Our New Open Source Framework That Brings Further Efficiency To Data Engineering Workflows

So, what is SAYN? In simple terms, SAYN is an open source data processing framework. We (the team at 173Tech) have built it to be the simplest framework whilst maintaining full flexibility. Users can select from multiple predefined task types and build their own ETL processes. SAYN is really unique and unlike anything you have seen before. Want to know more? Then read on!

Modern Analytics: The Context

Before we speak more about SAYN, let’s start with a quick refresher to place things in context. …

When people speak about “analytics” and “marketing” together, most likely they are talking about attribution and complex modelling to increase ROI. However there are many other analytics techniques which can increase marketing ROI much more significantly than complex attribution modelling — and usually with less effort.

This article presents a simple yet extremely efficient analytics framework which aims at maximising digital marketing’s long term value. It is composed of five stages to shift your optimisation from CPA towards ROAS / ROI and start using algorithms for optimisation recommendations. Please note that, although this article was written around Direct Response (DR), similar principles can be applied to Brand activity. …

About BI Debt And Avoiding Analytics Pitfalls

Smooth BI processes are key to forging the analytics team’s reputation within a company. However, data engineers and people maintaining the BI infrastructure often face an ironic challenge. Because solid BI infrastructures only have an indirect positive impact to business users (by facilitating more efficient analytics processes which in turn have a direct positive impact), it can be hard to justify resources to be spent on improving the infrastructure when the business needs insights quickly. …

In the first part of “Building World-Class Analytics For Startups”, we described how to best get started with advanced analytics through the implementation of five core layers: Extraction, Data Modelling, Reporting, Analysis and Data Science. This is the second part of this series and gives more detail about each layer. It recaps what the layer is about, what matters for each layer and provides some suggested tools. Those suggestions are based on today’s analytics ecosystem — analytics is an extremely fast-paced sector so it is always worth looking out for potential new solutions.

Exploring The Layers

Before we dive in, it might be worth doing a small refresh and having a look at the architecture of the five layers…

Analytics can be a tremendous competitive advantage for your startup, I am sure you already know this. If you get it right, it empowers business users to make the right decisions, it helps fuel growth via product and marketing ROI optimisation, it enables quick feedback cycles on product releases and the list goes on. The key question is: how do you get it right? If you have yet to leverage data to its full potential, or do not know where to even start, then this article is for you! …


Robin Watteaux

Co-Founder of analytics agency 173TECH, helping startups grow by leveraging data. Love new challenges and talking data :)

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