My first foray in to #novel #writing with #AI. – #Tensorflow #AI

TL;DR – Quick Summary

For all interested writers, authors and creative writing types…. I think it’s fairly safe to assume you’re safe for the time being.

Have a good day.

Can AI Write Me a Book?

First of all, this isn’t really about code it’s just about process. So there are no juicy code snippets or scripts to get all hot under the collar about. This whole (stupid) episode started out with a couple of questions.

  1. Could AI write a novel or a novella of a quality that it could be entered into a writing competition?
  2. Is it possible to make 50 Shades of Grey readable?

So before my usual working day I downloaded some recurrent neural network code, installed Tensor Flow and trained it on Shakespeare and left the laptop alone to do it’s thing. Yup, training takes a long time, in my case it was eight hours on a commodity Toshiba C70D laptop running Ubuntu. If you want to read more about RNN’s then there’s an excellent explanation here. Generating samples of text generated from the RNN is a doddle….. takes seconds.

That Flipping Book.

So, rinse and repeat with some different text. How about 161,528 words of that book. Now, I have a confession I’ve never read that book, or in fact novels, for some reason my brain is firmly planted in non-fiction. Now I’m wondering if I can get AI to write me an O’Reilly book…. wonder what animal I’d get?

Another eight hours pass, overnight this time.

So how quick is it to generate 500 words of AI driven wordery? No time at all it seems…..

In fact with some simple bash scripting I can write an 11,500 Novella in under two minutes, on a cheap laptop. I shall be rich after all!

Not So Fast….

While the output was, well okay, it needs A LOT OF WORK to make it actually work on a human readable level. The main reason is in the training, if you look at that book it trundles in at 900k long in text file length, for training that’s way too small. In the samples the AI would get stuck in a look at repeat the same phrases over and over. Sometimes it would actually add to the paragraph, most times it repeated so often it didn’t make sense.

“He looks so remorseful, and in the same color as the crowd arrives and in my apartment. The thought is crippling. But and I don’t want to go to me that I want to be beholden to you. I don’t want to be beholden to you. I don’t want to be beholden to you. I don’t want to be beholden to you. I don’t want to be beholden to you. I don’t want to be beholden to you. I don’t want to be beholden to you. I don’t want to be beholden to you. I don’t want to be beholden to you. I don’t want to be beholden to you. I don’t want to be beholden to you. I don’t want to be beholden to you. I don’t want to be beholden to you. I don’t want to be beholden to you. I don’t want to be beholden to you. I don’t want to be beholden to you. I don’t want to be beholden to you. I don’t want to be beholden to you. I don’t want to be beholden to you. I don’t want to be beholden to you. I don’t want to be beholden to you. I don’t want to be beholden to you. I don’t want to be beholden to you. I don’t want to be beholden to him — and I can tell him about 17 miles a deal.”

The only thing I can think of that came close was Adrian Belew’s repetative shouting of “I repeat myself when under stress, I repeat myself when under stress…..” (Warning: Youtube link contains King Crimson and a Chapman Stick).

Regardless, part of me thinks that’s naff, part of me thinks that’s rather cool, AI did that. So in theory and with a little cleaning up it’s possible to craft something.

What About Topic and Flow?

This is the thing with creative text, it has characters, themes and a story flow. What I’ve done so far doesn’t address any of that and that’s where everything falls flat on it’s bum for AI. Without some hefty topic wrangling it’s going to be difficult to craft something that’s actually going to flow and make sense.

My favourite book on text mining by a country mile is not one that has tons of code in it, it’s The Bestseller Code by Jodie Archer and Matthew Jockers. It’s a good attempt, while by their admission could be improved, investigation using multivariate analysis, NLP and other text mining tools to see if there were patterns in the best seller list.


Topic is important, that goes without saying. My AI version has no plot line whatsoever as it plainly isn’t told of such matters, if you want to know more about plot lines then there are the seven basic plot lines that are widely used. Baking those plot lines to an AI will take work.

The more text you generate the worse it’s going to be to get a basic plot going. A way to get the AI to focus on generating certain aspects of the story over a timeline would be beneficial but hard to do. Once again though, nothing is impossible.

