Kaleidoscope Data

Latest — Nov 27, 2023

Case Study: Kaleidoscope Data uses AI to improve data accuracy by as much as 83%

CLIENT Cookies, the most globally recognized cannabis company, has over 65 retail locations in over 20 markets across 6 countries. CHALLENGE Many of Cookies’ retail stores use different point-of-sale (POS) systems. Stores employ inconsistent product nomenclature and data entry formats, resulting in poor data quality across the board — as low

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Three methods for solving the dirty data dilemma

Originally posted on Metabase.com blog In a data-driven world, businesses need accurate and clean data to make informed decisions. Clean data is essential for reliable insight, efficient operations, and ultimately, success. Dirty data remains a significant challenge for many as it can lead to inaccurate analyses and misguided decision-making.

Quantitative content analysis with GPT-4

Everyone is talking about GPT-4, and for good reason. The generative capabilities are amazing, generating impressive and coherent text. On the other hand, I've been playing with the tool as a way to do traditional NLP tasks really quickly and with no training or examples (aka "zero

Backbone case study

In late 2021, seed-stage supply chain SaaS startup Backbone contacted Kaleidoscope Data about up-leveling their analytics offerings, including offering a paid "Data Warehouse" service to their customers and starting a data practice. Kaleidoscope Data worked with the CTO and engineering team to build out a scalable analytics infrastructure,

Let's Solve Elon's Twitter Bot Problem

In case you live under a rock, or rather, are smart enough to disconnect yourself from social media: Elon Musk has given in today and agrees to buy Twitter ($TWTR) at his original agreement's purchase price of $54.20/share. After months of Twitter insults to the executive

Integrate many data sources into your data warehouse with DBT

We walk through our DBT project structure and how it helps us transform data from many point-of-sale (POS) systems into a retail analytics data warehouse. * Background * File Structure * Base Tables * Staging Tables * Core Tables * Inventory * Conclusion Background For the last several years, we have been building and maintaining a retail

Continuous API Data ELT into BigQuery using Cloud Run

ETL/ELT frameworks can make working on big data pipelines a lot easier, as compared to writing extraction and transformation scripts from scratch. On the other hand, for very lightweight and standalone data extraction tasks, it's hard to beat a quick script run serverless on your cloud provider

Data Transformation architecture for your customer-facing Data Warehouse

Most data folks understand a "Data Warehouse" as an internal tool for reporting and analytics across different teams in your organization. However, it can also be used externally - by customers who want more granular access to their data (often your largest customers). Charging customers for this data

Multi-Tenancy in BigQuery

Background Multi-tenancy in BigQuery can be accomplished in several ways. This article by the folks at Google has many helpful tips for data isolation and security. The main idea is isolating tenant data in BigQuery datasets - all under a single client data project - rather than creating a new

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Kaleidoscope Data

Topics in Analytics Engineering and Data Product R&D from the consulting partners at Kaleidoscope Data

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