Data Intelligence

All smart businesses today require data analytics and business intelligence (BI), which we call data intelligence, for its survival and future growth. According to Forrester, “Business Intelligence is a set of methodologies, processes, platforms, applications, architectures, and technologies that transform raw data into meaningful and useful information used to enable more effective strategic, tactical, and operational insights and decision-making”. For example, through access to BI applications, a bank’s branch manager can determine who are the most profitable customers and which customers they should work on. Deriving such intelligence requires a number of steps to be followed as described below.

Our designers are excellent at showcasing any company on the Web. Our software engineers are very good at developing custom business applications for SMEs and large enterprises. They are highly skilled when it comes to scalability and security. We have a defined process to develop quality and reliable software.

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ETL stands for Extract-Transform-Load. The first step to get data intelligence is to extract data from one or more online/offline sources and then transform into common data formats. The transformation phase includes cleansing, reformatting, deduplication and blending of data that can be Loaded into the target database, data store or a data warehouse for further data analysis.

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Exploratory Data Analysis is the critical step to perform initial investigation to retrieve the underlying meaning of the data. This is done for discovering patterns, spotting anomalies, testing hypotheses and checking assumptions by pattern analysis, statistical testings and visual representations through charts or graphs etc. After thoughtful study and analysis features are extracted and engineered from the dataset by our expert team members.

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Model Training and Validation

This phase requires the right machine learning model technique selection and application, model training, model hyperparameter setting and adjustment, model validation, ensemble model development and testing, algorithm selection, and model optimization. Next, the trained models are tested and evaluated as to what extent the models can get predictions/outcomes related to the business interest. The prediction accuracy mostly depends on the right model selection and training; and our engineers are excellent at it.

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Deployment and Data Visualization

Finally the model is deployed to see how it works in the real world. This is the time to turn the predictions and insights from the fine tuned model into actions. The deployment can be in a cloud environment, in an on-premises or closed environment or within a closed, controlled group. Our engineers can deploy the system depending on the requirement. After that we continuously monitor for different aspects of the model to improve overall performance.

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