The 5 Steps Every Analytics Team Must Use To Identify ROI-Positive Projects??

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I. Spotlight:?The 5 Steps Every Analytics Team Must Use To Identify ROI-Positive Projects??

Today, when it comes to financial investments, we are spoilt for choice. How do you pick a stock portfolio that can beat the market? Should you invest in real estate during a bull run? How about snagging some cool crypto assets, such as NFT (non-fungible tokens) digital art?

Building a sound financial portfolio involves several considerations—risks vs. returns, short vs. long-term, and cookie-cutter portfolios vs. personalization. These options hugely influence your immediate lifestyle and your retirement plans.?

Similarly, organizational leaders run into several dilemmas while choosing their data and analytics (D&A) investments. With numerous technology capabilities within reach, which ones will deliver the best business returns? What are the quick wins, and which ones are for the long haul? Which competitor moves are worth replicating, and how should you tailor them for your organization?

These aren’t easy choices.

After all, 80% of analytics initiatives?fail?to deliver business value. Over 79% of artificial intelligence (AI) projects?end up?with a zero or negative return on investment (ROI). Ultimately, 81% of industry leaders?fail?to build a data-driven culture.

Here are five steps to pick the most impactful D&A projects and build a portfolio that will deliver business ROI. We’ll see how each step has striking parallels to investment planning.

Step 1: Identify your business priorities?

Never base your financial bets on the killing your friend made from dogecoin. Financial investments must be planned by reflecting on personal goals. How do you envision your retired life, and what critical life events should you anticipate?

Continue reading (Forbes)

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II. Industry Roundup:

1. Article: 7 Must-Read Books About Artificial Intelligence

05 minutes |?Forbes | Rob Toews

Looking to update your reading list? The author lists 7 thought-provoking books that explore the technology, business, politics, and ethics of AI. He provides compelling reasons why you must consider these books.

->?Read the Article

2. Podcast:?The AI Revolution is Just Beginning

47 minutes | Azeem Azhar's Exponential View | Nathan Benaich and Ian Hogarth

Here the co-authors of the annual ‘State of AI’ report discuss breakout developments in disparate fields, from defense to medical biology. And they offer their take on the flood of new investments into AI and how we can best keep the technology safe for humanity. The full report titled ‘State of AI Report 2022’ will be out this Oct.

->?Listen to the Podcast

3. Research Paper: The Effect of Computer-Assisted Learning on Students' Long-Term Development

50 min | NBER | Nicola Bianchi, Yi Lu, and Hong Song

This paper talks about the impact of computer-assisted learning on school students in China. The benefits include improved academic achievement, labor performance, and computer usage. The authors show how connecting China’s best teachers to rural students through satellite internet helps reduce the rural-urban education gap.

->?Read the Paper

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III. From my Desk:?

1. Video:?What's one of the top challenges in transforming orgs with data?

4 min | LinkedIn

I’ve often noticed a top challenge in organizations attempting to become data-driven - business and technology teams operate in silos. Data analytics steering committees can help orchestrate decisions across the organization effectively. I share the why, what, and how aspects of this idea in this whiteboard.

->?Watch the Video

2. Video:?Do you know the strange link between tooth-brushing and Data Science?!

3 min | LinkedIn

I share 3 steps to make decisions with data a habit in your org. I share an interesting story on how Pepsodent created a habit-loop in the early 1900s. Find out what you can learn from this narrative to influence data habits in your organization.

->?Watch the Video

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This AI Artist is more famous than Picasso…???Guess who?

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

Ganes.

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• DDF #42:?What are the 3 Stages where your Data Science Teams might Fail???
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