Data-centric Digital Transformation

"The reason that God was able to create the world in seven days is that she didn't have to worry about the installed base."
  — Enzo Torresi

We have seen the architecture behind enterprise digital platforms evolve over the years—from main-frame, client-server, N-tier, ERP, SaS, EDW, and Data Marts, to now Data Lakes. While each new generation brings additional capabilities essential to remain competitive, there is often no easy migration path. At Qualistec, we take a data-centric approach to crafting a transition without abruptly abandoning prior investments.

We believe Data Lakes offer many benefits (primarily for unstructured content) but remain fundamentally a distributed file system. Without a database to enforce transactional/referential integrity, lakes rapidly turn into swamps. There are a few newer offerings in the NoSQL (Not only SQL) category that address this need for a database, but the industry is still not mature enough for wide-spread adoption.

Some of the increased enthusiasm for data lakes can be attributed to the failure—after devouring enormous amounts of time/cost—of legacy data warehouse initiatives to deliver a nimble solution that can accommodate ever-changing business needs. Enterprise data warehouses (EDW) largely remain a vast landfill, leaving the onus of making sense of data to users.

At Qualistec we offer a methodical solution that can help transition to a modern digital platform systematically with frequent and easy-to-measure interim success metrics. Our proprietary platform Qualis® Refinery can be at the hub of your data infrastructure facilitating a controlled migration. While Qualis is sophisticated enough to be a comprehensive data platform, it is commonly deployed first to reap immediate ROI by solving a single pressing business problem.


  • Ability to start with a single, targeted app and scale without disrupting existing business operations
  • Elastic functionality to expand or contract to fill gaps in (and even replace) enterprise data systems
  • Chronologically growing database with unlimited time-series/history for deeper analytics
  • “Silo Buster” architecture to shield data scientists from source system hiccups; no “half-baked” data
  • Client self-sufficiency through machine-generated, self-healing, event-driven logic
  • Small IT footprint utilizing ubiquitous technology and commodity skill-set
  • Minimum 10-year design life