Currently, digital information is one of the main cornerstones of the business world. Organizations from all kinds of industries are looking for optimal ways to utilize it for lasting growth. After all, there is a lot of volatility in the modern day and being equipped to deal with it is a must for company leaders.
“Digitally native organizations that are “insight-driven by default” show much higher resilience and are able to tighten their dominant market positions, even growing share value while stock markets tumble. These organizations are equipped to manage the crisis better, and are expected to recover and excel faster once markets and regulatory efforts return to normal.”
— Deloitte
To become more data-driven, companies are increasingly turning to data engineering for help. However, as the technologies surrounding digital information are constantly evolving, there are several data engineering challenges that your company may face on its journey.
So, in today’s post, we want to shed light on some of these common difficulties and how you can overcome them. That way, should any arise during your data engineering process, you’ll be prepared.
How Does Data Engineering Work?
As you know, companies often have a multitude of data sources. ERP systems, CRM tools, inventory management solutions, and the like. All of this software generates valuable details that can be used to fuel business growth. However, to capitalize on this properly, all of the digital information has to work together, and this is where the concept of data engineering comes in.
In simple terms, data engineering is the process of building platforms for collection and usage of digital information in a way that benefits an organization. It is done to help manage the flow of data and to develop a comprehensive infrastructure that fuels business intelligence.
Data engineering will often involve the development of ETL and ELT pipelines, creation of data warehouses or lakes, and implementation of various types of data analysis. So, it is quite a wide-ranging practice, but definitely, one that many companies can benefit from.
Discover the Differences Between Data Lakes and Data Warehouses
5 Common Challenges in Data Engineering
Since data engineering projects are gaining popularity and use cases are growing in complexity, there are a few challenges that teams may encounter along the way. Below, we’ll discuss five of the most common ones and share what you can do to deal with them or to bypass them altogether.
1. End User Understanding
When your data engineering team starts working on a new initiative, it’s easy for them to get caught up in the nitty-gritty technical details of architecture design and lose track of what’s most important. Prioritizing the needs of end users.
You see, depending on the people who will be using particular data engineering tools, the expectations of what they should be able to do will vary. For example, a data pipeline that is created to support business analysts might be different from the one that is developed for the use of experienced data scientists.
Lacking the understanding of what your particular user group requires can create a real challenge within a data engineering project. So, make sure your team has conversations with a solution’s target audience and always maintains their needs front of mind.
User-Driven Approach
Watch our webinar and learn the top ways of reducing poor user satisfaction, low adoption rates, and decreased loyalty.
2. Data Management
Every day, more and more digital information is created by businesses. Data from mobile apps, IoT devices, and other platforms is constantly generated. It’s easy to get overwhelmed by the seemingly never-ending influx of data and having to face inconsistencies in values.
You see, if many systems are using the same digital information and there are no real-time updates, inaccuracies can appear. Naturally, this is something you want to avoid because poor quality data does nothing for your business.
A possible solution to this data engineering challenge is to establish a comprehensive data management strategy with a data governance plan. Doing so will help ensure that all data-related activities have someone in charge and that there are policies in place that help maintain integrity of all your digital information.
3. Regulatory Compliance
Another data engineering challenge you may encounter is having to deal with regulatory compliance. If your business operates within the finance sector or the healthcare industry, data-related regulations like HIPAA, PCI DSS, and GDPR are likely to affect it.
Read up on HIPAA Compliant App Development
On the regulatory landscape, things are always evolving, and ensuring that company operations are adhering to the latest requirements is a must. Unsurprisingly, this can pose a challenge.
The best way to deal with this is a combination of practices. Of course, it’s a good idea to keep monitoring any laws that may affect your business or even hire legal counsel. However, another good option is to work with data engineering specialists that have expertise in building compliant platforms and can share best practices with you.
4. System Integration
One of the primary goals of any data engineering project is to effectively connect disparate information sources and integrate data from a range of solutions. That, in and of itself, can be a challenge when you’re dealing with legacy systems that simply don’t have the built-in capabilities of connecting with modern software.
Find out how we performed VoIP System Integration with a CRM
In this regard, it’s a good idea to start by modernizing legacy software prior to doubling down on data engineering initiatives. Doing this before the start of a project will help minimize integration headaches down the line.
5. Human Mistakes
The last data engineering challenge we should mention is the human factor. Unfortunately, this one might be present across any software project your company chooses to pursue. After all, no matter which solution you’re building, it’s people that are working on it.
So, the human factor will always play a role. Yet, if you have proper data management practices and policies in place, you can reduce the risk of mistakes that leave a lasting impact.
Begin Your Data-Driven Journey
Preparation is key when you’re starting any data engineering project. Now that you’re aware of some common challenges that may arise along the way — you’re better prepared to handle them.
However, if you’re looking for some specialist advice or want to discuss a concrete initiative — don’t hesitate to reach out to our team. Velvetech’s experts are highly skilled in delivering successful data engineering services and would be happy to guide you on your journey or take development work off of your hands.
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