Why do Data Science projects fail?

Introduction

Data Science is the new buzzword in the business world. Companies are trying to figure out how to make sense of their data and use it to grow their business. However, there are many projects which fail due to a variety of reasons. This blog post will discuss some common reasons why Data Science Projects fail. The clue is in the result of a poll conducted by BCG!! The result is in the image above.

Data Scientists are Expensive

Data Scientists are Expensive!! With the growing demand for data scientists, the cost of hiring one is not going down anytime soon. How much does it cost to hire a Data Scientist? According to Indeed, their average salary is $122,000 per year. The same survey conducted by Glassdoor showed that Data Scientists have an average annual salary of $133K (Glassdoor). In addition to the high salaries offered by these companies, there’s also a shortage of available talent in this field which has caused employers to pay higher salaries than ever before.

Who Is In Demand For These Positions And Why? Data Science jobs are highly sought after because they require strong technical skills and knowledge of programming languages such as Python or R; however, they also require creative thinking and problem-solving abilities, which can be hard for companies to find in today's workforce, so many companies turn towards outsourcing firms like ours who specialize in providing these types of services at reasonable prices with qualified candidates.

Shrinking timelines

You may feel like you're the only company that's struggling to keep up with the shrinking timelines of your data science projects. If so, be comforted to know that you're not alone: many companies are in this same spot and trying desperately to catch up with companies who have already adopted AI. However, one thing is for sure: if your company fails to move quickly enough, you'll be left behind by those surpassing you in the AI adoption race.

There are two main reasons why timelines for data science projects are shrinking so rapidly. First and foremost is because of advances in technology—it's now possible for a single person to do what took an entire team years ago! The second reason is that everyone wants a piece of what they perceive as being too big, not just yet but also forever...

Lack of focus on scalable tools and automation

Data Science projects fail because they lack focus on tools and automation. Tools and automation are important to reduce the time, effort and cost of a Data Science project. Data wrangling tools like Pandas, NumPy or R can be used to clean the data before it is analyzed.

Data preparation tools like Spark MLlib or H20 can help in creating training datasets that can be used for machine learning algorithms to create predictions based on historical data. Data management tools like Hive/MapR-DB can be used to store large volumes of data efficiently using file formats such as ORC, which are optimized for memory usage rather than disk space usage.

These days, most companies use open source software like Apache Hadoop/Spark to process big data sets stored in the HDFS (Hadoop Distributed File System) storage system. HDFS is also known as MapReduce, where MapReduce splits up large datasets into smaller chunks called JOBs which are processed by several worker nodes running on commodity hardware or cloud servers (Amazon EC2 instances). The challenge is that there is not enough automation to pass on the knowledge to other team members.

Lack of Focus on Business Problems

A data science project can fail for many reasons, whether it's a new product feature or a research paper. Some common issues include:

• Not focusing on business problems (e.g., "We want to use machine learning to power a new product." vs "Our customers are having trouble with this particular kind of task.").
• Focusing on tools rather than business problems ("We have bought the latest and greatest machine learning library!" vs "We need to help our customers understand these patterns".)
• Focusing on data engineering instead of business problems ("Here is the architecture for our new graph database.") vs ("We want to know these five things about our users.")

A good rule of thumb is that if you're not sure whether your project will succeed, then ask yourself which problem(s) you're trying to solve and how they relate to the success of your business as a whole

80% of efforts are spent on data wrangling and preparation, Data Engineering

The 80/20 rule is a widely used principle in business and applies to Data Science projects. According to this rule, 80% of the effort is spent on data wrangling and preparation, while only 20% goes into the actual analysis.

Data Scientists are expensive. The average salary for top-tier data scientists in New York City is currently $162K per year, but that doesn't include bonuses or equity options—and those are definitely two things you want in your team members!

Data Scientists are also in short supply: In 2016 alone, there were over 2 million open jobs for IT professionals across the United States, according to Burning Glass Technologies' Job Demand Index Report; however, only 86K candidates with relevant skills applied during that same time period (5%). This lack of qualified applicants means that some companies have resorted to hiring foreign workers through H1B visas; however, these visas cap off at 85k per year, so they may not be available if your company needs more than just one or two new employees right now!

Companies are unable to focus enough on data engineering.

Data science projects fail because companies don’t focus or not able to focus enough on data engineering.

Data scientists can be expensive and hard to find, but no one is more expensive or harder to keep than a good data engineer. The best data engineers are also the most difficult ones to train and retain, so it’s no surprise that companies struggle with these roles.

Let’s look at why this is an issue:

• High turnover rate - Data scientists leave their jobs faster than any other professional in tech (upwards of 50% annually), which isn't surprising given they often work in small teams where there aren't many opportunities for advancement or career growth beyond their current role. Data engineers don't fair much better either, with a 20% annual turnover rate—and that's just for those who stick around long enough to get promoted into management positions! Moreover, only 9% of the poll respondents want to become a Data Engineer and 25% to become Data Scientist, and astonishingly 60% of the respondents want to apply for a Data Analyst position in a Data Science project, accordingly to the poll conducted by BCG.

The above scenarios create an ideal environment for Data Analysts to get into Data Engineering to complement the shortage of Data Engineers by leveraging data platform that requires little or no coding.?

Conclusion

Data Science projects fail because companies don't focus enough on data engineering. They spend a lot of time wrangling and preparing the data but don't spend enough time automating it. Data Engineering is a field that's growing in popularity, but not many people know about it yet.?

Data Analysts want to work on a Data Science project. Unfortunately, many don't have hands-on data science or data engineering skills.?It's an exciting time for Data Analysts who want to work on Data Engineering by leveraging scalable no-code tools and automation that requires little or no coding! (Check out Quantumics for a no-code data platform)