Why Data Science projects fail?

Brilliant ideas/algorithms are easy; execution is the hardest.

Companies are racing for data scientists and eventually set them up for failure. For most organizations, data science is a huge black hole of resources ending in desperation after the initial enthusiasm. Many things can go wrong being the lack of resources is the most unlikely reason.

Even organizations that have already invested millions in integrating their data infrastructure into a fancy data lake will unlikely see results. Data science takes time, resources, but, above all, a strategy.

So, why Data Science projects fails:

1. Undefined business goals/processes. Just because the data science team can map and predict customer satisfaction rates, recommend best products for cross-selling or integrate free text into risk models, doesn’t mean that your company is going to achieve a positive ROI. If the results demonstrate low customer dissatisfaction, predicting it will not solve the roots of the problem. You need to define what are the actionable insights, and most importantly, a framework to integrate the outcomes of the models into the daily processes and change them. Using the Henry Ford analogy, it’s not about making a better horse.

Unfortunately, many organizations keep following the misconception that smart data scientists can correctly define the business goals. They can’t!

1a. Data Science is not IT.

on a side note, treating data science as IT another big mistake. Why?

  • DS is experimental and do not easily follow a set predefined requirement.
  • DS is not a one-off thing but a continuous process
  • DS is mostly about business and cannot be isolated from it

2. Inability to build and apply a uniform data set across the organization.
While the politics in your organization may have inspired silos and fiefdoms, each department in your organization is ultimately driving toward the same goal – increasing ROI. To maximize the value of data science, you need to create a uniform data set that will deliver actionable results that every part of the organization can implement.

3. Investment in “sexy” algorithms instead of useful ones. You have just hired the data scientist who graduated in the top of her class. He has some great algorithms that can get some really cool results. However, are those algorithms going to deliver the results that can focus your organization and drive it forward? Just because something is old, tired, tried and true doesn’t mean it won’t work.

In most cases, the challenge you are facing isn’t new. Start from a problem not from a solution perspective.

4. Inappropriate data or infrastructure. Most organizations are built in silos. Large organizations have both an ERP system and a CRM system and many others, but those systems may not be in sync. They may not even be touching the inbound marketing and sales software. Until everyone in the organization can work with the same data and analyze the same data, siloed data science results aren’t as applicable or effective. Sometimes it takes much longer to determine whether specific data or the volume of data is sufficient for the specific task.

5. Failure to differentiate between academia or research and the real world. Data scientists generally go from the ivory tower directly into the data silos. They generally deliver great results in most properly defined challenges. Remember, though, at the end of the data scientist’s job, the hard work may only be beginning. The engineers need to implement it into production – many times from scratch. Don’t expect your data science team to learn how businesses actually work. It’s up to the engineers and systems analysts to determine your specific system requirements to get their work into production.

The most critical result of any data science activity is relevancy. Every “answer” generated by your data science team needs to answer specific questions derived from the overall corporate strategy. While investment in the “bells and whistles” of AI is fun and exciting, organizations need to make sure that they have a defined strategy, the right idea and a properly mapped roadmap before the first dollar of data science investment is spent.



Knowledge transfer In Neural Networks knowledge transfer is a very powerful technique to improve performance across disparate domains. Likewise, in organisations the most effective way to make adoption best practices is by following step by step advice from our experience alongside industry.