{"id":9781,"date":"2025-10-30T08:28:20","date_gmt":"2025-10-30T08:28:20","guid":{"rendered":"https:\/\/weareoakland.com\/?p=9781"},"modified":"2025-10-30T08:28:21","modified_gmt":"2025-10-30T08:28:21","slug":"four-pillars-of-a-data-platform","status":"publish","type":"post","link":"https:\/\/weareoakland.com\/blog\/four-pillars-of-a-data-platform\/","title":{"rendered":"The Four Pillars of a Data Platform"},"content":{"rendered":"\n

Data Decoded<\/span><\/a>: How to go from \u201cwe want to be data-driven\u201d to \u201cwe are data-driven\u201d.<\/strong><\/p>\n\n\n\n

We hate to break it to you, but there isn\u2019t any natural ROI in building a data platform<\/span><\/a>. The ROI happens when decisions get made <\/strong>from that platform. As a data engineer or data platform consultant<\/span><\/a>, you build around four key functions: Ingesting, storing, transforming, and serving the data. If your platform does these four things, you\u2019re in business. But just because you can tick all four pillars of a data platform doesn\u2019t mean you can tick one that says it provides ROI, too.<\/p>\n\n\n\n

Here\u2019s why.<\/p>\n\n\n\n

The Four Pillars of a Data Platform Build<\/h2>\n\n\n\n

 We\u2019ll start with the four functions in more detail:<\/p>\n\n\n\n

    \n
  1. Ingest <\/strong>\u2013 bringing data in from source systems (ERP, CRM, cloud apps, IoT, etc).<\/li>\n\n\n\n
  2. Store <\/strong>– persisting the data in a suitable repository (data lake, warehouse, lakehouse<\/span><\/a>).<\/li>\n\n\n\n
  3. Transform <\/strong>\u2013 modeling<\/span><\/a>, cleansing, structuring, and preparing the data for use.<\/li>\n\n\n\n
  4. Serve <\/strong>\u2013 providing access: dashboards, BI tools, ML models, APIs.<\/li>\n<\/ol>\n\n\n\n

    Many data engineering projects start with the diagram ingest \u2192 store \u2192 transform \u2192 serve<\/em>. And that\u2019s fine. But companies fall into the trap of believing: \u201cwe have dashboards, we have a lakehouse, we have streaming ingestion\u201d, therefore \u201cwe\u2019re data-driven.\u201d But that\u2019s wrong. If you haven\u2019t tied it to decision boundaries and measurable outcomes, you are spinning wheels.<\/p>\n\n\n\n

    A Platform Doesn\u2019t Equal Value<\/strong><\/h3>\n\n\n\n

    Data platforms have cost: Infrastructure, licences, engineers, maintenance. Without real usage and decision-making, you could have a negative ROI. For example, a data platform sitting idle still costs you \u00a35-10k\/month in cloud costs.<\/p>\n\n\n\n

    Jump into this further by reading: How to Manage Spiralling Cloud Costs<\/span><\/a>.<\/p>\n\n\n\n

    What Does A Data Consulting Company Do?<\/h2>\n\n\n\n

    A good data consultant doesn\u2019t just build a stack. They ask: <\/p>\n\n\n\n