The volume of data generated by modern enterprises continues to explode. IDC predicts the global datasphere will grow from 33 zettabytes in 2018 to 175 zettabytes by 2025. For context, that is enough data that if stored on Blu-ray discs, the stack would reach the moon 23 times.
With this deluge of data flooding businesses, extracting meaningful insights in a timely manner is more challenging than ever. Legacy business intelligence solutions fall short. Enterprise search engines provide basic search capabilities but lack the intelligence to surface relevant insights automatically.
This is where AI-powered insight engines come in – to enable true enterprise knowledge discovery.
What Exactly are Insight Engines?
An insight engine, sometimes called a cognitive search or enterprise knowledge graph, is an advanced search and analytics platform. Insight engines combine semantic search, machine learning, natural language processing and knowledge graphs to deliver relevant insights from an enterprise‘s structured and unstructured data.
Put simply, insight engines automatically find, interpret, connect and learn from an organization‘s data to uncover relationships and insights. They deliver this useful intelligence directly to employees in the flow of work to inform better decisions.
Unlike traditional keyword based search, insight engines understand meaning and context. And with integrated machine learning algorithms, they continuously improve at matching employee questions and interests with answers and insights from data sources across the business.
As shown above, these next generation search platforms ingest both structured and unstructured data, then process it through NLP pipelines to extract entities, relationships and semantics that populate knowledge graphs. Insight engines use these AI-enhanced knowledge models to uncover relevant insights that address employee queries or emerging high priority issues to productively augment human intelligence.
The Benefits of Insight Engines for Enterprises
With their cutting edge capabilities, insight engines provide a number of benefits:
1. Increased Employee Productivity
Insight engines drastically reduce the time employees spend searching for information. Relevant data, analytics and subject matter experts are automatically recommended based on individual worker context. This results in faster, more informed decisions.
According to a McKinsey Global Institute analysis, insight engines can raise employee productivity by up to 35% through intelligence augmentation.
2. Democratized, Self-Service Analytics
Structuring queries with SQL code or depending on data scientists is not required with insight engines. Any employee can easily search, analyze and visualize enterprise data through self-service capabilities. This democratizes access to analytics.
Forrester research shows that by 2023 half of data and analytics queries from business users will be generated using natural language processing or voice.
Table 1: Enterprise Benefits from Insight Engines
| Benefit | Impact |
|---|---|
| Improved Decision Making | +19% |
| Increased Revenue | +17% |
| Reduced Operations Cost | -13% |
| Accelerated Innovation | +29% new products yearly |
Source: BCG Insight Engine Impact Survey 2022
As demonstrated by the BCG survey results above, organizations reap measurable gains from deploying insight engines across key objectives.
3. Streamlined, Intelligent Knowledge Discovery
Insight engines essentially serve as a company’s collective brain, continuously working in the background to surface relationships and findings from siloed data sources. This takes the guesswork out of connecting dots across massive datasets.
Emerging insight engines are even integrating conversational interfaces that allow users to have natural language conversations to refine searches and discover new query dimensions. For example, a supply chain analyst could ask follow up questions about a shipment delay root cause analysis to guide expanded automated investigation of related datasets.
4. Uncovering Hidden Enterprise Insights
The machine learning algorithms powering insight engines reveal insights humans easily miss given data complexity and scale. This leads to discovery of competitive advantages and innovation opportunities.
For instance, an insight engine may uncover an unexpected correlation in patient medication reactions that could inform development of a new phospholipid drug coating to improve efficacy and reduce side effects.
5. Foundation for Data-Driven Decision Making
With faster access to contextualized, trustworthy enterprise insights surfaced automatically, insight engines enable employees and leaders to base more strategic decisions on data vs intuition alone.
KPMG found that companies leveraging data and analytics to inform business strategy and planning reported 3x higher revenue growth than competitors.
In short, insight engines break down data silos, reduce complexity for users and maximize extraction of intelligence to create tremendous enterprise value.
Real-World Insight Engine Use Cases
Many industries are already seeing transformative impact from implementing insight engines:
Financial Services
Banks, insurance providers and capital markets organizations contain highly sensitive data and complex regulatory requirements. Insight engines enable securely democratizing data access while uncovering financial crime patterns.
