8 Reasons Why Data Science Projects Fail (And How to Avoid)

Data Science has been booming in the past few years. From autonomous vehicles to intelligent content moderation systems, it has been at the core of every innovation. Moreover, it has given businesses a whole new perspective on how they view analytics and can use the customer data to their advantage.

Data science’s importance can be realized from the famous quote by Peter Skomoroch – Former Principal Data Scientist at LinkedIn.

“[Data scientists are] able to think of ways to use data to solve problems that otherwise would have been unsolved or solved using only intuition.”

However, as much as data science is helping the technological world advance, it’s no wonder that data science projects often end up wasting more resources than the value they add. The projects are usually long-term, and things don’t always turn out to be the way they were planned to.

So, why does practically only 1 out of every 10 projects make it? Where does the problem exactly lie? Worry not, as we’re going to investigate it thoroughly in this article today. Moreover, we’ll also see how you can avoid the factors responsible for most failures, to make it amongst the 13% of the teams that successfully get their projects up and running.

So, let’s get going without any further ado.

Why Data Science Projects Fail?

According to VentureBeat AI, 87% of data science projects never make it to production. Key reasons for projects failing are challenges in integrating solutions into the specific business problems, siloed data, changing business needs, and how well the organization’s goals are aligned with the project.

Meeting organizational goals is more than merely using algorithms and analytics. You have to have a specific vision. Solutions shouldn’t be based on ‘data’ alone, but instead, based upon the process and the people they’re affecting, with data acting merely as the ladder to help you reach there.

Hence, one thing is for sure; technology isn’t at fault here; people are. The top priority that should be kept in mind while designing solutions should be the people it will affect and the organizational goals it’s going to meet. It should never be about the kind of algorithms or analytical techniques you use.

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8 Reasons Why Data Science Projects Fail and How to Avoid Them

Now that we have a basic overview of where organizations are lacking while implementing the data science projects, let’s dig deeper into the specific reasons why most of the projects cannot reach the production level.

Siloed Data

When it comes to developing a new model, data is of the foremost essence. It acts as the most necessary fuel for the project. However, unfortunately, most of the data in practical problems are siloed; it’s spread over multiple databases in a variety of formats. Not only is it hard to collect such data, but also quite cumbersome to transform and later store all of it at a central location for easy access.

Added to the above, controlling the quality of data is another issue since low quality or inaccurate data could even result in a backfire to your model, making things even worse. So, organizations have to be very cautious. Unsurprisingly, most of the businesses new to data science fail at this very stage. It takes months to get the data at a single place, and even if it’s successfully done, the project costs already become unbearable till that time and carrying on becomes a risk.

Integration of Solutions with Key Business Needs

No matter how many actionable insights you successfully mine from the data, if you cannot integrate them successfully within your business hierarchy, they’re as good as gone. 

The results generated by the data science team are needed externally as much as they’re required internally, through a proper channel, of course.

When your team is generating the results as desired, they need to share them with other departments as well. So, if you’re also one of the people who’re generating the results but not appropriately channeling it, make sure you start doing it as soon as possible so that the relevant people can also get their hands on the data they need.

Another thing to keep in mind here is the transparency between the technical and non-technical teams. While the data scientists mostly have their eyes on the accuracy of models, the non-technical personnel are often looking at the results using other metrics, such as insight or financial benefits being achieved.

This lack of alignment often causes miscommunication within the hierarchy and needs to be adequately addressed using adequate channels. Moreover, the data should be conveyed in a form understood by the non-technical teams and with minimal jargon.

Over-Complexing the Problems

Overly complexing the problems is something every data scientist struggles with. Even when the simplest of the solutions could suffice to some problems, data scientists tend to add unnecessary complexities to the problem statement, making the solutions just as complex.

The thing is, solving a problem not always require rigorous mathematical and statistical concepts. It requires pinpointing the exact questions that need to be addressed. However, data scientists are sophisticated people who like to back their statements up with data and complex calculations.

Not only does this increase the time needed to come up with an efficient solution, but it also ends up using too many resources, putting the project’s continuation at risk. Hence, your approach should never involve getting things as complicated and fancy as possible, instead aim to tackle the problems as simply as you can. Because the simpler the problem is, the easier and quicker the solution gets.

Why Data Science Projects Fail?

Stakeholders Participation

Stakeholders’ participation is necessary for all commercial projects, let alone data science projects. However, the reason why it’s is a lot more important here is that data science projects tend to be agile in nature. 

