Why You Need to Implement Analytics Engineering
You might have started to notice companies are now implementing and developing teams termed “analytics engineering”. This is a relatively new role in the industry, but one that is essential to have to increase the productivity of your data analysts. It will put you on a good path to self serve analytics within your organization.
There has always been a huge push to develop more self serve analytics, The issue in the past when developing solutions towards this were rooted in the issue with the how analysts were managing their growing number of assets.
Lets get one thing straight…
The demand on an individual analyst should not be undervalued. Too many organizations claim that their data is their strongest asset and that they are driven by the data, but it is easy to see that those same organizations view their data teams as an after thought and typically will be providing them with leftover funding with increasing demands.
What is Analytics Engineering?
Analytics engineering can be seen simply as a functional process that provides support for any type of analyst team through data modelling and software principles. In essence, we are treating the analytics platform as its own product so that we can achieve high governance, stability, and repeatability.
You notice that a lot of teams are implementing “operational” roles within their sub-hierarchy (think DevOps, Design Ops, Biz Ops). Analytical engineering is sort of a form of operations, and depending on the strategy you choose (centralized or decentralized) it is easiest to digest it when you view it as a operational one.
How to Tell if You Need an Analytical Engineering Function?
Here are top 5 questions you should answer yourself that may suggest that you need to start thinking about incorporating this function:
- Is your team managing too many assets per person?
- Are you running the same queries multiple times to produce different outputs?
- Do you feel that data flowing through your organization lacks governance?
- Have you noticed stakeholders finding issues with consistency in numbers?
- Do your stakeholders have a yearning to self serve but find the data difficult to do so?
These are just a few questions you can ask yourself. The overall idea is that if you feel you don’t have as much control with your data processes and assets, then analytical engineering is for you!
Why is it Important to Implement?
The truth is a lot of companies like to talk the talk that their data is on-point. Anyone who has worked with technology knows that what something looks like on the front-end is never as close to what it looks like on that back end. In data, this is no different.
Imagine you work at a company and every department has 3–4 tools that they use. They are probably at least 10 data sources that your data team integrated with and the demand to integrate with more is still there. There is an expectation that your team is not only ingesting these sources in your ETL/ELT pipeline, but they are also doing reverse ETL and sending data back so that departments can use it. On top of that there is a huge demand that you are providing ad-hoc reporting, scheduled reporting, dashboards, analyses, A/B testing, and building machine learning models and maintaining them.
Data teams are under valued in what they provide to an organization.
Analytics engineering will soften the burden for all these functions.
The smartest decision you can make it equipping those closest to the problems with the right tools and methodologies to manage their assets so they can provide their stakeholders with quality work.
What Are Some Tools to Start Looking Into?
We will focus on two tools that will have a major impact in helping you get started on your journey. This is just a start and you won’t be successful with just these two tools alone.
dbt
This is a tool that that focused on the “T” (Transformation) in your pipelines. It helps you create consistent data models with good software engineering practices and can be used for free or comes with a nice GUI and additional features for a cost
Apache Airflow
This is an orchestration tool that is necessary to schedule your jobs. It is super important to get your jobs scheduled and running like a smooth machine. It is Not the only orchestration tool but it has a large community and high usage
Want to Get Started?
The best place to start in my opinion is by researching what these tools can provide, reading case studies and talking to those in the industry.
If you don’t have time for that, Kizmet Solutions can expedite the process for you and can set you up on the path forward!