An Industry Of Authorship, Automated?

The future automation of all things literature I think is a long way off. Though, let’s look at this from a 30,000ft view. I can generate an eleven thousand word book, while ropey, showing some promise if only needing an editor to sort the wording out.

API’s that exist now, well I could pick of for words to form a title. “rules are a hostile anthem” came out…. one Google Image Search for creative commons photos….


And automatically pick an image that fits a certain AI criteria. No text… pass that on to an overlay of the title of the book and the author’s name, “Alan Inglis” (geddit A.I.) and package that up in a Kindle format (that can be automated too) and off it goes to an Amazon account.

*I did check on Alan Inglis, there are no authors of that name but, rather ironically, one within clinical neurosciences….. I should write an algorithm to create a surname that doesn’t exist really. I just guessed this one out.

Perhaps Not Fiction Then….

Perhaps not, but with texts that tend to take the same form it could be easy to create fairly accurate drafts which require some form of editorial gaze afterwards. News reports, Invest NI jobs created press releases, term sheets and even business plans. Yup I think there’s sufficient scope for something to happen. I don’t think you’ll replace them human element but then again you don’t really want to.

Back, To Answer My Original Question

So, could my AI submit something to writing competition? Perhaps it could if it were less than 10,000 words. With enough corpus text it would be possible to do something of a quality that could be considered readable. Would a judge notice it as AI writing, who knows. There are some bits with the samples I’ve generated that are quite interesting, it looks like there’s heart in the prose but it’s simply not true.

I think the Alan Inglis’s of this world are safe for the time being….. I suppose I should go and read The Circle by Dave Eggers to see what my future holds.





The 30 Second Bayesian Classifier #machinelearning #bayes #classification

I’m putting this up as I got a nice email from a reader who was having trouble with running the Britney example. And as developers know, bad examples are enough to put people off…. actually they’re toxic.


See what I did there…


The Classifier4J library is old so it’s not on any Maven repository I’m aware of. So we have to go old school and go old fashion download jar file. You can find the Classifier4J library at

If you don’t have the code for the book you can download it from


Open a terminal window and go to the example code for the book. In chapter2 is the Britney code. Keep a note of where you’ve downloaded the Classifier4J jar file as you’ll need this in the Java compile command.

$ javac -cp /path/to/Classifier4J-0.6.jar


There should be a .class file in your directory now. Running the Java class is a simple matter. Notice we’re referencing the package and class we want to execute.

$ java -cp .:..:/path/to/Classifier4J-0.6.jar chapter2.BritneyDilemma
brittany spears = 0.7071067811865475
brittney spears = 0.7071067811865475
britany spears = 0.7071067811865475
britny spears = 0.7071067811865475
briteny spears = 0.7071067811865475
britteny spears = 0.7071067811865475
briney spears = 0.7071067811865475
brittny spears = 0.7071067811865475
brintey spears = 0.7071067811865475
britanny spears = 0.7071067811865475
britiny spears = 0.7071067811865475
britnet spears = 0.7071067811865475
britiney spears = 0.7071067811865475
christina aguilera = 0.0
britney spears = 0.9999999999999998

About the Book


You can find out more about the book on the Wiley website. As well as machine learning practical examples it also has sections on Hadoop, Streaming Data, Spark and R.

#Snapchat – The #IPO that will probably never profit.

The App That I Still Don’t Understand is IPOing

It used to be music that instantly classified you as “old”, with me it appears to be this app.

Snapchat has finally revealed plans for the much publicised IPO…..

And there’s one sentence that says everything about Silicon Valley IPO’s. You’ve read it a thousand times probably but here Snapchat were pretty plain about it.

“to incur operating losses in the future, and may never achieve or maintain profitability”

Which is about as wide open as you can get. The IPO therefore has the single purpose, as other Silicon Valley IPO’s tend to, recoup the money for the initial investors. There’s 24 of them… the idea of buying shares as investment is thinking that over the long term that the company will be striving to make a profit for the shareholders and therefore paying a dividend in the future. A company claiming it won’t make a profit to traditional shareholders is…..


