Data Analysis and Analytics Research
Introduction
Big raw data or information is not going to be of great use if not processed properly. What a company or an organization should do is to carefully analyze the raw data they will gather. According to Nelli (2015), data analysis is defined as a process that consists of various steps, which involves the transformation and processing of data in order to generate data visualizations and make forecasts, which are based on mathematical models that utilize the collected data. Furthermore, big data analytics is utilized to examine big data to discover hidden patterns, uncovered correlations and other information considered to be useful in arriving at decisions (Big Data Analytics, n.d.). Thus, data analytics is very important for people to be able to maximize the raw data, which is vital for the decision-making of a certain company or organization.
There are four types of data analytics used. These are prescriptive, predictive, diagnostic and descriptive. Prescriptive analytics is used to know what actions should be taken (Four Types of Big Data Analytics and Examples of their Use, n.d.). This kind of analytics is valuable and results in recommendations for next steps by a company or organization. Predictive analytics is used to predict what could happen and uses deliverables for forecast (Four Types of Big Data Analytics and Examples of their Use, n.d.). Diagnostic analytics is utilized to have a thorough study of the past performances of a company and the reason why they were done (Four Types of Big Data Analytics and Examples of their Use, n.d.). Descriptive analytics is used to describe the incoming data using real time dashboards or raw data (Four Types of Big Data Analytics and Examples of their Use, n.d.).
Data analytics is used widely in different aspects of everyday living. On example of it is the healthcare sector. People nowadays have higher expectations when it comes to the healthcare provided to them. Patients want to have easy access to their healthcare providers, information regarding their health, and have a more increased demand for the accountability of doctors, nurses and health plans (Cortada, Gordon & Lenihan, n.d.). In addition, the government places more focus on governance, accountability, and oversight in the healthcare industry, which means that the healthcare sector should be timely in providing healthcare to patients (Cortada et al., n.d.).
There were legislations and incentives to encourage data release and accessibility. These are the 2009 Open Government Directive, Affordable Care Act and Health Information Technology for Economic and Clinical Health (HITECH) (Groves, Kayyali, Knott, & Kuiken, 2013). The Department of Health and Human Sources (HHS) under the Health Data Initiative (HDI) started to release data from agencies like the Centers for Medicare and Medicaid Services (CMS), the Food and Drug Administration (FAD), and the Centers for Disease Control (CDC) (Groves et al., 2013).
Utilizing analytics can provide hospitals and healthcare providers with more insight. In managing details – from the small to the large ones -- analytics serves as a tool in the discovery of new techniques for healthcare; it aids in the designing and planning of policies and programs; and it enhances service delivery and operations; boosts sustainability and is an aid for gauging and valuing critical organizational data (Cortada et al., n.d.).
Data analytics is helpful in the improvement of financial and administrative performance as it should target the following objectives to make sure that it will bring positive results: (1) increase revenue and the return on investment or ROI; (2) enhance the utilization of machines and other treatments provided by the hospital; (3) boost human capital management and supply chain; (4) augment regulatory compliance and risk management; and (5) diminish abuse and fraud (Cortada et al., n.d.).
Analytics is also used for the improvement of the healthcare provided by hospitals. According to Berg (2015), delivering the right intervention at the right time is one of the main purposes of analytics in healthcare. With a huge volume of real time information from patients, it will be difficult to monitor all of them; hence, the ability to provide the right intervention at the right time to patients is needed. As patients understand, monitor and access their health information, healthcare will be improved (Berg, 2015). Moreover, with an improved data analysis, the healthcare provider or the system will be able to have a faster identification of people at risk of illnesses together with its right intervention (Berg, 2015).
The impact of economics to data extraction method is valuable as it will help lessen the cost paid by the patients and the budget given by the government (Groves et al., 2013). As mentioned earlier, easy access of the patients and doctors with the same information means the proper and immediate response to their healthcare need. In the US healthcare, costs were reduced from $ 500 billion to $ 300 billion because of the following drivers: targeted disease prevention, alignment of care across providers, innovation and alignment on payment and improvement of trial operations (Groves et al., 2013). Moreover, Groves et al., (2013) stated that if there will be transparency and alignment between the healthcare provider and the patients, the time consumed for consultations will be lessen and patients will be able to monitor themselves by simply looking at their information.
References
Berg, G. (2013). 3 Ways Big Data is Improving Healthcare Analytics. Retrieved from
http://www.healthcareitnews.com/blog/3-ways-big-data-improving-healthcare-analytics
Big Data Analytics. (n.d.). Retrieved from http://www.sas.com/en_us/insights/analytics/big-data-
analytics.html.
Four Types of Big Data Analytics and Examples of their Use. (n.d.). Retrieved from
http://www.ingrammicroadvisor.com/data-center/four-types-of-big-data-analytics-and-
examples-of-their-use
Groves, P., Kayyali, B., Knott, D., & Kuiken, S. (2013). The ‘big data’ revolution in healthcare:
Accelerating value and innovation. New York, USA: McKinsey & Company.
Nelli, F. (2015). Python data analytics. Delaware, USA: Apress.
