#69: 3 Data Analytics Challenges: How Decision Intelligence Can Help You Tackle Them💡
#69: 3 Data Analytics Challenges: How Decision Intelligence Can Help You Tackle Them💡
Hello,
There are decades where nothing happens, and there are weeks where decades happen! This quote by Vladimir Ilyich Lenin perfectly encapsulates activity in today's Generative AI space. OpenAI’s first developer conference this week got a lot of press, with many exciting announcements. More on that soon.
This newsletter will take you about 4 minutes to read.
I. Spotlight: 3 Data Analytics Challenges: How Decision Intelligence Can Help You Tackle Them💡
Today, most organizations believe in the power of data and analytics. Almost every executive aspires to build a data-driven organization. However, a survey by New Vantage Partners found that just 26.5% of companies have done so successfully.
What holds teams back from this coveted goal? While advising business leaders on building a data culture, I’ve encountered three common data analytics challenges. Below, we’ll look at those challenges and review how decision intelligence can help address them.Â
Why is it so difficult to build a data-driven organization?
A data-driven firm is one in which decision-making at all levels is firmly grounded in data and analytics. There are three significant roadblocks leaders face while operationalizing data and analytics to deliver business benefits:
Ineffective collaboration amongst business and technology teams
Technology, data, and business teams operate in silos in many organizations. Suboptimal collaboration impacts the kind of data and analytics projects that get picked. It affects how projects are executed and gets reflected in the poor adoption of technology solutions by end users. As a result of poor adoption of data analytics solutions (ex. BI dashboards or AI solutions), it’s no wonder that data is disconnected from these firms’ business decisions.Lengthy timelines to deliver business insights
Firms that get past the collaboration challenge often need help to deliver insights effectively and in a timely manner. It takes a painfully long time to source data, compute the business metrics, identify insights from analytics, and present them for actionable decisions.
II. Industry Roundup:
1. Article: The most impactful AI events of 2023
09 minutes | Â AI Supremacy | Charlie Guo
This article discussed some of the most important AI stories of 2023 - stories that are both noteworthy in their own right, and can also teach us something about where AI is headed. This includes the launch of AutoGPT - a toolkit that quickly became the fastest-growing GitHub repository in history, Facebooks’ 'arm-the-rebels' policy with open-sourcing Llama 2 and many more interesting stories.
->Â Read the Article
2. Article: AI powered startup gives Rwandan cancer patients better access to life-saving treatments
05 minutes | Amazon | Amazon Staff
Hurone AI, a Seattle-based firm with African origins, innovates in cancer care with its AI technology for simulating oncologist-patient chats. The initiative aims to bridge the vast oncologist gap in Rwanda and beyond. Its "Gukiza" app facilitates vital communication, offering hope in regions where cancer care is scarce and stigmatized. This pioneering approach promises to reshape cancer treatment accessibility in Africa and potentially worldwide.
->Â Read the Article
III. From my Desk:Â
1. Announcement: Gramener has been acquired!
02 min | LinkedIn
Exciting news: After over a decade of pioneering in data visualization and storytelling, Gramener reached a significant milestone – acquisition by Straive, a BPEA EQT company! From our early days, pre-dating the big data buzz, our journey has been remarkable, thanks to our dedicated team (current & alumni), supportive clients, and encouraging partners. Now, as part of Straive, Gramener is geared to scale new heights. Onward and upward! 🚀🎉
->Â Read the Post
2. Article: AI Revolution In Diabetes Care - How Technology Is Beating This Silent Killer
05 min | Forbes
Insulin resistance, a silent global health threat, leads to diabetes, burdening healthcare systems. Traditional blood sugar management is invasive and limited. AI-driven solutions offer a proactive approach, transforming diabetes care with personalized lifestyle adjustments. While challenges persist, this technological shift marks a critical advancement. Learn more about this innovation in my latest article.
->Â Read the Article
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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