Big Data

 Big Data examples in Health care With a variety of data analytics tools and methods, healthcare analysts use big data to inform health prevention, intervention and management. Efforts such as these can help enhance the patient experience, improve efficiency and quality of care and lower healthcare costs. Big data analytics for healthcare makes it possible to get a more complete picture of something to make smarter decisions. Big data in healthcare can benefit patients and providers alike in many different ways. Here are just a few other big data examples in healthcare:  Patient outcomes. Big data can be used in healthcare to identify individual and community trends and develop better treatment plans or predict at-risk patients.  Staffing and operations. Healthcare analytics can use big data to forecast patient admissions trends at specific times of the day and schedule the right number of staff during peak or slow periods.  Product development. Big data in healthcar...

  While the recent trend towards cloud computing might make it seem like virtualization is new, it has existed in some form for many decades. As far back as IBM’s mainframe era in the 1960s, virtualization played a part in the way computing was handled to make it more efficient and reduce the load on the individual machine. The 5 most popular types of virtualization are below. 1. Desktop: allows multiple virtual machines to run cloud based desktops. 2. Application: Creates a virtual instance of the applications needed for core business operations, which keeps app software off of local OS 3.Server: Creates a virtual server in place of the physical one, allowing for management of the server through the cloud. 4. Storage: Stores the enterprise’s data in a secure cloud, removing the need for physical data storage and potentially reducing the costs associated with space in a data centre. 5.  Network: Uses physical and virtual components to create a hybrid network, allowing adminis...

 Data mining is a technique used to process data and explore patterns in large data sets to develop practical and data-driven insights. The vital aspects of data mining include data cleaning, data transformation, and data integration. A few data mining methods are down below 1. Pattern Tracking:  Pattern tracking is one of the fundamental data mining techniques. It entails recognizing and monitoring trends in sets of data to make intelligent analyses regarding business outcomes. For a business, this process could relate to anything from identifying top-performing demographics or understanding seasonal variations in the customer’s buying behaviour. 2. Classification:  It’s a useful data mining technique used to derive relevant data and metadata based on a defined attribute, for example, type of data sources, data mining functionalities, and more. Basically, it’s the process of dividing large datasets into target categories. 3. Clustering:  Like classification, cluster...

 A well-executed big data strategy can streamline operational costs, reduce time to market and enable new products. Below are listed some strategies that some organisations use. 1. Managing large volumes of data: Big data by itself definition typically involves large volumes of data. Once you have a sense of the data that's been collected it is easier to narrow in on insights by making small adjustments. 2. Finding and fixing quality issues:    The analytics algorithms and artificial intelligence applications built on big data can generate bad results when data quality issues creep into big data systems. These problems can become more significant and harder to audit as data management and analytics teams attempt to pull in more and different types of data 3. Dealing with data integration and preparation complexities:   Some enterprises use a data lake as a catch-all repository for sets of big data collected from diverse sources, without thinking through how the dispa...

Generally, most organisations that deal with Big Data have several goals for Big Data projects. While the primary goal for most organisations is to enhance the experience for the customer, other goals are also considered. When considering Big Data organisations will consider a few things. 1. Trying to decide whether there is true value in Big Data or not. 2. Evaluating the size of the market opportunity. 3. Developing new services and products that will use Big Data 4. Already using Big Data solutions, Repositioning existing services and products to utilise big data 5. Already utilizing Big Data solutions

 The implication of big data for society are wide ranging, affecting various aspects of everyones lives 1. Health and safety: We can use Big data for an impact on the public health industry, diseases can be tracked and help keep people stay hungry. 2. Making cities better: We can use data to make cities safer and more efficient. By looking at things like crime rates and traffic patterns, we can make cities run smoother and be safer for everyone. 3.Protecting the Environment: Big data can help us take care of the environment better. We can use it to use less water in farming, detect pollution faster, and manage natural resources smarter. In conclusion Big data has a lot of potential to make life easier and better for everyone, however it needs to be used properly and responsibly

Big Data significantly affects individuals across the world. With big data becoming so popular for businesses, the issue around big data privacy is also becoming important. Businesses are increasingly using the insights that big data generates in order to grow their businesses. This is happening while business owners fail to consider big data privacy issues and how they can affect their business. Below are 2 reasons how big data has implications in individuals lives. 1. personalization: Big data enables companies to collect large amounts of data of information about individuals, such as behaviours and habits. this data is then used to personalize experiences, such as targeted advertising. 2. Privacy concerns: The collection and analysis of this personal data raise concerns about privacy and data security. Individuals may worry about how their information is being used and who has access to it.  

  Predictive analytics is a branch of data analytics that uses statistical algorithms and machine learning techniques to analyse historical data and make predictions about future events. Limitations Limitation 1: Data quality One of the most significant limitations of predictive analysts is the quality of the data, predicative models rely on large, accurate and relevant datasets to produce their accurate predictions. Limiitation 2: Overfitting Another limitation of predictive analytics is overfitting. Overfitting occurs when a model is trained on a specific dataset and becomes too complex making it difficult to generalize to new data. Limitation 3: changing conditions Predictive analytics models are designed to predict future outcomes based on historical data. However, the future is inherently uncertain, and conditions can change quickly. In conclusion predictive analytics is a powerful tool that can help organizations make informed decisions and plan for the future

  Big Data Technology is primarily divided into two types: Operational Big Data Technologies and Analytical Big Data Technologies.  Operational Big Data Technologies This type of big data technology focuses on the data that people use to process. Typically, the operational-big data includes data such as online transactions, social media platforms, and data from any particular organization Analytical Big Data Technologies Analytical Big Data is an enhanced version of Big Data Technologies. This type of big data technology is complex when compared to operational big data. Analytical big data is mainly used when performance metric is used and important business decisions are to be made based on reports created by analysing operational analytics. Top big data technologies are split into four different sections Data Storage Data Mining Data Analytics Data Visualization