Big Data

  Application of big data techniques to a problem Identifying problems and providing data to back up the solution is beneficial as you can track whether the solution is solving the problem, improving the situation or has an insignificant effect.  As a relatively new field, big data in healthcare is still evolving to keep up with the fast pace and changing nature of technology. With such vast amounts of data available to work with, organizations and leaders can struggle with knowing where and how to start with data analytics in healthcare to find the information that is meaningful. Making use of all of this data raises concerns of healthcare cyber security and information privacy. The issue of governance who owns and is responsible for overseeing the planning, implementation and management of big data is also a common concern among healthcare organizations. Many healthcare organizations lack adequate systems and databases — and the skilled professionals to handle them. As such...

  Types of visualizations   The most common categories of data visualization are graph, charts and maps.  By choosing the right type of visualization for your data, you can reveal insights, tell a story, and guide decision-making.  some examples of types of visualization: Pivot tables-    pivot tables aren’t always the most visually inspiring form of data , they are useful in the right context. For instance, highlight tables, as shown in the image, use different shades or colors to easily flag the highest and lowest values in a dataset. Scatter-plot - also known as a scatter-graph, scatter-gram, or scatter chart displays the relationship between two variables on an x- and y-axis. Each item of data is shown as a single point, creating the chart’s visual ‘scatter’ effect. When there are three interrelated data points. line graphs -  Line graphs, or line charts, are a simple but effective staple for representing time-series data. They are visually similar...

  Data mining methods    Data mining refers to filtering, sorting, and classifying data from larger datasets to reveal subtle patterns and relationships, which helps enterprises identify and solve complex business problems through data analysis. Data mining software tools and techniques allow organizations to foresee future market trends and make business-critical decisions at crucial times The most popular types of data mining techniques include association rules, classification, clustering,neural networks and predictive analysis . Association rules, also referred to as market basket analysis, search for relationships between variables.  Association rule -  The association rule refers to the if-then statements that establish correlations   and relationships   between two or more data items. The correlations are evaluated using support and confidence metrics, wherein support determines the frequency of occurrence of data items within the dataset. ...

Types of problem suited to big data analysis Some problems that are suited to big data analysis:   Data Quality - The analytics algorithms and artificial intelligence applications built on big data  can generate bad results when data quality   issues creep into big data analysis.  Data security-   is a challenge of big data  where organizations need to ensure that their data  is protected against unauthorized access, breaches.  Data security  and protection are overlooked. More data  means more opportunity for security  breaches. constant data changes -   Implementing the infrastructure and management of data cannot be a set-and-forget task. The nature of data is that it’s constantly changing. Your customers details and orders are always changing, as well as their interactions with your company. Data integration-  consists of taking data from various sources and combining it to create valuable and usab...

Strategies for limiting the negative effects of big data        some strategies that could limit the negative effects of big data are: Data minimization refers to the practice of gathering only the information required for a certain purpose and then deleting it once finished.  Ethics-related factors Make sure that data is gathered and used ethically, fairly, and in accordance with the rights and freedoms of each individual. Security of sensitive data: Put in place strong security measures to guard against data breaches and unauthorized access. Implementing procedures and rules for data collection, storage, and use will help to assure its accuracy, confidentiality, and security. Implement organisational and technical safeguards to safeguard personal data and ensure compliance with data privacy regulations.

Implications of big data for society Big data analytics takes the small pieces of each individual life and fits them into the bigger puzzle of our shared reality. That puzzle reveals a broader picture what we search for, where we go, that benefits all of us. The important attributes associated with the data sets are;  Data is a transmissible and storable computer information knowledge derives an understanding from the data and  more data the more information, therefore, more knowledge embedded in it.  In both the public and private sectors, decisions are increasingly being guided by data. Whether it's in forecasting market trends, optimizing supply chains, or developing new products, data analytics provides tailored and specific business insights for organisations.  Big data can reduce costs in storing all of a business's data in one place. Tracking analytics also helps companies find ways to work more efficiently to cut costs wherever possible.

  Implications of big data for individuals    Big data could increase access  people have to information. It could also make it easier for us to find more data to be analysed. But having access to vast amounts of data can give an individual person influence on people and power.  Big data can be used to tailor a persons experience to be unique and meaningful to them, it has enough data to make predictions on what other items the person would most likely like based on their current likes/ looked at or previously bought.  The main challenge is when collecting data for big data is the protection of peoples privacy, this is because collecting the data also includes collecting peoples private information. this becomes a bigger concern when the person who had their data collected doesn't agree on how the data will be used.    

  Limitations of predictive analytics    One of the most significant limitations of predictive analytics is data quality  . Predictive models rely on large, accurate, and relevant datasets to produce accurate predictions. If the data used to train the model is incomplete, inaccurate, or biased, the model's predictions will also be flawed.  While predictive analytics might seem like the ideal inclusion for application teams, it's worth noting the risks. These include  data privacy and security concerns, model accuracy and bias challenges, users perception and trust issues and the dependency of data quality and availability.   

  Technological requirements of big data     Big data technologies can be categorized into four main types: data storage -  Big data technology that deals with data storage has the capability to fetch, store, and manage big data. It is made up of infrastructure that allows users to store the data so that it is convenient to access. data mining -  Data mining extracts the useful patterns and trends from the raw data. Big data technologies such as Rapid miner and Presto can turn unstructured and structured data into usable information.  data analytics -  In big data analytics  , technologies are used to clean and transform data into information that can be used to drive business decisions. This next step (after data mining) is where users perform algorithms, models. data visualization -  data technologies can be used to create stunning visualizations from the data. In data-oriented roles, data visualization is a skill that is beneficial fo...

  Future applications of big data    Predictive analytics will play a huge role in the future of big data. As the name suggests, it utilizes historical data to make predictions. For example, you can use predictive analytics on your big data sets to determine when your product will spike in demand.  Another future application is  Big data analytics evolution continue to focus around  machines learning and AI systems . Increasingly, AI is used by organizations of all sizes to optimize and improve their business processes.  some examples of future application of big data : healthcare-    Big data analysis has the potential to revolutionize the healthcare industry. It can change the way we do things. From predictive devices to enhanced diagnostic accuracy, from real-time imaging data to optimized treatment plans you can have it all at the time of your fingers. By generating an ever-increasing volume of big data future opportunities are bound to ...

  Contemporary applications of big data in society    Applications of big data in society Whether it be helpful in technology fields or social media marketing or advertising,  big data  affects all of our daily lives .  some examples of big data in society are: Transportation - assist in GPS navigation, traffic and weather alerts Government and public administration - track tax, defense and public health data.  Business - streamline management operations and optimize costs.  Healthcare -access medical records and accelerate treatment development. Improving security and law enforcement- using predictive analysis to prevent crimes based on where crimes are most likely to occur after recent crimes  https://builtin.com/articles/big-data-examples-applications