It’s not long before the development team sees an alternative approach or more effective algorithms and shifts to them.

Moreover, data science projects are long-term, and stakeholders’ disengagement could leave the organizational goals isolated and the required solutions in the dark. So, the stakeholders’ continuous involvement is the key as they’re going to be the ones getting all the value from the project in the end.

Moreover, if the stakeholders stay in contact but not very frequently, they could surely provide their feedback, but there’s a chance it might be too late to entertain their feedback when they suggest it.

Alignment with Business Goals

Data science is all about helping businesses grow themselves and providing them with more efficient ways to target their customers while minimalizing resources consumed. Hence, being able to shape the project according to the business goals is crucial. Failure to do so could never result in a successful data science project, no matter how advanced technologies you use or how skilled data scientists you hire.

So, always ensure the business goals are your priority, not the technology or the tools being used. Even if you’re using a low-level algorithm, you’re good to go if it’s delivering the results as required by the business objectives. Spending resources on solutions that drift off your initial goals is never fruitful.

Shortage of Skills

Ever since the dawn of data science, companies are facing a hard time finding the talent they want. Since data science is a multi-disciplinary field that packs a lot of sub-fields under its hood, it’s not so easy to find people mastering all the skills. Not only do data scientists need to have a strong grasp of mathematical and statistical concepts, but they also need to have a solid command of programming languages and other analytical tools.

Eventually, companies end up recruiting a whole team of professionals to cope with the need for various skills, instead of just data scientists. This, in turn, gives rise to further complexities such as transparency issues, coordination, and so forth. More importantly, it increases the budgets as well, way beyond what they had initially apprehended.


Unjustified ROI

If you can develop something for the comfort of your operations, it doesn’t necessarily mean you NEED it. Most of the time, data science projects are initiated out of goals that could never justify the budgets. The investment made into the project is way more than the value it could provide. Such projects are not thought of thoroughly and the ROI isn’t calculated properly.

Before starting any project, you should analyze it critically and make some hard, but important questions about its ROI. For example, a business may develop a machine learning model for analyzing its HR performance, hoping that it would save the HR resources being wasted and work without any manual intervention.

Unless the organization is relatively large-scale based on hundreds of employees spread into multiple locations, the HR can manually handle it. Starting a data science project, on the other hand, would require numerous resources. Multiple data scientists would need to be involved. Quality assurance would need to be done, bug fixing, and so forth.

Eventually, considering the project isn’t already dropped midway due to the shortage of funds, it doesn’t provide enough value for the money invested.

Changing Business Needs

As I mentioned previously, data science projects are not something you can accomplish in a short time. They are generally long-term projects since the pipeline involves many steps, and the analytical part alone takes much time. According to an estimate, data scientists spend 60% of their time only cleaning and organizing the data!

So, what happens is that before the data science project could be completed and integrated into the business model to meet the needs, the business changes its direction, and the scenario gets completely different. The needs are changed; the goals might also be a bit altered. As a result, the same data science project might not be as relevant anymore, and there’s no other way than to waste the efforts of months of the data science team.

Not only is this a huge waste of resources, but this also results in the data scientists losing commitment and motivation in future projects as well. The only solution to this specific problem is top-notch management that doesn’t focus only on short-term goals. 

The future prospects of data science projects should be strictly kept in mind in the planning phase. Moreover, if the business is evolving in nature, it should be analyzed where the project would stand when the business experiences a paradigm shift.


While data science has certainly played its part in bringing commendable innovations in the past decade, it surely hasn’t stayed back in draining a lot of resources as well. There are hundreds of stats showing data science projects fail way often than they succeed. What’s even more bothering is that these trends don’t seem to hit a stop!

Throughout the article, we’ve taken a detailed look at the most frequent reasons why data science projects end up as a failure. Moreover, we discussed the tips you should keep in mind if you don’t want your project amongst those failures. So, make sure you go through the article thoroughly and keep in mind to avoid all the mistakes people make in their data science projects.

There you have. Top 8 Reasons why data science projects fail and how to avoid them! Really hoping that you can get interesting insights here and increase the success rate of your data science projects.

Emidio Amadebai

As an IT Engineer, who is passionate about learning and sharing. I have worked and learned quite a bit from Data Engineers, Data Analysts, Business Analysts, and Key Decision Makers almost for the past 5 years. Interested in learning more about Data Science and How to leverage it for better decision-making in my business and hopefully help you do the same in yours.

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