5 data pipeline mistakes that lose business owners serious $$$

What is a data pipeline?

Being a business owner or someone leading a function, you’re obsessed with the numbers that drive your business forward and create the most value. Whether it’s sales, marketing or operations, you’re watching out for what moves the needle, and there is nothing more frustrating than numbers that don’t make sense, are missing or inconsistent.  

That’s where a data pipeline comes in. A data pipeline, also called a data engineering or processing pipeline, is exactly what it sounds like – a process through which data flows from its source to a predetermined destination to be used for various business needs. During this process, the data will also go through certain transformations to make sure it’s in the correct form before it can be utilised.  

As businesses realise the impact that efficient data processing and usage can have, demand for data engineering services is on the rise and the industry is expected to grow by 15% between 2023 and 2028.

How do you know that you need a data processing pipeline for your business, or need to rebuild the one you already have? Some indications might be:  

  • More data coming in than before, causing your spreadsheets to freeze.  
  • Discrepancies between data in the source and the destination.
  • Data failing to pull through on some days (coincidentally, right when you have to send an important email update quoting numbers for the day)
  • The time required to manually update data daily is increasing to the point where you don’t have available capacity in your team to accommodate this.  

If you have decided to embark on this mission of building a data engineering pipeline, you need to know the top 5 things you should NOT be doing if you want to invest your money wisely:


#1 Beginning the buildout without having a data architecture in place  

Not knowing exactly where you have to pull your data from, where it needs to go and what the reporting or visualisation layer should look like is a strategic error that may result in having to get the entire pipeline disassembled and rebuilt - incurring DOUBLE the amount of cost it would’ve taken to build right in the first place, not to mention the additional time you’ll have to wait to get the pipeline in place.  

Even if you have minimal data sources right now and relatively simple analytics requirements, having a data architecture in place will help you take a more structured approach towards building a data engineering pipeline. If this isn’t within your expertise, the best route may be to get in touch with a consultant who knows the ins and outs (book a free call with us to find out how Data Pilot can do this for you).  

#2 Not exploring multiple data extraction options  

You might have heard the term ETL - extraction, load, transform. Most often, this is the process being followed to build a data engineering pipeline. You want to make sure this is done in a cost-efficient manner but also with a high degree of accuracy, so the data is pulling through the pipeline at the required frequency and in the correct format. If the ETL process has gaps, the data being viewed by end users in the form of dashboards or reports will show discrepancies.  

ETL is also often the most time taking part of the process. It can either be done using an ETL tool or a script run using programming languages like Python. The latter is more time consuming, whereas the former is quicker but can be more expensive depending on which ETL tool you use. Researching and comparing different options before you start may help you identify areas where you can save a hefty cost or save time, depending on your priorities and budget.

#3 Ignoring scalability needs

Every business has a vision for how it wants to scale – this may be more defined for some than for others, also depending on what stage the company is at. This vision will define the roadmap for all business functions, and for a data-driven company, data will be at the center of all of these.  

Businesses today are more dynamic than ever before, operating in multiple locations around the world, engaging in international transactions and leveraging highly nuanced consumer information flowing in every minute of the day. To scale efficiently, knowing the volume of data you have today AND how it could grow in the future is essential in setting up a robust data processing pipeline. This ties in closely to how you store your data, and whether you have a data storage strategy in place.

#4 Not having a data storage strategy  

Each business requires a tailored approach towards data storage, depending upon the format and volume of your data, number of data sources, and your analytics needs. Knowing the answers to these questions will help you decide how you want to store your data, which is an essential piece for building a data engineering pipeline.  

An important decision, for instance, may be to choose between on premise and cloud-based storage. Increasingly, the top cloud-based services globally like Microsoft Azure, Amazon Web Services and Google Cloud Platform are marketing the scalability features that cloud storage offers, allowing businesses to ingest, process and store steadily increasing amounts of data. While this is a significant advantage of cloud-based storage, this type of storage also has the downside of posing higher security risks; comparatively, on premise storage may be more secure but will cost a business more to scale its services considering the hardware constraints involved (Source: Forbes).  

#5 Lacking buy-in from important stakeholders in your business

As a business owner, you may be privy to all the major pieces that need to be aligned to decide a data strategy that will then inform the creation of your data engineering pipeline. But to make sure you take into account the day-to-day problems that your teams are hoping to solve using data, you may want to involve the important stakeholders in your business from the get-go.  

Getting buy-in from the relevant people during the initial phases of data understanding, exploration and requirement gathering is key to building a data pipeline process that meets all your business needs. Marketing and growth teams, for example, will know exactly what the customer journey looks like, what the various touchpoints are within your marketing channels, website landing pages and CRMs, and where the major bottlenecks lie in the entire funnel. This knowledge will help inform which specific metrics can be monitored now based on available data, and what are the specific trend lines and charts that can be used to visualize these.  


Before you get to an exciting finished product like a PowerBI dashboard with beautifully built out visuals, you’ll need to go through an introspective planning process to make sure the inputs to your data pipeline process are set up strategically, so you don’t waste precious $$ building something that won’t work the way you need it to. To this end, it may help to do your research and get expert opinions on your data architecture and ETL process, clearly define business requirements with other stakeholders you work with and think about scalability needs.

By Manaal Shuja

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