Posts tagged big data
Text Analytics: It's Not Just for BIG Data

In a world focused on the value of Big Data, it's important to realize that Small Data is meaningful, too, and worth analyzing to gain understanding. Let me show you with a personal example. If you're a regular reader of the OdinText blog, you probably know that our company President, Tom Anderson, writes about performing text analytics on large data sets.  And yes, OdinText is ideal for understanding data after launching a rapid survey then collecting thousands of responses.

However for this blog post, I'm going to focus on the use of Text Analytics for smaller, nontraditional data set:  emails.

SMALL Data (from email) Text Analytics

I recently joined OdinText as Vice President, working closely with Tom on all our corporate initiatives. I live in a small town in Connecticut with an approximate population of 60,000.  Last year I was elected to serve our town government as an RTM member along with 40 other individuals.  Presently, our town's budget is $290M and the RTM is designing the budget for the next year.

Many citizens email elected members to let them know how they feel about the budget.  To date, I have received 280 emails. (Before you go down a different path with this, please know that I respond personally to each one -- people who take the time to write me deserve a personal response.  I did not and will not include in this blog post how I intend to vote on the upcoming budget, nor will I include anything about party affiliations. And I certainly will not share names.)

As the emails were coming in, I started to wonder … what if I ran this the data I was receiving through OdinText?  Would I be able to use the tool to identify, understand and quantify the themes in the people’s thoughts on how I should vote on the budget?

The Resulting Themes from Small Data Analytics

A note about the methodology:  Each email that I received contained the citizen's name, their email address and content in open text format.  Without a key driver metric like OSAT, CSAT or NPS to analyze the text against, I chose to use overall sentiment. Here is what I learned

280-emails-1024x600.png

Emails about the town budget show that our citizens feel Joy but RTM members need to recognize their Sadness, Fear and Anger

280-emotions-1024x775.png

Joy:

“I have been a homeowner in Fairfield for 37 years, raised 4 kids here and love the community.”

Sadness:

“I am writing you to tell you that I am so unhappy with the way you have managed our town.”

Fear:

“My greatest concern seems to be the inability of our elected members to cut spending and run the town like a business”

Anger:

“We live in a very small house and still have to pay an absurd amount of money in taxes.”

Understanding the resulting themes in their own words

Reduce Taxes (90.16%)

“Fairfield taxes are much higher than surrounding communities.”

“Fairfield taxes are out of line with similar communities”

“The town has to stop raising taxes at such a feverish rate.”

“High taxes are slowly eroding the town of Fairfield.”

Moving if Taxes are Increased (25.13%)

“I am on a fixed income at 64, and cannot afford Fairfield’s taxes now. Please recognize that I cannot easily sell my house, due to the economy & the amount of homes on the market here”

“regret to say most of our colleagues and friends have an "exit strategy" to leave Fairfield”

“Our town is losing residents who are fed up and have moved or are moving to Westport and other towns with lower mil rates”

Reduce Spending (33.33%)

“... bring spending under control”

“Stop the spending please”

“... needs to trim fat at the local level, cut services, stop spending money”

“We need to keep taxes down as much as possible - even if it means spending cuts.”

Education ‘don’t cut’ (8.74%)

“… takes great pride in its education system”

“… promise of an excellent public education”

“… fiscal responsibility; however, not at the expense of the children and their right to an excellent education.”

Education ‘please cut’ (9.83%)

“Let's shave funding from all programs including education”

“... deeply questioning our education budget”

“... reduce the Education budget”

“I have a cherished budgetary item that I want protected--the library. Cut that last, after you cut education, police, official salaries”

Big Value from Small Data in Little Time

I performed this text analysis in 30 minutes. Ironically, it has taken me longer to write this blog post than it did to quantify the text from all those emails. Yet the information and understanding I have gleaned will empower me as I make decisions on this important topic. A small investment in small data has paid off in a BIG way.

Tim Lynch - @OdinText

Text Analytics Tips

Text Analytics Tips, with your Hosts Tom & Gosia: Introductory Post Today, we’re blogging to let you know about a new series of posts starting in January 2016 called ‘Text Analytics Tips’. This will be an ongoing series and our main goal is to help marketers understand text analytics better.

