Data modernization

The problem is rarely storage

Most organizations have more data than they can act on. The gap is between where it sits and where it would have to be for anything to reason over it — and it usually shows up in the same three places.

• Systems that hold the data and will not give it up. AS/400, mainframe, and relational systems old enough that the people who shaped the schema have moved on.
• Pipelines built by different teams with different tools, so when one breaks nobody can say why — or whether the number that came out of it last month was right.
• Reporting assembled by hand. A spreadsheet rebuilt every month, from data that was already weeks old when it was pulled.

What the work covers

Five areas. Most engagements start in one of them and widen only when the first has paid for itself.

• Legacy migration — getting data out of AS/400, mainframe and ageing relational systems without losing the history held in them.
• Pipelines — design, build and operation, so that when one breaks somebody can say why.
• Reporting — analytics and self-service reporting in place of a spreadsheet assembled by hand every month.
• Streaming — event and streaming pipelines where the decision cannot wait for tonight’s batch.
• AI readiness — retrieval pipelines, vector stores and feature engineering, so that an AI project fails on its own merits rather than on the state of the data.

The platform is the one you already bought

Azure Synapse, Snowflake, Databricks, Microsoft Fabric, Redshift, PostgreSQL. Data Factory, dbt, Spark, Glue, Kafka. Power BI, Tableau, Qlik. The right platform is the one that fits the investments you have already made, and where an existing choice is wrong for what you are trying to do, that is said at scoping rather than discovered at the end.

Where this stops, and what it leads to

The data and the platform beneath an application are what this covers. What the application itself does is application modernization, and it has its own answer. In the other direction, this is the foundation LightHouse Enterprise Intelligence stands on — a unified pipeline layer is what it connects, a semantic model is what it reasons over, the decision traces are the memory. Someone who wants clean data and nothing more stops here.