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A goal of Data warehouse and business intelligence is to support and enable ineffective business analysis and decision making by knowledge workers.
The acronym CMDB stands for:
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.
Principles for data asset accounting include:
Use business rules to support Data Integration and Interoperability at various points, to:
Issues caused by data entry processes include:
All organizations have the same Master Data Management Drivers and obstacles.
An organization will create an uncover valuable Metadata during the process of developing Data Integration and Interoperability solutions.
Data models are critical to effective management of data. They:
Wat data architecture designs represent should be clearly documented. Examples include:
Primary deliverables of the Data Warehouse and Business Intelligence context diagram include:
Data governance requires control mechanisms and procedures for, but not limited to, assignment and tracking of action items.
A sandbox is an alternate environment that allows write-only connections to production data and can be managed by the administrator.
ECM is an abbreviation for:
Data parsing is the process of analysing data using pre-determined rules to define its content or value.
Orchestration is the term used to describe how multiple processes are organized and executed in a system.
Through similarity analysis, slight variation in data can be recognized and data values can be consolidated. Two basic approaches, which can be used together, are:
Examples of business processes when constructing data flow diagrams include:
Inputs in the data storage and operations context diagram include:
The best preventative action to prevent poor quality data from entering an organisation include:
Data and text mining use a range of techniques, including:
Data asset valuation is the process of understanding and calculating the economic value of data to an organisation. Value comes when the economic benefit of using data outweighs the costs of acquiring and storing it, as
There are numerous methods of implementing databases on the cloud. The most common are:
Logical abstraction entities become separate objects in the physical database design using one of two methods.
Business Intelligence, among other things, refer to the technology that supports this kind of analysis.
ANSI 859 recommends taking into account the following criteria when determining which control level applies to a data asset:
Possible application coupling designs include:
Please select the three types of data models:
Business activity information is one of the types of data that can be modelled.
The goals of Metadata management include:
What ISO standard defines characteristics that can be tested by any organisation in the data supply chain to objectively determine conformance of the data to this ISO standard.
Typically, DW/BI have three concurrent development tracks:
The information governance maturity model describes the characteristics of the information governance and recordkeeping environment at five levels of maturity for each of the eight GARP principles. Please select the correct level descriptions:
The impact of the changes from new volatile data must be isolated from the bulk of the historical, non-volatile DW data. There are three main approaches, including:
A change management program supporting formal data governance should focus communication on:
Obtaining buy-in from all stakeholders
Business requirements is an input in the Data Warehouse and Business Intelligence context diagram.
The operational data quality management procedures depend on the ability to measure and monitor the applicability of data.
Risk classifications describe the sensitivity of the data and the likelihood that it might be sought after for malicious purposes.
The most informal enterprise data model is the most detailed data architecture design document.
In the Abate Information Triangle the past moves through the following echelons befor it comes insight:
Enterprise data architecture project-related activities include:
Please select the answers that correctly describes where the costs of poor quality data comes from.
Please select correct term for the following sentence: Any collection of stored data regardless of structure or content. Some large databases refer to instances and schema.
The data warehouse and marts differ from that in applications as the data is organized by subject rather than function.
Instant Messaging (IM) allows a user to message each other in real-time.
Please select the 2 frameworks that show high-level relationships that influence how an organization manages data.
Control activities to manage metadata stores include:
When selecting a DMM framework one should consider of it is repeatable.
There are several methods for masking data:
Data governance requires control mechanisms and procedures for, but not limited to, facilitating subjective discussions where managers’ viewpoints are heard.
Device security standard include:
Barriers to effective management of data quality include:
Disciplines within the enterprise architecture practice does not include:
Data governance requires control mechanisms and procedures for, but not limited to, escalating issues to higher level of authority.
Changes to reference data do not need to be management, only metadata should be managed.
Two risks with the Matching process are:
A data governance strategy defines the scope and approach to governance efforts. Deliverables include:
SOA stands for:
In Resource Description Framework (RDF) terminology, a triple store is composed of a subject that denotes a resource, the predicate that expresses a relationship between the subject and the object, and the object itself.
In the Data Warehousing and Business Intelligence Context Diagram, a primary deliverable is the DW and BI Architecture.
For each subject area logical model: Decrease detail by adding attributes and less-significant entities and relationships.
Different storage volumes include:
Inputs in the data modelling and design context diagram include:
Several global regulations have significant implications on data management practices. Examples include:
Machine learning explores the construction and study of learning algorithms.
Reference and master data require governance processes, including:
Oversight for the DMMA process belongs to the Data Quality team.
Data professionals involved in Business Intelligence, analytics and Data Science are often responsible for data that describes: who people are; what people do; where people live; and how people are treated. The data can be misused and counteract the principles underlying data ethics.
Layers of data governance are often part of the solution. This means determining where accountability should reside for stewardship activities and who the owners of the data are.
Data warehousing describes the operational extract, cleaning, transformation, control and load processes that maintain the data in a data warehouse.
The four main types of NoSQL databases are:
Value is the difference between the cost of a thing and the benefit derived from that thing.