Chapter 8 Big Data

What are two methods of data mining?

-Verification - drill-down -Discovery Model - looking for information about the data - what does it say

What are the three main sources of Big Data?

1. Operational Data- transactions (sales, PO's, Checks) 2. Social Media Data 3. Dark Data- Data collected from business activities that can analyzed, but haven't been yet. (Untapped, unanalyzed) Not a bad thing

What is variety of data?

A lot of different data. Ex: typed, video, audio, etc

What is volume of data?

How much data there is. Terabytes, petabytes, exabytes. Huge amounts

What are descriptive data analytics?

Just a description. No prediction or prescription. Ex: B/S and I/S

What are some risks or Big Data?

Privacy & Data Security. Must protect data - Firewalls, access controls, password controls, audit trails Legal issues and prohibited uses (medical data and HIPPA - Dark data that needs to stay dark. Where and how will we protect this data?

What is data mining?

Process of selecting, exploring, and modeling data to uncover relationships and global patterns

What are diagnostic data analytics?

Review past performance and why things happened. Diagnose the problem.

What are predictive data analytics?

Statistical likelihoods of future events. Use past data. Forward looking

What are prescriptive data analytics?

Tells what users what actions they should be taking. Solves/answers questions. Like a prescription. Forward looking

What is Big Data?

The creation, analysis, storage and dissemination of extremely large sets of data. Mainly feasible due to advances in computer storage technologies (the cloud)

What is velocity of data?

The speed data needs to be analyzed. -This leads to machine learning and AI -Because of volume and need to analyze - have to use big data analytics techniques

What are the data sources of Big Data?

They include Ubiquitous computing (i.e., smart phones and wearables, e.g., the Fitbit), the Internet of Things, biometrics (i.e., automated human recognition)

What is a data warehouse?

•A centralized relational database, separate from organization's operational database designed to meet needs of analysis -Has all the data of the operations (sales, purchases, etc.) -But also adds in additional data

How do you manage big data projects? Approach and understanding

•Approach and understanding—top management informed of and engaged in big data initiative" Understand scope and risks?

How do you manage big data projects? Availability

•Availability—Are disaster recovery and event response plans in place and reliable?

Conclusion

•Big data sets are here and growing in frequency and importance •Varied uses and applications •Must consider ethical and legal risks •Must consider governance and controls over big data projects

How do you manage big data projects? Data quality

•Data Quality—Does the data collection and storage process result in accurate, reliable, complete, timely data?

How do you manage big data projects? Data confidentiality and privacy

•Data confidentiality and privacy—Does the organization comply with external laws and regulations, and its own internal standards for data confidentiality and privacy?

What is the value of big data?

•Help monitoring & evaluate performance in operations and finance •Improve risk and compliance management •Assist with product and service innovation •Improve customer experiences and loyalty •Data monetization (data sales)- everyone else wants the data you have.

How is the Governance of Big Data accomplished?

•Must establish a clear governance structure for big data projects: -Responsibilities, scope and limits of project -Require a clear purpose, scope and plan -Consider qualitative characteristics of information in formulating big data plans (see COBIT lesson) New, emerging (largely unexplored) possibilities for monitoring of accounting and internal control systems.

Why do CPA's care about this?

•Possible new roles for CPAs—data scientists of financial information •Applications of big data in auditing, tax work, and risk analysis -Big data analysis of risk of material misstatement of an audit client -Big data analysis of our identified risk areas -"Continuous audit" of big data streams

What are the implications of big data?

•Will result in expanding existing data warehouses •Big data analytics and "smart data:" -Emerging focus of big data—data mining (discovering data trends), expanded OLAP (online analytical processing) -Another name for big data is "smart data", which generally refers to both big data and the use of advanced analytic methods on the data -E.g., big data-based audits