Data governance requires control mechanisms and procedures for, but not limited to, assignment and tracking of action items.

Preparation and pre-processing of historical data needed in a predictive model may be performed in nightly batch processes or in near real-time.

The flow of data in a data integration solution does not have to be designed and documented.

The operational data quality management procedures depend on the ability to measure and monitor the applicability of data.

Self-service is a fundamental delivery channel in the BI portfolio.

A goal of reference and master data is to provide authoritative source of reconciled and quality-assessed master and reference data.

Customer relationship management systems manage Master Data about customers.

Data quality issues only emerge at initial stages of the data lifecycle.

Deliverables in the Metadata Management context diagram include:

In gathering requirements for DW/BI projects, begin with the data goals and strategies first.

Dimensions of data quality include:

Data warehouses are often loaded and serviced by a nightly batch window.

Please select the transition phases in Bridges’ Transition process:

Lack of automated monitoring represents serious risks, including compliance risk.

Control activities to manage metadata stores include:

Data modelling tools are software that automate many of the tasks the data modeller performs.

JSON is an open, lightweight standard format for data interchange.

SOR Stands for:

Effective data management involves a set of complex, interrelated processes that enable an organisation to use its data to achieve strategic goals.

CMA is an abbreviation for Capability Maturity Assessment.

Enterprise data architecture usually include the following work streams:

Malware refers to any infectious software created to damage, change or improperly access a computer or network.

Subtype absorption: The subtype entity attributes are included as nullable columns into a table representing the supertype entity

Temporal aspects usually include:

Possible application coupling designs include:

Device security standards include:

Technical Metadata provides data about the technical data, the systems that store data, and the processes that move between systems.

A roadmap for enterprise data architecture describes the architecture’s 3 to 5-year development path. The roadmap should be guided by a data management maturity assessment.

Data profiling also includes cross-column analysis, which can identify overlapping or duplicate columns and expose embedded value dependencies.

Small reference data value sets in the logical data model can be implemented in a physical model in three common ways:

Those responsible for the data-sharing environment have an obligation to downstream data consumers to provide high quality data.

Class operations can be:

Business glossaries have the following objectives:

Veracity refers to how difficult the data is to use or to integrate.

Examples of concepts that can be standardized within the data quality knowledge area include:

Business activity information is one of the types of data that can be modelled.

Controlling data availability requires management of user entitlements and of structures that technically control access based on entitlements.

Data quality rules and standards are a critical form of Metadata. Ti be effective they need to be managed as Metadata. Rules include:

All organizations have the same Master Data Management Drivers and obstacles.

The IBM Data Governance Council model is organized around four key categories. Select the answer that is not a category.

Data warehousing describes the operational extract, cleaning, transformation, control and load processes that maintain the data in a data warehouse.

To mitigate risks, implement a network-based audit appliance, which can address most of the weaknesses associated with the native audit tools. This kind of appliance has the following benefits:

MPP is an abbreviation for Major Parallel Processing.

The minority of operational metadata is generated as data is processed.

Product Master data can only focus on an organization’s internal product and services.

Some common data quality business rule types are:

The first two steps in the data science process are:

Issues caused by data entry processes include:

Improving data quality requires a strategy that accounts for the work that needs to be done and the way people will execute it.

Data can be assessed based on whether it is required by:

Effective data management involves a set of complex, interrelated processes that disable an organization to use its data to achieve strategic goals.

When constructing models and diagrams during formalisation of data architecture there are certain characteristics that minimise distractions and maximize useful information. Characteristics include:

Please select the correct general cost and benefit categories that can be applied consistently within an organization.

A synonym for transformation in ETL is mapping. Mapping is the process of developing the lookup matrix from source to target structures, but not the result of the process.

Validity, as a dimension of data quality, refers to whether data values are consistent with a defined domain of values.

The implementation of a Data Warehouse should follow these guiding principles:

Modeling Bid data is a non-technical challenge but critical if an organization that want to describe and govern its data.

Three data governance operating models types include:

CIF stands for:

Elements that point to differences between warehouses and operational systems include:

A goal of data governance is to enable an organisation to manage its data as a liability.

Field overloading: Unnecessary data duplication is often a result of poor data management.

A limitation of the centralized approach include: Maintenance of a decentralized repository is costly.

Issues caused by data entry processes include:

A control activity in the metadata management environment includes loading statistical analysis.

If the target system has more transformation capability than either the source or the intermediary application system, the order of processes may be switched to ELT – Extract Load Tranform.

An image processing system captures, transforms and manages images of paper and electronic documents.

Managing business party Master Data poses these unique challenges:

Several global regulations have significant implications on data management practices. Examples include: