Implement Custom Analytics

At DiLytics, we understand that your analytics requirement is as unique as your business is. So many a times, the product philosophy of one size fits all approach doesn’t work. We realize that in such situations trying to force fit a product warrants so much extension of the out of the box product that it defeats the purpose of ROI.

We evaluate a product’s fit vs. gap and recommend custom analytics solution to suit your unique business needs and address your specific data needs. DiLytics offers the right skills and expertise to cater to your data needs with the help of the right set of tools and modern technology.


Implementing Custom Analytics involves multiple stages, from evaluating the current state to implementing the analytics solution:

  • Current state assessment and build Analytics implementation roadmap: Documenting the findings by reviewing the existing business functions, activities, technical environment and any planned/ongoing implementations.
  • Future state requirement: Identifying the future state requirements based on current state assessment and recommendations, in alignment with the organization’s objectives and strategic goals
  • Gap Analysis: identifying what is missing in terms of people, process and technology required to move from the current state to the desired state
  • Analytics implementation roadmap: Building the implementation roadmap based on the findings and recommendations to implement the analytics solution that helps the organization to progress to the desired state of analytics maturity
  • Identify and analyze the objects: Identifying the source to extract metadata from. Flat files and tables can be imported as data objects to analyze the data structure
  • Determine Facts and Dimensions: Identifying the tables and views that represent facts (numerical values), and those representing dimensions (ways to aggregate these figures)
  • Finalize Star Schemas and Build the Data Warehouse: Developing or building a data warehouse and dimensional data marts. Pinpoint Source System tables and columns for Data Warehouse Objects
  • Design and Build ETL: Extraction of data from multiple source systems, transformation of data to usable resource, and loading it to be used by end-users to solve real business problems
  • Execute UAT: Planning, designing and executing user acceptance testing to confirm adherence to requirements and business objectives and ready to be deployed to Production


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