Blog

The idea of the Modern Data Stack has become the de-facto standard for data platforms. This stack is widely used to collect first and third-party data, process, analyse and serve out data for purposes such as reporting, self-serve analytics and, increasingly, machine learning. The key components of the stack are data pipelines, a transformation engine, data storage in the form of a data lake or warehouse and something to “activate” this data by passing from the data warehouse to downstream systems.

Eric Thanenthiran

We have built reconfigurable dbt package for subscription analytics that empowers both our analytics engineers and data analysts by locking in the definitions and making them available to everybody on the team. We are now ready to share this tool with the world 🙌

Tom Shelley

In 2023, Google began deprecating support for third-party cookies, and this trend will accelerate later in this year. While the loss of third-party cookies brings a number of new challenges (most notably around advertising performance measurements) it also brings massive opportunities (for instance for better data quality).

Tom Shelley

We have spent years building refining a reconfigurable dbt package that empowers both our analytics engineers and data analysts. We are now ready to share this tool with the world 🙌

Caro Boeri

As a data person, your most important working environment is likely going to revolve around a datawarehouse. We have worked with a plethora of different cloud datawarehouses in the past, and this is the second post in our series — talking about what we like about Google BigQuery.

Abbey Bowen

For the last 5 years, we have been improving our Tasman attribution engine to make sure that it covers not only the basics but allows marketing and data teams to make adjustments based on knowledge gained over time. We’d love to lift the veil on our methods and are doing so in a series of blogposts.

Caro Boeri

From all the applications of a centralised data platform, attribution is still the most critical one. It has however become progressively harder to get to useable, actionable insights as new privacy frameworks and other (good!) evolutions take hold in the ecosystem. In a series of blogposts, we'll try to lift the veil on how we think about attribution and what can be done to make sure it is A/ efficient to set up; B/ delivering value for your business.

Caro Boeri

In the ever-evolving landscape of data-driven decision-making, the strategic importance of data modeling cannot be overstated. At its core, data modeling is the exercise of simplifying complex data within possibly multiple information ecosystems of any business. Effectively, it allows a much wider audience to understand concepts which natively are very technical and complex. Data modeling is also laying the foundation upon which actionable insights and foresights are built.

Erik Melin

Within the data industry, new tools emerge every so often, all aiming to address very similar problems. At Tasman, we like to stay informed about what's new, explore them a little further and considering adoption if there is significant value for our clients. Having said that, we have, time and again, recommended Snowflake when it comes to picking a data warehouse solution for the majority of our clients. This is pretty interesting, given that they operate in differing industries and thus have unique problems to solve. We have listed and summarised the reasons to why we love using Snowflake.

Ho Yin Wong

During a recent analytics conference in London, one of the attending companies presented a staggering fact: they currently manage 85,000 models — which translated into an astounding 150+ models for each active developer. While this might first seem like an impressive figure, it begs the question: are we becoming overwhelmed by data? Such immense quantities teeter more towards a data swamp rather than a purposeful, streamlined collection.

Tom Shelley

There are only two types of models in a data warehouse: event data, and entity data. We will dive into this crucial distinction, its implications on data change management, and the best practices we employ at Tasman.

Tom Shelley

Data does not lie but it really needs interpretation. There are plenty of ways that you are vulnerable to misleading conclusions and worse. We will go through some of the most common ones.

Ben Henshall

At Tasman, we’ve had the privilege of partnering with some 40+ organisations over the past 6 years. These business have ranged in size and complexity, some had existing (and mature) data capabilities and others were completely greenfield, however there is one challenge that has been common across all of them - tracking.

Tom Shelley

At Tasman, we work with attribution model a lot. Our clients like the fact that we can bring a very sophisticated approach to the table, while still maintaining first-party benefits and retaining customer privacy. This landscape however is changing a lot — most recently by the launch of Apple's new Ad Network kits. We'll go into detail how it works, where the limitations are, and what it means for analytics.

Caro Boeri

Good web & app analytics is hard. As the complexity of web & mobile applications grows, understanding how users interact with those applications is more critical that ever. The best way to go about this is by what we call event tracking. What is it? How do you do it well? How do you make sure user journeys are identifiable, but privacy is also guaranteed? We will take you through our approaches and lift the veil on some of the event techniques we have developed.

Anna Selway

Scaling companies is hard. If you have raised series A a while back, and are now gearing up for your next round of funding, then a lot of things will be on your mind— not in the least how to manage all the challenges of your growing team!

Thomas in't Veld

When we start an engagement with a new client, most often there already is some event analytics in place that typically will need to be reviewed and optimised. Here are the fundamental questions we typically ask — and how we get to our Tracking Plan. There’s some useful tips & tricks in here so please read on :)

Anna Selway

At Tasman, we find that the biggest problem in analytics for startups is that they risk ending up with a messy, organically grown data model. Those models are nasty beasts, often taking up vast amounts of resources in small, already overworked data teams. It is a big problem for our clients: most of them are fast-growing businesses, which means that tactical assumptions change all the time. That means business questions change all the time as well. Recognise the feeling? Read on!

Thomas in't Veld

We talked about tips & tricks for when you raised seed — this week it’s what we think you should consider doing just after raising an A round! Lots of work to do, and finally you have budget to build out an analytics function. What should you prioritise?

Thomas in't Veld

So — you raised Seed. Congratulations! Now what? You likely have a team to hire, a product vision to execute, and a growth strategy to draw up. Plenty to keep you busy for the next months, if not years. Data analytics might be the last thing on your mind now and so it might be tempting to push it out until later.

Thomas in't Veld

Hey, startup founder! You already know you need analytics in your product or on your website. What you might not know, is that you need less tools than you think. Yes, that’s controversial — but trust us and read on for our first proper stack recommendation post.

Thomas in't Veld

In the last 10 years behavioral data, including data from web, mobile, connected devices and wearables has been leveraged in more and more use cases. Different teams across the business need this data to understand and tailor experiences to smaller segments of users and individuals, and using new techniques like AI. This growth in usage and importance of behavioral data means that companies have stricter requirements on the quality - the accuracy and completeness of that data, than ever before. Delivering on that quality is hard, because it requires a concerted effort from everyone involved in the data production pipeline.

Thomas in't Veld