Podcast Beta
Questions and Answers
What type of analytics is primarily concerned with understanding past performance?
Which type of analytics aims to answer the question 'What should be done to prevent future issues'?
Which analytics type focuses on explaining the reasons behind past events?
What is the primary output of descriptive analytics?
Signup and view all the answers
What differentiates data science from data analytics in terms of the nature of work?
Signup and view all the answers
What is a characteristic of traditional Business Intelligence (BI)?
Signup and view all the answers
Which of the following best describes the capability of Big Data as compared to traditional BI?
Signup and view all the answers
Which question aligns with typical inquiries made in Data Science?
Signup and view all the answers
Which of the following represents a type of data used in Big Data?
Signup and view all the answers
What is a common method used in Business Intelligence for reporting?
Signup and view all the answers
What is the primary focus of predictive analytics in Data Science?
Signup and view all the answers
Which statement about data warehouses is true?
Signup and view all the answers
Which question is typically not asked in the context of Business Intelligence?
Signup and view all the answers
Which analytic techniques are commonly used in the Consumer Packaged Goods sector?
Signup and view all the answers
In wireless telecom, which of the following methods is NOT listed as an analytic technique used?
Signup and view all the answers
What is an important aspect of the model building process?
Signup and view all the answers
Which of the following tools is specifically mentioned for executing statistical tests and managing data?
Signup and view all the answers
For model training, what is the typical division of data segments used?
Signup and view all the answers
Which of these options incorrectly lists a tool used for advanced analytics?
Signup and view all the answers
What is the role of automatic relevance determination (ARD) in model planning?
Signup and view all the answers
What is a primary function of SQL in the context of data analytics?
Signup and view all the answers
What is a primary characteristic of a data warehouse?
Signup and view all the answers
Which type of analytics involves understanding historical data trends?
Signup and view all the answers
What does the term 'non-volatile' refer to in data warehousing?
Signup and view all the answers
What is the main purpose of prescriptive analytics?
Signup and view all the answers
What characterizes operationalized analytics?
Signup and view all the answers
Which type of data is primarily sourced for data warehouses?
Signup and view all the answers
What key benefit does an analytic sandbox provide?
Signup and view all the answers
What are the two main types of analytics in the second school of thought?
Signup and view all the answers
What is a significant challenge associated with big data regarding storage?
Signup and view all the answers
Which role is focused on providing technical expertise for analytical projects in the big data ecosystem?
Signup and view all the answers
What is a major concern about security in big data platforms?
Signup and view all the answers
Which aspect is crucial when maintaining data quality in a big data environment?
Signup and view all the answers
What describes the role of data analytical talent in the big data ecosystem?
Signup and view all the answers
Which characteristic is essential for big data systems to manage hardware and software failures?
Signup and view all the answers
What is a major issue regarding data retention in big data management?
Signup and view all the answers
Which emerging technology aids in managing infrastructure for big data?
Signup and view all the answers
Study Notes
Business Intelligence (BI) and Big Data
- BI focuses on straightforward reports and performance measures, aiding management decisions with recent trends.
- Traditional BI relies on a central server and offline data analysis, managing structured data.
- Big data operates on a distributed file system, offering real-time streaming and handling structured, semi-structured, and unstructured data.
BI vs. Data Science
- Key distinction lies in analytical techniques: Data Science emphasizes predictive analytics and data mining.
- BI utilizes standard reporting, dashboards, alerts, and queries primarily on structured data from traditional sources.
- Common questions explored in BI include historical performance, scenarios, and problem identification.
Model Planning in Data Science
- Loading data into a warehouse requires thorough understanding; local systems may emerge as departmental warehouses.
- Data is analyzed via applications, and the need for either a single model or model ensemble is established.
- Various analytic techniques are applied across industries, from linear regression in consumer goods to neural networks in telecommunications.
Common Tools for Model Planning
- SAS: High-quality interpretive models, statistical analysis, and quality plotting capabilities.
- SQL: Enables in-database analytics for predictive modeling and data manipulation.
- RapidMiner and Tableau Public: Advanced analytics with no programming required, supporting real-time data usage and sharing.
Model Building Importance
- Extracting insights informs business strategies and decisions.
- Data is divided into training (60-80%), validation (10-20%), and testing data (10-20%) to develop predictive models.
Data Warehousing Characteristics
- Central repositories for structured and semi-structured data, facilitating regular analysis.
- Key characteristics include being subject-oriented, integrated, non-volatile, and time-variant.
Types of Analytics
- Descriptive, Diagnostic, Predictive, and Prescriptive analytics offer various insights from data.
- Classification of analytics ranges from basic insights to operationalized and monetized analytics.
Evolution of Analytics
- Analytics have evolved from descriptive and diagnostic analyses (2005-2012) to predictive and prescriptive analyses (2012-present).
- The focus has shifted from historical reporting to forecasting and optimizing future actions.
Data Science vs. Data Analytics
- Data Science leans towards exploratory and predictive analytics, while Data Analytics focuses on reporting and optimization.
- Outputs differ: Data Science aims for comprehensive data products, whereas Data Analytics yields reports and dashboards.
Traditional BI vs. Big Data
- BI encompasses data mining and performance benchmarking, relying on central servers for offline analysis.
- Big data enables comprehensive analytics from multiple sources and supports large-scale data operations.
Drivers and Ecosystem of Big Data
- Emerging technologies include data devices, collectors, and aggregators, creating a new ecosystem for data usage.
- Key roles in this ecosystem demand advanced training in quantitative disciplines, with a simultaneous need for data savvy professionals.
Challenges of Big Data
- Key challenges include scale, security, schema flexibility, and ensuring continuous availability.
- Data volume increases exponentially, raising questions about usefulness and retention.
- Organizations face a talent gap, requiring skilled professionals to manage and exploit big data analytics effectively.
Studying That Suits You
Use AI to generate personalized quizzes and flashcards to suit your learning preferences.
Related Documents
Description
This quiz explores the differences between traditional business intelligence and big data analytics. It highlights key concepts like performance measures, data storage methods, and analysis modes relevant for management decisions. Test your knowledge on the latest trends in the BI landscape.
