YELP Data Literacy (FPA 2022) - Call List

(Print) Use this randomly generated list as your call list when playing the game. There is no need to say the BINGO column name. Place some kind of mark (like an X, a checkmark, a dot, tally mark, etc) on each cell as you announce it, to keep track. You can also cut out each item, place them in a bag and pull words from the bag.

1. EU law on data protection and privacy in the European Union (EU) and the European Economic Area (EEA)
   GDPR
2. Conceptual framework or set of processes that connect data sources and users
   Data Hub
3. Provides technical support and system analysis to FPA
   Business System Analysts
4. Process of obtaining , importing, and processing data for later use/Tools that retrieve data from data sources
   Data Integration and Extraction
5. Creates transparency into multiple potential data formats and is used to foster collaboration
   Cross functional data management
6. Responsible for building predictive and prescriptive models for future scenarios and recommending best course of action
   Data Scientist
7. Responsible for deriving business insights from data
   Data Analyst
8. Repository that pools data unmodified from its original form or source for exploratory analysis
   Data Lake
9. Stores operational and business definitiions for critical data elements
   Data Glossary
10. Inventories of distributed data assets acting as the interface for data discovery They maintain an inventory of data assets through the discovery, discription, and organization of data sets
    Data Catalogues
11. Responsible for ensuring the data assets serve their intended purpose, comply to policies and standards and communicate data value
    Data Owner
12. Classification of data according to category and subcategory comprising a standardized set of definitions and metrics used throughout the enterprise
    Data Taxonomy
13. Downstream processing locations some traditional BI platforms concurrent to a DataWarehousse
    OLAP cubes
14. Allows finance to transform data, create visualizations and output analytics
    Analytics and BI Architecture
15. Responsiblle for executing analytics across the enterprise without being formally trained, supported by technology like smart data discovery tools
    Citizen Data Scientist
16. Shows the processes and controls applied to data
    Data Flow
17. Responsible for developing BI Reports, dashboards and analyses published for others to consume
    BI Developer
18. Offers self-service analytics, data oreparation and data discovery tools
    Analytics workbench
19. Scheduled nightly, hourly or daily, refers to the process of extracting data from sources, transforming is and loading it into a target location in batches
    ETL
20. Shows frequency and timing of data updates
    Update Schedule
21. Manages Yelp DW and tableau experts
    BIDI
22. Storage architecture designed to hold data extracted from transaction systems, operational data stores and external sources
    Data Warehouse
23. Specialized Repository organized for a single category of analysis.
    Data Mart
24. Unsupervised learning where machine is is trained to take action to maximize rewards in a particular situation, Reacts to positive events by increasing or decreasin strength and freq of its behave (Computers learning to play games or direve vehic
    Reinforement Learning
25. Traces the source of the data and the applications through which it has passed
    Data Lineage
26. Company that created ML pilot in AR without Data Scientists or IT support that decreased time to settlement by 40%
    Iron Mountain
27. Uses ML models to more rapidaly repare data for analytic use - data profiling/data quality, harmonizationn, data mdeling, manipulation, enrichment/interference, metadata deceolpment, and data cataloging
    Augmented Data Prep Tools
28. Two functional processes finance is responsible for providing a basis for Information Security
    Data Documentation & Data Policies/Standards
29. Lack of clarity surrounding the origins, meaning or uses of different data sets
    Data Mistrust
30. ML works on its own to discover information by working with unlabled data or not tagged with a right or wrong answer (IE topic modling or clustering)
    Unsupervised Learning
31. Aggregated or system level data (IE demographic Data on people or customers)
    Macro Data
32. Method for ensuring the uniformity, accuracy, stewardship, sematic consistency and accountability of the orgs shared data assets within a single point of referene
    Master Data Management (MDM)
33. Includes: Usability, precision, Timeliness, Accuracy, no duplication, real-time, validity, consistency, completeness
    Data Hygiene
34. Data Curator
35. Enables the basic level of data quality and consistency needed to derive value from data for purposes specific to the finance function
    Functional Data Management
36. Information assets that org collects, processes and stores in the course of regular business activity but generally fails to use for other purposes
    Dark Data
37. Process of organizing how data is collected, processed and analyzed - used to ensure data quality
    Data Documentation
38. Competency to READ, WRITE and COMMUNICATE data in context including understanding of data sources and constructs, analytical methods and techniques applied and ability to describe data's use and application and its resulting values
    Data Literacy
39. DS train the machine with labeled data or data already tagged with a correct answer(IE recognition of a pattern of :user" input behavior - think bots)
    Supervised Learning