CTS Layoffs & Why Data Science Failed in Predicting Election Results

Over the past few weeks, we have seen many companies and analysts attempt to predict election results using Data Science.

However, most of these predictions turned out to be incorrect.

Why does this happen?

If we look at the situation from a technical perspective, the biggest issue is often data collection and sampling.

In any data science project, the quality of the outcome depends heavily on the quality of the data.

If the data collected is biased, incomplete, or incorrectly sampled, the predictions generated by the model will also be flawed.

In simple terms:

Bad data leads to bad predictions.

The Role of Data Engineering

This is where data engineering plays a crucial role.

Before any model is trained or deployed, the data must go through multiple processes such as:

• Data collection
• Data cleaning
• Data transformation
• Data pipeline management

These processes ensure that the data used by machine learning models is reliable and noise-free.

Without strong data engineering practices, even advanced AI models cannot produce meaningful results.

If poor-quality data is fed into AI systems, the outputs may contain bias, incorrect predictions, and misleading insights.

This is why data engineering has become the backbone of modern AI systems and technology platforms.

What the CTS Layoffs Tell Us

At the same time, the tech industry is also seeing restructuring and layoffs. Recently, Cognizant Technology Solutions announced plans to lay off around 12,000 employees as part of its transformation strategy.

Many companies are now shifting toward automation, AI-driven systems, and higher productivity models.

This raises an important question:

How can professionals stay relevant in this changing technology landscape?

Becoming Indispensable in the AI Era

One key lesson is simple but powerful:

Deliver projects and create real value.

In the AI era, companies increasingly value professionals who can:

• Solve real-world problems
• Build practical systems
• Deliver working solutions
• Take ownership of outcomes

Tools and technologies may evolve, but the ability to think critically and deliver results will always remain valuable.

Friday Talk with Aru – This Week’s Episode

In this week’s episode of Friday Talk with Aru, I discuss these topics in detail:

• CTS layoffs
• Why many data science projects fail
• How engineers can adapt in the AI era

🎧 Listen on Spotify:
https://open.spotify.com/episode/4W61STwTmWIUsAy6nckSAz?si=gnK4-D4nRNSrE9HYEqQvKQ

The episode is also available on YouTube.

Happy Weekend!

If you have feedback or thoughts, feel free to reach out:
https://www.instagram.com/transformwith_aru/