Index SWOT Analysis Strengths Weaknesses Opportunities Threats Data Analytics in Manufacturing Conclusion Bibliography Data analytics is the process of examining data sets in order to draw conclusions on the information contained therein. This is done with the help of specialized systems and software. In this report I will talk about data analytics and discuss some of the strengths, weaknesses, opportunities and threats associated with it. Finally, I will discuss the importance of data analytics in manufacturing today. Data analytics technologies and techniques are widely used in business sectors to enable organizations to make more informed business decisions, and by scientists, engineers and researchers to verify or disprove scientific models, theories and hypotheses. Data analytics can help companies increase revenue, improve operational efficiency, optimize marketing campaigns and customer service efforts, respond more quickly to emerging market trends, and gain a competitive advantage over rivals, the all with the ultimate goal of increasing business performance. Depending on the application, the analyzed data may consist of historical records or new information processed for real-time analytics uses. Additionally, it can come from a mix of internal systems and external data sources. Say no to plagiarism. Get a tailor-made essay on "Why Violent Video Games Shouldn't Be Banned"? Get an Original Essay Strengths of SWOT Analysis The fundamental strength of data analytics or big data lies in the three Vs it represents: volume, velocity, and variety. The enormous volume, velocity and variety of data that is collected opens up a new range of business opportunities across all functional areas, from marketing, operations, accounting, finance and human resources management, and across all types of organizations from industry, government and non-profit. In short, data itself represents an opportunity for innovation. Weaknesses As Big Data analytics becomes more popular and becomes a more standard part of modern business processes, it will be necessary to provide more training and knowledge transfer for small and medium-sized businesses so that they are able to analyze collected data to get a better view of what their customers want and need. It has been pointed out that there are not enough people comfortable dealing with large amounts of data and that big data should be incorporated into all aspects of a degree program so that more graduates have at least a moderate level of understanding of the sector. Another big problem with data analytics is the risk of unintentionally or deliberately violating people's privacy. As companies analyze large amounts of data, the risk of this happening can be high. Opportunities Data analytics offers an exciting array of opportunities across many different industries, including healthcare, education, manufacturing, supply chain, and transportation. Likewise, the promise of big data spans all functional areas, including marketing, accounting, finance, operations, and human resource management. Big data can be used to identify specific customer needs and wants and develop products and services that meet those needs. Another area where data analytics could benefit a good majority of the population would be education. Having the ability to crunch data to see whether teachers are effective in improving their students' performance would not only raise test scores to boost the school system's standing, butit would make the future workforce more productive and educated. more data is collected, there is a risk that some of this data may be used inappropriately. For example, in healthcare, if a third party analyzed the data, the data would have to be stripped of some identifying information. Leaving someone's name or other personally identifiable information in a data set sent outside the company could not only put the customer in danger, either through identity theft or some type of fraud; but it could also impact the company that released the information. Data Analytics in Manufacturing Data analytics is extremely important to today's manufacturing industry, as it is essential for achieving productivity and efficiency gains and uncovering new insights to drive innovation. With big data analytics, manufacturers can discover new insights and identify patterns that allow them to improve processes, increase supply chain efficiency, and identify variables that impact production. Manufacturing company leaders understand the importance of data analytics in today's industry. A study by Honeywell Process Solutions-KRC found that 67% of manufacturing executives planned to invest in data analytics, even in the face of pressure to reduce costs. The majority understand that data analytics is necessary to successfully compete in a data-driven economy and are investing in data integration and management resources to achieve digital transformation and gain a competitive advantage. With the right analysis, manufacturers can focus on each segment of the production process and examine supply chains in detail, taking into account individual activities and tasks. This ability to narrow the focus allows manufacturers to identify bottlenecks and reveal underperforming processes and components. Data analysis also reveals dependencies, allowing manufacturers to improve production processes and create alternative plans to address potential pitfalls. Data analytics also allows you to accurately predict demand for customized products. By detecting changes in customer behavior, data analytics can offer manufacturers more lead times, providing the opportunity to produce customized products as efficiently as goods produced on a larger scale. Innovative features include tools that allow product engineers to collect, analyze and visualize customer feedback in near real-time. By providing manufacturers with the tools they need to perform a process review, data analytics allows them to identify points within the manufacturing process where they can profitably insert customized processes using internal capabilities or postpone production for allow a partner to perform customization before the manufacturing process is complete. According to a Deloitte study on the rise of mass customization, the ability to postpone production gives manufacturers new flexibility that allows them to accept made-to-order requests. Deloitte also notes that the ability to postpone production can “help reduce inventory levels and ultimately increase plant efficiency.” A streamlined manufacturing process is not only beneficial in itself, but it gives manufacturers a way to maintain efficiency when making customizations. Please note: this is just an example. Get a custom paper from our expert writers now. Get a custom essay Conclusion From my report:: 27/09/18.