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  • Posts
  • Databricks
    • Databricks Labs Data Generator
    • Using Auto Loader on Azure Databricks with AWS S3
    • Using and Abusing Auto Loader's Inferred Schema
    • Scheduling Databricks Cluster Uptime
  • Data Lake
    • Introduction to Data Lakes
    • Deep Dive Into Data Lakes - SQL Bits
  • Data Mesh
    • What is Data Mesh?
    • Data Mesh Deep Dive
    • Data Domain Fictional Case Study in Retail
    • Data Product Fictional Case Study in Retail
  • Data Science
    • Forecasting Methods and Principles
  • SQL
    • Triangulation Arbitrage in SQL
    • CI CD with Synapse Serverless
  • Testing
  • Strategy
    • Why Data Quality is Important
    • What do we mean by Self-Serve
  • Data Modelling
    • Tabular Automation with TMSL and PowerShell - SQL Bits
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What do we mean by Self-Serve

Self-serve, or self-service, has been around in various guises for decades from Automatic Teller Machines (ATMs) through to self-serve analytics. With the advent of spreadsheeting tools, self-serve analytics has arguably been around for a long time, and with the more recent introduction of dedicated self-serve Business Intelligence tools, self-serve has become embedded in the analytics vernacular. But what do we really mean when we say “self-serve”? In my experience, we all have different interpretations and understanding of what we mean - and those interpretations will be specific to the context in which we operate.

August 12, 2022 Read
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Why Data Quality is Important

Data is among the most valuable assets for any organisation. Without data, the ability to make informed decisions is diminished. So it stands to reason that Data Quality is incredibly important to any organisation. If data doesn’t meet the expectations of accuracy, validity, completeness, and consistency that an organisation sets it, then the data could have severe implications for the organisation. Conversely, if data does meet those expectations, then it is a real asset that can be used to drive value across an organisation.

February 21, 2022 Read
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January 1, 0001 Read
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