The 3 Disciplines You Absolutely Need To *Succeed* In Any Data Science Project✌️
Hello,
“When gratitude becomes an essential foundation in our lives, miracles start to appear everywhere.” - Emmanuel Dalgher
#Thanksgiving is a great reminder to practice it consciously - not just today, but every single day.
When you give your thanks to the Universe, it always returns that energy multiplied. Happy Thanksgiving to all our readers around the world!
This newsletter will take you about 4 minutes to read.
I. Spotlight: The 3 Disciplines You Absolutely Need To *Succeed* ✌️In Any Data Science Project
Digital transformation has been the flavor of the past few years. Every company has accelerated its efforts to digitize operations, gather intelligence, and rapidly respond to a changing market.
McKinsey senior partner Kate Smaje said that organizations are now accomplishing in 10 days what used to take them 10 months.
As a result, most organizations are trying to adopt data-driven decision-making. They hire data scientists, buy the best tools, and greenlight big-bang analytics projects.
However, none of these efforts alone will deliver results. They can lead to a build-up of activity, expectations, and expenses, but the business outcomes will not just magically happen.
A whopping 80 percent of data science projects fail. Wonder why? There is a critical element missing from these initiatives: Decision intelligence.
Decision intelligence is the application of data science within the context of a business problem, and it’s achieved by factoring in stakeholder behavior to influence adoption and decision-making.
Decision intelligence augments data science with two disciplines that are often ignored when it comes to data: social science and managerial science. It’s only when you combine all of the principles and skills from these three disciplines – data science, social science, and managerial science – that you can unlock business decisions. It is essential to contextualize data insights with social behavior in an organizational context to enable decision-making.
How can decision intelligence help you?
So many data science projects fail because IT leaders are not applying all three disciplines of decision intelligence in tandem.
Here are the roles each discipline plays and how you can implement them in your organization:
II. Industry Roundup:
1. Article: The Four Cringe-Worthy Mistakes Too Many Startups Make with Data
17 minutes | First Round Review
Amanda Richardson, Chief Data and Strategy Officer for Hotel Tonight, explains the four common mistakes many startup data teams make. They are: starting without a goal, rampant personalization, hiring dedicated data scientists, and chasing after the latest toolsets. She shares compelling reasons why data must be treated like a product.
2. Article: 5 Harvard Business Review articles that will resonate with CIOs right now
04 minutes | Enterprisers Project | Katie Sanders
As a CIO / IT leader, are you looking to update your reading list? Here are 5 thought-provoking HBR articles covering critical topics from AI to digital transformation and everything in between. Katie shares a summary and provides compelling reasons to consider each of these articles.
III. From my Desk:
1. Podcast: Successful Frameworks for Scaling Data Maturity
45 min | DataFramed
I was recently invited to DataFramed, the DataCamp podcast. I shared our experience helping Gramener’s clients implement frameworks to become data-driven. We discussed many common questions leaders ask and how to tackle common challenges. Listen in and let me know what resonated with you.
2. Article: How You Can Unlock Decision Intelligence At Scale by Aligning BI with AI
06 min | AtScale
“How can we transition from BI to AI?” is a question leaders often ask me. There are two big misconceptions bundled into this apparently benign question. In this article, I bust these misconceptions and discuss why one needs to align BI and AI to deliver business value.
3. Talk: How You Can Create Impactful Data Stories Using Power BI
UpGrad
This week, I conducted an immersive session on data storytelling. The talk covered the 4 key steps to building powerful data stories. I illustrated the concepts using real-world case studies and showed how to implement the concepts hands-on in Power BI. To learn more about data storytelling, check out the whitepaper below.
Online retailers waste an awful amount of cardboard packaging. Here’s a computer vision innovation to tackle this and save Nature!
Thank you for subscribing and reading the newsletter. I appreciate your attention,
Ganes.
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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.
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