Units 1 - 9

The True or False section of Sir Dave's exam

Data is a valuable asset for identifying trends, making decisions, and taking action before competitors. The new position of data in the value chain is leading organizations to actively seek better ways to derive value from this new capital.

True

Data management is the development, execution, and supervision of plans, policies, programs, and practices that deliver, control, protect, and demote the value of data and information assets throughout their life-cycles.

False

Data management systems as we know them today weren’t common until the 1970s. These data management systems were strictly un-operational. They provided records (reports) of business operations at a given point in time, pulled from a relational database that stored information in rows and columns (typically a data warehouse).

False

A data science environment automates as much of the data transformation work as possible, streamlining the creation and evaluation of data models. A set of tools that eliminates the need for the manual transformation of data can expedite the hypothesizing and testing of new models.

True

Measurement is a program that gets different areas (both inside and outside the business) to work together through Data sharing and Electronic Data Interchange.

False

Analytics is a program that builds quantitative processes for a business to arrive at optimal decisions, however, it does not perform Business Knowledge Discovery.

False

Event mining is a process management technique that allows for the analysis of business processes based on event logs. The basic idea is to extract knowledge from event logs recorded by an information system.

False

Predictive analytics relies on capturing relationships between explanatory variables and the predicted variables from past occurrences, and exploiting it to predict future outcomes.

True

Event mining is a process management technique that allows for the analysis of business processes based on event logs. The basic idea is to extract knowledge from event logs recorded by an information system.

False

Data management is the development, execution, and supervision of plans, policies, programs, and practices that deliver, control, protect, and demote the value of data and information assets throughout their life-cycles.

False

Data is a valuable asset for identifying trends, making decisions, and taking action before competitors. The new position of data in the value chain is leading organizations to actively seek better ways to derive value from this new capital.

True

Pearson captures how linearly dependent two variables are whereas Spearman captures the monotonic behavior of the relation between the variables.

True

Discovery of data is an iterative process, so it does not require a complete model be constructed at the start of analysis. 

True

Techniques for pattern discovery consist in the ability to detect patterns in noisy and missing data – which makes them essentially probabilistic – and to detect low order patterns based on the interrelationship between data features.

False

The growing use of big data systems in organizations can also complicate the MDM process by adding new forms of unstructured and semi-structured data stored in a variety of platforms.

True

The DW today includes many heterogeneous data sources.

True

Master data doesn't include transactions processed in the various data domains.

True

In addition to a master data storage repository and software to automate the interactions with source systems, a master data management framework typically includes change management, and workflow but not with collaboration tools.

False

The potential benefits of master data management decrease as the number and diversity of systems and applications in an organization expand.

False

A coexistence approach, in which sets of master data are pulled from various source systems and consolidated in the MDM hub.

False