1. Lean enterprise and operations for business agility of people, process, and systems
2. Lean data principles and modelling across heterogeneous platforms, methods and data types.
3. JIT insights for KPI-driven actions
Data Kaizen is lean approach to optimize the process of underlying data workflows and data transactions; the lack of which often results in false transactional reporting and enterprise inefficienciesMore
It is about process and data standards and definitions for mission-critical data domains, which become the pathways to data integrity and trust-worthiness; the lack of which produces multiple interpretation of business operationsMore
Data governance is the process of permeating a healthy accountability of form and function across the enterprise, thereby, fostering accountability and quicker resolutions to many data and process issuesMore
A multiple data sources for the customer experience is what forces many corporations into isolated IT initiatives. With a Single Source for Truth (Hub and Spoke or likes) Master Data for Customers, Vendors, Suppliers Finances garners trusted and relied upon data without the need for frequent scrubbing and normalizationMore
With the advent of Big Data, Data Lakes, BI, DS, DW…to name a few, the need is paramount to have a data architecture framework that governs the leading practices of the enterprise. The lack of which, fosters significant data challenges for Data Science teams and reporting functionsMore
With the increasing need for predictive analysis of customer buying patterns, risks management…Data Science offers a methodical framework for the enterprise to deploy different Machine Learning functions that shapes mission-critical revenue generating and risk avoidance decisions.More
Competitive advantage is predicated by the enterprise’s ability to sanction these dimensions and orchestrate a harmonious eco-system between individual, contributor and enterprise as one collective.