What are some of they key things that cause big data projects to fail?

Q:

What are some of they key things that cause big data projects to fail?

A:

It mostly starts with unrealistic expectations and the wrong people doing the job. Big data is not an extension or the logical extension of data warehousing. It's much more complex. This often creates an ability gap in companies whose big data team is made up of database administrators. This why a data engineer or data scientist is often worth their higher salary. Adding big data to a person’s skill set is not easy and may not be possible for everyone. Data engineers and data scientists already have the skill set. Plus, even for those who are capable of learning these skills, it takes time and resources, which slows momentum on many projects.

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Written by Jesse Anderson
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Jesse Anderson is a Data Engineer, Creative Engineer and Managing Director of Big Data Institute.

He trains and mentors companies ranging from startups to Fortune 100 companies on Big Data. This includes training on current edge technologies and Big Data management techniques. He’s mentored hundreds of companies on their Big Data journeys. He has taught thousands of students the skills to become Data Engineers. He is widely regarded as an expert in the field and his novel teaching practices. Jesse is published on O’Reilly and Pragmatic Programmers. He has been covered in prestigious publications such as The Wall Street Journal, CNN, BBC, NPR, Engadget and Wired.

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