Big data is absolutely relevant. With the type of people that take this class I
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Big data is absolutely relevant. With the type of people that take this class I will be eagerly looking through the responses for someone that says it is not relevant. One thing that keeps coming up in the news is that Big Data and Artificial Intelligence is going to take away jobs. This is something that always happens, people need to adapt. In the 1800s when bridges were being built in Rankin and Homestead, to move all the steel workers back and forth, there were ferrymen that screamed at the people building the bridge from below. Cursing them because they were going to put them out of business, they were stealing their jobs. I think the argument that a new technology will remove old jobs Is irrelevant. There's is no good reason not to move forwardExplanation / Answer
Big Data is everywhere. We’ve seen its value in recent acquisitions, such as Oracle buying cloud-based big data platform, BlueKai, and Apple buying social media analytics company, Topsy. Among these billions of terabytes, there’s a treasure trove of marketing data that spans search, social, local and mobile analytics. As the amount and accessibility of data reaches new heights, marketers need to be able to understand how to discern what information is relevant and how to leverage it for better business results. So , we can say that Big data is relevant . But , The pharma industry now has an incredible amount of data at its fingertips thanks to electronic medical records, social media, wearables, and more. But just having access to this data isn’t enough—marketers need a way to turn all of this unstructured information into actionable insights. Not only that, but they need to do it as quickly as possible so they can act on these insights and in way that doesn’t require too much help from IT.
Not all data is relevant in fact, most of it isn’t. Recognizing the fit between the data sources, the analytic tools, and the problem you want to solve is paramount.However, it is important to remember the basic tenet of data analysis that necessitates formulating questions as the first step before data sources are chosen and analysis begins. Only with clear objectives and strategy can one determine what to measure—and what to ignore.Once the problem is identified, then the plan can be mapped out and decisions made on what data sources to include and what analytic methodology to apply.
Harnessing the full potential of data requires developing an organization-wide data science strategy. Such strategies are now commonplace in most industries such as banking and retail. Banks can offer their customers targeted needs-based services and improved fraud protection because they collect and analyze transactional data. Retailers such as Amazon routinely collect data on shopping habits and preferences to profile their customers and use sophisticated predictive algorithms to tailor marketing strategies to customer demand.
Health care is a glaring exception. Individual pieces of data can have life-or-death importance, but many organizations fail to aggregate data effectively to gain insights into wider care processes. Without a data science strategy, health care organizations can’t draw on increasing volumes of data and medical knowledge in an organized, strategic way, and individual clinicians can’t use that knowledge to improve the safety, quality, and efficiency of the care they provide.
Without a data science strategy, health care organizations can’t draw on increasing volumes of data and medical knowledge in an organized, strategic way, and individual clinicians can’t use that knowledge to improve the safety, quality, and efficiency of the care they provide.
A comprehensive data science strategy needs to address the quality of the underlying data, effective ways to analyze the data, and a framework for keeping it secure. If an organization tries to aggregate and analyze poor-quality data, it may derive useless or even dangerous conclusions. An inadequate security framework may lead to unauthorized access and undermine trust of patients and providers.
A carefully developed data science strategy will help achieve both precision medicine (helping to tailor treatments to patients) and the creation of learning health systems (helping to predict outcomes and identifying specific areas for improvement). Ideally, every decision a provider makes about a patient should be informed by the data of both that specific patient and other similar patients. In a learning health system, prior experiences improve future choices.
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