As the digital revolution gains momentum, the multifaceted world of Data Science and Analytics has ascended to be the backbone of contemporary business paradigms. Venturing deeper into this vast and dynamic domain, we uncover a complex interplay of evolving trends, inherent challenges, and a future teeming with promise and potential:

In-depth Exploration of Emerging Trends:

1. Rising Demand for Data Expertise: The digital renaissance has catalyzed an insatiable appetite for data-savvy professionals. Citing a detailed forecast from the US Bureau of Labor Statistics, it’s projected that the demand for seasoned data scientists and analysts will witness a staggering 31% surge from 2020 to 2030. This statistic underscores the pivotal role that data-driven decision-making plays across industries.
2. Mainstreaming of AI and ML: These once niche technologies have now firmly entrenched themselves in the business lexicon. Companies, large and small, are harnessing the power of Artificial Intelligence and Machine Learning to drive transformative changes, foster innovation, and achieve unparalleled predictive accuracy.
3. Prioritizing Data Integrity: As digital interactions multiply, so does the vulnerability to data breaches. This stark reality has galvanized organizations into action, prompting a more rigorous emphasis on data security. To counteract potential threats, businesses are ardently investing in advanced data protection protocols, encryption techniques, and privacy-enhancing technologies.

Granular Look at Prevailing Challenges:

1. Talent Shortage: The burgeoning allure of the data realm paradoxically coexists with a pronounced talent gap. Organizations are in a frenzied scramble to onboard adept data professionals, often facing stiff competition in their recruitment endeavors.
2. Data Consistency Hurdles: Data, in its voluminous glory, pours in from a mosaic of sources, each with its unique attributes. Weaving this diverse data into a cohesive, reliable, and actionable analytical narrative is a challenge that many organizations grapple with daily.
3. Scaling Dilemmas: The astronomical rise in data generation rates has thrust scalability concerns into the spotlight. As organizations strive to stay afloat in this data deluge, the quest for robust, flexible, and scalable analytical solutions becomes paramount.

Broad Spectrum of Promising Opportunities:

1. Cloud-Centric Analytics: Beyond mere storage, the cloud represents the next frontier in data analytics. Platforms like AWS, Azure, and Google Cloud have democratized access to powerful analytical tools. By leveraging cloud infrastructure, businesses can dynamically allocate resources, effortlessly scale operations, and tap into advanced machine learning algorithms without the overhead of maintaining physical hardware.
2. Proximity Processing with Edge Computing: The promise of edge computing lies in its ability to decentralize data processing. By shifting the analytical workload closer to the data source, it minimizes latency, ensuring businesses can act on real-time insights, thereby enhancing responsiveness and decision-making agility.
3. Revolutionizing Data Visualization: The intricate dance of numbers finds its rhythm in visualization. Pioneering visualization tools are enabling businesses to transform dense data clusters into visual masterpieces, fostering clearer communication, and driving stakeholder engagement.

Futuristic Industry Projections:

Immediate Outlook: The confluence of a resurgent demand for data mavens, coupled with rapid technological strides, paints a buoyant picture for the immediate future of the data science and analytics sector.

Distant Horizon: Gazing further into the temporal expanse, we envision a landscape reshaped by quantum computing, advanced AI models, and neural network innovations. This transformative phase will herald deeper analytics, uncovering layers of insights previously deemed unfathomable.

Contemporary Influences & Global Impacts: No industry operates in isolation. The undulating currents of global events, be it geopolitical shifts, economic tremors, or public health crises, cast their shadows on the data realm. These externalities, especially concerns surrounding data ethics, privacy, and governance, will indubitably sculpt the trajectory of data science and analytics, emphasizing its quintessential role in navigating the complexities of the modern world.