The hope then is the share price is more than what you paid for it and sell it on.

And if you think the unicorn eye-watering valuation of $25 billion is impressive then the loss figures are equally sphincter tightening. You can also see this in Uber, Lyft and other media luvvied ventures. These aren’t businesses, they’re planned investor flip schemes where profit isn’t a measure, just a bonus. The focus is on the IPO.

What is Business?

If you need a reminder.

“Business is a very specific, limited activity, whose defining purpose is maximising owner value over the long term by selling goods or services.  Accordingly business is not an association to promote social welfare, spiritual fulfillment or full employment; such organisations are legitimate, but they are not businesses” Elaine Sternberg – Just Business – Business Ethics In Action

I’m just wondering if the buyers of Snap shares will be left with a donkey, not a unicorn once the original investors have cashed out. It’s a well documented approach, hardly new.

The ongoing issue of data transparency in Northern Ireland. #data #opendata #charity

There’s an interesting data parallel happening at the moment, the ability to champion open data and all the trimmings: reports, dashboards, findings (with smatterings of bias thrown in for good measure). And on the other hand the one in the cogs of government, this kind of thing.


During the Detail Data Conference last week the main word that delegates were interested in most of all was one of “transparency” and while Northern Ireland is getting there in some measure, there’s a portal of open data, I can even see Translink real time rail data now, something un-imaginable five years ago.

If you look at the mainstream media it appears to be the opposite, data is requested, demanded and downright expected so someone can back up their claims. It’s been an interesting few weeks indeed.

The harsh reality is that while things like the open data portal for Northern Ireland are a great idea, under good management and have the potential to let others do good things, well we are far behind with some catching up to do.

Three Key Requirements for 2017

Open up the Postcodes

It comes up time and time again but on Wednesday it was coming from some heavy hitters in the open data sector. The GrantNav utility in the 360Giving website lets you find details on all levels of giving in the UK. Their main issue (if I picked it up correctly) is the ease of searching on Northern Ireland, with postcode searches still requiring a licence it makes things very difficult for them and others on the mainland.

The work around was to map to ward level, it feels like a stop gap to me though, I’m hoping from NI’s side of things it can be resolved. The mainland is taking interest in what we do, there are times we just make it difficult to access the information.

file-15-01-2017-11-46-14Open Up Grant Funding

I know that Detail Data got part the way with this, here’s the report on Invest NI funding.  These usually require freedom of information requests, phone calls, sarcastic tweets and so on. There has to be an easier way. Who benefits from the RHI money? And I’m not just talking about the ones with boilers, I mean the suppliers too? Invest NI money, as it’s essentially public money to aid the economy, so the old eSynergy fund or the newer TechStartNI management. The Propel Programme, who’s going through that and what were they getting at the end? StartPlanetNI? I’m scratching the surface…. there’ll be hundreds.

Now Tell Me The Ones That Got Turned Down

One conversation I had during the Detail Data Conference was with 360Giving CEO Rachel Rank, while the site has all the parties who received money it doesn’t list the ones who applied and got turned away. I was interested to see if i) that data existed and ii) would it be publicly available.

The short answer is no. I personally believe this needs to happen. There’s actually a lot to learn here and I’m thinking on a algorithmic level. Neural networks predicting Darcey Bussell’s scores are all well and good but it would be better for all to put it to good use.

How about a startup who securely feeds their idea into a system that can predict their probability of getting funding from various bodies? To train a system like that you need the accepted, the declined and the undecideds.

Northern Ireland’s Panama Papers Moment?

It’s better to start opening up as much data as possible. What could follow is an opening up of data by other means. Back door sifting and publishing with connections to all the funding received. The media would love this and baking cakes with pictures on will be a walk in the park compared to the explaining some could potentially have to do.

Rest assured, it won’t be me having the Panama Papers moment, I have enough to do.


Filtering the Streaming #Twitter API with #Onyx Plugin – #clojure #twitter #data #firehose #onyx

Twitter Fashion Analytics Revisited?

Not quite but I have been working on revisiting the original work I did in SpringXD and moving it to the Onyx Framework.