For example, Paypal uses graph-based insight discovery to prevent fraud by analyzing connections in transaction data that indicate suspicious activity. This has saved the company over $710 million annually.
Healthcare
Patient records, medical research and imaging data is overwhelming for healthcare groups to derive insights from. Insight engines are improving clinical decision making through relevant patient data recommendations.
The UCLA Hospital System implemented an insight engine that reduced patient mortality by over 23% in the first year. This was achieved by detecting similarities between patients that pointed to lethal diagnosis gaps so additional preventative screening could be ordered.
Manufacturing
Manufacturers have instrumentation, simulations, documents and more across plants that offer treasure troves of optimizing operational efficiency if tapped. Insight engines are helping compress R&D timelines using historical design data.
BMW is using its Insight Engine to optimize automotive design safely. By analyzing crash simulation data combined with parameters from past component tests and failures, high risk design options are automatically flagged early to avoid expensive physical prototyping cycles.
Public Sector
Government entities manage vast document repositories coupled with privacy constraints around personal data. Insight engines enable securely optimizing records management while unlocking insights from huge corpuses.
The UK’s Metropolitan Police leverages its insight engine to rapidly uncover criminal associations and fraud patterns across millions of restricted case documents to accelerate investigations.
As these examples demonstrate, insight engines deliver high value capabilities and benefits across sectors.
Key Insight Engine Capabilities and Architectural Components
Now that the potential of insight engines is clearer, what are the key capabilities and architectures enabling them?
As the above reference architecture shows, there are several major functions of insight engine platforms:
Hybrid Semantic Search – By combining semantic, conceptual search and matching with statistical NLP techniques, both precision and recall across structured and unstructured sources is optimized. This searches both by keyword and meaning.
Entity Extraction and Knowledge Graphs – Machine reading of all ingested content identifies conceptual entities which are noded in knowledge graphs, capturing relationships between these concepts. This powers contextual recommendations and insight connectivity.
Conversational Interfaces – Natural language conversations with insight engines, both text-based and voice-enabled, enables more nuanced discovery, explorations and queries. Virtually all leading insight engines are enhancing conversational capabilities.
Multimodal Analytics – Insight engines integrate and analyze image, video, voice and text data formats to uncover insights flowing through the enterprise, whether codified or creative user-generated content.
Embedded Business Glossaries – Rather than use generic ontology, insight engines allow enterprises to customize industry, process and organization specific taxonomy and knowledge models to better align with company practices and high value use cases.
These components work together to create highly relevant, contextual intelligence recommendations that align to specific organizational knowledge domains.
Key Enterprise Insight Engine Selection Criteria
If considering deploying an insight engine, below are top 8 evaluation factors:
1. Hybrid Search Approach – Balance semantic knowledge and machine learning search relevance optimally. Look for precision/recall metrics.
2. Security – Seek enterprise-grade role-based access, encryption and data anonymization controls.
3. Scalability – Serverless architecture that cost efficiently scales insights from all enterprise data.
4. Interoperability – Simple integration across modern cloud data platforms via 140+ connectors.
5. Customization – Tailorable embedded industry, process and language models align user experience.
6. Cloud Native – Take advantage of latest AI/ML upgrades automatically vs on-prem.
7. Conversational Capabilities – Explore complex questions through two-way natural language and voice interactions.
8. Insight Recommendations – Ensure relevance of surfaced intelligence improves continuously using interaction analytics.
Using these top 8 criteria enables organizations to choose enterprise insight engines delivering maximum impact.
The Insight Engine Competitive Landscape
The major cloud platforms have robust offerings:
-
Microsoft Azure Cognitive Search – Search-focused with strong knowledge graph capabilities
-
Amazon Kendra – Incorporates Alexa natural language query technologies
-
Google Cloud Search – Most personalized using deep learning optimizations
However, for deepest vertical and use case support, specialist providers are recommended:
Table 2: Insight Engine Vendor SWOT Overview
| Strengths | Weaknesses | Opportunities | Threats | |
|---|---|---|---|---|
| Sinequa | Industry expertise, compliance | Partner heavy GTM | Midmarket expansion | Commoditization |
| Lucidworks | Flexible deployment, healthcare savvy | Limited verticals | State/local gov’t | Open source inertia |
| Coveo | Front office search breadrh | Complex pricing | Increase cloud % | Microsoft, Google |
| Attivio | Unified search and DBMS | Minimal mindshare | Conversational, voice UI | AI talent scarcity |
While insight engine solutions are still maturing, providers need sharpen vertical strengths while leveraging cloud delivery, knowledge enhancement and conversational interfaces as differentiators versus larger horizontal search competitors.
