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— Dr. Igor Mosyagin, Data Consultant, ex-Klarna
— Johanna G., Analytics Lead, ARD
— Josh Richman, Business Analytics Manager, Arrive
— Jean-Mathieu, Engineering Director, Analytics Data Platform, Datadog
— Liu Yang, BI Manager, Monee
— Daniel Lim
— Ahmad P.
#85 Not all analytics is truth-seeking; much of it is persuasion, and that changes what we should expect from BI tools and governed data layers. Sep 10, 2026 #84 More and more people are starting to vibecode analytics, and we're seeing it at Holistics too. There are the obvious effects, but the second-order effects of AI are probably much more interesting. Aug 27, 2026 #83 We explain the what and why of self-service analytics through a story of a fictional pizza restaurant. May 30, 2025 #82 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. Nov 13, 2024 #81 For this week’s episode of Designing Data Organizations, we spoke with Christine Jiang, former Director of Core Analytics at Vimeo, who was with the organization between 2020 and 2024. Jun 19, 2024 #80 Designing Data Organizations is brought to you by Holistics, where we delve into the intricate process of creating and optimizing data teams within organizations. May 24, 2024 #79 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. May 7, 2024 #78 Designing Data Organizations is brought to you by Holistics, where we delve into the intricate process of creating and optimizing data teams within organizations. May 2, 2024 #77 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. Mar 25, 2024 #76 Designing Data Organizations is brought to you by Holistics, where we delve into the intricate process of creating and optimizing data teams within organizations. Mar 14, 2024 #75 Are dashboards really 'dead'? A response to a vendor trend that seems rather premature. Feb 12, 2024 #74 Treating your dashboards like a product means thinking about ALL of your user's needs — rational or otherwise. Jan 23, 2024 #73 10 practical tips/strategies I extracted myself when doing analytics case study as part of job interview process. Jan 16, 2024 #72 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. Nov 13, 2023 #71 Why are so few data analysts get good at business problem-solving? Oct 10, 2023 #70 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. Jul 17, 2023 #69 Jun 28, 2023 #68 How would an ideal semantic layer look like? Apr 28, 2023 #67 Data clinic is one of many ways you create value and drive your organization toward better data literacy. Here's how. Oct 19, 2022 #66 What actual values do business stakeholders get from data analysts? Sep 22, 2022 #65 Investment analyst is an entry-level investment position. So is data analyst an entry-level data position, too? Jul 21, 2022 #64 What happened to us; and the subtle difference between customer-centric metrics vs business-centric metrics. Jul 7, 2022 #63 The silos between Data Engineers and Data Analysts. Oct 20, 2021 #62 Why the Data Org doesn't work well with each other, the arc of data adoption and defining data stack as code. Oct 13, 2021 #61 The goldilocks rule of being data-driven, why Data Teams should run like Product Teams, and what to do if your non-data friends ask you how to get into data. Oct 6, 2021 #60 A short story about my history with programmatic configuration (or lack thereof) and how it's related to analytics. Sep 30, 2021 #59 How do you balance between ad-hoc queries that may change your business and the mundane reality of day-to-day operational analytics? Sep 15, 2021 #58 The first data modeling approach since Kimball worth paying attention to. Sep 8, 2021 #57 The definitive explainer for OLAP cubes, where we cover every single possible definition for the phrase. Sep 2, 2021 #56 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. Aug 29, 2021 #55 A hack for when you don't understand data jargon. Aug 26, 2021 #54 An unrigorous form of thinking you might encounter in your work with data. Aug 18, 2021 #53 We take a look at an exhaustive list of reasons people give when they want to ignore anomalous data. Aug 12, 2021 #52 How a data person might fit into your company's learning cadence, and what that means for company performance. Aug 11, 2021 #51 Defining what self service analytics is, by looking at what it isn't. Jul 28, 2021 #50 The origins and intuition behind a famous SaaS business metric. Jul 22, 2021 #49 We're back! Also: what does self-service mean, anyway? Jul 14, 2021 #48 Why Amazon's notion of measuring controllable input metrics is a lot more profound than you might think. Mar 24, 2021 #47 How Amazon uses metrics. A summary of chapter 6 of Working Backwards, the first book to explain how Amazon really works. Mar 17, 2021 #46 Simple data people tricks, a hierarchy of feature stores, and Shreyas