Introduction to Big data and data analytics (1).pptx

Bigdata - Introduction Data, Information and Bigdata Data & Information Data - Facts and statistics collected for reference or analysis. Six (6) key characteristics of data: 1.Accuracy, 2.Validity, 3.Reliability, 4.Timeliness, 5.Relevance, 6.Completeness. In...

Bigdata - Introduction Data, Information and Bigdata Data & Information Data - Facts and statistics collected for reference or analysis. Six (6) key characteristics of data: 1.Accuracy, 2.Validity, 3.Reliability, 4.Timeliness, 5.Relevance, 6.Completeness. Information - Organized or classified data, which has some meaningful values for the receiver. Information is the processed data on which decisions and actions are based.  Data Process Information 1.Accuracy 1. Accurate 2.Validity 2. Valid 3.Reliability 3. Reliable 4.Timeliness 4. Timely 5.Relevance 5. Relevant 6.Completeness 6. Fit for purpose 7. Accessible Decisi 8. Cost effective on 9. Level of detail Makin 10. Reliable source 11. Understandable by user g Characteristics of data 1. Structured 2. Semi-structured 3. Unstructured Data Growth over the years Traditional Data Vs. Big Data Traditional Data vs. Big Data Traditional data is structured. Big data can refer to Businesses can use both a large and traditional data for complex data set, as tracking sales or well as the methods managing customer used to process this relations or workflows. type of data. However, it generally provides less sophisticated insights and more limited benefits than big data. Main characteristics of Big Data The multi-V model 3Vs 5Vs 7Vs Big data: The key characteristics Big Data – 3Vs Big data: The key characteristics Big Data – 5Vs Big data: The key characteristics - The multi-V model Variety Velocity Variability 7V Volume Veracity s Variability is different from variety. The constantly changing the data. This will have a huge impact on Value data homogenization. Variability Visualization Visualization Using charts and graphs to visualize large amounts of complex data is much more effective in conveying meaning than spreadsheets and reports chock-full of numbers and formulas. Data volumes – unit of measures A day in Data The exponential growth of data is undisputed. This explosion is fueled by If this number is correct, IOTs, and the it will mean there are 40 connected times more bytes than devices. there are stars in the observable universe. A day in Data A day in Data What is Business Analytics The use of data, information technology, statistical analysis, quantitative methods, computer-based models to make better fact-based decisions. “ It Is a process transforming data into actions through analysis and insights in the context of organizational decision making and problem solving” Scope of business data analytics 1. Descriptive analytics 2. Diagnostic analytics 3. Predictive analytics 4. Prescriptive analytics Scope of business data analytics 1. Descriptive analytics Descriptive analytics are more about summarizing and reporting data to answer the question of what has happened. This is to use of data to understand past and current business performance and make informed decisions. (Use of plots, charts, graphs, etc. to analyze data);  How much we paid as incentives?  What were sales last quarter?  What was the shortage of product X last year?  How are the sales representatives performing across regions?  Who are the best performers in the production line?  How many and what type of complains did we resolve? Scope of business data analytics 2. Diagnostic analytics The historical data can be measured against other data to answer the question of why something has happened. To find out dependencies and to identify patterns. These analytics are looking on the processes and causes, instead of the result. The typical methods used are:  Time series analysis and Components analysis to understand the data patterns.  Sensitivity analysis  Regression and correlation analysis. Scope of business data analytics 3. Predictive analytics This analytics is focused on what is likely to happen, and to predict future trends, through forecasting. Used historical data to build mathematical models, that can be used to make inferences about what will happen in a future (yet unrealized) scenario. This will help to answer questions such as:  What will happen to the level of productivity if incentive payments are reduced by 10% or if overall salary increase by 5%. Scope of business data analytics 4. Prescriptive analytics The goal here is for the output of the model to inform on the best actions to achieve a goal. (Eg. How much should we produced to maximize profit? ) These analytics are often formulated as optimization and simulation problems where a business or manager is trying to maximize (or minimize) some objective (e.g. profit, efficiency, cost, employee satisfaction, etc.) These analytics are commonly used in supply chain, routing and operations where the number or decisions are too vast for a human to efficiently manage. How do we use organizational data?