CRISP-DM stands for cross-industry process for data mining. The CRISP-DM methodology provides a structured approach to planning a data mining project. It is a robust and well-proven methodology
There have been several attempts to make the process of discovering knowledge scientific. And those attempts are called the KDD process, the CRISP-DM process, and the CCC Big Data Pipeline.
By leveraging different computer software to apply statistical and other mathematical principles to data.
Group together things that are “similar” according to some definition of “similar”. Example: Are there groups of customers with similar buying/purchase habits? In marketing, cluster analysis is what is used to divide customers into “segments”. Used to make effective product offerings Pregnancy coupons/ gift cards based on buying habits
Assign a probability that something belongs to 1 of several mutually exclusive classes. Example: Is this credit card transaction fraudulent? (A: probability Yes/No) Will this person donate to my charity? (A: probability Yes/No) Is this person suffering from a heart attack, or some other mimic condition? (A: probability of Attack) K-NN, K nearest Neighbors where examples are categorized based on shared attributes. anomaly detection (also outlier detection) is the identification of rare items, events or observations which raise suspicions by differing significantly from the majority of the data
Predict the most likely value of a continuous variable. Example: what will sales be next quarter? How much will this group of customers spend over the next year? What will be the market share of our new product? Linear Regression Neural Network
Data Analytics: Improving Business
Symptai Consulting Limited
What is Data Analytics?
Data Analytics is the process of extracting meaning from raw data
using computer software. This process;
• organizes; and,
• models data to draw conclusions and identify patterns.
Why is Data Analytics Important?
Data Analysis is the best way for a business to understand their
• It organises , interprets, structures and presents data as useful
• This context is then used to make informed decisions to enhance
productivity and business value.
To improve your approach to data analytics
1. Evaluate the data to identify gaps and use this to improve data
quality over time.
2. Clearly define the objectives and choose appropriate tools.
3. Focus on objectives that have the most impact on business
• Be creative about how you approach data analysis.
• Invest in the people and tools necessary for success.
• Provide value for management from data analytics.
• Where possible eliminate sampling risk.
• Use data analytics to make informed decisions.