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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 there a need for it? What are its challenges? Is there a way to make it easier?
What does the future of self-service business intelligence look like?
We’re introducing a simpler, faster way to explore data in Holistics: you just need to chat with Holistics AI.
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.
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.
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.
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.
10 practical tips/strategies I extracted myself when doing analytics case study as part of job interview process.
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.
Why are so few data analysts get good at business problem-solving?
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.
Data clinic is one of many ways you create value and drive your organization toward better data literacy. Here's how.
Investment analyst is an entry-level investment position. So is data analyst an entry-level data position, too?
A short story about my history with programmatic configuration (or lack thereof) and how it's related to analytics.
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.
How a data person might fit into your company's learning cadence, and what that means for company performance.
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.
Simple data people tricks, a hierarchy of feature stores, and Shreyas Doshi on product metrics.
How Amazon uses input metrics, a data pipeline is a materialized view, and the case against self-service data and analytics.
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, Superset is also a thing, and how to make dashboards using a product thinking approach.
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.
We're releasing The Analytics Setup Guidebook, a first-principles approach to data analytics. Get it free today!
Why data quality is an ongoing, people, process, and tools problem, and how to think about getting better at it.
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.
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.
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.
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 resurgence of SQL-based data modeling is a something we should all be interested in. Here's why it's important.
“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.
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.
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.
Occasionally, we get asked “when should we consider getting a data warehouse?”. The answer is a lot simpler than you think!
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.
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.
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.
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.
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.
Preparing for a data analyst interview can be intimidating. Learn a few tips to help you prepare your interview better.
What to choose between BigQuerry and Amazon Redshift when it comes to build a cost-efficient, fast, and reliable data warehouse?
High level guide for data analysts and engineers starting their first data warehouse project.
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.
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 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.
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.
Analyze how the way different vendors addresses the problem of combing data from different sources