Predictive analytics common mistakes
What is Predictive Analytics?
Predictive analytics helps to extract information from old data’s set in order to determine the future outcomes, trends and policies.
For Example: In business organization, Predictive analytics help to increases the business operation, performance, productivity among various competitors.
Common mistakes in predictive analytics:
- Poorly Structured Predictive Model:
Using very high-quality data is minor part in predicative analytics. Too many business organization using billions of man power and his hours to collect the old data to determine the future outcomes, but selection of poor predictive model will collapse all the efforts and energy of man powers.
Over Come: To combine customer data from a customer relationship management tool with behavioural data from your model.
Clarify your Objective:
Data Science is not a business objective or a learning objective. Smart people doing creative things to find the value in their data. Predictive analytics is technology that learns from experience to predict the future behaviour of individual in order to make better decision.
Overcome: You create a new kind of value proposition to analyse the data. It will clarify the objective and increases your leadership quality.
- Invalid Predictions:
Built on data with a certain attributes are applied to data that has very different attributes.
For Example: Scoring a model for consumer with income ranges of say $30k to $120k, and income is a critical predictor in my model, it is not a good idea for me to use this model to score customer that perhaps have income greater than $130k or less than $20k.
Overcome: When implementing a model, it is a good idea to ensure that the data attributes for data used are similar in most respects to data used to build the model.
- Specific objective in mind before you start:
For Example: In large company, that engaged research to start working with its data to predict that one executive could go out and sell to his business Units. While the research company start doing work, the company create a model to analysis future trends without knowing the information clearly about the products of the company and this leads to poor analytics to know the objective of the company.
Overcome: First of all, before start the research, analysis the information of the data to achieve the objective clearly.
Seeking Purified Data:
It is not true that companies need good data to use predictive analytics. While doing research, collection of data will be new and it may be purified data to identify the exact future outcomes to increases our business among various competitors. If the data is not good, anyone cannot do the advanced analytics.
Overcome: Good data is useful, but business organization must start with the business decision they want to make, and then look for data that might help them predict outcomes.
- Focusing on Single channel Data Sets:
To analyse the data, focusing on all data sets is important to make correct predictive analytics. Focusing on one data sets, will cause insufficient result and lack of understanding.
Overcome: To treat all data sets as Equal. It is important to examine the credibility of their data sources and cleanliness of their data sets or risk making decision on outdated and error free.
About the Author
DataFactZ is a professional services company that provides consulting and implementation expertise to solve the complex data issues facing many organizations in the modern business environment. As a highly specialized system and data integration company, we are uniquely focused on solving complex data issues in the data warehousing and business intelligence markets.