Big Data Analysis
Using this Presentation Template
This presentation is designed to provide an adaptable format for presenting information related to Big Data projects to your organization. On its own, it is an instruction about the basic concepts of Big Data and how they are related.
We encourage you to use the format to expand each section to support the individual needs of your organization.
Data Mining versus Data Analytics
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.
Why Data Mining?
What value does data mining provide?
Supports decisions using unbiased information
Predicts 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
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
Increasing Data Analysis Efforts
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 started.
Be practical; don’t oversell.
General Implementation Process
Choose a problem area.
Define data inclusions and exclusions.
Define business rules.
Translate rules into analytical queries and algorithms.
Choose appropriate presentation of results.
Maintain and improve analytics.
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.
Primary Capabilities of Data Analytics
Locating Data – identifying data sources, extracting the data from the source, and validating the data.
Normalizing Data – imposing 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 – identifying any significant trends, patterns, or differences which should be investigated and/or communicated.