What are the 3 Stages where your Data Science Teams might Fail?🤯

Sep 18, 2022

Hello,

👋 Long time, I know! The last time I sent you this newsletter was in May, after a 2-month break - the longest till then. Then came a 4-month pause.

I’m back again and hope to be third-time lucky🤞

Not just hoping, but have been planning for this. Let’s resume our chat about data, business value, and the future🔮

This newsletter will take you about 4 minutes to read.

I. Spotlight: What are the 3 Stages where your Data Science Teams might Fail?🤯

Today, most organizations claim to be AI-driven. What this really means is open to interpretation. Some organizations might be churning out excel reports, while others might be building cognitive models. But, the truth is that everyone aspires a play in analytics.

But the big question is, where should one get started?

Where should one source the talent from? How should teams be organized? How do they scale up to avoid the inevitable, slow death that stares most teams in the face? What works in the early stages of data science teams doesn’t hold true a few years after they deliver on their initial promise.

With data science space still evolving, there are no standard guidelines to follow. This conversation aims to address this gap.

woman placing sticky notes on wall — Photo by Jason Goodman on Unsplash

Firstly, what’s the need for Data Science teams?

Going by any analyst estimate, hundreds of billions are being thrown in by companies to solve problems with data. The key ask is to draw actionable insights that can drive business decisions. The very mention of the word ‘analytics’ brings up images of predictive models and fancy algorithms.

However, data science delivers value only by applying pertinent techniques in a relevant business context. When done right, even the simplest of exploratory analysis delivers substantial returns. AI has its rightful place, but it’s not a silver bullet for every data problem.

What are the 3 stages of the Evolution of Data Science teams?

Let’s see how one can go about incubating a data science practice…

II. Industry Roundup:

1. Report: What do data-driven companies have in common?

07 minutes | Tableau | Ashley Howard Neville

Research shows five trends drive a successful data culture and help organizations become data-leading. These are talent, trust, mindset, sharing, and commitment. Taking incremental steps can help organizations grow their data culture, regardless of type, size, and industry. Check out the report for more details.

-> Read the Report

2. Article: Why AI Failed to Live Up to Its Potential During the Pandemic

13 minutes | HBR | Bhaskar Chakravorti

Some think AI was of help during the pandemic and others consider it an utter failure. It’s worth listening and learning from both camps. This author summarizes four key reasons for failure: a) bad datasets, b) embedded bias, c) susceptibility to human error, and d) a complex, uneven global context. Read on for the next steps.

-> Read the Article

3. Report: 12 Data and Analytics Trends to Keep on Your Radar

07 min | Gartner | Laurence Goasduff

These 12 trends from Gartner on data and analytics have three themes: Activate diversity and dynamism, Augment people and decisions, and Institutionalize trust. Leaders must anticipate change, manage uncertainty, and accelerate growth by investing in trends that are aligned with strategic business goals. Take a look.

-> Read the Report

III. From my Desk:

1. Post: "How can I turn everyone in our org into a Data Scientist?"

2 min | LinkedIn

I was asked this question by the Head of Analytics of a fast-growing Advertising firm. This is a popular but misplaced question. I explained to the executive what one should really ask to become data-driven. This post resonated strongly on LinkedIn.

-> Read the Post

2. Whiteboard Video: The Most Important Data Science Role You Should Never Hire

5 minutes | Gramener - Youtube

Data Champions are people who evangelize data and analytics within an org, improve data adoption, and drive decision-making with data. With strong domain skills, communication skills, and data exposure, they help sustain momentum within an org. Find out how to identify and nurture them.

-> Watch the Video

3. Talk: "Top Data Science Career Opportunities For Project Managers"

60 minutes | PMI-Metrolina

I’ll be presenting the top career opportunities for PMs in Data Science (DS) for the Metrolina chapter of Project Management Institute. The intersection of DS and project management is important but underplayed. I’ll talk about the responsibilities of DS PMs and what’s critical for success in this role.

-> Check out event Details (Needs registration)

Have you seen this hilarious meme on AI taking over all jobs, including counting cows...🤣

Thank you for subscribing and reading the newsletter. I appreciate your attention,

Ganes.

PS: Did someone forward this to you? You can subscribe here.

My Website | LinkedIn | Twitter | YouTube

I’m Ganes Kesari. I publish ‘Data-Driven Future’ to help understand how data shapes our world, explore key trends, and explain what they mean for you today. I speak and write to demystify data science for decision-makers and organizations.

Recent Issues:

Our Data-driven Future

Discussion about this post