Advanced Analytics is solving business puzzles using data and statistics.

The results can be communicated via visual reports or as a data product that automates the entire process of delivering the result on dashboards.

  • What are the salient traits of a highly efficient employee?
  • What drives your revenue the most?
  • What devices can fail, and when?
  • What drugs and dosages are the most impactful for a particular patient and disease?

Get unbiased answers based on numbers, not opinions.

The prime version of analytics is an iterative process that begins with identifying business needs and pains.

The data preparation and coding are done by hand every cycle. 

Although this statement may sound excessive, the analysis goals are often so diverse that there is no other alternative except to start from scratch.

DEFINE THE BUSINESS NEED COLLECT THE DATA PREPARE THE DATA VISUALIZE THE FINDINGS ANALYZE THE DATA DELIVER THE OUTCOMES DEFINE THE BUSINESS NEED COLLECTTHE DATA PREPARE THE DATA VISUALIZE THE FINDINGS ANALYZE THE DATA DELIVER THE OUTCOMES

Define the business need

This stage involves understanding the business goals and objectives or the problem to be solved.
It identifies business stakeholders, what data is available and what is the deliverable from the analysis.
Every new iteration starts from this step.

Collect the data

The data is required to start the exploration and analysis phases.
At this point, engineers collect appropriate information from databases, flat files, live measurements from physical devices, scraped web data, or any of the myriad of data services.

Prepare the data

This stage includes a thorough study of the available data. Exploration consists of investigating features, missing values, distributions, etc. Cleaning, making imputations for missing data, refining, and normalizing data steps are performed as necessary. Data preparation involves transforming data to create new metrics or variables.

Deliver the outcomes

This stage includes a thorough study of the available data. Exploration consists of investigating features, missing values, distributions, etc. Cleaning, making imputations for missing data, refining, and normalizing data steps are performed as necessary. Data preparation involves transforming data to create new metrics or variables.

Analyze the data

This stage includes a thorough study of the available data. Exploration consists of investigating features, missing values, distributions, etc. Cleaning, making imputations for missing data, refining, and normalizing data steps are performed as necessary. Data preparation involves transforming data to create new metrics or variables.

Find the solution

The goal of this step is to put the insights derived from the analysis into action. The optimal action is selected from the list of choices aligned with estimated outcomes.

Estimate the impact 

This step emphasizes techniques to uncover insights and relationships that can help predict an outcome.
Then, the solutions are aligned with estimated consequences for communicating later.

Visualize the findings

Here the findings suggested solutions, and the reasoning behind them are visualized as plots, histograms, and other visuals for the ease of understanding.