The awesome presentation of all time. Find out about: Cross Industry Standard Process for Data Mining Knowledge Management Presentation. MBA semester 2

1. Knowledge
Management
TANIYA SRIVASTAVA (A64)
PRN: 15020441287

2. Company: Teradata (Pune)
 Address: Tower 12, Level 5, Cyber City, Magarpatta Inner Circle,
Magarpatta City, Hadapsar, Pune, Maharashtra 411028
 Teradata Corporation (NYSE: TDC) is the world's largest company focused
on raising intelligence through data warehousing and enterprise analytics.
 It is the global leader in data warehousing and enterprise analytics.
 Teradata Professional Services enables Teradata customers to use their
enterprise data warehouse for decision making and to support business
operations providing active enterprise intelligence to frontline workers
throughout the enterprise.

3. Cross Industry Standard Process for Data
Mining (CRISP-DM)
 A data mining process model that describes commonly used approaches
that data mining experts use to tackle problems.
 Was the leading methodology used by industry data miners
 Was called the "de facto standard for developing data mining and
knowledge discovery projects.” by miners in a recent survey
 CRISP-DM breaks the process of data mining into six major phases

4.  Phrase One: Business Understanding
This initial phase focuses on understanding the project objectives and requirements from a
business perspective, and then converting this knowledge into a data mining problem
definition, and a preliminary plan designed to achieve the objectives.
 Phrase Two: Data Understanding
The data understanding phase starts with an initial data collection and proceeds with
activities in order to get familiar with the data, to identify data quality problems, to discover
first insights into the data, or to detect interesting subsets to form hypotheses for hidden
information.
 Phrase Three: Data Preparation
The data preparation phase covers all activities to construct the final dataset (data that will
be fed into the modeling tool(s)) from the initial raw data.

5.  Phrase Four: Modeling
In this phase, various modeling techniques are selected and applied, and their parameters
are calibrated to optimal values. Typically, there are several techniques for the same data
mining problem type.
 Phrase Five: Evaluation
At this stage in the project, a model (or models) is built that appears to have high quality,
from a data analysis perspective.
 Phrase Six: Deployment
The deployment phase can be as simple as generating a report or as complex as
implementing a repeatable data scoring (e.g. segment allocation) or data mining process

6.  Teradata has adopted the Data Mining
procedure and is using this to it’s full capacity.
 The company has been using this technique
since a very long time and through this, it gives
it’s customers the best possible results/reports.