7 Predictive Analysis Tips for Hadoop

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7 Predictive Analysis Tips for Hadoop

7 Predictive Analysis Tips for HadoopIntroduction to predictive analysis
It’s hard to find a good analysis tool, in today’s technical era that fits and suits our business requirements. Predictive analysis is a technique that highly correlates with big data and hadoop. In this article we will discuss about the predictive model used for big data and some useful tips for any organization to lead to success. Here we will discuss 7 useful tips for predictive analysis using hadoop framework.

Predictive analysis is highly recommended technique used to extract the information out of data sets (large amount of data, used for research and development purpose) to determine patterns and future predictions for some conclusions and trends.

It has been never said that predictive analytics will tell what might happen in future; the fact is that it can forecast a set of possibilities under an acceptable level of reliability and robustness.

Predictive models
These models are built in order to analyze the current data records in combination with some historical data and facts for making a better understanding using a CPP model (Customer, Product, and Partner) for identifying potential risks and growing opportunities (Just like what E commerce companies are doing now a days).

Techniques and tools used for Predictive Models
Some of the popular techniques that are used with predictive analysis are machine learning, Data mining (A traditional approach for knowledge extraction using predicates) and statistical modeling helping analyst and data scientist to make better future forecasting.
Techniques and tools used for Predictive Models

Predictive model Markup language
It is a standard (just like UML (Unified Modeling Language)) developed by DMG (Data mining group) that is a parameter to depict a predictive analysis model. PMML is now days, widely accepted by IT business leaders like IBM, Oracle and SAP. The advantage of using PMML is that it enables predictive analytics model to switch amongst different models and applications, making it portable to move from one application to another and testing against various data sets.

Predictive analytics with big data
Business organizations collect huge amount of data and uses historical data taking customer’s insight in consideration and provides with a predicted future events. It also enables the organizations to allow the use of big data, including real time and stored data (in HDFS) to move from a historical view to a futuristic view of a consumer of a particular product.
Software vendors taking advantage of predictive analytics
Many of the software giants are using predictive analytics techniques and have also developed valuable software’s for analysis.

• SAS has developed Predictive analytics suite.
• IBM developed SPSS Statistics.
• Microsoft developed Microsoft Dynamics CRM Analytics Foundation.

Some vendors also make proprietary solutions for making an open source solution based technology. Predictive analysis based software are deployed in various premises for enterprise users whether in cloud based environment or just for project based team initiatives.

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Predictive Analysis: Useful tips, Challenges and solutions

1. Making predictive analytics as a useful asset
Predictive analysis model is not a distinctive model and is a biggest asset for many organizations. We should always ensure that any IP (Intellectual property) should play an active role to make ensure the safety and security of Critical data.
This enables the users of the data to ensure various roles and visibility level of who can access what and other user and critical files permission. This asset is pretty useful in environment where peoples from different domains have collaborated for creation and management of data.

2. Beware of any competitive advantage
This is a very important to carefully select and implement the predictive model so as to keep the model up to date and being two steps ahead with other market competitors. Predictive models do change based on various factors such as changing of the customer’s base data, disruptive technology variations and competitors behavior. There comes a time when you need to change the version or need to retire the software itself.

3. Analytics concern with open source software
The hadoop ecosystem itself has various pros and cons regarding the open source options for predictive analysis. While some of the statistical programming languages like R programming will offer great depth and insight for various open source analytic programs. Some open source systems like apache mahout (used for machine learning), apache spark (used for in memory cluster computing for predictive analysis) are specifically designed, keeping hadoop ecosystem in mind. This might change in future and we need to develop the systems that has a quick adaptability factor while keep scalability in mind.

4. Security concerns for large scale system and Predictive modeling
The biggest disadvantage of hadoop ecosystem is the lack of being secure and safe and doesn’t incorporate with the features such as authentication and authorization. The focus on data predictability and data management needs to have a secure framework for data modeling.

5. Exploiting challenges
Transformation of the data from being suited in a batch processing mode to a real time predictive analysis mode sometimes doesn’t works for the ad hoc queries. Some companies that develops the predictive tools includes and bridging the gap and filling these loopholes by developing user friendly NoSQL (Not only SQL) databases.

6. Making a suitable workflow environment
Workflow management is a key point for developing the project and for the of ease management, many open source technologies are available like apache oozie scheduler (used for managing map reduce jobs and other processing). To effectively manage the workflows for predictive analysis we need some graphical management tools for better data visualization.

7. Stratifying Risk in terms of Predictive modeling
Risk stratifying is useful in managing the workflow of data for reducing system waste (optimization and management of data visualization) and developing financially efficient. Risk management is also based on predictive risk score ranking.

Conclusion
In this article we have discussed about some useful tips for management of predictive analysis tool and techniques, taking some preventive measures we can easily manage the data and also efficiently implement predictive data models with keeping the risk measures in mind for working properly.

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