The 3 biggest mistakes to avoid when it comes to data pipeline implementation

All day, every day, businesses are creating massive volumes of data. Contained inside that data is the insight needed to bring better products and services to market. It contains the intelligence needed to better understand who they are serving, thus allowing them to always get the right solutions in front of the right people at precisely the right time. That, in essence, is what a data pipeline is all about facilitating – unlocking the true story hidden beneath that information so that organizational leaders can always make the right decision at exactly the right time.

At its core, a data pipeline is a series of steps that help process that data. The pipeline is made up of A) the source of the data in question, B) a series of processing steps, and C) the destination for what is uncovered.It’s an invaluable resource for organizations in every industry that you can think of, but when implementing one of your own there are a number of potential pitfalls that you can encounter. Avoiding those issues isn’t necessarily difficult, but it does require you to keep a few key things in mind.

Mistake #1: Failing to include users in the beginning of the process

One of the biggest mistakes that organizations commonly make when implementing a data pipeline involves failing to get the insight of the most important people of all: those who actually need that data to do their jobs on a daily basis. Remember that when it is not leveraged properly, data is little more than a series of 1s and 0s sitting on a hard drive somewhere. If the only insight being given during this process is at the leadership level, it could ignore the very real needs of those operating on the “front lines” of the business. Therefore, almost before the implementation process even begins, all key stakeholders need to be engaged to find out what they need in terms of a solution. This involves conversations about communication and collaboration between departments and more.

Mistake #2: Failing to understand the difference between structured and unstructured data

Another mistake that many businesses make during this process involves the assumption that all data is created equally. Not only is data quality of paramount importance, but paying attention to whether or not data is structured or unstructured is a key to success as well.

Structured data is anything that has already been processed. Depending on your industry this can include customer information, invoices, information drawn from sources like an enterprise resource planning (ERP) system and more. Unstructured data is essentially the opposite – it’s a term referring to typically large sets of data that aren’t stored in a structured database and that thus need additional processing before they can be of use to the organization.

If you pull both structured and unstructured data into the same data pipeline, you’ll essentially end up in a similar situation to the one that you were in when you started. You need to be mindful about what data you are working with and, most importantly, where it is coming from, to derive the maximum amount of value from your efforts.

Mistake #3: Underestimating the migration process

Finally, it’s important to not underestimate how complicated the migration process to a new data pipeline can be – particularly if you are coming from some type of legacy system that has been used for many years. This is common of older organizations in particular who have not updated their underlying IT infrastructure in quite some time.

In no uncertain terms, don’t take anything for granted. Always be sure to review the documentation for any database engine you plan on investing in as thoroughly as possible. The only way that you will be able to write adequate queries to meet your needs is if you know the ins and outs of the database engine itself. A failure to do so won’t just result in it taking a far longer amount of time than it should to bring your data pipeline to life, but it will also probably create situations where you’re creating many small problems that add up to much bigger hassles down the road.

The Lytics approach

At Lytics, we pride ourselves on the reputation we’ve been able to earn over the years as a customer-focused data platform provider. We leverage machine learning, artificial intelligence and similar technologies to help organizations accomplish all of their goals. This includes breaking down the types of data silos that harm internal collaboration, improving communication between departments and more. We also aid in improving data security, data governance and other important matters.

If you’d like to find out more information about some of the most common mistakes that you should avoid when implementing a new data pipeline (or an ETL pipeline), or if you just have any additional questions that you’d like to discuss with an expert in a bit more detail, please don’t hesitate to contact Lytics today.