Seth Grimes discusses text analytics market trends. Text analytics applies natural language processing to extract business insights from text sources. It has been part of business intelligence, data science, and analytics for over 15 years. While the vendor landscape is fragmented with no clear leader, visual analytics platforms and customer experience platforms like Medallia and InMoment have seen increased market activity and private investment. Users are advised to start small, test multiple tools, and focus on use cases and business benefits over accuracy when evaluating text analytics solutions.

1. Text Analytics
Market Trends
Seth Grimes
Alta Plana Corporation
@sethgrimes
November 6, 2019

4. 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.

5. 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

6. Atsushi Takayama , Coursera: Text Mining and Analytics
https://medium.com/@taka.atsushi/coursera-text-mining-
and-analytics-bf314d7e130e.

8. 2016

9. Name some text analytics providers…
•
•
•
•
•
•
•
•
•
•

10. 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.”

11. 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…

16. Visual Analytics Platforms

17. Visual Analytics Platforms
https://www.kenflerlage.com/2019/09/text-analysis.html

20. 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).
• …

22. 2019?

27. 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.)

29. Text Analytics
Market Trends
Seth Grimes
Alta Plana Corporation
@sethgrimes
November 6, 2019