The Twitter Plugin for Onyx Workflows

The original Onyx Twitter plugin acted as a handy input stream from the Twitter firehose. Nice and easy to setup too, just put your Twitter app credentials as environment variables and add a catalog. For example:

{:twitter/consumer-key (env :twitter-consumer-key)
 :twitter/consumer-secret (env :twitter-consumer-secret)
 :twitter/access-token (env :twitter-access-token)
 :twitter/access-secret (env :twitter-access-secret)
 :twitter/keep-keys [:id :text]}

The plugin used the .sample call from Twitter4J library which gives you the 1% of tweets from the public feed. Fine but the data coming out was wayward, it’s very random.

The .filter function

For a more refined look at the Twitter firehose you’re better off using the .filter function which takes a FilterQuery and uses an array of Strings that you want to monitor, this then refines the matches against the firehose and then you get 1% of the matching tweets and not a bunch of random noise.

So, to that end, I’m delighted to say that the Onyx Twitter plugin now supports the tracking of strings in the stream.

Just add:

:twitter/track ["#fashion" "#louboutins" "#shoes"] the catalog and you’re away. If you leave this option off then you get the usual random sample from the public stream.

I might get around to revisiting the whole Twitter Fashion analytics thing in Clojure, with the Onyx Platform soon.

Quick Recipe for #Kafka Streams in #Clojure


Kafka Streams were introduced in Kafka 0.10.x and act as a way of programatically manipulating the data from Kafka. William Hamilton from Funding Circle introduced the concepts in a lightening talk during ClojureX. As discussed by myself and William, make Java Interop your friend.

I’ve based my example from James Walton’s Kafka Stream example which you can find on GitHub.

The Quick and Dirty Basic Stream Demo

First add the dependencies to your project.

[org.apache.kafka/kafka-streams ""]


First of all some configuration, the properties we’re going to use give the application a name, the Kafka broker to work with and the key/value classes to use for each message (in this example they are both strings). With those properties we then create a StreamsConfig class.

(def props
 {StreamsConfig/APPLICATION_ID_CONFIG, "my-stream-processing-application"
 StreamsConfig/BOOTSTRAP_SERVERS_CONFIG, "localhost:9092"
 StreamsConfig/KEY_SERDE_CLASS_CONFIG, (.getName (.getClass (Serdes/String)))
 StreamsConfig/VALUE_SERDE_CLASS_CONFIG, (.getName (.getClass (Serdes/String)))})

(def config
 (StreamsConfig. props))

Creating the Builder

The main builder is defined first then we’ll add the topic and config on when the stream is created.

(def builder

Defining the Topic

Just a string array of topic names, Kafka Streams can read more than one topic.

(def input-topic
 (into-array String ["topic-input"]))

Working with the Stream

While the stream is running every event passed through the topic becomes a KStream object, it’s a case of passing that through a method to do some work on the content of that stream. In this case we’re mapping the values (.mapValues) and converting the value of the key/pair (v) to a string then counting the length. That thing to do is print out the results to the System.out.

 (.stream builder input-topic)
 (.mapValues (reify ValueMapper (apply [_ v] ((comp str count) v))))

It’s worth looking at the actual Java API for the Kafka KStream class. There are lots of methods to manipulate the data passing through, this might result in a value being sent to another Kafka topic or it just being written out to a file. Take the time to study the options, you’ll save yourself time in the long run.

Setting It All Off

The final parts of the puzzle.

(def streams
 (KafkaStreams. builder config))

(defn -main [& args]
 (prn "starting")
 (.start streams)
 (Thread/sleep (* 60000 10))
 (prn "stopping"))

The main function starts the service and will keep it alive for ten minutes.

Packaging it all up

I’m using leiningen, it’s a simple case of creating an uberjar.

$ lein uberjar
Compiling kstream-test.core
log4j:WARN No appenders could be found for logger (org.apache.kafka.streams.StreamsConfig).
log4j:WARN Please initialize the log4j system properly.
Created /Users/jasonbell/work/dataissexy/kstream-test/target/uberjar+uberjar/kstream-test-0.1.0-SNAPSHOT.jar
Created /Users/jasonbell/work/dataissexy/kstream-test/target/uberjar/kafka_streams.jar

Testing the Service

So straight out of the box, Kafka 0.10 is installed in /usr/local, I’m going to be the root user while I run all this (it’s just a local machine).