We realize Text Analytics is a subject with incredibly high awareness, yet sadly also a subject with many misconceptions.

The first generation of text analytics vendors over hyped the importance of sentiment as a tool, as well as ‘social media’ as a data source, often preferring to use the even vaguer term ‘Big Data’ (usually just referring to tweets). They offered no evidence of the value of either, and have usually ignored the much richer techniques and sources of data for text analysis. Little to no information or training is offered on how to actually gain useful insights via text analytics.

What are some of the biggest misconceptions in text analytics?

  1. “Text Analytics is Qualitative Research”

FALSE – Text Analytics IS NOT qualitative. Text Analytics = Text Mining = Data Mining = Pattern Recognition = Math/Stats/Quant Research

  1. It’s Automatic (artificial intelligence), you just press a button and look at the report / wordcloud

FALSE – Text Analytics is a powerful technique made possible thanks to tremendous processing power. It can be easy if using the right tool, but just like any other powerful analytical tools, it is limited by the quality of your data and the resourcefulness and skill of the analyst.

  1. Text Analytics is a Luxury (i.e. structured data analysis is of primary importance and unstructured data is an extra)

FALSE – Nothing could be further from the truth. In our experience, usually when there is text data available, it almost always outperforms standard available quant data in terms of explaining and/or predicting the outcome of interest!

There are several other text analytics misconceptions of course and we hope to cover many of them as well.

While various OdinText employees and clients may be posting in the ‘Text Analytics Tips’ series over time, Senior Data Scientist, Gosia, and our Founder, Tom, have volunteered to post on a more regular basis…well, not so much volunteered as drawing the shortest straw (our developers made it clear that “Engineers don’t do blog posts!”).

Kidding aside, we really value education at OdinText, and it is our goal to make sure OdinText users become proficient in text analytics.

Though Text Analytics, and OdinText in particular, are very powerful tools, we will aim to keep these posts light, fun yet interesting and insightful. If you’ve just started using OdinText or are interested in applied text analytics in general, these posts are certainly a good start for you.

During this long running series we’ll be posting tips, interviews, and various fun short analysis. Please come back in January for our first post which will deal with analysis of a very simple unstructured survey question.

Of course, if you’re interested in more info on OdinText, no need to wait, just fill out our short Request Info form.

Happy New Year!

Your friends @OdinText

Text Analytiics Tips T G

[NOTE: Tom is Founder and CEO of OdinText Inc.. A long time champion of text mining, in 2005 he founded Anderson Analytics LLC, the first consumer insights/marketing research consultancy focused on text analytics. He is a frequent speaker and data science guest lecturer at university and research industry events.

Gosia is a Senior Data Scientist at OdinText Inc.. A PhD. with extensive experience in content analytics, especially psychological content analysis (i.e. sentiment analysis and emotion in text), as well as predictive analytics using unstructured data, she is fluent in German, Polish and Spanish.]

 

Text Analytics, Big Data and Marketing Research

Analytics and Data Science in Consumer Insights In case you missed yesterday’s screencast discussion on text analytics, big data and market research with OdinText CEO Tom H. C. Anderson and Paul Kirch of Boss Academy. Please excuse the video quality at some points due to connection issues, but sound quality is pretty good.

 

 

If you have any other questions for OdinText or for Tom please contact us here.

@OdinText

Forbes: Text Analytics Gurus Debunk Four Big Data Myths

Four Big Data and Text Analytics Myths Debunked [Visit Forbes.com for more detailed article]

There are a lot of myths out there surrounding next generation research techniques around data from small to big. Text analytics, if done correctly, can offer insights into problems from a new more encompassing and accurate perspective.

OdinText CEO, Tom H. C. Anderson, and CTO Chris Lehew recently spoke to Forbes Magazine about some of the common analytics and text analytics myths they frequently encounter, and gave real examples of how OdinText users are benefiting from superior insights provided by the Next Generation Text AnalyticsTM platform.

