2018年智能企业应当关注的八大分析趋势（英文版）_22页

8 Analytics Trends to Watch in 2018 for the Intelligent Enterprise

Core Content Overview

This document outlines eight key analytics trends that enterprise organizations should be aware of and act on in 2018. These trends are drawn from insights by leading analysts and experts in the field of business intelligence, data analytics, and digital transformation. The Intelligent Enterprise is positioned to thrive by leveraging these trends to drive innovation, efficiency, and competitive advantage.

Main Trends and Key Insights

Trend 1: AI Will Reshape Analytic and Business Innovation

• Summary: Artificial intelligence (AI) will play a transformative role in analytics and business innovation in 2018.
• Key Points:
  • 25% of firms will use conversational user interfaces alongside traditional analytics tools.
  • 20% of firms will rely on AI to make decisions and provide real-time instructions.
  • Data lakes will either evolve or face decline, with many enterprises adopting a cloud-first strategy.
  • 66% of firms will create customer insight centers of excellence, and data engineers will become a sought-after role.
  • The insights market will become more complex, with 80% of firms relying on service providers for some of their insights capabilities.

Trend 2: Competition for Data Science and Analytics Talent

• Summary: The demand for data science and analytics talent will intensify in 2018 and beyond.
• Key Points:
  • By 2020, there will be 2.7 million job postings for data science and analytics roles.
  • 59% of employers expect data science and analytics skills to be required for all finance and accounting managers by 2020.
  • The shortage of qualified candidates is growing, with only 23% of graduates in 2021 expected to have these skills.
  • Enterprises need to focus on both acquiring and retaining top talent, and investing in internal training programs.

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

• Summary: Real-time and batch-based analytics will increasingly converge to offer more comprehensive and actionable insights.
• Key Points:
  • The analytics market will see convergence across vendors, emerging technologies, and tools.
  • M&A activity will continue, with smaller analytics firms either being acquired or going out of business.
  • Emerging technologies such as AI, IoT, and cloud computing will integrate with analytics tools.
  • Organizations will standardize on a single platform to support their analytics ecosystem, reducing the number of tools needed.
  • Next-gen analytics will combine with AI and cloud computing for smart-scaling and real-time analysis.

Trend 4: Voice and Natural Language Interfaces Become Mainstream

• Summary: Voice and natural language interfaces are expected to become the primary means of interacting with analytics systems.
• Key Points:
  • Voice and natural language processing will be a major enhancement to human-computer interfaces.
  • These interfaces will allow a broader audience to access and understand analytics.
  • Natural language generation will reduce ambiguity in data displays and provide written summaries.
  • By 2020, 50% of all searches are predicted to be voice searches.
  • Analytics systems should explore integrating these interfaces to enhance user experience and data accessibility.

Trend 5: Convergence of Analytics Tools and Technologies

• Summary: The convergence of analytics tools and technologies will lead to more integrated and efficient systems.
• Key Points:
  • Organizations will move towards a unified analytics platform, merging multiple projects and integrating third-party tools.
  • The consumerization of IT has empowered users to adopt a variety of tools for immediate problem-solving.
  • Powerful enterprise software and failed projects will drive a shift towards standardized, scalable solutions.
  • This trend will reduce the need for multiple tools, leading to better governance and fewer IT management burdens.

Trend 6: Emergence of Augmented Analytics

• Summary: Augmented analytics will automate and enhance the analytics process, making it more accessible and efficient.
• Key Points:
  • Augmented analytics combines AI with traditional analytics to provide predictive and prescriptive insights.
  • It automates data preparation, cleansing, feature engineering, and insight discovery.
  • Early adopters report unmatched speed to insight and competitive advantage.
  • The quality of insights depends heavily on the accuracy and representativeness of the input data.

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

• Summary: Machine learning and AI will be integrated into edge and video analytics to enable real-time insights and decision-making.
• Key Points:
  • AI and ML will drive real-time data and analytics infrastructure, enhancing efficiency and decision-making.
  • Edge analytics will address challenges of managing vast volumes of streaming data from connected devices.
  • Video analytics, powered by AI, will become a key source of data for businesses, particularly in retail, law enforcement, and city surveillance.
  • By 2022, it is forecasted that there will be 29 billion connected devices, with 18 billion related to IoT.

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

• Summary: Organizations will need to balance access to data with ownership of analytics streams.
• Key Points:
  • 60% of mission-critical data is now outside traditional data warehouses and data lakes.
  • Insight streams will come from both obvious and less obvious sources, such as power consumption and foot traffic.
  • Organizations should focus on building a strong data foundation with governance, data prep, and streaming capabilities.
  • Infinite ambient orchestration will enable mass personalization and AI-driven smart systems to support regulatory compliance and reduce recalls.

Key Takeaways

• The Intelligent Enterprise must embrace digital transformation to remain competitive.
• AI and machine learning will be central to future analytics and business innovation.
• Talent acquisition and retention will be critical, especially for data science and analytics roles.
• Real-time and batch analytics will converge, leading to more integrated and efficient systems.
• Voice and natural language interfaces will become mainstream, enhancing data accessibility.
• Augmented analytics will automate the entire analytics lifecycle, making insights faster and more actionable.
• Edge and video analytics will play a growing role, driven by IoT and AI capabilities.
• Organizations must adapt to the shift from data ownership to data access and integration.