Our Approach to Analytics
The data tells a story about your business and customers. But data, on its own, is of little value without interpretation. Analytics is the language of data. Analytics finds the insights hiding inside your data and tells the story. We approach every project with fresh perspectives and an open mind.
Advanced Analytic Services
New Model Development
New to analytics? No problem. Most projects start with simple questions, like, “Why do I have customer turnover?” or “How can we make our marketing spend more effect?” You ask the questions and we'll take it from there.
Model Optimization & Retraining
Predictive models should be retained every 12 to 18 months as your business and customers’ needs change. Additionally, we will also optimize them for performance.
Code Migration
We convert and optimize model code from one language to another ensuring model integrity. For example, we can convert R to Python or SAS to to Python.
Optimize for Accuracy & Efficiency
We take a practical approach to predictive model development by selecting techniques that are best suited to deliver the most accurate results with the greatest efficiency.
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Selecting the wrong technique can blow up your analytics cloud environment and leave you with a massive AWS or GCP bill with no additional insights. Remember, predicting the future is both an art and science.
Machine Learning Lifecycle
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Understand the meaning of critical data elements, and the rules applied to generate data elements.
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Develop modeling strategy including determining observation and performance periods, population sampling, target selection, and data extraction methods.
Modeling Approaches
We use various machine learning algorithms and fit the best algorithm(s) to the project. Both supervised and unsupervised learning will be explored to create more efficient and accurate solutions. Then we will select the best approach. Here are some of the ML approaches we will evaluate:
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Variable Clustering
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K-mean clustering
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Dynamic Ranking
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Principal Components
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Factor Analysis
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Linear Regression
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Logistic Regression
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Decision Trees
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Random Forests
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Nonlinear Regression
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Deviation Detection
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Outlier Detection
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Support Vector Machines
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Neural Networks
Modeling Languages
The models can be written in a language of preference, SAS, R or Python languages based on the client’s preference. The selection of the best language is generally based on the internal technology, preferences, and use case.
Python
Python is an open-source object-oriented programming language. It has proven to be popular among data analysts and software developers. Python is suggested because it supports a variety of programming techniques.
R
R is an open-source platform. It is mainly used in the academics, data science and research settings. As it is open-source, it is highly extensible and there are quick releases of the software with the latest techniques.
SAS
SAS is market leader in statistical data analysis software. The program is for complex jobs in large enterprises. It has a wide range of statistical features.
* SAS is a registered trademark of the SAS Corporation.
** Python is a registered trademark of the Python Software Foundation.
