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Text Analytics Market Trends

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Text Analytics Market Trends

  1. 1. Text Analytics Market Trends Seth Grimes Alta Plana Corporation @sethgrimes November 6, 2019
  2. 2. Level setting: Text analytics is a term for software and business processes that apply natural language processing to extract business insights from social, online, and enterprise text sources. 1. Software. 2. Business processes. 3. Business insights. 4. Data sources. Note: Commercial product images & logos on the slides that follow are included for illustration only.
  3. 3. Search BI Text Analytics Semantic search Information access / question answering Integrated analytics Data Mining Text data mining Text analytics has been part of the BI, data science, and analytics toolkit for over fifteen years. Data science? NLU Statistics
  4. 4. Atsushi Takayama , Coursera: Text Mining and Analytics https://medium.com/@taka.atsushi/coursera-text-mining- and-analytics-bf314d7e130e.
  5. 5. 2016
  6. 6. Name some text analytics providers… • • • • • • • • • •
  7. 7. From a user survey I ran earlier this year… One respondent’s comment: “Since language technologies are still immature the vendor landscape is highly fragmented and with no clear market leader. Most of the vendors provide APIs for development staff requiring specific technical expertise, new skills and systems to learn. Other simplified text analytic tools are usually narrow domain dedicated and require plenty of manual work, manually built knowledge bases, and long training.”
  8. 8. Market / opportunity spaces • Platform and integrations. • Delivery mode: tool, workbench, solution, service. • Industry/function/market. • Data provisioning. • … What’s new and notable in the last couple of years? Let’s look at examples…
  9. 9. Visual Analytics Platforms
  10. 10. Visual Analytics Platforms https://www.kenflerlage.com/2019/09/text-analysis.html
  11. 11. Market activity: • Medallia IPO (July 2019) – CX platform, $MDLA up 40%, $3.75B current market value • Crimson Hexagon -> Brandwatch (Oct. 2018). “We’ll be offering our customers instant access to the world’s largest library of consumer conversations. That includes: 1.2 trillion+ posts back to 2008 • 15 billion posts added each month • 100 million sources across the globe.” • InMoment (uses IBM Watson): private equity buy-out (May 2019). • OTOH: “ServiceNow to acquire Attivio’s cognitive search platform” (Oct. 23, 2019). • …
  12. 12. 2019?
  13. 13. Create test cases and run a pilot with real data. Focus more on data connectivity ease of building custom adopters. Approach as a text processing platform, not single application. Use a POC (large or small) to make final decision. Ask the experts. 🤡 Hire a consultant to help get you started with one project and explore the potential for greater value before making a big investment. Read the available literature about TA and NLP. Do not be afraid to test several tools to find the one for you. I would urge the users to gauge the appetite for text analytics within their organization before making an investment and be a power user of this awesome technology. 🔥 Set your expectations low. This is an emerging area and the maturity levels of even market leaders are pretty low. 1. Focus on quantitative evaluation. 2. Compare vendors based on your own samples. Don't take their word for it. Study the theory and mathematics before starting anything. Then clearly define objectives and analytic roadmap. 🔥 Text analytics without vehemence and without cultural context is useless. Be thoughtful about what it takes to set it up and how flexible it can be. Take open source seriously, invest in enablement/staff/training. 🔥 Don't focus too much on accuracy. Customers need to be okay with classification mistakes. Start small and slowly expand your category tree. Trying to do it all at once is bound to end in failure. Compare at least 2 solutions. 🤡 Focus on use case and business benefit. Take your time. Think big, start small. Get real linguists. Don't expect too much. 🔥 Ask vendor to show that their accuracy is superior to competitors in a robust way, e.g., report accuracy values. Understand the technology and how it's different even if the explanation is not super technical. 🤡 Speak to someone neutral who understands the business need. Learn on the job. Exploratory data analytics can be a good first step. 🔥 Use a vendor that supplies a text API, experiment with recall and precision on each vendor and keep your metrics for benchmarking one against the other. Consider text analysis tools as a helpful and time/money saving "recommendation" software and not as the ultimate solution to text mining or content management. Get an expert in the field (i.e., areas of Natural Language Processing, Information Retrieval, Semantic Technologies, etc.)
  14. 14. Text Analytics Market Trends Seth Grimes Alta Plana Corporation @sethgrimes November 6, 2019

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