Start Zookeeper

$KAFKA_HOME/bin/ config/

Start Kafka

$KAFKA_HOME/bin/ config/

Create the Topic

$KAFKA_HOME/bin/ --create -zookeeper localhost:2181 --replication-factor 1 --partitions 1 --topic topic-input

Created topic "topic-input".


Start a Producer and Add Content

$KAFKA_HOME/bin/ --broker-list localhost:9092 --topic topic-input
This is some data
Thisis some more data
This is more data than the last time

Start the Uberjar’d Stream Service

$ java -jar target/uberjar/kafka_streams.jar
log4j:WARN No appenders could be found for logger (org.apache.kafka.streams.StreamsConfig).
log4j:WARN Please initialize the log4j system properly.
null , 5
null , 3
null , 3
null , 3
null , 3
null , 6
null , 3
null , 3
null , 6
null , 0
null , 3
null , 4
null , 5


A really quick walkthrough but it gets the concepts across. Ultimately there’s no way of doing things that’s better than the other. Part of me wants to stick with Onyx, the configuration works well and the graph workflow is easier to map and change. Kafka Streams is important though and certainly worth a look if you are using Kafka 0.10.x, if you are still on 0.8 or 0.9 then Onyx, in my opinion, is still the best option.



@jennschiffer Delivers the Best Talk on the State of Tech I’ve Seen #tech #talks


I love the tech industry, the diversity, the people, everything. I don’t like the nonsense that goes with it, it’s pointless and not needed. And it’s not often I’ll watch something that leaves me peeing myself laughing but also angry about the treatment that some get.

So, Jenn Schiffer, your talk at the XOXO Festival, brilliant and thank you. As for everyone else, her talk is definitely required viewing.


So #ClojureX was excellent, now here’s my corrections – @skillsmatter @onyxplatform

My first #ClojureX done and I’ve already spent the morning thinking about what I could possibly talk about in 2017. Hands down one of the best developer conferences I’ve attended. It’s all about the community and the Clojure community gets that 100%, it showed over the two days.

Many many thanks to everyone at SkillsMatter for looking me, the weary traveler, the tea was helpful. Also great to meet some of the folk I regularly talk to on the Clojurians Slack channel.


A couple of things following my talk now I’ve watched it back. If you want to watch then this is the link:

Firstly, when you create the Onyx app with lein it does create the docker compose file but it only has the Zookeeper element, not the Kafka one – that has to be added in afterwards.

Secondly, the percentage scheduler adds up to 100, I said zero. Brain detached for a second, thought one thing and something else came out.

Apologies, I don’t like giving out wrong information.

Craig and Darcey would have been proud, kind of, perhaps.

Here’s to 2017.

Running Investor Metrics on NI Startup @Mattermark Scores – #investing #clojure #stats #startups

In the last post I argued (mainly with myself) on how Mattermark scores could be used as a gauge for a NI startup’s performance. No response, not that I was really expecting one but no one complaining either. I did say that I would delve into the numbers a little deeper, so here it is.

Got a drink ready? Let’s go.


“So How’s [Startup] Doing?”

It’s a question I’m often asked but one I have now stopped asking myself. The main reason: location, location, location. I’m nowhere near the action.

With the best will in the world it’s either be in Belfast or pretend you are in Belfast. The thing is when someone asks that question, “How’s so and so doing” the answer is usually based on hearsay, rumour and the 99% confirmation bias of the founders, regardless of how nice they are everything will be going fine. They’ve got to keep the positive mindset going, I won’t knock them for doing what they have to do, it’s business.

One of the reasons I’ve relied on the Mattermark scores more and more, they are a good gauge as to how a startup is performing from a social and investor sentiment profile standpoint. It also means I can compare against others in the same sector.

So how is doing? Easy, inspect the growth score numbers.

