1. Big Data Analysis

2. Data Mining versus Data Analytics DATA ANALYSIS HYPOTHESIS CONCLUSION

3. Structured and Unstructured Data Nearly 80% of all data is unstructured. Data analysis is traditionally performed only on structured data. Unstructured data must become structured in order to be analyzed: this can be a complex and expensive endeavor.

4. Why Data Mining? What value does data mining provide? Supports decisions using unbiased information. Predict future trends based on historical trends. Influences business focus and priorities. What limitations face data mining activities? The security and privacy of original data unmanaged. Misuse of information. Inaccuracies in Information.

5. Why Data Analytics? What are the benefits of Data Analytics? Targeted analysis of risk areas. Leveraging analysis across several projects. Increased frequency of high-risk activities. What are the limitations of Data Analytics? Cost of increased data quality. Data Volume – finding the necessary value. Improperly budgeting efforts. Specialized skill sets required.

6. Increasing Data Analysis Efforts Continuous Monitoring Centralized Repeatable Ad Hoc Analysis Source: Data Analytics – A Practical Approach (ISACA)

7. What to avoid in Big Data Be realistic, not optimistic. Don’t put all your eggs into software. Change the way you think. Learn from mistakes. Find the people who know. Finish what you start. Be practical, don’t oversell.

8. General Implementation Process 1. Choose a problem area. 2. Define data inclusions and exclusions. 3. Define business rules. 4. Translate rules into analytical queries and algorithms. 5. Choose appropriate presentation of results. 6. Maintain and improve analytics.

9. Anomalies and False Positives Anomalies – something occurs that is unique or distinctly different from what is expected. False Positive – a result indicating the presence of a given condition when it is not.

10. Primary Capabilities of Data Analytics Locating Data – identifying data sources, extracting the data from the source and validating the data. Normalizing Data – imposes regulatory and business standards on the data: ensures the data is in a usable format, organized, and deals with anomalies and false positives as required by procedure. Analyzing Data – identifies any significant trends, patterns, or differences which should be investigated and/or communicated.