6 Tips for Text Analytics Success with NLP
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Unstructured, ‘dark data’ accounts for 80% of all enterprise data, and it’s where some of your most valuable information is hiding out: in customer service interactions, employee feedback, reports, notes, RFPs and contracts and social media data. Unfortunately, turning it into valuable business insights remains a challenge. That’s why companies are turning to Natural Language Processing (NLP), and Forrester Research has named NLP a “Top 10 Emerging Technology to go Mainstream.”¹
NLP and text analytics break down language data to analyze it automate operations and find untapped, actionable text data insight. No longer a nascent innovation, NLP has reached a technical maturity that can be relied upon to solve complex business challenges. Today’s NLP applications go beyond simple chatbots and are used in complex business processes like claims, email management and robotic process automation.
Listen as Luca Scagliarini, Chief Product Officer at expert.ai, and guest speaker Boris Evelson, VP and Principal Analyst at Forrester Research discuss the text analytics technology market.
Hear key findings and recommendations from Forrester’s 2022 text analytics research including:
- How a Hybrid AI approach provides accuracy and adaptability
- The importance of semantic understanding (aka NLU)
- Which use cases should impact NLP platform decision making
- When companies should build vs buy NLP solutions
¹”The Top 10 Emerging Technologies In 2022”, Forrester, September 15, 2022