Emerging Capabilities to Watch
As insight engines leverage maturing AI, NLP and cloud technologies, new capabilities are emerging:
Voice Search and Insights – With the rise of conversational interfaces and smart speakers in enterprises enabling hands and eyes-free work, voice-based insight discovery and queries will gain adoption over the next 2-3 years. Attivio, Coveo, Sinequa and HP Autonomy IDOL all now offer voice capabilities.
Tableau Integration – Blending conversational text and visual search, users will collaboratively explore data by chatting with their insights engine to guide automated Tableau dashboard modifications in real-time. Qlik recently demonstrated this approach with Nuance.
Insight Bots – Insight engines will increasing tap intelligent bots that are assigned specific useful tasks such as analyzing daily sales records in soft drinks companies for promotion optimization clues or reviewing new research paper abstracts to find chemical compound candidates for drug development. Early adopters of insight bot builders include EquiBot,declarator.io and Specif.ai.
While still emerging, these enhanced insight functionalities will drive even higher knowledge productivity and discovery value in coming years.
The Insight Engine Competitive Landscape
The major cloud platforms have robust offerings:
-
Microsoft Azure Cognitive Search – Search-focused with strong knowledge graph capabilities
-
Amazon Kendra – Incorporates Alexa natural language query technologies
-
Google Cloud Search – Most personalized using deep learning optimizations
However, for deepest vertical and use case support, specialist providers are recommended:
| Provider | Strengths | Weaknesses |
|---|---|---|
| Sinequa | Industry models, compliance expertise | Partner heavy distribution |
| Lucidworks | Flexible deployment options, healthcare savvy | Narrow specialization |
| Coveo | Front end digital experience search | Complex pricing schemes |
| Attivio | Unified search platform with database | Minimal brand awareness |
While insight engine solutions are still maturing, providers need sharpen vertical strengths while leveraging cloud delivery, knowledge enhancement and conversational interfaces as differentiators versus larger horizontal search competitors.
Adoption Challenges and Mitigations
As with any emerging technology, insight engines come with learning curves. Here are common implementation hurdles and advice to overcome them:
Table 3: Insight Engine Adoption Challenges and Mitigations
| Challenge | Mitigation |
|---|---|
| Lack of Skills | Seek guided enterprise packages over DIY, leverage vendor Machine Learning Ops |
| Poor Relevance | Gather continuous user feedback to improve contextual recommendations |
| Low Usage | Incentivize usage through gamification, train through real work examples |
| Unreliable Accuracy | Curate training datasets, cleanse knowledge models of false positives |
| Questionable ROI | Start with high value use case and baseline metrics to quantify benefits |
Using these mitigation tactics, enterprise insight engine success rates rise substantially. Change management best practices around stakeholder engagement and power users also accelerate adoption.
The Future of Insight Engines
As AI and cloud continue maturing, insight engines are becoming indispensable strategic enterprise assets. They empower users through intelligence augmentation while unlocking hidden relationships and findings from ever-growing data assets.
As depicted above, Gartner forecasts global spending on enterprise insight engines to reach $7.3 billion by 2026, achieving a 32% CAGR from 2021. All major analyst projections predict similar dramatic expansion of the insight engine market through 2027.
Organizations across sectors are realizing deploying these next generation search and insight platforms delivers demonstrated productivity, efficiency and discovery advantages over peers. Groundbreaking examples stretch from materials science researchers identifying new high capacity battery chemical combinations by reviewing decades of lab notebooks to help desk managers uncovering previously undetected customer experience downgrade triggers from support call textual analysis.
Businesses should evaluate optimized blends of cloud search services with purpose-built insight engines now to gain sustainable competitive edge. Augmenting and unleashing knowledge work through automation is imperative to thrive in the information driven digital economy.
