DATAECONOMY makes the data agree.

A bank has the transactions. It has the accounts. It has the customer records. Yet the important story can remain scattered across separate systems, waiting for someone to connect the pieces. In a case study published by DATAECONOMY, a regional bank used the company’s KNODE platform to uncover what it describes as a $650 million money-laundering operation in hours rather than weeks. The bank is unnamed. The result is the vendor’s account. The underlying problem, however, is wonderfully concrete: seeing the records was easier than seeing their relationships.

KNODE brought customers, accounts, transactions, and beneficial ownership into an interactive graph. Investigators could follow money between counterparties and explore ownership hierarchies in one interface. The useful unit of information became the connection. A customer record acquired a neighborhood.

That is a useful way into DATAECONOMY, a company whose catalog can otherwise sound like an enterprise technology shopping list. It engineers data platforms, modernizes cloud infrastructure, and builds AI applications. Its more interesting proposition is that these jobs belong together. An intelligent answer depends on the less glamorous work of making information usable.

02An 80-person-year reality check

Consider another of its banking projects. A risk-and-finance platform ran on DataStage, DB2, and Oracle. Its architecture tied workloads closely together, duplicated data, and stretched batch-processing windows. Here, the initial constraint was the machinery carrying the information. Faster analysis required changing the machinery.

DATAECONOMY describes a migration involving more than 5,000 DataStage components, approximately 800 tables, 225 billion records, and about 60 terabytes. The work included assessment, architecture, migration, reconciliation, and post-migration support. Engineers added more than 200 data-quality checks. The delivery model was roughly 80 percent offshore and 20 percent onshore.

The revealing number is approximately 80 person-years of effort. That is labor accumulated across a team, not an 80-year calendar schedule. It gives the phrase “data modernization” some weight. Moving a bank means preserving the logic embedded in its reports, mappings, and regulatory processes. A missing dependency can matter more than a new dashboard.

For a buyer, the cost discussion therefore starts with scope: which systems, how much history, what validation, and who supports the result. DATAECONOMY’s AWS Marketplace offerings use custom private offers. Its commercial model sells engineering and implementation expertise alongside reusable software accelerators, with cloud operations and support extending the relationship.

03Five tools, one underlying argument

The product names describe different points along that journey. xPIER provides enterprise self-service data and analytics capabilities, including pipelines, catalogs, lineage, quality controls, and a data marketplace. The idea is to help people discover and consume data products without rebuilding the same engineering scaffolding each time.

MDM Core addresses identity: matching, merging, and validating records to create a trusted view of business entities. xContext addresses meaning, using semantic models and enterprise knowledge to give analytics and AI shared business context. KNODE addresses relationships, particularly the connections investigators need to explore.

xCompanion tackles the engineering work itself, including legacy code analysis and modernization. In a case involving a US property-and-casualty insurer, DATAECONOMY says fragmented Informatica mappings were consolidated into reusable dbt transformations and canonical models, with Apache Iceberg as the target. Its reported results include 50 percent less conversion effort and 75 percent less quality-assurance effort.

The choice that matters in that example is architectural. The insurer’s repeated mappings were reorganized into reusable components. Simply translating every old pipeline would have carried the duplication into a new environment. The practical lesson is to decide what deserves to survive before automating its conversion.

04The consultancy inside the product company

DATAECONOMY occupies the space between enterprise consulting and packaged data software. A buyer could assemble cloud services, governance tools, and an internal engineering team. It could also hire a broad systems integrator. DATAECONOMY’s pitch combines specialist delivery with its own accelerators, applied within the customer’s technology environment.

Its AWS partner page names work for State Street, Citizens Financial, Johnson & Johnson, Guardian Life, Sun Life, and Farmers Insurance. Those examples span infrastructure automation, data platforms, governance, and legacy modernization. Banking and insurance dominate the case-study library; healthcare and life sciences also appear. These are buyers with existing systems and substantial obligations around how information moves.

The ecosystem is part of the proposition. Snowflake lists DATAECONOMY as a Select partner and describes its migration, reconciliation, and self-service tools. DATAECONOMY also maintains dedicated AWS and Databricks practices. These platforms supply the underlying infrastructure; the consultancy’s work connects them to business processes, data definitions, and operating controls.

For customers, this makes the handover important. A pipeline that only its original builder understands creates another dependency. Documentation, reconciliation rules, visible lineage, and operational ownership determine whether a delivered platform becomes an everyday capability. They belong in the buying decision alongside the demonstration.

“Intelligence Must Be Engineered.”DATAECONOMY / the company’s stated belief

05A garage with a production deadline

Founded in 2018, DATAECONOMY is led by co-founders Ravi Kopuri, CEO; Siva Kondamadugula, COO; and Reginald Mathews, CTO. Its delivery footprint spans the United States, India, and Australia. LinkedIn places it in the 201-to-500-employee range.

The company calls its innovation lab DE Garage and its engineers “Ninjas.” The vocabulary is playful for a business concerned with audit trails. Its stated culture emphasizes learning across disciplines and taking ownership of delivery. The garage’s stated purpose is to evaluate technologies and move useful ideas into production.

There is evidence of commercial momentum: Inc. ranked DATAECONOMY No. 24 on its 2026 Midwest Regionals list, reporting 220 percent two-year growth. That measures growth, not the quality of every implementation. The more instructive evidence remains the work: reconciling old records, documenting transformations, and making an investigation easier to follow.

06Start with one troublesome handoff

A reader can borrow a sensible sequence from these projects. Pick a consequential workflow. Identify where its information originates, who owns the definitions, and how the output is checked. Then decide which pieces can be reused. For a bank, that might be a risk report. For another enterprise, it might be a customer record shared between sales and operations.

This approach requires access to source systems, knowledgeable business owners, and willingness to fix inherited logic. A small team with clean data and a simple reporting task may need a much narrower tool. In a regulated enterprise, the attraction is more specific: building a path from an individual record to an answer someone can inspect, explain, and use.