(160 173 175 170 174 172 170 169 176 178 185 196 196 196 188 196 196 198 199 206 205 204 207 197 187 187 187 182 173 173 170 167 160 154 153 146 150 149 147 145 144 140 138 136 135 134 129 126 129)

That’s all I care about, the previous 52 week Mattermark scores.

Annual Return

Taking the end and start figures I can return a percentage of theoretical returns (what did it make), if I were to treat the Mattermark score as stock price for example then how much am I making over the annual period.

The annual return is easy to calcuate, it’s a percentage.

end / start - 1

Seesense started at 160 and ended at 129, which gives me -0.19375 or -19.3%.

Daily Returns

Exactly the same calculation as annual returns, just done on an each reading basis over the period.

(inv/daily-returns seesense)
(0.08125000000000004 0.011560693641617936 -0.02857142857142858 0.02352941176470602 -0.011494252873563204 -0.011627906976744207 -0.00588235294117645 0.041420118343195034 0.01136363636363602 0.039325842696628976 0.059459459459459074 0.0 0.0 -0.04081632653061218 0.042553191489361986 0.0 0.010204081632652962 0.005050505050504972 0.035175879396984966 -0.004854368932038833 -0.004878048780487809 0.014705882352940902 -0.048309178743961345 -0.050761421319796995 0.0 0.0 -0.0267379679144385 -0.0494505494505495 0.0 -0.01734104046242768 -0.01764705882352935 -0.041916167664670656 -0.03749999999999998 -0.006493506493506551 -0.04575163398692805 0.027397260273972934 -0.00666666666666671 -0.01342281879194629 -0.013605442176870652 -0.006896551724137945 -0.02777777777777779 -0.014285714285714346 -0.01449275362318836 -0.007352941176470562 -0.007407407407407418 -0.03731343283582089 -0.023255813953488413 0.023809523809523947)

Remember these are based as percentages.

Measuring Risk

Risk is measured as the standard deviation of daily returns.

(defn risk-measure [coll]
 (stats/standard-deviation (daily-returns coll)))

In the above figures we get:

:risk-measure 0.02866805600574157

A 2% standard deviation from the mean, not bad going at all. It’s consistent.

Using The Sharpe Ratio

A common measurement used in finance, the Sharpe Ratio is a reward vs risk measurement. I’m basically taking the average of daily returns and dividing it by the standard deviation, the average is then multiplied against the square root of the number of trading days.

There are variations on the Sharpe Ratio but this version serves me well as a reward/risk measurement.

(defn sharpe-ratio [coll trading-days]
 (let [k-num (Math/sqrt trading-days)
 dr (daily-returns coll)
 risk (risk-measure coll)]
 (* k-num (/ (stats/mean dr) risk))))

Let’s have a look with Seesense’s readings.

(inv/sharpe-ratio seesense 52)

I’m looking for a ratio of 1 as a general good return with low risk, if it were 2 then my eyes would be wide open looking to invest if the company had IPO’d. As Seesense’s downward curve (why? they’re a good company in NI) is pretty consistent I personally wouldn’t be looking to invest, the -1 Sharpe ratio confirms it.

Take six companies

Let’s take this a little further. At the start of November I looked at the data from a number of companies from the mainland and Northern Ireland. Good companies doing great things, what’s important is that there’s a consolidated number within Mattermark for all of them.

I ran the investor metrics against the Mattermark company data and got the following data back.  Disclaimer: The idea is for me to remove myself from the biases of the startup and get a response purely from the numbers, the companies below are all great I just picked them for the purpose of this blog.

Company Annual Ret Risk Sharpe
Adoreboard -0.0645 0.0171 -0.4950
Airpos 0.9833 0.0408 2.5306
App Attic 0.5625 0.2118 0.8072
Brewbot -0.25 0.0182 -2.1838
Get Invited 0.1428 0.1084 0.5443
Taggled TV -0.0064 0.0252 0.0520

Interesting results, there’s one standout “investment” and that’s Airpos. With a Sharpe ratio of 2.53 and only a 4% risk factor an investor would be putting money on it if it were IPO’d.

Talking of risk, is App Attic the riskiest company? From the numbers it says so, at 21% but the annual return was good too at 56%. There was plenty of volatility in the raw data to back it up, especially at the early stage of the year.

