University:
California State University, Northridge
Course:
MGT 450 | Organization Change and Development
Academic year:

2024
Views:

197

Pages:

18
Author:

stwnsiwrdg8

3.3 • • • • • Big Data Defining Big Data Characteristics of Big Data Issues with Big Data Managing Big Data Putting Big Data to Use 26 Defining Big Data: Gartner Diverse, high volume, high-velocity • information assets that require new forms of processing to enable enhanced decision making, insight discovery, and process optimization. 28 Defining Big Data: The Big Data Institute (TBDI) • Vast Datasets that: Exhibit variety Include structured, unstructured, and semi-structured data Generated at high velocity with an uncertain pattern Do not fit neatly into traditional, structured, relational databases Can be captured, processed, transformed, and analyzed in a reasonable amount of time only by sophisticated information systems. 29 Characteristics of Big Data • Volume • Velocity • Variety 30 Issues with Big Data (P. 79) • Untrusted data sources • Big Data is “dirty” • Big Data changes, especially in data streams 31 Managing Big Data • Big Data can reveal valuable patterns, trends, and information that were previously hidden: tracking the spread of disease tracking crime detecting fraud 32 Managing Big Data (continued) • First Step: Integrate information silos into a database environment and develop data warehouses for decision making. • Second Step: making sense of their proliferating data. 33 Managing Big Data (continued) • Many organizations are turning to NoSQL databases to process Big Data 34 Putting Big Data to Use • Making Big Data Available • Enabling Organizations to Conduct Experiments • Micro-Segmentation of Customers • Creating New Business Models • Organizations Can Analyze Far More Data 35 Putting Big Data to Use • • • • • • • Sentiment Analysis 360-Degree Customer View Multi-Channel Marketing Customer Micro-Segmentation Clickstream Analysis Fraud Detection Ad-hoc Analysis “Big Data” is stored on and organized in… 36 3.4 Data Warehouses and Data Marts • Describing Data Warehouses and Data Marts • A Generic Data Warehouse Environment 37 Figure 3.4: Data Warehouse Framework 38 Describing Data Warehouses and Data Marts • Organized by business dimension or Use online analytical processing (OLAP) • Integrated • Time variant Data warehouse • Nonvolatile supports data mining • Multidimensional 39 A Generic Data Warehouse Environment • • • • Source Systems Data Integration Storing the Data Metadata Data about data: type, structure, constraints, owner, source, revision, … • Data Quality • Governance • Users 40 Figure 3.5: Relational Databases 41 Figure 3.6: Data Cube 42 Figure 3.7: Equivalence Between Relational and Multidimensional Databases 43 ’S ABOUT BUSINESS 3.4 Data Warehouse Gives Nordea Bank a Single Version of the Truth  What are other advantages (not mentioned in the case) that Nordea Bank might realize from its data warehouse?  What recommendations would you give to Nordea Bank about incorporating Big Data into their bank’s data management? Provide specific examples of what types of Big Data you think Nordea should consider. 44

Big Data

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