In this comprehensive guide, we look at all
    aspects of data engineering to understand
    how this fundamental discipline is the
    foundation for all things data.

  • Introduction to

    Data Engineering


    So what is data engineering? And why does it matter?

    Perhaps this is best explained with a metaphor. Imagine that it’s 1970, and you’ve just opened a bookstore. For the grand opening you stock your store with the 10 most popular books from the New York Times bestseller list, but soon realize that customers are looking for a variety of books to purchase in addition to the most popular books. So you add books from the top-20, then the top-100.


    But now customers get confused when they enter the store, because you’ve gone from stacking books in 10 piles to stacking them in 20 and then 100 different piles. Worse yet, customers start mixing the different piles, which makes it even harder for them to find what they want.


    So you add a system of bookshelves, putting the books in order of popularity. Customers entering the store can now walk past the most popular books and make their way to the slowest sellers.


    But then certain customers ask you to make special orders.

    You decide that it makes sense to order a few extra since it’s a sure sign of demand.


    See a pattern here? This constant improvement and iteration of the organization of this bookstore’s data has strong parallels to the discipline of data engineering.


    Data engineers are charged with the mission of helping their company to organize its data in such a way as to be not only accessible to the widest audience, but useful for their particular needs. Some employees just need to look at a chart. Others need to do comprehensive analysis. And other employees need to structure the data to allow for advanced use cases such as machine learning and artificial intelligence.


    Data engineering is the discipline devoted to making all this possible, while simultaneously dealing with an ever-changing landscape, both internally and externally.

    Learn More

  • Why bother with

    Data Engineering?


    Again a metaphor will help. 

    Imagine your company as being either a shed, a house, a multi-family complex or a high-rise tower. The owner of a shed probably doesn’t spend much time concerned about engineering permits or maintenance since this person also doesn’t need to worry about maintaining services like heating, running water or electricity. And if the shed isn’t going to be upgraded anytime soon, there is little need to plan for future changes.


    The owner of a house will need to devote significantly more effort to keeping things running when compared with a shed, but even then they won’t need to concern themselves with the needs of themselves and their family.


    A multi-family complex requires more planning, more resource integration, and a more structured approach to dealing with issues as they arise, particularly since multiple tenants will live in the unit.


    And a high-rise tower will have a completely different set of engineering requirements, permitting requirements, safety requirements, and so on.


    And a high-rise tower will have a completely different set of engineering requirements, permitting requirements, safety requirements, and so on. The high-rise tower will also be much more complicated to manage, particularly if things like fixtures have not been standardized across different units in the building.


    See a trend here? The more complicated the structure, the greater the need for better infrastructure. More subtle is the fact that earlier planning becomes even more important for bigger buildings, as mistakes and omissions can become costly later on.


    The same holds true for data engineering within a company. While the smallest companies may not have an immediate need for tools and structures to support data access, companies with aspirations to become bigger will need to consider this issue at some point in their growth. And the longer they wait, the more expensive the effort will be, with more work required to fix past mistakes and omissions.

    Learn More

  • Developing a

    Data Strategy

    Without a clear data strategy, companies run the risk of carving off small data projects (or worse yet no projects at all) that don’t connect, resulting in a sum that is less than the parts.

    Forward thinking companies understand the value of knowing where they are, and where they want to go. From a data perspective, this assessment involves understanding where they fit in the analytics maturity model.

    The majority of companies that have not formally developed a data strategy tend to fall in the descriptive category, performing basic reporting. Those that make a conscious decision to focus on improving their reporting will advance to the diagnostic phase of their journey, where they not only centralize data

    to improve the efficiency of their reporting, but also examine the question

    of “why” when doing analysis. More advanced stages of maturity include predictive and prescriptive, but in reality few companies achieve this level

    of expertise across the entire organization.


    One common mistake made by many companies is to jump into advanced stage use cases without getting the basics right, or even knowing where

    they are in terms of analytics maturity.

    Doing so not only potentially ignores lower-hanging opportunities for improvement, but in the worst case may set the company up for failure if it chooses to take on a project that ignores earlier stage prerequisites. For example, a company looking to do predictive analysis to support production planning will not be able to efficiently or reliably perform analysis if all the source data is spread across hundreds of spreadsheets managed by dozens of people.


    At 3AG, we developed the Data Coach with the specific purpose of helping companies to assess their current analytics maturity level, their target, and the necessary steps required to achieve this goal. Other organizations also offer similar roadmap development services – the important this to consider is that you have a clear plan of attack.

    Learn More

  • Data Governance

    Data governance is where your company gets to set it policies related to

    how data is collected. It is tightly integrated with data strategy in that

    it is the implementation of the rules defined in the former.


    A solid data governance policy will not only look to identify which sources to consolidate, but it will define who will be able to access data, when the data is pulled, and what do to when conflicts might occur. The governance policy also defines other issues such as retention policies and how to integrate new sources of data as they come online.

  • Wrapping Up

    We hope that you have found this resource helpful. Data engineering is a complex topic, made more so by the fact that there are countless ways for an organization to tackle it.



    For organizations looking to get more control over their data but who lack experience in this area, a guide can be invaluable. Consider bringing in experts who can give an unbiased assessment of your current situation, and of course we would be honored if you were to consider the 3AG Data Coach as an option.


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Customer service
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