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. 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
2. Process of obtaining , importing, and processing data for later use/Tools that retrieve data from data sources
   Data Integration and Extraction
3. Storage architecture designed to hold data extracted from transaction systems, operational data stores and external sources
   Data Warehouse
4. Responsiblle for executing analytics across the enterprise without being formally trained, supported by technology like smart data discovery tools
   Citizen Data Scientist
5. Process of organizing how data is collected, processed and analyzed - used to ensure data quality
   Data Documentation
6. 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
7. 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
8. Aggregated or system level data (IE demographic Data on people or customers)
   Macro Data
9. 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
10. Responsible for ensuring the data assets serve their intended purpose, comply to policies and standards and communicate data value
    Data Owner
11. Includes: Usability, precision, Timeliness, Accuracy, no duplication, real-time, validity, consistency, completeness
    Data Hygiene
12. Allows finance to transform data, create visualizations and output analytics
    Analytics and BI Architecture
13. Lack of clarity surrounding the origins, meaning or uses of different data sets
    Data Mistrust
14. Provides technical support and system analysis to FPA
    Business System Analysts
15. 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
16. Traces the source of the data and the applications through which it has passed
    Data Lineage
17. Company that created ML pilot in AR without Data Scientists or IT support that decreased time to settlement by 40%
    Iron Mountain
18. Shows frequency and timing of data updates
    Update Schedule
19. Shows the processes and controls applied to data
    Data Flow
20. Offers self-service analytics, data oreparation and data discovery tools
    Analytics workbench
21. 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
22. Responsible for building predictive and prescriptive models for future scenarios and recommending best course of action
    Data Scientist
23. Conceptual framework or set of processes that connect data sources and users
    Data Hub
24. Responsible for deriving business insights from data
    Data Analyst
25. Downstream processing locations some traditional BI platforms concurrent to a DataWarehousse
    OLAP cubes
26. 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)
27. 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
28. Creates transparency into multiple potential data formats and is used to foster collaboration
    Cross functional data management
29. Manages Yelp DW and tableau experts
    BIDI
30. Stores operational and business definitiions for critical data elements
    Data Glossary
31. Specialized Repository organized for a single category of analysis.
    Data Mart
32. Data Curator
33. Classification of data according to category and subcategory comprising a standardized set of definitions and metrics used throughout the enterprise
    Data Taxonomy
34. 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
35. Responsible for developing BI Reports, dashboards and analyses published for others to consume
    BI Developer
36. 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
37. Repository that pools data unmodified from its original form or source for exploratory analysis
    Data Lake
38. Two functional processes finance is responsible for providing a basis for Information Security
    Data Documentation & Data Policies/Standards
39. EU law on data protection and privacy in the European Union (EU) and the European Economic Area (EEA)
    GDPR