A guide to understand all about predictive analytics & data mining focusing the best practices in Data mining and Predictive analytics.

1. Published on : Feb 7, 2012
Hexaware E-book
on Predictive Analytics
Business Intelligence & Analytics
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2. Hexaware E-book on Can you name some specific uses of data mining?
Some Specific uses of data mining include:
Predictive Analytics Market segmentation - Identify the common characteristics of customers
who buy the same products from your company.
What is Data mining?
Customer churn - Predict which customers are likely to leave your
Data mining, or knowledge discovery, is the computer-assisted process of
company and go to a competitor.
finding hidden patterns in data. Data mining tools predict behaviors and
future trends, allowing businesses to make proactive, knowledge-driven Fraud detection - Identify which transactions are most likely to be
decisions. Hence it is also called predictive analytics. fraudulent.
Data mining tools can answer business questions that traditionally were Direct marketing - Identify which prospects should be included in a
time consuming to resolve. They scour databases for hidden patterns, mailing list to obtain the highest response rate.
finding predictive information that experts may miss because it lies outside Interactive marketing - Predict what each individual accessing a Web site
their expectations. is most likely interested in seeing.
When and where the data mining and predictive analytics could be Market basket analysis - Understand what products or services are
useful? commonly purchased together; e.g., beer and diapers.
The amount of raw data stored in corporate databases is exploding. From Trend analysis - Reveal the difference between typical customers this
trillions of point-of-sale transactions and credit card purchases to month and last.
pixel-by-pixel images of galaxies, databases are now measured in
gigabytes and terabytes. Raw data by itself, however, does not provide Will sharing data to do data mining raise privacy related issues?
much information. What needs to be done in such scenarios?
In today's fiercely competitive business environment, companies need to There is a way to deal with sensitive data like credit card numbers,
rapidly turn these terabytes of raw data into significant insights into their
insurance policy numbers and account numbers. Data need to be masked
customers and markets to guide their marketing, investment, and
or recoded to maintain the privacy.
management strategies.
In these scenarios data mining would help you unlock the hidden potential
of your data and deliver actionable insights.
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3. What are all steps involved in data mining? Name some tools used for Data mining?
Data mining process involves predefined steps starting from a. Microsoft BI stack has SSAS as part of SQL Server 2008
• Business case understanding or Problem understanding, b. There is an open source tool called R which offers data mining
• Data understanding solution
• Data extraction c. Rapid Miner is an another open source tool
• Pre-processing d. SAS is of course is a highly sophisticated tool with enormous
computational power
• Mining model building
e. IBM has it’s tool called SPSS
• Testing and Evaluation
f. Oracle’s ODM – Oracle Data Miner
Is there any maintenance to be done for the mining model once it is The above list is not exhaustive.
deployed?
What are all the industries in which Predictive Analytics is
Yes, the mining model needs to be calibrated at least once in six months. applicable?
The frequency varies based on the business need and the volume of data
We have recently provided predictive analytics solutions for following
flow
Industries:
Calibration involves
• Insurance
• Checking the predicted output with the actual output
• Education
• Modifying the mining model if required Public
• Mining
Can data mining solution be offered on the cloud? • Logistics
Yes, Data mining solution can be offered on the cloud. • Health & Hygiene
Organizations can adopt “Pay per use” method without investing on the In short, predictive analytics can be deployed for diverse industries.
infrastructure required. Here the challenge is to upload huge amount of
data on the cloud.
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4. What are all the risks involved in using predictive analytics solution? Can you list some of the best practices in Data mining and
• Wrong understanding of business problems and data will result in a Predictive analytics?
prediction model with complex statistical algorithms, but it will be of no • Executive Support: Support from the decision makers and middle
use to the business. management would make a world of difference.
• Wrong interpretation of the results would lead to wrong decisions. • Business problem specificity: Identification of correct business problem
• Poor data quality would result in poor predictions. to apply predictive analytics is vital to the success of the mining model.
• Absence of maintenance of the mining model would make predictions • Availability of historical data: Richer the data, the more robust will be
obsolete. the mining model.
• Building the predictive analytics solution with resources with less • Good quality data: It is the most important factor for an accurate mining
statistical knowledge will lead to less accurate models. model.
• Pre-processing: While building the mining model, one of the main
What is Text mining?
activities is pre-processing where the data is cleansed, sliced, diced
Text Mining is the process of deriving high quality information from
and categorized to suit to the mining model. Good business
unstructured text data. There are various techniques used to derive high
knowledge and a sound data mining knowledge is required to do this
quality information from textual data, such as computational linguistics,
as this is the base for the predictive model.
information retrieval, statistics, machine learning, etc.
• Selection of statistical techniques: Experienced data mining resource
Various forms of text mining include categorization, classification,
clustering, concept extraction, summarization, sentiment analysis, etc. can choose the correct statistical technique and can compare the
accuracy of other techniques.
Are the open source tools sufficient and robust in providing • Interpretation of output: It is extremely important to interpret the output
answers to tough business cases?
in the correct way and link it back to the business problem stated
Open source tools like R and Rapid miner provide excellent flexibility to initially.
build the model. Online R community constantly updates algorithms and
industry specific solutions as packages to R after validating. So far there
are around 3500 packages built in R.
R’s popularity has been increasing over the other predictive analytics tools.
In a recent survey Kdnuggets.com reports that R has 24% of market share
and R is the most sought after statistical programming language.
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