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Staff writer at Holistics. Enjoys Python, coffee, green tea, and cats. I'd love to talk to you about the future of business intelligence!
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.
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.
Are dashboards really 'dead'? A response to a vendor trend that seems rather premature.
Treating your dashboards like a product means thinking about ALL of your user's needs — rational or otherwise.
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.
The definitive explainer for OLAP cubes, where we cover every single possible definition for the phrase.
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.
A hack for when you don't understand data jargon.
We take a look at an exhaustive list of reasons people give when they want to ignore anomalous data.
The origins and intuition behind a famous SaaS business metric.
Why Amazon's notion of measuring controllable input metrics is a lot more profound than you might think.
How Amazon uses metrics. A summary of chapter 6 of Working Backwards, the first book to explain how Amazon really works.
Looking for an analytics engineering job in your locale? Read this.
Everything we know and don't yet know about the emerging role of analytics engineering.
Three changes in the data landscape we're investigating over the next few months.
Five quick things we know to be true, written for the end of the year.
Every cool data project has some schlep up front. Here's why it's a good idea to keep that in mind.
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.
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.
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.
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 better rival: Michael Stonebraker's storied query language, QUEL.
Stuck at home during the pandemic? We give you Holistics's top five data books to read in 2020.
Why does data analytics and business intelligence lag behind the best practices and tooling of software? Two anecdotes and a theory.
What we can learn from Softwar, a book about Larry Ellison's attempt to take over the enterprise software world.
What intermix.io's forced acquisition tells us about the two philosophies of cost in data analytics.
A look into metadata hubs, which might just be the hottest category of data tools of the past two years.
We take a look at the business fundamentals of the operations stage — the last of three stages in a company's life.
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).
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.
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.
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.
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.
Why data quality is an ongoing, people, process, and tools problem, and how to think about getting better at it.
What a new approach to slowly changing dimensions tell us about the future of dimensional data modeling.
Why maturity models can be a bad idea, and why using a capability model is a better idea for digital transformation.
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 your CEO is so obsessed with cash flow metrics, and what you can do to help as an analytics person in your company.
There appears to be two philosophies today when it comes to managing costs in a modern data stack. We explore what they are.
An explanation of the SaaS Quick Ratio that focuses on the intuition behind the metric. Written with the data analyst in mind.
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.
A definitive history of the rise of the OLAP cube, how it's affected our industry, and what comes after.
In Holistics, you are now able to create data models on top of external data sources like MongoDB and Google Sheets. Here's a video tutorial on how to do exactly that.
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.
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.
Using custom SQL for your Tableau workbooks is usually a really bad idea. Here's how using Holistics with Tableau makes this a non-problem.
Tableau may be the best data visualizer on the planet today, but Holistics makes Tableau more awesome, by making data prep trivially easy.
Occasionally, we get asked “when should we consider getting a data warehouse?”. The answer is a lot simpler than you think!
Holistics's data modeling layer allows data teams to radically increase their productivity. No repeated queries. Self-serve reports and dashboards. Here's how it works.
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.
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.
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.
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.
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.