The four data myths covered today in Forbes were:

Myth 1: Big Data Survey Scores Reign Supreme

 A myth propagated by Bain Consulting, without any evidence to justify the claims, is that a survey metric asking 'likeliness to recommend' is related to business growth. OdinText found absolutely no link between survey metrics like NPS and revenue when analyzing 800,000 customer surveys for Shell Oil/Jiffy Lube.

Actual customer text comments proved to be the single best predictor of real behavior from loyalty/returns to revenue.

 

Myth 2: Bigger Social Media Data Analysis Is Better

 textanaltyicsCocaCola

Many believe big data must have value simply because it's big - but it’s more about smart data than big data. Using OdinText Coca-Cola’s Social Media Hub found that only smarter text analytics and data translate to better insights.

 

Myth 3: New Data Sources Are The Most Valuable

 Many companies go looking for new data on social sites and elsewhere while failing to first look too what valuable data they may already be collecting . Campbell’s Soup for instance realized that they could get better insights by using OdinText to listen to the quarter of a million customers that contact their customer service department each year than relying on anything found on Twitter.

 

Myth 4: Keep Your Eye On The Ball By Focusing On How Customers View You

 Sometimes online discussion boards and other consumer generated media is the best place to find insights. But companies often focus just on what is said about them. Starwood hotels among others have realized the benefit of doing comparative analysis VS. their competition, arguably the single best use of this type of data.

 

You can read more detail about the four data myths discussed above in Forbes Magazine article here.

Please visit our blog in the coming months as we roll out a new series of “Text Analytics Tips” with experts from OdinText discussing more myths and sharing tips and techniques on how to drive better more actionable insights through smarter Next Generation Text AnalyticsTM

Coca-Cola Social Media Team Chooses OdinText Text Analytics Software and Wins 2015 ARF Award

Coca-Cola Leverages OdinText analytics to develop new approach to social listening and wins ARF’s 2015 RE:Think Your Future “Make Your Mark” Award!

At the Advertising Research Foundation’s (ARF) annual RE:Think Conference this week?

Please join OdinText and The Coca-Cola Company this coming week at the 2015 Advertising Research Foundation’s annual RE:THINK event to learn more about the future of big data and text analytics.

 

On Monday in SuttTomCokeTextAnalyticson South, 2nd Floor at 3:30, OdinText Founder and CEO Tom H. C. Anderson will be joined by several other industry leaders in what promises to be a very interesting panel entitled “Applying Mobile Big Data at the Intersection of Content, Context & User Analytics”. As unstructured data is the key to the future of mobile, text analytics promises to be a key part of the discussion.

 

 

On Wednesday afternoon in the Grand Ballroom, The Coca-Cola Company’s Digital Anthropologist, Allison Barnes and Global Media Insights Director, Justin De Graaf, are being honored for their very interesting work and award winning paper entitled “Digital Anthropology: Researching Audiences Online”.

CokeAllisonTextAnalytics

CokeJustinTextAnalytics

Allison and Justin will briefly discuss how Coca-Cola is leveraging OdinText to understand specific consumer groups, developing an innovative new approach that works across the Coca-Cola system. An approach that is scalable, utilizes existing tools, works across all markets and adapts to custom requirements.

“The new technique moves beyond word clouds [and other first generation social listening features] which represent the old way, and towards true analytics, which we’re getting from OdinText”

As Allison points out in their paper, great text analytics is critical for The Coca-Cola Company since “Social is a new avenue our consumers use to share their lives, and tapping in to those shared thoughts allows us to become more closely tied to the things that matter most to them. Our ability to continue innovating communications, products and happiness is at the heart of our future and harnessing social allows us to do exactly that”.

Congratulations to Justin, Allison and the rest of the social media team at Coca-Cola for a job well done!

We look forward to seeing you at The ARF Monday or Wednesday. And as always we welcome your questions about OdinText and text analytics anytime!

Big Data Insights

Big Data Insights presented by Silicon Alley Network big+data+silicon+alley+new+york

In Manhattan this Monday? If so please join OdinText founder Tom H. C Anderson along with Phil Leig Bjerknes of AllDayEveryDay and Afshim Goodarzi of 1010 data. Great networking event for anyone interested in big data analytics and tech startups. This is an invitation only event, so please contact us if you are interested in attending and we’ll try to get you on the list if space allows. More info here.

