A goal of data architecture is to identify data storage and processing requirements.
Deliverables in the document and content management context diagram include:
Improving data quality requires a strategy that accounts for the work that needs to be done and the way people will execute it.
SOA stand for Service Orchestrated Architecture
Data Warehouse describes the operational extract, cleansing, transformation, control and load processes that maintain the data in a data warehouse.
A data dictionary is necessary to support the use of a DW.
Data governance and IT governance are the same thing.
SOA stands for:
Databases are categorized in three general ways:
Value is the difference between the cost of a thing and the benefit derived from that thing.
Domains can be identified in different ways including: data type; data format; list; range; and rule-based.
The term data quality refers to only the characteristics associated with high quality data.
Examples of transformation in the ETL process onclude:
Change only requires change agents in special circumstances, especially when there is little to no adoption.
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.
While the focus of data quality improvement efforts is often on the prevention of errors, data quality can also be improved through some forms of data processing.
RACI is an acronym that is made up of the following terms.
Enterprise Architecture domains include:
Data integrity is the state of being partitioned – protected from being whole.
One common KPI of e-discovery is cost reduction.
Within the Data Handling Ethics Context Diagram a key deliverable is the Ethical Data Handling Strategy.
The load step of the ETL is physically storing or presenting the results of the transformation into the source system.
Data management professionals who understand formal change management will be more successful in bringing about changes that will help their organizations get more value from their data. To do so, it is important to understand:
Release management is critical to batch development processes that grows new capabilities.
Data Integration and Interoperability is dependent on these other areas of data management:
In the context of big data the Three V’s refer to: Volume, Velocity and Validity
The goals of data security practices is to protect information assets in alignment with privacy and confidentiality regulations, contractual agreements and business requirements. These requirements come from:
Resource Description Framework (RDF), a common framework used to describe information about any Web resource, is a standard model for data interchange in the Web.
Data handling ethics are concerned with how to procure, store, manage, use and disposeof data in ways that are aligned with ethical principles.
The ethics of data handling are complex, but is centred on several core concepts. Please select the correct answers.
With reliable Metadata an organization does not know what data it has, what the data represents and how it moves through the systems, who has access to it, or what it means for the data to be of high quality.
Typically, DW/BI projects have three concurrent development tracks, including:
Many people assume that most data quality issues are caused by data entry errors. A more sophisticated understanding recognizes that gaps in or execution of business and technical processes cause many more problems that mis-keying.
A data governance strategy defines the scope and approach to governance efforts. Deliverables include:
Drivers for data governance most often focus on reducing risk or improving processes. Please select the elements that relate to the reduction in risk:
Some document management systems have a module that may support different types of workflows such as:
Principles for data asset accounting include:
Controlling data availability requires management of user entitlements and of structures that technically control access based on entitlements.
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:
In a SQL injection attack, a perpetrator inserts authorized database statements into a vulnerable SQL data channel, such as a stored procedure.
A completely distributed architecture maintains a single access point. The metadata retrieval engine responds to user requests by retrieving data from source systems in real time.
Data security internal audits ensure data security and regulatory compliance policies are followed should be conducted regularly and consistently.
The first two steps of the Reference data Change request process, as prescribed DMBOk2, include:
One of the deliverables in the Data Integration and Interoperability context diagram is:
ECM is an abbreviation for:
Data replication can be active or passive.
Project that use personal data should have a disciplined approach to the use of that data. They should account for:
Access to data for Multidimensional databases use a variant of SQL called MDX or Multidimensional expression.
Data can be assessed based on whether it is required by:
The process of building architectural activities into projects also differ between methodologies. They include:
A content strategy should end with an inventory of current state and a gap assessment.
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:
There are three recovery types that provide guidelines for how quickly recovery takes place and what it focuses on.
Type of Reference Data Changes include:
Data and enterprise architecture deal with complexity from two viewpoints:
Elements that point to differences between warehouses and operational systems include:
The Belmont principles that may be adapted for Information Management disciplines, include:
Disciplines within the enterprise architecture practice does not include:
Deliverables in the data management maturity assessment context diagram include:
Data governance requires control mechanisms and procedures for, but not limited to, identifying, capturing, logging and updating actions.
Big data primarily refers specifically to the volume of the data.
What are the primary drivers of data security activities?
BI tool types include:
The language used in file-based solutions is called MapReduce. This language has three main steps:
Tools required to manage and communicate changes in data governance programs include
Effective data management involves a set of complex, interrelated processes that disable an organization to use its data to achieve strategic goals.
Examples of technical metadata include:
Content management includes the systems for organizing information resources so that they can specially be stored.
Inputs in the data modelling and design context diagram include:
Reference and Master Data Management follow these guiding principles:
Triplestores can be classified into these categories:
Lack of automated monitoring represents serious risks, including compliance risk.