From an investment standpoint the numbers, while live, are purely theoretical. The question we need to ask ourselves is this: is a VC, Angel or other investor going to use these scores as part of their due diligence process. Taken even further could something like the gamblers fallacy take over?

The Binary Decision

Would data driven decision making work for long term returns? If an investor algorithm for example worked on two basic rules:

if risk < 10% and sharpe > 1 then invest

Out of the seven companies we’ve looked at all together only one would be positive as a result of the algorithm, that’s Airpos.


It’s been an interesting exercise and one I’ll be keeping an eye on. The question that remains unanswered though, do actual investors use the Mattermark scores as part of their due diligence and use the metrics available to steer the basic decision of to invest or decline?



Is the @Mattermark score the best NI Startup Score metric? #mattermark #data #startups #clojure

How To Measure a NI Startup?

It’s been a question on my mind for a good five years, to the point that in 2015 I bought a domain called nitechrank to collate all the data from various sources and see if it could be resold. No sooner as I started I saw that Mattermark was gaining momentum and I did the sensible thing and stopped.

The question though still remains in my mind, is there a good metric to tell me how a Northern Ireland startup is doing. One way is to ask the CEO of the company and hands down, 100% they will talk utter rubbish and say, “yeah it’s great, we’re just about to…..“. Let me make this simple, the phrase “just about to” means, “we haven’t”.

As there aren’t many publicly listed companies from Northern Ireland. Off the top of my head I can only think of two: Kainos and First Derivatives. Andor now belong to Oxford Instruments and UTV are now nested within ITV with the reaction from the action being that Julian has been removed from our screens. With listed companies there is a metric of performance and shareholder value. With a small startup there’s non of that just basic here say, hype and questionable cash flow projects.

So I’ve been thinking more and more about one metric that can tell me how a early stage Northern Ireland startup is doing. And I think it all points back to Mattermark.

All Hail The Growth Score

The Mattermark Growth score is the baseline on how a startup is looking to the outside world. It’s calculated on a number of sources but the main ones to think about are social sources like Linkedin, Facebook, Twitter and Instagram as well as the more entrepreneurial sources like Crunchbase and

With this information fed in, ran through a nice model and distilled to a number of key scores (Growth, Mindshare and Momentum) we have a good idea how a company is doing from an outsider’s perspective. Interaction with the brand will increase the mindshare in your Mattermark score for example.

There’s Nothing Like Exposure To Public Ridicule To Galvanise The Attention*….

If you are Northern Ireland CEO/Founder and you’re not looking at your Mattermark score then I suggest you get your skates on and watch it religiously.

While it’s all very nice with the “Our wee country” schizz, no one outside of Northern Ireland really gives much of a hoot about what you are saying but they will be keeping an eye on Mattermark, Crunchbase and other sources to see how things are performing.

In all fairness while it’s good to be on the Propel Programme and accelerators like StartPlanet NI, that the first foot up from the ground to the first rung on the ladder. That’s about getting you ready to be let out in the world and those two glorious words “investor ready”. Historically I’ve not see much gain on the hype cycle once a company has completed these types of things, just a few quid in the bank to last another quarter. And you know what, fair enough.

Back to the point, what I am saying is that your Mattermark Growth Score is your stock exchange price that others will look at, judge you on and figure if you’re worth looking at.

If we look at GetInvited’s Mattermark score it’s doing okay. A growth score of 48, okay there was a dip during the year but it’s on the up again.


Now, I and others know that GI raised money. And it’s something to shout about so get it on Crunchbase! Would that raise the growth score, possibly. Would it show other potential investors that there was enough faith in the team to put money in it, definitely. Showing incremental growth on these platforms sends out a strong message that things are happening within the company.

Basically every NI company needs to figure out a way of getting positive PR, up to date information on Crunchbase and some good hype on the likes of to start getting those scores up. Once that happens then perhaps startups from “our wee country” will get in front of the noses that matter.

Non of this is secret, it’s just common sense.

Coming Soon….

In the next part I’ll start exploring the growth score numbers and seeing what we can learn and apply from them.