MindTree has developed a range of frameworks than can be used extensively in design, architecture, and development of DW/BI solutions.

Business-Driven Requirement Gathering Framework

This framework integrates role-based functional requirements with non-functional aspects of those requirements, like performance, security, frequency, latency, interface, and history. Oriented towards collecting current and future requirements, MindTree ensures a sustainable and scalable solution.

Corporate Data Model Framework

This framework helps scale a data warehouse gracefully when business environments change, and warehouse requirements are revisited. One of the core areas addressed within MindTree’s framework is conformance of dimensions across subject areas, often viewed as a major challenge in data warehousing.

Robust Metadata Framework

MindTree’s emphasis on metadata as part of the methodology ensures sufficient information is captured in a structured manner as a part of the development process.

Generic Modelling Constructs

MindTree’s repository of ‘Design patterns for data warehousing’ best practices provides a literary format for capturing the wisdom and experience of expert designers, and communicating this to novices. The generic design patterns adopted are made up of modelling approaches (universally accepted as best practice models, re-utilized by MindTree without reinventing the wheel) and approaches based on distilled knowledge generated in earlier projects. Examples of generic constructs include the party-role transaction construct, contract construct, and recursive relationship construct. These constructs provide much needed flexibility, ensuring that solutions are nimble when business environments change.

Generic Data Architecture Principles

MindTree’s generic data architecture principles can become guiding values for designers and developers in development projects. Examples include guidance in terms of when to use procedural language with ETL and when to use canned reports versus OLAP delivery versus dashboards. Data architecture principles can also help in circumstances where redundant structures like ‘materialized views’ need to be created.

Information Delivery Architecture

A foundational requirement in any DW/BI implementation is a strong information delivery mechanism. This needs to provide role-based access to content in the integrated data warehouse.

Alternate modes of information delivery like, Static Web-based ‘bread and butter’ reports for operational roles, Dashboard/scorecard reporting for senior managers, Exploratory OLAP reporting for analysts, Proactive rule-based alerts in cases where exception-based alerts are all required.

At MindTree, we understand the need for all of these to work in tandem. Our structured approach identifies specific needs and defines a robust architecture that caters to all these requirements. Standard reusable reporting components and format templates are also defined, easing the effort to churn out new reports.

Data Quality Framework

MindTree recommends establishing at least 4 - 6 core metrics to quantitatively assess the quality of ‘measure’ and ‘dimension’ data in every engagement. To do this effectively, we have a standard library that is maintained for all known data quality issues.