Five Key Trends Shaping Enterprise Search and Unstructured Data Analysis

Most organizations effectively use structured data such as tables and spreadsheets, but many critical business insights remain hidden in unstructured data.

80% of organizations are realizing that 80% of their content is unstructured.

Nearly 80% of enterprise data consists of unstructured formats—job descriptions, resumes, emails, text documents, research and legal reports, recordings, videos, images, and social media posts. New developments in neural networks, search engines, and machine learning are expanding the ability to discover knowledge, perform search, gain business insights, and take action on this content.

Search Combined with AI Solves Real‑World Problems

Applications like Siri, Alexa, Shazam, Lyft, and others rely on powerful search engines that blend search with AI techniques such as natural language processing, neural networks, and machine learning to process voice or text input, query multiple data sources, and return accurate real‑time answers.

Within enterprises, these technologies can connect employees to the information and answers they need, regardless of where the answers reside—documents, financial systems, HR systems, or policy databases.

Search has evolved from merely locating files to providing direct answers.

By 2020 we expect more AI‑driven search and analytics applications to support enterprises.

Five Trends Worth Watching in Search and Unstructured Data Analysis

1. Neural Networks and Search Engines

Accenture’s Fjord Trends 2020 shows neural networks are a key technology for AI systems, learning tasks through pattern recognition. By analyzing large volumes of digital data, neural networks can recognize photos, understand voice commands, and respond to natural‑language queries, moving beyond simple keyword search to understand user intent and deliver personalized results.

Modern neural networks (e.g., BERT and its derivatives) create a “semantic space” that abstracts enterprise content for:

Deep search: identifying sentences with the same meaning rather than just matching keywords.

Better classification: organizing content for navigation or management (e.g., compliance, filtering, remediation).

Question/answer: extracting facts from documents to answer specific queries such as “What was the US revenue last quarter?”

These networks are already used for highly managed content like knowledge‑base articles, policies, documents, and test standards, and we expect broader adoption for contextual answers.

2. Semantic Search

Semantic search extends neural networks to handle a wide range of enterprise queries, delivering precise, instant answers—including short‑tail and long‑tail queries—by understanding entities, intent, mapping requests to answer agents, and presenting results.

Factors driving semantic search include the growth of data warehouses, data lakes, and content ingestion technologies that break down data silos; new tools designed for semantic search integration; and advanced machine‑learning methods that improve query understanding.

3. Document Understanding

Computers traditionally ignore visual cues such as font size, layout, and graphics, which convey important semantics. AI is now able to analyze these presentation elements, enabling extraction of insights from documents. Example use cases include automatic PDF invoice processing, digitizing paper workflows, searching PowerPoint content, extracting geological report elements, routing emails, converting engineering drawings, and searching policy documents.

4. Image and Voice Search

Digital voice assistants (Siri, Alexa, Google Assistant, etc.) are becoming ubiquitous, providing deeper natural‑language understanding and new ways to locate information. In enterprises, voice assistants allow employees to ask questions like “Who is our data‑science expert in Europe?” or “How do I book a meeting room in Paris?” enhancing interaction with corporate data.

By 2021, early adopters redesigning websites for visual and voice search will see a 30% increase in digital commerce revenue.

These tools naturally complement semantic search, often replacing chatbots with robust semantic search back‑ends.

5. Knowledge Graphs

Knowledge graphs aggregate existing data into a repository (often a data lake) and add context, relationships, and meaning using natural‑language‑understanding algorithms, creating an interconnected information network that can instantly provide insights when users ask questions.

Knowledge graphs can range from modest interconnections (employees, offices, products) to richly linked structures (organizational hierarchies, equipment relationships, customer‑product interactions). As new data points and relationships grow, knowledge graphs continuously expand.

Beyond Search

Looking ahead to 2020 and beyond, we anticipate these five developments will mature and see broader enterprise adoption, focusing on leveraging intelligent technologies to discover and maximize the value of unstructured data. New AI‑driven use cases will emerge daily, delivering greater value and better results while reducing costs and solving technical and organizational challenges.