The future of (big) “text analytics”

The future of (big) “text analytics”: Dr. Kristof Coussement interviews Tom H.C. Anderson  

Dr. Kristof Coussement and his colleagues at IESEG in France have been doing some interesting research in the field of text analytics and he recently interviewed OdinText founder Tom H. C. Anderson on the future of text analytics and big data.

BigTextAnalytics

Find out more and read the full interview via IESEG’s Website or Dr Coussement’s site.

 

[Tom H. C Anderson is Founder and CEO of OdinText. To find out more about OdinText please visit INFO REQUEST]

Mr Big Data VS. Mr Text Analytics

[Interview re-posted w/ permission from Text Analytics News]

Mr. Big Data & Mr. Text Analytics Weigh In Structured VS. Unstructured Big Data

 

kirk_borne Text Analytics News

If you pay attention to Big Data news you’re sure to have heard of Kirk Borne who’s well respected views on the changing landscape are often shared on social media. Kirk is professor of Astrophysics and Computational Science at George Mason University. He has published over 200 articles and given over 200 invited talks at conferences and universities worldwide. He serves on several national and international advisory boards and journal editorial boards related to big data

 

 

tom_anderson Text Analytics News

Tom H. C. Anderson was an early champion of applied text analytics, and gives over 20 conference talks on the topic each year, as well as lectures at Columbia Business School and other universities. In 2007 he founded the Next Gen Market Research community online where over 20,000 researchers frequently share their experiences online. Tom is founder of Anderson Analytics, developers of text analytics software as a service OdinText. He serves on the American Marketing Association’s Insights Council and was the first proponent of natural language processing in the marketing research/consumer insights field.

 

Ahead of the Text Analytics Summit West 2014, Data Driven Business caught up with them to gain perspectives on just how important and interlinked Big Data is with Text Analytics.

 

Q1. What was the biggest hurdle that you had to overcome in order to reach your current level of achievement with Big Data Analytics?

KB: The biggest hurdle for me has consistently been cultural -- i.e., convincing others in the organization that big data analytics is not "business as usual", that the opportunities and potential for new discoveries, new insights, new products, and new ways of engaging our stakeholders (whether in business, or education, or government) through big data analytics are now enormous.

After I accepted the fact that the most likely way for people to change their viewpoint is for them to observe demonstrated proof of these big claims, I decided to focus less on trying to sell the idea and focus more on reaching my own goals and achievements with big data analytics. After making that decision, I never looked back -- whatever successes that I have achieved, they are now influencing and changing people, and I am no longer waiting for the culture to change.

THCA: There are technical/tactical hurdles, and methodological ones. The technical scale/speed ones were relatively easy to deal with once we started building our own software OdinText. Computing power continues to increase, and the rest is really about optimizing code.

The methodological hurdles are far more challenging. It’s relatively easy to look at what others have done, or even to come up with new ideas. But you do have to be willing to experiment, and more than just willingness, you need to have the time and the data to do it! There is a lot of software coming out of academia now. They like to mention their institution in every other sentence “MIT this” or ‘UCLA that”. The problem they face is twofold. On the one hand they don’t have access to enough real data to see if their theories play out. Secondly, they don’t have the real world business experience and access to clients to know what things are actually useful and which are just novelty.

So, our biggest hurdle has been the time and effort invested through empirical testing. It hasn’t always been easy, but it’s put me and my company in an incredibly unique position.

Q2. Size of data, does it really matter? How much data is too little or too much?

THCA: Great question, with text analytics size really does matter. While it’s technically possible to get insights from very small data, for instance on our blog during the elections one of my colleagues did a little analysis of Romney VS. Obama debate transcripts, text analytics really is data mining, and when you’re looking for patterns in text, the more data you have the more interesting relationships you can find.

