Last updated on Mar 29, 2024

What are the most common data engineering challenges in ML projects?

Data engineering is the backbone of any machine learning project, as it involves collecting, processing, and transforming data for analysis and modeling. However, data engineering also poses many challenges that can affect the quality, efficiency, and reliability of ML solutions. In this article, we will discuss some of the most common data engineering challenges in ML projects and how to overcome them.

1 Data quality

One of the most critical aspects of data engineering is ensuring that the data is accurate, consistent, and relevant for the ML problem. Poor data quality can lead to biased, inaccurate, or ineffective models that fail to meet the business objectives or user expectations. Some of the common data quality issues include missing values, outliers, duplicates, errors, noise, and inconsistency. To address these issues, data engineers need to apply various techniques such as data validation, cleansing, normalization, imputation, and standardization.

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Jasmine Josan BComm, PD Bus Admin, MS Azure, Certified PM / IT

"Your Partner in Business Transformation. Leveraging Technology to Overcome Obstacles & Achieve Goals"
ML is often used to launder old systems' decisions and pass accountability. Machine learning projects are no different from any other kind of project that starts on the back of a napkin and has a lot of assumptions that have to be tested at the beginning and along the way. Keeping the algorithm fixed and varying the training is really in my view, the most efficient way to build the system
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2 Data integration

Another common challenge in data engineering is integrating data from different sources, formats, and systems. Data integration involves combining data from various sources into a unified and coherent view that can be used for ML purposes. However, data integration can be complex and time-consuming, as it requires dealing with heterogeneous data structures, schemas, formats, and semantics. Some of the common data integration challenges include data inconsistency, duplication, incompleteness, and conflicts. To overcome these challenges, data engineers need to use various methods such as data extraction, transformation, loading (ETL), data pipelines, data lakes, and data warehouses.

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Jack Dyer

Founder at Leftshifted | Contract Data Consultant
Starting data science projects before you’ve done the foundational data work can really damage the data science roadmap. You absolutely need data, in the format required for data science, with enough history to learn patterns from, in an accessible place. Typically this requires planning and automation from a data engineering function, or someone to effectively be performing a data engineering task. Once this is done, it’s important to get to an MVP fast, then iterate. If you don’t have a model yet, you’re not driving value. Get a prototype model in the ground with even a handful of features to prove out a simple pipeline. Once you find out which features are predictive and valuable, you can go back and improve them.
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3 Data scalability

As ML projects grow in scope and complexity, so does the amount and variety of data that needs to be handled. Data scalability refers to the ability of data engineering systems and processes to handle large volumes, velocities, and varieties of data without compromising performance, reliability, or quality. Data scalability can be challenging, as it requires dealing with issues such as storage capacity, processing speed, concurrency, availability, and security. To achieve data scalability, data engineers need to leverage various technologies such as cloud computing, distributed systems, parallel processing, and streaming data.

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4 Data governance

Data governance is the set of policies, standards, and practices that ensure the proper management, security, and compliance of data throughout its lifecycle. Data governance is essential for ensuring the trustworthiness, accountability, and transparency of data and ML solutions. However, data governance can also be challenging, as it involves dealing with issues such as data ownership, access control, privacy, ethics, and regulations. To implement data governance, data engineers need to collaborate with various stakeholders such as business users, analysts, developers, and regulators, and use various tools such as data catalogs, metadata management, data lineage, and data audits.

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5 Data operations

Data operations, or DataOps, is the practice of applying agile and DevOps principles to data engineering workflows. DataOps aims to improve the efficiency, quality, and reliability of data engineering processes by enabling continuous integration, delivery, testing, and monitoring of data and ML solutions. However, DataOps can also be challenging, as it requires changing the culture, mindset, and skills of data engineering teams, as well as adopting new tools and frameworks. To adopt DataOps, data engineers need to embrace collaboration, automation, iteration, and feedback loops.

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Jasmine Josan BComm, PD Bus Admin, MS Azure, Certified PM / IT

"Your Partner in Business Transformation. Leveraging Technology to Overcome Obstacles & Achieve Goals"
A hugely underrated part of the job (and every job) is building relationships and communicating. I know it sounds oversimplified and tired but it's true. Spending a lot of time planning and executing not only our products but how we communicate them to management is very crucial. Data Science can never work without proper leadership and this is beyond the capacity of data scientists. If there is any way to even metaphorically sit with your stakeholders, get to know them, and for them to get to know you, that is the best way to help them.
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6 Data innovation

Data innovation is the process of creating new value from data by exploring new possibilities, opportunities, and insights. Data innovation is crucial for staying ahead of the competition and meeting the changing needs and expectations of customers and users. However, data innovation can also be challenging, as it requires dealing with uncertainty, risk, and complexity. To foster data innovation, data engineers need to cultivate a culture of curiosity, experimentation, and learning, as well as use various techniques such as data visualization, data science, and ML.

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Jasmine Josan BComm, PD Bus Admin, MS Azure, Certified PM / IT

"Your Partner in Business Transformation. Leveraging Technology to Overcome Obstacles & Achieve Goals"
"ML" doesn't mean 100% automation and 100% success and it's uneasy to convey that message to the policymakers. -Start by understanding the Company well, what does the company produce? -What are the key metrics? What are the key decisions? -What parameters are involved in those decisions? --Which of those can you improve with ML? -How much would that impact the metric? Partnering with like-minded in the organization is the key.
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7 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?

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What are the most common data engineering challenges in ML projects?

1

2

3

4

5

6

7

1 Data quality

2 Data integration

3 Data scalability

4 Data governance

5 Data operations

6 Data innovation

7 Here’s what else to consider

Data Engineering

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What are the most common data engineering challenges in ML projects?

1

2

3

4

5

6

7

1 Data quality

2 Data integration

3 Data scalability

4 Data governance

5 Data operations

6 Data innovation

7 Here’s what else to consider

Data Engineering

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