How to pursue data science even if you don’t have big data or AI skills🐢
How to pursue data science even if you don’t have big data or AI skills🐢
Hello,
Last week, I launched my newsletter on LinkedIn by republishing from Substack. I was surprised by the traction: 3,300+ subscribers in 6 days. This is way over the base I’ve built on Substack over the past 16 months!
Substack is rising in popularity, and I’m grateful to all of you for keeping me going all along. But, there’s a learning here - Be present across mediums, particularly where your target audience thrives.
I’ll republish all future editions on both Substack and LinkedIn, so no action is needed. LinkedIn doesn’t seem to offer detailed audience stats, though. I’ll be running newsletter experiments on both platforms and will share what I learn.
This newsletter will take you about 5 minutes to read.
I. Spotlight: How to pursue data science even if you don’t have big data or AI skills🐢
Have you come across organizations that pour money into data science before getting their data in place? My earlier newsletter talked about a manufacturing organization that attempted just that.
I shared *one* question to help spotlight this disconnect and convince leaders against burning their money. (If you missed the edition, here’s a link)
We walked through a simple yet powerful framework to help you get the most from your data and analytics investments. (See the 2x2 quadrant below)
When you review the poor ROI of advertising campaigns, if your marketing team has disowned the underlying data, you’ve been in the ‘zone of disbelief.’ When a team painstakingly runs customer surveys, stores all customer interactions but uses them for basic summary reporting, they are in the ‘zone of complacency.’
Most organizations start with a clean slate, on the bottom-left. How do they reach the ‘zone of nirvana’ without slipping into the zones of ‘disbelief’ or ‘complacency’?
Yes, you must steer clear of AI until you get the data in place. But you need NOT shelve all analytics efforts until you build a perfect data warehouse.
You must parallel-track data and analytics (D&A). Focus on incremental gains to level up in your D&A maturity. Here’s a 6-step iterative process to help you get there:
1. Identify small, impactful problems: Identify the target audience and their short-term business problems. Ensure that addressing them will deliver business impact. For example, find which advertising channel offers the most bang for the buck.
2. Assess data feasibility: Assess the data needs of each initiative. Some moonshot projects may have no data. If it aligns with your strategy, plan for data acquisition in the future. Note down projects for which data is immediately available.
3. Pick quick wins: Based on the business impact and feasibility, pick projects that can be executed immediately - these are your quick wins. If predicting customer churn needs time, diagnosing factors that cause churn could be done earlier.
4. Choose simple analytics: Find out how to achieve project outcomes with the simplest analytics techniques and existing skills/tools. Early on, these could be descriptive or diagnostic analytics done on tools such as MS Excel.
5. Help users adopt & build trust: Make insights consumable and help users adopt them. Partner on change management and help build user trust in data. This takes time. For example, embed data stories in a business workflow for ready actioning.
6. Plan the next iteration: Build a roadmap of incremental solutions and features to meet your users’ rising appetite for insights. Revisit the business initiatives to see your new ‘quick wins’ and which additional skills you can acquire next.
Rinse and repeat this process to keep moving along the diagonal in the earlier chart, towards the ‘zone of nirvana.’ As your data quality and analytics depth rise, so will your business value.
Leaders often bet their careers on big-bang data science projects but forget to leverage the power of small gains. Remember the 1% improvement rule from ‘Atomic Habits’?
If you can get 1 percent better each day, you’ll end up thirty-seven times better by the end of the year.
Small habits are underestimated. So are small wins in data and analytics.
II. Industry Roundup:
1. Why your ROI from personalized advertising might be inflated
6 minutes | HBR | Bart de Langhe, Stefano Puntoni
Big tech companies such as Facebook, Google, and Twitter claim impressive returns from dollars invested in their platforms. Their A/B tests might be fundamentally flawed, claims this article. It shares how the companies mix causal and associative factors and why you can’t take these numbers at face value.
2. First human trial begins for a drug entirely discovered by AI
4 minutes | Outsourcing Pharma | Jenni Spinner
Insilico Medicine, a company that uses AI in drug discovery, has begun human trials to treat a rare, chronic lung disease. The company claims this is the first-ever AI-discovered novel molecule based on an AI-discovered target. This has been achieved in 18 months on a budget of $2.6m, a breakthrough achievement.
3. Top 7 Analytics predictions for 2022
12 pages | IIA | Bill Franks, Tom Davenport, Drew Smith
IIA recaps the top trends and what to expect in the coming year. The authors share helpful guidance on preparing for each prediction. Spanning areas such as low/no-code, ModelOps, analytics product management, talent strategies, and analytics going mainstream, this is a good refresher of top trends in analytics.
III. From my Desk:
1. Video: How data science can help optimize work schedules
38 minutes | Workforce.com
How can analytics help small business owners tackle the current staffing crisis? When the availability of employees is scarce, how can organizations optimize schedules? We talk about these in the chat with Workforce CTO and Research Analyst.
2. Video: How can you tackle cognitive biases in decision-making?
39 minutes | 500 Startups
What are the top 3 biases that impact our decisions? Here’s how decision intelligence can come to our rescue. Find out why an awareness of our blind spots is the first crucial step to making better decisions at work and in life.
3. Board of Studies: DUK’s course on Ecological Informatics
DUK School of Informatics
Digital University Kerala has launched an MSc course at the intersection of ecology, informatics, computational and social sciences. I'm happy to join the illustrious board of advisors to help shape the curriculum & mentor students. I look forward to seeing this course contribute to the environmental, social, and governance areas.
Have you struggled with decision paralysis of late? You’re not alone in this COVID year, claims this comic from Marketoonist.
Thank you for subscribing and reading the newsletter. I appreciate your attention,
Ganes.
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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.
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