KB: Size of data doesn't really matter if you are just getting started. You should get busy with analytics regardless of how little data you have. The important thing is to identify what you need (new skills, technologies, processes, and data-oriented business objectives) in order to take advantage of your digital resources and data streams. As you become increasingly comfortable with those, then you will grow in confidence to step up your game with bigger data sets. If you are already confident and ready-to-go, then go! The big data revolution is like a hyper-speed train -- you cannot wait for it to stop in order to get on board -- it isn't stopping or slowing down! At the other extreme, we do have to wonder if there is such a thing as too much data. The answer to this question is "yes" if we dive into big data's deep waters blindly without the appropriate "swimming instruction" (i.e., without the appropriate skills, technologies, processes, and data-oriented business objectives). However, with the right preparations, we can take advantage of the fact that bigger data collections enable a greater depth of discovery, insight, and data-driven decision support than ever before imagined.

Q3. What is the one thing that motivates and inspires you the most in your Big Data Analytics work?

KB: Discovery! As a scientist, I was born curious. I am motivated and inspired to ask questions, to seek answers, to contemplate what it all means, and then to ask more questions. The rewards from these labors are the discoveries that are made along the way. In data analytics, the discoveries may be represented by a surprising unexpected pattern, trend, association, correlation, event, or outlier in the data set. That discovery then becomes an intellectual challenge (that I love): What does it mean? What new understanding does this discovery reveal about the domain of study (whether it is astrophysics, or retail business, or national security, or healthcare, or climate, or social, or whatever)? The discovery and the corresponding understanding are the benefits of all the hard work of data wrangling.

THCA: Anyone working with analytics has to be curious by nature. Satisfying that curiosity is what drives us. More specifically in my case, if our clients get excited about using our software and the insights they’ve uncovered, then that really gets me and my whole team excited. This can be challenging, and not all data is created equal.

It can be hard to tell someone who is excited about trying Text Analytics that their data really isn’t suitable. The opposite is even more frustrating though, knowing that a client has some really interesting data but is apprehensive about trying something new because they have some old tools lying around that they haven’t used, or because they have a difficult time getting access to the data because it’s technically “owned” by some other department that doesn’t ‘Get’ analytics. But helping them build a case and then helping them look good by making data useful to the organization really feeds into that basic curiosity. We often discover problems to solve we had no idea existed. And that’s very inspiring and rewarding.

Q4. Which big data analytics myth would you like to squash right here and now?

KB: Big data is not about data volume! That is the biggest myth and red herring in the business of big data analytics. Some people say that "we have always had big data", referring to the fact that each new generation has more data than the previous generation's tools and technologies are able to handle. By this reasoning, even the ancient Romans had big data, following their first census of the known world. But that's crazy. The truth of big data analytics is that we are now studying, measuring, tracking, and analyzing just about everything through digital signals (whether it is social media, or surveillance, or satellites, or drones, or scientific instruments, or web logs, or machine logs, or whatever). Big data really is "everything, quantified and tracked". This reality is producing enormously huge data volumes, but the real power of big data analytics is in "whole population analysis", signaling a new era in analytics: the "end of demographics", the diminished use of small samples, the "segment of one", and a new era of personalization. We have moved beyond mere descriptive analysis, to predictive, prescriptive, and cognitive analytics.

THCA: Tough one. There are quite a few. I’ll avoid picking on “social media listening” for a bit, and pick something else. One of the myths out there is that you have to be some sort of know it all ‘data scientist’ to leverage big data. This is no longer the truth. Along with this you have a lot dropping of buzz words like “natural language processing” or “machine learning” which really don’t mean anything at all.

If you understand smaller data analytics, then there really is no reason at all that you shouldn’t understand big data analytics. Don’t ever let someone use some buzz word that you’re not sure of to impress you. If they can’t explain to you in layman’s terms exactly how a certain software works or how exactly an analysis is done and what the real business benefit is, then you can be pretty sure they don’t actually have the experience you’re looking for and are trying to hide this fact.

Q5.What’s more important/valuable, structured or unstructured data?

