Data Analyst vs. Data Scientist vs. Business Analyst

1. Data Analyst
- Primary Role:
Focuses on analyzing existing datasets to find patterns, trends, and insights. Their main goal is to provide reports and dashboards that help businesses make informed decisions.

- Skills Required:
SQL (for querying databases)
Data Visualization (charts, dashboards, reports)
Reporting & Basic Statistics
Data Wrangling (cleaning and preparing raw data)
ETL (Extract, Transform, Load) processes

- Common Tools:
SQL
Tableau
Power BI
Excel
Python (basic usage for analysis & visualization)

- Example Work:
Preparing monthly sales reports, customer segmentation dashboards, or visualizing marketing campaign results.


2. Data Scientist
- Primary Role:
Goes beyond descriptive analysis — builds predictive and prescriptive models using machine learning. They handle large, complex datasets and generate insights that can influence future strategy.

- Skills Required:
Advanced Math & Statistics
Programming (Python, R)
Machine Learning & Deep Learning
Data Wrangling & ETL Processes
Big Data technologies

- Common Tools:
Python, R
TensorFlow, PyTorch
Scikit-Learn
Hadoop, Spark

- Example Work:
Building a predictive model for customer churn, training NLP models for sentiment analysis, or developing fraud detection algorithms.


3. Business Analyst
- Primary Role:
Works as a bridge between business and technology. They focus more on understanding business processes, stakeholder needs, and challenges, and then suggest data-driven solutions.

- Skills Required:
Communication & Presentation skills
Stakeholder Management
Business Process Modeling
Problem-Solving & Strategic Thinking

- Common Tools:
Microsoft Office Suite
Business Intelligence Tools
Project Management Tools

Innovation isn’t always about big leaps — sometimes it’s about timeless design.
Look at the humble safety pin: from 1849 to 2025, its design has hardly changed. Why? Because it solved the problem so perfectly that it didn’t need reinvention.
A reminder for all of us in business and technology:
Start by solving the real problem.
Refine quietly instead of chasing novelty.
Great design lasts for centuries.
Whether you’re building products, processes, or teams — simplicity and reliability often win over complexity.
hashtag#Innovation hashtag#BusinessGrowth hashtag#Leadership hashtag#DesignThinking hashtag#Entrepreneurship hashtag#Simplicity hashtag#Technology hashtag#ProductDesign

The recent MIT research on enterprise AI use has often been incorrectly reported as 95% of AI fails.

The proper headline is 95% of AI approaches fail.

Why?

Last summer we shared concerning research - enterprises spending $5m on $1m problems.

Early this year we published analyses on how such directionless spend and immoderate expectations from AI could soon tank sentiment below the rising potential of AI, leading to missed opportunities.

We see 5 common patterns at firms with underperforming programs from case work and discussion with executives, many of which are alluded to in the study:

1) Setup AI committees and innovation labs with sprawling red tape, low velocity and ivory tower thinking.

2) Appointed bureaucrats to lead the program who thought it would be a wonder drug and lacked the intuition and agility to cope with applying AI.

3) Lacked a strategic plan and let overzealous engineering and data teams run without clarity to develop in-house AI wrappers and tools, often for commodity solutions available in the market.

4) Spent little effort on user experience, workflow integration and managing adoption and behavioral change.

5) Ignored the hard task of designing operating model shifts. Executives looking to shave off weekly 4hrs/FTE had no plan to leverage the 10% productivity gain. More time for cafeteria foosball?

The committees have had great fun, held countless meetings, massaged metrics on slides, churned out platitudes and burned millions of corporate dollars,

Meanwhile, no one else has had fun. Many workers are quietly using shadow AI where they can for personal productivity gains. And AI shows up as cost on the P&L to fund this bureaucracy.

This is the structure of technology revolutions. Years ago similar reports stated that most digital transformations fail yet many companies ultimately found value. An inability to understand novel developments and resulting frame shifts leads to fear, uncertainty and unwise deployment of attention and capital.

Some organizations learn and thrive, new natives emerge and others get bought or fold. Fundamentals of human and organizational behavior and fallibility hasn't evolved much if we study history keenly.

hashtag#ai hashtag#transformation hashtag#mit

Six E’s of Leadership

Leadership in today’s dynamic world goes beyond authority—it is about inspiring, guiding, and empowering people. Dr. Timothy Tiryaki’s Six E’s of Leadership offers a modern framework for effective leaders.

The first is Envisioning, where leaders act as strategists, shaping the future with a clear vision. Next is Executing, which highlights the leader’s role as an operator who ensures plans turn into results. Enabling emphasizes the digital integrator role, helping teams adopt technology and innovation for growth.

Equally important is Empowering, where leaders mentor and coach, building confidence and skills in their teams. Engaging focuses on culture building, ensuring trust, inclusion, and collaboration flourish in the workplace. At the center lies Embodying, the North Star of leadership—being a role model who demonstrates integrity, resilience, and accountability.

Together, these six qualities create leaders who are not only result-driven but also people-focused, ensuring sustainable success for organizations in the modern era.

