Developing a Successful Data Science Project

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data science project

While Data Science practitioners, aspirants, and enthusiasts often get caught up in the business benefits of Data Science, it is equally important to keep a close watch on the common pitfalls that need to be avoided to launch a successful Data Science project. By identifying and exploring why some initiatives fail, data scientists can learn to better leverage their data assets for maximum gain.

Let’s begin with two Data Science roles: data scientist and data analyst. A data scientist must investigate a business problem by asking the right questions, gather the required data from different sources and prepare it for analysis, extract the actionable insights and communicate the results to others, and finally, deliver data-enabled solutions for positive business outcomes.  

A data analyst works with a particular problem and finds results by organizing and analyzing the data provided. Organizations throughout the industry depend on data analysts to work collaboratively with different departments to analyze large data sets and historical data, perform A/B tests, and operate data visualization tools like Tableau. 

The Reality of Data Science Projects 

In the real world of business, data scientists spend the majority of their time preprocessing data – making sure that data is consistent – before they can analyze it. As a result, many organizations do not get the best out of their data scientists because they are not able to give them the proper context and feeds. Without established, well-defined methodologies to manage Data Science projects, organizations frequently fall back on an ad-hoc process for managing projects, which may result in poor information sharing, missing steps, and ill-informed analyses.

Here are some statistics related to Data Science projects:

  • In 2019, Gartner reported that “80% of analytics insights will not deliver business outcomes through 2022.”
  • In 2019, VentureBeat AI stated that “87% of Data Science projects never make it into production.” 

Common Data Science Project Pitfalls

What specifically is holding back so many initiatives ? Below are a few issues that might prevent a successful Data Science project – as well as probable solutions to help avoid them.

The Knowledge Gap

In the real world, practicing data scientists often fall short of adequate skills or experience levels. Data scientists who are new to their jobs must often spend long hours with mathematics, coding, descriptive statistics, inferential statistics, or data visualization to understand their data sets. This process can be intimidating and emotionally taxing. Another problem is that entry-level data scientists often list unrelated skills on their resumes just to impress potential employers. 

Too often, junior data scientists oversell themselves by showing off a lot of skills and experience on paper. As actual projects can become quite complex and labor-intensive, inexperienced data professionals who take on big projects early on may just be setting themselves up for failure. The data scientist must fit in seamlessly with corporate strategies since the ultimate purpose of Data Science is to drive and enhance decisions within their organizations.

Solution: Data professionals can explore Data Science competitions or online classrooms to augment their skills in Data Science, besides engaging in problem-solving and brainstorming exercises with lots of people. For example, the International Data Analysis Olympiad is designed to boost Data Science skills to adequately meet industry demands in this field. Since the challenges are to address problems and gain knowledge of Data Science, code submitted by winners is released under open-source licenses to enable everybody to learn and improve. 

Technology Complexity

For stress-free Data Management, the recent trend in many organizations is to mix and match cloud service providers, based on “best-fit capabilities.” This means choosing one cloud provider to govern Data Science and another one to govern applications. Is this approach really stress-free, or does it lead to more interoperability and compliance issues in the long run?

Solution: As modern-day Data Management is shifting to the cloud, the best approach is to discuss your business’s needs and goals with specific cloud providers to see if they can provide a desired solution. Mix-and-matching solutions in specific cases may be useful, but too many providers can cause technology mismanagement.

Data Quality Issues

Data sources need to be verified. Erroneous data will be useful for neither building models nor performing analytics. Improving Data Quality by ensuring your data is free from errors and of high quality will help to increase the precision and robustness of your models. 

Solution: Business leaders should consider choosing from the wide variety of AI- and ML-driven Data Quality management tools available.

Model Testing Issues

Overfitting is a major problem with training models, where overfit models trained on a few data points perform very well with the training data, but perform poorly with new, unseen data.

Solution: Models must be tried with undersampling or holding data sets. Additionally, it is a good practice to use a variety of testing and training sets so that you can ensure that the model is well-placed to summarize results. Data scientists must explain how models achieve precision, what features are crucial, why they chose a specific algorithm, how certain algorithms behave differently, and so on. 

Inherent Biases

Subconsciously, data scientists may inject biases into the algorithms and models they develop. The inherent biases within the source data are often overlooked. Ideally, the training data should be free of biases and truly representative of the intended data category.

Solution: Use automated data collection tools to remove biases from source data.

The Data Trap

The hardest part of Data Science is arguably not building a precise model or getting good, clean data, but rather it is identifying the business problems and coming up with sensible ways of developing solutions with the help of data. Data Science is about answering questions and discovering hidden insights, whereas analytics is more focused on processing the data itself and performing statistical analyses on data sets. 

Solution: Data scientists need to spend time understanding and evaluating the data sets before using them for any project. One approach is to use AI- or ML-powered data architectures and data platforms to automate all routine Data Management tasks so that data scientists can concentrate on understanding the business problems and exploring data-enabled solutions for the problems.

Lack of Leadership and Communication Skills

Modern-day data scientists need two essential soft skills to survive: leadership and communication skills. Data scientists have to work with other business stakeholders like the C-suite, domain experts, and business users. Without strong communication skills, they will never be able to communicate the problems they are trying to solve or the solutions. 

Solution: Experience is the best teacher: Working on real-world projects will provide first-hand exposure to teamwork and collaboration. 


Data Science involves skills from computer science, statistics, information science, mathematics, information visualization, data integration, graphic design, complex systems, communication, and business. Thus, it is a serious business requiring cross-discipline and cross-functional skills and experience. Aspiring data scientists should start slow and small to learn the ropes. Once they have worked through some projects solving real-world problems, they will develop an intuitive sense for how to approach each problem. 

In addition to building sophisticated quantitative algorithms and synthesizing a vast amount of information, data scientists need to be skilled at communications and leadership skills – both essential for creating a successful Data Science project and driving measurable, tangible results for the different stakeholders of the company.

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