KB: Someone said recently that there is no such thing as unstructured data. Even binary-encoded images or videos are structured. Even free text and sentences (like this one) are structured (through the rules of language and grammar). Even some meaning this sentence has. One could say that analytics is the process of extracting order, meaning, and understanding from data. That process is made easier when the data are organized into databases (tables with rows and columns), but the importance and value of the data are inherently no more or no less for structured or unstructured data. Despite these comments, I should say that the world is increasingly generating and collecting more "unstructured data" (text, voice, video, audio) than "structured data" (data stored in database tables). So, in that sense, "unstructured data" is more important and valuable, simply because it provides a greater signal on the pulse of the world. But I now return to my initial point: to derive the most value from these data sources, they need to be analyzed and mined for the patterns, trends, associations, correlations, events, and outliers that they contain. In performing that analysis, we are converting the inherent knowledge encoded in the data from a "byte format" to a "structured information format". At that point, all data really become structured.

THCA: A trick question. We all begin with a question and relatively unstructured data. The goal of text analytics is structuring that data which is often most unstructured.

That said, based on the data we often look at (voice of customer surveys, call center and email data, various other web based data), I’ve personally seen that the unstructured text data is usually far richer. I say that because we can usually take that unstructured data and accurately predict/calculate any of the available structured data metrics from it. On the other hand, the unstructured data usually contain a lot of additional information not previously available in the structured data. So unlocking this richer unstructured data allows us to understand systems and processes much better than before and allows us to build far more accurate models.

So yes, unstructured/text data is more valuable, sorry.

Q6. What do you think is the biggest difference between big data analysis being done in academia vs in business?

KB: Perhaps the biggest difference is that data analysis in academia is focused on design (research), while business is focused on development (applications). In academia, we are designing (and testing) the optimal algorithm, the most effective technique, the most efficient methodology, and the most novel idea. In business, you might be 100% satisfied to apply all of those academic results to your business objectives, to develop products and services, without trying to come up with a new theory or algorithm. Nevertheless, I am actually seeing more and more convergence (though that might be because I am personally engaged in both places through my academic and consulting activities). I see convergence in the sense that I see businesses who are willing to investigate, design, and test new ideas and approaches (those projects are often led by data scientists), and I see academics who are willing to apply their ideas in the marketplace (as evidenced by the large number of big data analytics startups with university professors in data science leadership positions). The data "scientist" job category should imply that some research, discovery, design, modeling, and hypothesis generation and testing are part of that person's duties and responsibilities. Of course, in business, the data science project must also address a business objective that serves the business needs (revenue, sales, customer engagement, etc.), whereas in academia the objective is often a research paper, or a conference presentation, or an educational experience. Despite those distinctions, data scientists on both sides of the academia-business boundary are now performing similar big data analyses and investigations. Boundary crossing is the new normal, and that's a very good thing.

THCA: I kind of answered that in the first question. I think academics have the freedom and time to pursue a research objective even if it doesn’t have an important real outcome. So they can pick something fun, that may or may not be very useful, such as are people happier on Tuesdays or Wednesday’s? They’ll often try to solve these stated objectives in some clever ways (hopefully), though there’s a lot of “Pop” research going on even in academia these days. They are also often limited in the data available to them, having to work with just a single data set that has somehow become available to them.

So, academia is different in that they raise some interesting fun questions, and sometimes the ideas borne out of their research can be applied to business.

Professional researchers have to prove an ROI in terms of time and money. Of course, technically we also have access to both more time and more money, and also a lot more data. So an academic team of researcher working on text analytics for 2-3 years is not going to be exposed to nearly as much data as a professional team.

That’s also why academic researchers often seem so in love with their models and accuracy. If you only have a single data set to work with, then you split it in half and use that for validation. In business on the other hand, if you are working across industries like we do, while we certainly may build and validate models for a specific client, we know that having a model that works across companies or industries is nearly impossible. But when we do find something that works, you can bet it’s going to be more likely to be useful.

Text (AkA Buzz) Analytics

[Re Posted from Next Gen Market Research Blog] First Ever Text Analytics Cartoon

It's been a while since I've posted a cartoon here on the blog. However, all the buzzwords (Big Data, Hadoop, Natural Language Processing and Machine Learning etc.) constantly bandied about in our field inspired me - plus I don't think I've ever seen a text analytics cartoon before.