Doshi on product metrics. Mar 11, 2021 #45 How Amazon uses input metrics, a data pipeline is a materialized view, and the case against self-service data and analytics. Feb 24, 2021 #44 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. Feb 17, 2021 #43 The data lakehouse is a thing, Superset is also a thing, and how to make dashboards using a product thinking approach. Feb 3, 2021 #42 Looking for an analytics engineering job in your locale? Read this. Jan 21, 2021 #41 Everything we know and don't yet know about the emerging role of analytics engineering. Jan 14, 2021 #40 Three changes in the data landscape we're investigating over the next few months. Jan 7, 2021 #39 Five quick things we know to be true, written for the end of the year. Dec 17, 2020 #38 Every cool data project has some schlep up front. Here's why it's a good idea to keep that in mind. Dec 3, 2020 #37 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. Nov 26, 2020 #36 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. Nov 14, 2020 #35 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. Oct 29, 2020 #34 Instead of an article this week, I thought to share something in a different medium instead. Oct 21, 2020 #33 Goodhart's law says that 'when a measure becomes a target, it fails to become a good measure. Here are four ways that occurs. Oct 15, 2020 #32 A short story about SQL's better rival: Michael Stonebraker's storied query language, QUEL. Oct 7, 2020 #31 Stuck at home during the pandemic? We give you Holistics's top five data books to read in 2020. Sep 30, 2020 #30 Why does data analytics and business intelligence lag behind the best practices and tooling of software? Two anecdotes and a theory. Sep 24, 2020 #29 What we can learn from Softwar, a book about Larry Ellison's attempt to take over the enterprise software world. Sep 10, 2020 #28 What intermix.io's forced acquisition tells us about the two philosophies of cost in data analytics. Sep 2, 2020 #27 A look into metadata hubs, which might just be the hottest category of data tools of the past two years. Aug 27, 2020 #26 We take a look at the business fundamentals of the operations stage — the last of three stages in a company's life. Aug 20, 2020 #25 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). Aug 6, 2020 #24 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. Jul 23, 2020 #23 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. Jul 16, 2020 #22 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. Jul 10, 2020 #21 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. Jul 2, 2020 #20 Why data quality is an ongoing, people, process, and tools problem, and how to think about getting better at it. May 27, 2020 #19 While writing The Analytics Setup Guidebook, we realized that the tools we recommend (apart from Holistics!) were really drawn from a small handful of ideas. Apr 30, 2020 #18 What a new approach to slowly changing dimensions tell us about the future of dimensional data modeling. Apr 2, 2020 #17 Why maturity models can be a bad idea, and why using a capability model is a better idea for digital transformation. Mar 18, 2020 #16 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. Mar 12, 2020 #15 Why your CEO is so obsessed with cash flow metrics, and what you can do to help as an analytics person in your company. Mar 5, 2020 #14 There appears to be two philosophies today when it comes to managing costs in a modern data stack. We explore what they are. Feb 21, 2020 #13 An explanation of the SaaS Quick Ratio that focuses on the intuition behind the metric. Written with the data analyst in mind. Feb 13, 2020 #12 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. Feb 6, 2020 #11 A definitive history of the rise of the OLAP cube, how it's affected our industry, and what comes after. Jan 30, 2020 #10 This week's post comes from Holistics team member Yahya Apristianto, who wrote it after attending a talk on digital transformation by a CEO in his home base of Indonesia. Jan 22, 2020 #9 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. Jan 3, 2020 #8 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. Dec 22, 2019 #7 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. Dec 17, 2019 #6 A data dictionary is a document that assists you in navigating your team's mountain of data. Dec 11, 2019 #5 The resurgence of SQL-based data modeling is a something we should all be interested in. Here's why it's important. Nov 15, 2019 #4 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. Nov 6, 2019 #3 Occasionally, we get asked “when should we consider getting a data warehouse?”. The answer is a lot simpler than you think! Aug 15, 2019 #2 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. Jul 27, 2019 #1 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. May 29, 2019