What are Critical Success Factors?
Key areas of activity in which favorable results are
necessary for a company to obtain its goal.
There are four basic types of CSFs which are:
Industry CSFs
Strategy CSFs
Environmental CSFs
Temporal CSFs
ETL Testing
Key areas of activity in which favorable results are
necessary for a company to obtain its goal.
There are four basic types of CSFs which are:
Industry CSFs
Strategy CSFs
Environmental CSFs
Temporal CSFs
Data cubes are commonly used for easy interpretation
of data. It is used to represent data along with
dimensions as some measures of business needs. Each dimension of the cube represents some attribute of the database. E.g profit per day, month or year.
Data cleaning is also known as data scrubbing.
Data cleaning is a process which ensures the set of data
is correct and accurate. Data accuracy and consistency,
data integration is checked during data cleaning. Data cleaning can be applied for a set of records or multiple sets of data which need to be merged.
An extension of data mining can be used for slicing the
data the source cube in discovered data mining.
The case table is dimensioned at the time of mining a
cube.
A stage of data mining is a logical process for searching
large amount information for finding important data.
Stage 1: Exploration:One will want to explore and prepare data. The goal of the exploration stage is to find important variables and determine their nature.
Stage 2: pattern identification: Searching for patterns and choosing the one which allows making best prediction,
is the primary action in this stage.
Stage 3: Deployment stage. Until consistent pattern is
found in stage 2, which is highly predictive, this stage
can not be reached. The pattern found in stage 2, can be applied for the purpose to see whether the desired
outcome is achieved or not.
Data mining can be used in a variety of fields/industries
like marketing of products and services,
AI, government intelligence.
The US FBI uses data mining for screening security and intelligence for identifying illegal and incriminating e-information distributed over internet.
Deleting data from data warehouse is known as data
purging. Usually junk data like rows with null values or spaces are cleaned up.
Data purging is the process of cleaning this kind of junk values.
what is BUS schema?
A BUS schema is to identify the common dimensions
across business processes, like identifying conforming dimensions. It has conformed dimension and
standardized definition of facts
Non additive facts are facts that cannot be summed up
for any dimensions present in fact table. These columns
cannot be added for producing any
results.
Conformed fact in a warehouse allows itself to have
same name in separate tables. They can be compared
and combined mathematically. Conformed dimensions
can be used across multiple data marts. They have a
static structure. Any dimension table
that is used by multiple fact tables can be conformed dimensions.
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