Business Intelligence (109)

AQL: A Better Output Query Language for Agentic Analytics

AI analytics needs a purpose-built output language. We compare SQL, metric DSLs, and measure-aware SQL, then explain why we designed AQL for composable, governed, verifiable analytical reasoning.

A Quick Way To Get On Top Of Your Company’s Data: Create a Spreadsheet-Based Data Dictionary!

A data dictionary is a document that assists you in navigating your team's mountain of data. We show you how to create a spreadsheet-based data dictionary with Excel and dbdiagram.io.

Why is Text-to-SQL so hard?

Why is Text-to-SQL so hard? Why is there a need for it? What are its challenges? Is there a way to make it easier?

The Future Of Self-Service BI Is AI-Guided

What does the future of self-service business intelligence look like?

Slice n' Dice: A Self-Service Story

Introducing Holistics AI: Your Thought Partner for Self-Service Analytics

We’re introducing a simpler, faster way to explore data in Holistics: you just need to chat with Holistics AI.

The Goldilocks Rule of Getting Data Driven

The 4 Types of Data Scales

Numbers aren't all created equal; they fall into four distinct categories known as data scales. Understanding this, you'll learn how to handle each type correctly to make your data work for you.

The Self-Service Analytics Maturity Scorecard

Metrics Deserve Better Composition Than What SQL Allows

The Three Types of Fact Tables

A concise tour of one of the basic components of Kimball dimensional data modeling. We explore the three types of fact tables, and then analyze why they have lasted the test of time.

4 levels of Analytics-as-Code

How did Analytics-as-Code evolve? We explain the history behind the movement, and present the 4 levels of analytics as code over the years.

You’re a Data Leader About to Get Laid Off. Here’s What You Can Do

Death to Dashboards, Really?

Are dashboards really 'dead'? A response to a vendor trend that seems rather premature.

The Emotional Needs of Dashboarding

Treating your dashboards like a product means thinking about ALL of your user's needs — rational or otherwise.

Nailing An Analytics Interview Case Study: 10 Practical Strategies

10 practical tips/strategies I extracted myself when doing analytics case study as part of job interview process.

The Two Forms of Metrics: Interval vs Point

In actual business conversations, the metric's form is sometimes ignored and implicitly understood. Understanding this concept gives you much better clarity in dealing with metrics.

The skills chasm of the data analyst career

Why are so few data analysts get good at business problem-solving?

Data Careers: Dealing With Being in a Cost Center

Building a career in data often means building a career in a business cost center. This isn't necessarily bad. Here's how to think about it.

LLM Is Not Enough (For Self-Service Analytics)

How Would An Ideal Semantic Layer Look Like?

How to run a Data Clinic (aka Data Office Hours)

Data clinic is one of many ways you create value and drive your organization toward better data literacy. Here's how.

Data analysts, think about your work from the business stakeholders perspective

The Misleading Data Analyst Job Title (and Career Ladder)

Investment analyst is an entry-level investment position. So is data analyst an entry-level data position, too?

Business Metrics vs Customer Metrics

The Silos between Data Analysts and Data Engineers

The Silos of Data Organization: Data Analysts vs Business Users

Analytics as Code

A short story about my history with programmatic configuration (or lack thereof) and how it's related to analytics.

What is an OLAP Cube? An Exhaustive Explainer

The definitive explainer for OLAP cubes, where we cover every single possible definition for the phrase.

A 2020 Reader's Guide to The Data Warehouse Toolkit

Ralph Kimball's classic book The Data Warehouse Toolkit introduce the world to the practice of data modeling. Here's how to read it in 2022.

Read Data Research Papers

A hack for when you don't understand data jargon.

The Six Ways People Deflect Inconvenient Data

We take a look at an exhaustive list of reasons people give when they want to ignore anomalous data.

Fitting Into Your Company's Learning Cadence

How a data person might fit into your company's learning cadence, and what that means for company performance.

The Intuition Behind The Rule of 40

The origins and intuition behind a famous SaaS business metric.

Obsess Over Controllable Input Metrics

Why Amazon's notion of measuring controllable input metrics is a lot more profound than you might think.

This is How Amazon Measures Itself

How Amazon uses metrics. A summary of chapter 6 of Working Backwards, the first book to explain how Amazon really works.

Simple Data People Tricks

Simple data people tricks, a hierarchy of feature stores, and Shreyas Doshi on product metrics.

How Amazon Uses Input Metrics

How Amazon uses input metrics, a data pipeline is a materialized view, and the case against self-service data and analytics.

Snowflake and the Data Lakehouse

A quick look at what Snowflake's been up to re: the Data Lakehouse, an inside look at Amazon's data-driven decision making, and how Airbnb customized Superset to fit their needs.

The Data Lakehouse is a Thing

The data lakehouse is a thing, Superset is also a thing, and how to make dashboards using a product thinking approach.

So You Want To Find an Analytics Engineering Job?

Looking for an analytics engineering job in your locale? Read this.

What We Know and Don't Know About Analytics Engineering

Everything we know and don't yet know about the emerging role of analytics engineering.