𝗔𝗜 𝗘𝗻𝗴𝗶𝗻𝗲𝗲𝗿𝘀 𝗧𝗵𝗲𝗻 𝘃𝘀. 𝗡𝗼𝘄 𝗪𝗵𝗮𝘁 𝗖𝗵𝗮𝗻𝗴𝗲𝗱 𝗮𝗻𝗱 𝗪𝗵𝘆 𝗜𝘁 𝗠𝗮𝘁𝘁𝗲𝗿𝘀

Just a few years ago, AI engineers were deep into building models from scratch:

• Training 𝗖𝗡𝗡𝘀 for image classification

• Using 𝗹𝗼𝗴𝗶𝘀𝘁𝗶𝗰 𝗿𝗲𝗴𝗿𝗲𝘀𝘀𝗶𝗼𝗻 for churn prediction

• Optimizing 𝗿𝗮𝗻𝗱𝗼𝗺 𝗳𝗼𝗿𝗲𝘀𝘁𝘀 for fraud detection

• Implementing 𝗟𝗦𝗧𝗠𝘀 for sentiment analysis

These tasks required deep mathematical knowledge, coding expertise, and hands-on experience with data pipelines.

𝗙𝗮𝘀𝘁 𝗳𝗼𝗿𝘄𝗮𝗿𝗱 𝘁𝗼 𝘁𝗼𝗱𝗮𝘆:

Much of that complexity is abstracted away by 𝗟𝗮𝗿𝗴𝗲 𝗟𝗮𝗻𝗴𝘂𝗮𝗴𝗲 𝗠𝗼𝗱𝗲𝗹𝘀 (𝗟𝗟𝗠𝘀) like ChatGPT. Instead of writing models line by line, many AI tasks are now reduced to calling an API or fine-tuning pre-trained models.

This shift has sparked debate:

• Some argue AI engineering has become “too easy.”

• Others see it as 𝗱𝗲𝗺𝗼𝗰𝗿𝗮𝘁𝗶𝘇𝗮𝘁𝗶𝗼𝗻—making AI accessible to far more people.

𝗪𝗵𝗮𝘁 𝘁𝗵𝗶𝘀 𝗺𝗲𝗮𝗻𝘀 𝗳𝗼𝗿 𝗽𝗿𝗼𝗳𝗲𝘀𝘀𝗶𝗼𝗻𝗮𝗹𝘀 (𝗯𝗲𝗴𝗶𝗻𝗻𝗲𝗿𝘀 → 𝗲𝘅𝗽𝗲𝗿𝘁𝘀):

𝟭 𝗕𝗲𝗴𝗶𝗻𝗻𝗲𝗿𝘀: You can start experimenting with powerful models without a PhD in ML. Focus on prompt engineering, data handling, and ethical use.

𝟮 𝗜𝗻𝘁𝗲𝗿𝗺𝗲𝗱𝗶𝗮𝘁𝗲 𝗽𝗿𝗮𝗰𝘁𝗶𝘁𝗶𝗼𝗻𝗲𝗿𝘀: Learn how to integrate LLMs into real systems (APIs, apps, automation). The value lies in application, not just model building.

𝟯 𝗔𝗱𝘃𝗮𝗻𝗰𝗲𝗱 𝗽𝗿𝗼𝗳𝗲𝘀𝘀𝗶𝗼𝗻𝗮𝗹𝘀: Shift towards scalability, optimization, and governance—how to make LLMs safe, efficient, and business-ready.

𝗧𝗵𝗲 𝗯𝗮𝗹𝗮𝗻𝗰𝗲 𝗵𝗮𝘀 𝗰𝗵𝗮𝗻𝗴𝗲𝗱:

• Before → Build models

• Now → Apply, adapt, and govern models

The core skill today isn’t just “training models”—it’s 𝘂𝗻𝗱𝗲𝗿𝘀𝘁𝗮𝗻𝗱𝗶𝗻𝗴 𝗽𝗿𝗼𝗯𝗹𝗲𝗺𝘀, 𝗱𝗮𝘁𝗮, 𝗮𝗻𝗱 𝗵𝗼𝘄 𝘁𝗼 𝗿𝗲𝘀𝗽𝗼𝗻𝘀𝗶𝗯𝗹𝘆 𝗹𝗲𝘃𝗲𝗿𝗮𝗴𝗲 𝗽𝗼𝘄𝗲𝗿𝗳𝘂𝗹 𝗔𝗜 𝘁𝗼𝗼𝗹𝘀 𝗮𝘁 𝘀𝗰𝗮𝗹𝗲.

Whether you’re just starting or already working in the field, the key takeaway is 𝗔𝗜 𝗶𝘀 𝗺𝗼𝘃𝗶𝗻𝗴 𝗳𝗿𝗼𝗺 𝗺𝗼𝗱𝗲𝗹-𝗰𝗲𝗻𝘁𝗿𝗶𝗰 𝘁𝗼 𝗮𝗽𝗽𝗹𝗶𝗰𝗮𝘁𝗶𝗼𝗻-𝗰𝗲𝗻𝘁𝗿𝗶𝗰. The winners will be those who can bridge technology with real-world impact.

𝗕𝗼𝗻𝘂𝘀 𝗧𝗶𝗽: If you're looking to level up in your Ai career, explore 𝗔𝗜 & 𝗗𝗮𝘁𝗮 𝗦𝗰𝗶𝗲𝗻𝗰𝗲 𝗖𝗼𝘂𝗿𝘀𝗲 𝘄𝗶𝘁𝗵 𝗖𝗲𝗿𝘁𝗶𝗳𝗰𝗮𝘁𝗶𝗼𝗻 from 𝗧𝗲𝗰𝗵𝗩𝗶𝗱𝘃𝗮𝗻 to stay ahead of industry trends.