaches to use to address the predicted events. Using the trends and patterns in data to forecast for the organization, health care organizations are equipped to redesign their strategies and ideas to fit into the direction the industry trend shifts towards. Understanding the trends can also help organizations make informed decisions about the organization’s future and long-term strategies (Tiwari et al., 2016). For instance, if a high rate of medical errors is reported in the surgical unit, the organization can look at this trend and realize that it is attributed to a low nurse-to-patient ratio. Consequently, the organization may opt to increase the number of nurses in the surgical unit.
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Understanding patterns and trends in data can help organizations save time through the facilitation of resources allocation. It also helps organizations to predict and plan for strategies to apply, thus helping in saving time. It can also save time by identifying the future supply of skills and workforce (Tiwari et al., 2016).
National Center for Health Statistics, Center for Disease Control, & Prevention. (2017). Health, United States, 2016, with chartbook on long-term trends in health.
Phinyomark, A., & Scheme, E. (2018). EMG pattern recognition in the era of big data and deep learning. Big Data and Cognitive Computing, 2(3), 21. https://doi.org/10.3390/bdcc2030021
Tiwari, V., Tiwari, B., Thakur, R. S., & Gupta, S. (Eds.). (2016). Pattern and data analysis in healthcare settings. IGI Global.