Hope you like it?

@TomHCAnderson

[Full Disclosure: Tom H. C. Anderson is Managing Partner of Anderson Analytics, developers of patented Next Generation Text Analytics™software platform OdinText. For more information and to inquire about software licensing visit ODINTEXT INFO REQUEST]

Selecting the Best Text Analytics Software

The Non-Dummies Guide for Selecting a Text Analytics (or any other) Partner WhatToLookForWhenBuyingBestTextAnalyticsSoftwareSolution

Text Analytics is a Process, Not and End!

What would you say should be the goal of good text analytics software?

Based on the questions we get from clients investigating text analytics solutions there seems to be no small amount of confusion. The fault isn’t theirs, it’s the fault of the early text analytics and social media monitoring vendors who overpromised and under delivered.

Rather than explaining to clients what kind of analysis and insights they should rightfully expect they choose instead to hide the fact that they know very little themselves about how text analytics can and should actually be applied, instead most text analytics sales staff preferred to talk theoretically using as many technical buzzwords like “natural language processing” as possible.

Here are questions you can safely set aside when investigating the right text analytics solution. They have next to no meaning whatsoever in terms of efficacy for your use case:

-How do you handle xyz stemming, semantic ABC, Ontologies and ______? [Insert other favorite buzz word you’ve heard but don’t really understand]

-What does the output look like, do you have a pretty dashboard? [If you buy text analytics software for pie charts and word clouds you’ll be in trouble. Dashboards, even if you find they make sense need serious customization]

-Do you have a cool black sci-fi looking background with neon colored maps? [If you plan to put a bunch of monitors up and pretend you or on the bridge of starship enterprise I suppose this may make sense?!?!]

Instead, these kinds of questions are what you should be asking:

-Tell me about a client with the same kind of data that I have. How have they benefited from the tool? [They better be darn specific]

-Show me how it works with my own data!? [It’s easy to give a demo of poorly working software with canned data. Always make then use your data and never give them more than a day or two max to set it up]

Even better Text Analytics tools are becoming easier to use, and I admit, keeping OdinText intuitive as we add more features is challenging. However, one of the biggest single misconceptions about text analytics software is that they somehow have this magical “artificial intelligence” power. Some sort of power to discern everything and automatically write the report for you. I’m really not exaggerating.

Text analytics is not an end, it is a process. Find a vendor who understands this and whose software is not black box. Here simple is better. If how the software does its coding is hidden in a black box, and the sales person throws buzz words at you to make you feel safe/confused about the fact you have no idea about how the sausage is made, it’s not because they have valuable “linguistic” or “machine learning” rules (more buzz words) -those can only be developed after carefully studying your own data, it’s because their software doesn’t actually work too well and will require a lot of expensive and time consuming customization for unproven performance.

After choosing a text analytics software tool that is powerful and intuitive, a software that you can trust, then the fun begins. You or your analyst should be able to learn how to use the tool relatively quickly, but as with anything, you should expect to get better with experience.

Remember the early statistical software tools like SPSS and SAS. They worked very well on smaller data and you could trust that they actually did what you expected them to. However you still needed to know what clustering and factor analysis was, and why to look at a mean VS. a median. Just like these tools text analytics software also requires an analyst who can think about the data and how to get the most valuable insights for management.

Unfortunately, people who have never analyzed big data or conducted text analytics for real clients are building text analytics and “social listening” software. Find a vendor who understands your business. Their products will make you a data scientist. You’ll have to do a little more than press one button to understand the data, but since when has anything worthwhile been that easy?

To answer the question I posed earlier - what should be the goal of good text analytics software? – the answer depends on what field you’re in…

If you’re a marketer, then the main question you should be asking is how will this text analytics software help me sell more product to more customers less expensively?

@TomHCAnderson

 

[Full Disclosure: Tom H. C. Anderson is Managing Partner of Anderson Analytics, developers of patented Next Generation Text Analytics™ software platform OdinText. For more information and to inquire about software licensing visit ODINTEXT INFO REQUEST]

[Above also posted on the Next Gen Market Research blog]