A look at key processes and data engineering

Avoid rubbish in, rubbish out, 4 key questions to ask about your data readiness, what's different about engineering and data science.

Without data engineering there is no Artifical Intelligence. Data engineering creates and maintains the pipeline of usable data

Data Engineering is the foundation of AI

Without data engineering there is no Artifical Intelligence. Data engineering creates and maintains the pipeline of usable data.

Data Engineering is the foundational requirement for great Artificial Intelligence. It is fundamental and anyone who tells you differently is not telling the truth. Interestingly companies sometimes start at the top, the output. So they start by hiring a data scientist. After some time, the data scientist will find that they don’t have the right data, the useful data and they have no real way of adding value. Because data engineering is missing. So what does Data engineering mean and what does it do?

Avoid rubbish in, rubbish out

Before you can get any meaningful work out of your data it needs to be cleansed, organised and structured. It will probably also need to be repeatable. That’s where the phrase rubbish in, rubbish out comes from. If you put a load of unclean, higgledy piggledy data into a ‘system’ you will definitely get very unclear answers out. In order to be able to focus on the right outcomes, to get the intelligence you need and why you are doing this, you have to get your data sources sorted.

That’s the core job of a data engineer. They serve up a reliable pipeline of data for analysis. They will do this in a efficient way in order that those services are scaleable and are able to be put into production.

We talk about being able to ingest data. You will have many sources of data. Most of this will be housed in systems that may be difficult to extract from, that can’t be joined up, that comes out a bit gobbledy gook, that has weird characters in it. It’s often what is termed a non-tivial task. That means it’s hard.

Forward-facing Analytics

Go from reporting to advance analytics, from rear-view to forward facing

First thing, expect to do a lot of data engineering when you start your project, especially if your systems are old. With more modern platforms, it’s easier. But none of this is trivial. This again takes you some way up the pyramid of data needs, but still only into the realm of being able to do some business intelligence. This is usually performed on the data which is easy to use, transactions, product sales and feeds from more modern systems, such as your website. Often, via a data warehouse, this data can be linked together to be able to create more meaningful insight allowing for segment reporting and the linking of behaviours.

What data engineering really brings to the party is the ability to create data processing systems. These systems will create a data-driven ecosystem. This means you can move from point in time rear-view mirror reporting, to much more insightful advanced analytics. Which is exactly what you want.

4 Key Questions:

Four Key Questions to ask yourself:

  1. What data are you collecting?
  2. Is your data compiled in a central repository and is it accessible?
  3. Is your data mobile? Is it stuck in that database? Can it get out?
  4. Is your data organised in a meaningful way? Chronologically, by customer, by demographic, segment, category? So that you can use these to be able to tell the data story for your business.


How you are able to answer those questions will help you understand how much engineering you require. It may be that your data warehousing is starting to feel a little out of date. You may want to review your architecture as you identify data sources that you want to use. We’ll take a look at this when we look at data strategy.

It is also important to note that Data engineering will also take you some way in order to be able to use and utilise some algorithms and some machine learning off the shelf. You will potentially be able to run some projects. But, the major role of engineering is to be able to continue to provide high quality data for the data science team. It is the data science team that can then create the custom AI and ML models that go into production.

Real-time decision making

Data engineering gets your data ready to enable real time decision making. The pipeline and speed of data that they deliver can create the significant value that is delivered via the AI models. It has to work together.

Where we see failure is where engineering is being expected to be done by the science team or the science team are doing the engineering role. It is so important not to get these roles muddled up.

But that is also why we are focused on making sure that we have more efficient processes for getting to grips with your data.

Key Takeaways

Data Engineering is there to create a pipeline of quality data that can be analysed.

Key skills include being able to meld different technologies to create the right solution.

Help activate end-to-end data driven decision making.

Good data engineering creates consistency and reduces risk.

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