MicroStrategy-2018年智能企业应当关注的八大分析趋势（英文）-2018-22页

8 Analytics Trends to Watch in 2018 for the Intelligent Enterprise

Core Content Overview

The document outlines eight key analytics trends expected to shape the Intelligent Enterprise in 2018, emphasizing the need for organizations to embrace digital transformation through data-driven strategies. These trends highlight the integration of advanced technologies like AI, machine learning, and real-time analytics, alongside the growing importance of talent acquisition and the evolution of analytics interfaces.

Key Trends and Insights

Trend 1: AI Will Reshape Analytic and Business Innovation

• Core Content: AI will transform how businesses use analytics, enabling real-time decision-making and predictive insights.
• Main Points:
  • 25% of firms will use conversational interfaces alongside traditional analytics.
  • 20% of firms will use AI to make decisions and provide real-time instructions.
  • Data lakes will either evolve or decline, with many moving to a cloud-first strategy.
  • Organizations will need to create customer insight centers of excellence and invest in data engineers.

Trend 2: Competition for Data Science and Analytics Talent

• Core Content: The demand for data science and analytics skills will grow significantly, leading to fierce competition for talent.
• Main Points:
  • By 2020, there will be 2.7 million job postings for data science and analytics roles.
  • 59% of employers expect data science skills in finance and accounting managers by 2020.
  • Only 23% of graduates in 2021 are expected to have these skills.
  • Organizations must focus on both recruiting and retaining talent, and invest in internal training programs.

Trend 3: Convergence of Real-Time and Batch-Based Analytics

• Core Content: Real-time and batch-based analytics are merging, enabling more comprehensive insights and decision-making.
• Main Points:
  • Real-time analytics must integrate with historical data to provide full value.
  • 80% of retailers expect IoT to drastically change business operations in the next three years.
  • The analytics market will see more vendor consolidation and M&A activity.
  • Organizations should standardize on a single platform for their analytics ecosystem.

Trend 4: Voice and Natural Language Interfaces Become Mainstream

• Core Content: Voice and natural language interfaces will redefine how users interact with analytics tools.
• Main Points:
  • These interfaces will make analytics more accessible and user-friendly.
  • Natural language generation will reduce ambiguity in data presentation.
  • By 2020, 50% of all searches will be voice searches.
  • Organizations should explore and integrate these technologies into their systems.

Trend 5: Convergence in the Analytics Market

• Core Content: The analytics market is converging across vendors, technologies, and tools.
• Main Points:
  • Vendor convergence will continue with M&A activity.
  • Emerging technologies like AI, IoT, and cloud computing will integrate more deeply.
  • Tool convergence will lead to fewer tools being used as organizations standardize on a single platform.
  • The convergence will enable more powerful, scalable, and user-friendly analytics ecosystems.

Trend 6: Emergence of Augmented Analytics

• Core Content: Augmented analytics will automate the analytics process and provide actionable insights.
• Main Points:
  • Augmented analytics will combine human intuition with AI.
  • It automates data preparation, feature engineering, and insight generation.
  • Early adopters will gain speed to insight and competitive advantage.
  • Data quality is critical for the success of augmented analytics.

Trend 7: Machine Learning, AI, and Edge/Video Analytics

• Core Content: Machine learning and AI will drive real-time analytics, while edge and video analytics will process data at the source.
• Main Points:
  • AI and ML will be integrated into mainstream software applications.
  • Edge analytics will address data management challenges from IoT-enabled devices.
  • Video analytics will become a key data source, using AI for object recognition and behavioral analysis.
  • The number of connected devices is expected to reach 29 billion by 2022, with 18 billion related to IoT.

Trend 8: Access vs. Ownership of Analytics and Insight Streams

• Core Content: Organizations will increasingly rely on external data sources and insight streams.
• Main Points:
  • 60% of mission-critical data is outside traditional data warehouses.
  • Insight streams will come from both obvious and unexpected sources.
  • Companies should focus on data governance, preparation, and agility.
  • Infinite ambient orchestration and recommendation engines will be key for delivering personalized insights.

Summary of Key Information

• The Intelligent Enterprise is essential for survival in the era of digital disruption.
• AI and machine learning will play a central role in transforming analytics and business processes.
• Talent shortages in data science and analytics will drive competition for skilled professionals.
• Real-time and batch-based analytics are converging to provide more actionable insights.
• Voice and natural language interfaces will make analytics more accessible to a broader audience.
• Augmented analytics will automate the entire analytics lifecycle, from data preparation to insight delivery.
• Edge and video analytics will be key for processing and analyzing data at the source.
• Organizations must shift from data ownership to data access, leveraging external data streams and insight brokers.

Experts and Resources

• Boris Evelson (Forrester Research): Predicts AI will reshape analytics and business innovation.
• Michele Goetz (Forrester Research): Highlights the growing demand for data science and analytics skills.
• Tim Lang (MicroStrategy): Discusses the convergence of real-time and batch-based analytics.
• David Menninger (Ventana Research): Focuses on voice and natural language interfaces becoming mainstream.
• Hugh Owen (MicroStrategy): Explores the convergence in the analytics market.
• Jen Underwood (Impact Analytix): Talks about the emergence of augmented analytics.
• Ronald van Loon (Top Analytics Influencer): Discusses the role of AI, ML, and edge analytics.
• Ray Wang (Constellation Research): Highlights the shift from data ownership to data access and the importance of insight streams.

Conclusion

The Intelligent Enterprise of 2018 will be defined by its ability to harness AI, data science, and real-time analytics to drive innovation and competitive advantage. Organizations must prepare for these trends by investing in talent, technology, and a data-driven culture. The future belongs to those who can adapt quickly and leverage the power of analytics across all aspects of their operations.