Three Data Industry Questions We're Investigating For The New Year

Three changes in the data landscape we're investigating over the next few months.

Five Things We're Pretty Sure About

Five quick things we know to be true, written for the end of the year.

Embrace The Data Schlep First

Every cool data project has some schlep up front. Here's why it's a good idea to keep that in mind.

Applying The Principles of Agile to Data Modeling

In our third and final part on Agile Data Warehouse, we take a look at the origins of the Agile Manifesto, from which Corr and Stagnitto took inspiration.

A Business Process Worth Measuring

The second part of our series on Agile Data Warehouse Design, a 2011 attempt at applying the principles of agile to the practice of data modeling.

First Impressions from Agile Data Warehouse Design

Agile Data Warehouse Design is a 2011 attempt at applying the principles of agile to the practice of data modeling. This is the first post in a series on the ideas from the book.

The Four Flavors of Goodhart's Law

Goodhart's law says that 'when a measure becomes a target, it fails to become a good measure. Here are four ways that occurs.

A Short Story About SQL’s Biggest Rival

A short story about SQL's better rival: Michael Stonebraker's storied query language, QUEL.

Top 5 Data Books To Read in 2020

Stuck at home during the pandemic? We give you Holistics's top five data books to read in 2020.

Why is Data Analytics So Far Behind Software Engineering?

Why does data analytics and business intelligence lag behind the best practices and tooling of software? Two anecdotes and a theory.

Business Intelligence Lessons from Softwar: A Portrait of Larry Ellison And Oracle

What we can learn from Softwar, a book about Larry Ellison's attempt to take over the enterprise software world.

Redshift, Snowflake, and the Two Philosophies of Cost

What intermix.io's forced acquisition tells us about the two philosophies of cost in data analytics.

A Dive Into Metadata Hubs

A look into metadata hubs, which might just be the hottest category of data tools of the past two years.

Measuring a Company When a Company Is Mature

We take a look at the business fundamentals of the operations stage — the last of three stages in a company's life.

Understanding the Scale Stage

The scale stage is the second of three stages in a startup's life. In this post, we discuss what product-market fit means, and then we look at healthy growth (the kind that leads to winning) and unhealthy growth (the kind that leads to company death).

Measurement Before Product Market Fit

A startup's first job is to create a customer. We take a closer look at how that affects the metrics you measure at the product stage of a startup's lifecycle.

How Metrics Change When Your Company Grows

When your company grows, the metrics that matter changes along with it. Here's a look at the three growth stages of every company, along with the metrics that matter most at each stage.

The Magic Money Machine Model

One way to get better at business communication is to learn the fundamentals of business. We explain Return on Invested Capital from first principles, written with the data analyst in mind.

Clearer Communication: How To Use The Ladder of Inference When Communicating Data To Your Business Users

If you work in data analytics, communicating complex information is just part of your job. Here's how to use the Ladder of Inference to get better at it.

Announcing The Analytics Setup Guidebook: The First Principles Approach to Data Analytics

We're releasing The Analytics Setup Guidebook, a first-principles approach to data analytics. Get it free today!

Data Quality from First Principles

Why data quality is an ongoing, people, process, and tools problem, and how to think about getting better at it.

When Doing Digital Transformation, Think Capabilities, Not Maturity

Why maturity models can be a bad idea, and why using a capability model is a better idea for digital transformation.

How to Measure Software Development, from ‘Accelerate: The Science of Lean Software and DevOps’

Tracking the performance of your software development team is really, really difficult. The 2018 book Accelerate: The Science of Lean Software and DevOps gives us a fantastic way to measure just that. Here's how.

Why Measuring Cash Flow is More Important Than Measuring Profitability

Why your CEO is so obsessed with cash flow metrics, and what you can do to help as an analytics person in your company.

The Two Philosophies of Cost in Data Analytics

There appears to be two philosophies today when it comes to managing costs in a modern data stack. We explore what they are.

Developing an Intuitive Understanding of the SaaS Quick Ratio

An explanation of the SaaS Quick Ratio that focuses on the intuition behind the metric. Written with the data analyst in mind.

OLAP != OLAP Cube

There's a tendency for people to conflate OLAP with OLAP cube. We take a quick look at how this happens, why it shouldn't, and why it matters if you're a data practitioner.

The Rise and Fall of the OLAP Cube

A definitive history of the rise of the OLAP cube, how it's affected our industry, and what comes after.

How We Combine Data Sources at Holistics

Good data teams combine data from different sources in order to do their jobs well. Here's what we've learnt while putting this into practice at Holistics.

Judge Your Company: Using The Three Levels of Data Analysis For Data Career Decision-Making

Data team careers are different from equivalent careers in software engineering, product management, or UI design. Here's how to evaluate prospective employers as part of your data career.

Why Google Analytics sucks and what you can do about it

Google Analytics is an incredible tool to have in your toolbox. But it only offers aggregated data, which limits your ability to track visitor behavior. In this post, we discuss a simple, affordable alternative to GA that we've implemented at Holistics.

