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Data_analyst_types of data, Structured, Unstructured and Semi-structured Data
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- 5. Difference Between Structured,Semi-structured, and Unstructured Data
- 6. Parameters Structured DataSemi-Structured Data Unstructured Data Data Structure The information and data have a predefined organization. The contained data and information have organizational properties- but are different from predefined structured data. There is no predefined organization for the available data and information in the system or database. Technology Used Structured Data works on the basis of relational database tables. Semi-Structured Data works on the basis of Relational Data Framework (RDF) or XML. Unstructured data works on the basis of binary data and the available characters. Flexibility The data depends a lot on the schema. Thus, there is less flexibility. The data is comparatively less flexible than unstructured data but way more flexible than the structured data. Schema is totally absent. Thus, it is the most flexible of all.
- 7. Management of Transaction It hasa mature type of transaction. Also, there are various techniques of concurrency. It adapts the transaction from DBMS. It is not of mature type. It consists of no management of transaction or concurrency. Management of Version It is possible to version over tables, rows, and tuples. It is possible to version over graphs or tuples. It is possible to version the data as a whole. Robustness Structured data is very robust in nature. Semi-Structured Data is a fairly new technology. Thus, it is not very robust in nature. – Scalability Scaling a database schema is very difficult. Thus, a structured database offers lower scalability. Scaling a Semi- Structured type of data is comparatively much more feasible. An unstructured data type is the most scalable in nature. Performance of Query A structured type of query makes complex joining possible. Semi-structured queries over various nodes (anonymous) are most definitely possible. Unstructured data only allows textual types of queries. Parameters Structured Data Semi-Structured Data Unstructured Data
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- 9. Analytics is thediscovery and communication of meaningful patterns in data. What is Data Analytics? In this new digital world, data is being generated in an enormous amount which opens new paradigms. As we have high computing power as well as a large amount of data we can make use of this data to help us make data-driven decision making. The main benefits of data-driven decisions are that they are made up by observing past trends which have resulted in beneficial results.
- 10. In short, wecan say that data analytics is the process of manipulating data to extract useful trends and hidden patterns which can help us derive valuable insights to make business predictions. What is Data Analytics?
- 11. Types of DataAnalytics There are four major types of data analytics: 1.Predictive (forecasting) 2.Descriptive (business intelligence and data mining) 3.Prescriptive (optimization and simulation) 4.Diagnostic analytics
- 12. Predictive Analytics Predictive analyticsturn the data into valuable, actionable information. predictive analytics uses data to determine the probable outcome of an event or a likelihood of a situation occurring. Predictive analytics holds a variety of statistical techniques from modeling, machine learning, data mining, and game theory that analyze current and historical facts to make predictions about a future event. Techniques that are used for predictive analytics are: •Linear Regression •Time Series Analysis and Forecasting •Data Mining Basic Corner Stones of Predictive Analytics •Predictive modeling •Decision Analysis and optimization •Transaction profiling
- 13. Descriptive Analytics Descriptive analyticslooks at data and analyze past event for insight as to how to approach future events. It looks at past performance and understands the performance by mining historical data to understand the cause of success or failure in the past. Almost all management reporting such as sales, marketing, operations, and finance uses this type of analysis. Common examples of Descriptive analytics are company reports that provide historic reviews like: •Data Queries •Reports •Descriptive Statistics •Data dashboard
- 14. Prescriptive Analytics The finaltype of data analysis is the most sought after, but few organizations are truly equipped to perform it. Prescriptive analysis is the frontier of data analysis, combining the insight from all previous analyses to determine the course of action to take in a current problem or decision. Prescriptive analysis utilizes state of the art technology and data practices. It is a huge organizational commitment and companies must be sure that they are ready and willing to put forth the effort and resources. Artificial Intelligence (AI) is a perfect example of prescriptive analytics. AI systems consume a large amount of data to continuously learn and use this information to make informed decisions. Well- designed AI systems are capable of communicating these decisions and even putting those decisions into action. Business processes can be performed and optimized daily without a human doing anything with artificial intelligence. Currently, most of the big data-driven companies (Apple, Facebook, Netflix, etc.) are utilizing prescriptive analytics and AI to improve decision making. For other organizations, the jump to predictive and prescriptive analytics can be insurmountable. As technology continues to improve and more professionals are educated in data, we will see more companies entering the data-driven realm. For example: Prescriptive Analytics can benefit healthcare strategic planning by using analytics to leverage operational
- 15. Diagnostic Analytics In thisanalysis, we generally use historical data over other data to answer any question or for the solution of any problem. We try to find any dependency and pattern in the historical data of the particular problem. For example: companies go for this analysis because it gives a great insight into a problem, and they also keep detailed information about their disposal otherwise data collection may turn out individual for every problem and it will be very time-consuming. Common techniques used for Diagnostic Analytics are: •Data discovery •Data mining •Correlations
- 16. Future Scope ofData Analytics 1.Retail: To study sales patterns, consumer behavior, and inventory management, data analytics can be applied in the retail sector. Data analytics can be used by retailers to make data-driven decisions regarding what products to stock, how to price them, and how to best organize their stores. 2.Healthcare: Data analytics can be used to evaluate patient data, spot trends in patient health, and create individualized treatment regimens. Data analytics can be used by healthcare companies to enhance patient outcomes and lower healthcare expenditures. 3.Finance: In the field of finance, data analytics can be used to evaluate investment data, spot trends in the financial markets, and make wise investment decisions. Data analytics can be used by financial institutions to lower risk and boost the performance of investment portfolios.
- 17. 4. Manufacturing: Dataanalytics can be used to examine production data, spot trends in production methods, and boost production efficiency in the manufacturing sector. Data analytics can be used by manufacturers to cut costs and enhance product quality. 5. Marketing: By analyzing customer data, spotting trends in consumer behavior, and creating customized marketing strategies, data analytics can be used in marketing. Data analytics can be used by marketers to boost the efficiency of their campaigns and their overall impact. 6. Transportation: To evaluate logistics data, spot trends in transportation routes, and improve transportation routes, the transportation sector can employ data analytics. Data analytics can help transportation businesses cut expenses and speed up delivery times.