The rise of SQL-based data modeling and DataOps

The resurgence of SQL-based data modeling is a something we should all be interested in. Here's why it's important.

How To Choose A BI Tool - Insights from Datanest

“There is nothing so terrible as activity without insight.”  Insight has become an essential need for every company in order to understand what the company is experiencing, why it happens, and what might come in handy in the future.

Hire Your Data Analysts First

In the past, setting up an analytics department meant hiring data engineers first. But in a cloud-first world, you can and should hire data analysts first. Here's why.

Data Analytics for the App Generation

We live in the age of apps — and therefore, the age of data. This presents us with an unparalleled opportunity for data analytics, as well as an unparalleled challenge! Here's how to not get left behind.

When Should You Get a Data Warehouse?

Occasionally, we get asked “when should we consider getting a data warehouse?”. The answer is a lot simpler than you think!

When Measuring Performance, Find Lines in the Sand

One of the most useful ideas from the 2013 book Lean Analytics is the notion of 'lines in the sand' — concrete values that tell you how well you're doing on a metric that matters.

Lean Analytics Part 2: The Stages of a Data Driven Startup

In Part 2 of our summary of Lean Analytics, we cover the five stages of a data driven startup, and the book's tips for creating a data-driven culture in your company.

Lean Analytics Part 1: An Introduction to Analytical Thinking

In the first part of our comprehensive summary of Lean Analytics, we examine the basics of analytical thinking, explore six startup business models, and examine the metrics that matter the most to each.

The Two Types of Operating Indicators

In operational analytics, you're either looking at a leading indicator, or you're looking at a lagging trend indicator. Here's why this particular categorisation is so useful.

Beware What You Measure: The Principle of Pairing Indicators

When you're implementing company-wide analytics, it's easy to fall into the trap of measuring only one metric. Don't. The principle of pairing indicators is why.

How Data Analytics Helps MyanLearn’s Operations: Holistics Data Platform (Guest Post)

As we are still a very small startup, we do not have a dedicated data team but we want to democratize data access. Holistics plays a big role in our company by providing visualization and reporting features via connecting with our database.

How Planogram and Share of Shelf (SOS) Reports Maximise Revenue for Retail and FMCG Companies

Planogram and Share of Shelf/Space (SOS) reports are key field execution reports that are important concerns for decision makers to carry out the marketing strategy. In this article, we will explain what Planogram and SOS reports are.

4 Case Study Questions for Interviewing Data Analysts at a Startup

A good data analyst is one who has an absolute passion for data, he/she has a strong understanding of the business/product you are running, and will be always seeking meaningful insights to help the team make better decisions.

How To Setup Reporting Analytics For MongoDB

Many Mongo users get stuck going from MongoDB data to analytics. Getting data out of Mongo and into a relational database lets you build your reporting workflow.

What is a Pivot Table?

The 4 Basic SQL Commands: SQL Basics 101

How Do I Prepare for A Data Analyst Interview?

Preparing for a data analyst interview can be intimidating. Learn a few tips to help you prepare your interview better.

3 Things To Consider When Choosing The Right BI Tool!

3 Points to Consider When Deciding Between Google BigQuery and Amazon Redshift!

What to choose between BigQuerry and Amazon Redshift when it comes to build a cost-efficient, fast, and reliable data warehouse?

The Analyst Guide to Designing a Modern Data Warehouse

High level guide for data analysts and engineers starting their first data warehouse project.

Data Analyst or Excel Monkey? (Becoming a 10x Data Analyst)

When people think of data analytics, they often think of charts and visualizations. However, the 10x data analyst cannot just be a visualizer. There are several abilities and techniques that a data analyst requires to truly become fully empowered and effective.

Learning SQL - Why it is critical for Analysts

As a derivative of the development of technology, the importance of data to business is indisputable. Converting that information into insights becomes a business advantage. Understanding data flows to be able to work with data is essential for all Analysts.

How To Interview A Data Analyst Candidate

How do you usually interview a data analyst candidates? In this article, I'll share some of the guidelines and areas you can focus on when interviewing a data analyst candidate. These serve as pointers to aid with your interview.

Data Lake vs Data Warehouse vs Data Mart

ETL vs ELT: How ELT is changing the BI landscape

Understanding Your Average Customer Value Over Time (CVO)

4 Ways to Improve Dashboard Performance

Knowing Your Key Metrics

In his early days of investing, Warren Buffett drove to the airport at Omaha to pick up David Strassler, a New York based businessman whose family bought and fixed distressed companies.

Quick Guide: Calculate Cohort Retention Analysis with SQL

Useful PostgreSQL Queries For Monitoring Purpose

Quick Guide: Converting Rows to Columns — Pivoting with SQL & BI Tools

Combining Data Sources: Approaches & Considerations

Analyze how the way different vendors addresses the problem of combing data from different sources

Moving From Redshift to Presto - Data Engineering at Grab

SQL Is The New Excel

Quick Guide: Setting Up Your Own MySQL Instance Using Google CloudSQL

Why You Should Use Postgres Over MySQL For Analytics Purpose