DATA DATA DATA – January 2023

Do you want to grow your business, innovate, drive efficiencies, &/or create new revenue streams? Of course, you do! Well data is the starting point…and this is the only data-focused roundup you’ll ever need!


How can I not mention ChatGPT? It’s all over the news. Even Ryan Reynolds is tweeting about it – Ryan Reynolds on Twitter: “You knew it was just a matter of time until we did this (extend the @MintMobile savings with @OpenAI, that is).” / Twitter. So, what’s it all about and what does it mean for the world of data?

Let’s start with the acronym – ChatGPT stands for Chat Generative Pre-Trained Transformer…goodness! Put simply, it’s a chatbot created by OpenAI (ChatGPT: Optimizing Language Models for Dialogue ( They say they’ve…

“trained a model which interacts in a conversational way…[and it’s] possible for ChatGPT to answer follow-up questions, admit its mistakes, challenge incorrect premises, and reject inappropriate requests.”

ChatGPT has been trained on a massive amount of text data and it’s currently free to use.

And, as much as it’s dominating the press now, it will only get bigger – Microsoft is the largest investor and is looking to invest even more to take a dominant stake in OpenAI, so watch out for ChatGPT in Bing, Word, Excel and Power BI (Microsoft to expand ChatGPT access as OpenAI investment rumors swirl | Reuters). 

So what does this mean for us data people? I see three opportunities in the short term:

  1. FAST-TRACK YOUR CODING – data engineers using SQL, Python, R or DAX can ask for coding examples, coding help, translate code into different languages, convert natural language questions to code, classify sentiment in sentences or create data visualisations. You just need to ask ChatGPT.
  2. RESEARCH DATA TECHNIQUES – want to get some quick answers to some tech questions before an interview? No worries, ask ChatGPT to explain data science models, statistical techniques and more.
  3. FIND DATASETS – looking for some external data to enrich your own data? Ask ChatGPT or set up automated data enrichment in Excel.

Sound too good to be true? Like any technology solution, ChatGPT has its limitations including:

  • Dependence on available data – ChatGPT is a machine learning model that has been trained on a large amount of data. Therefore, the quality of the responses will depend on the quality of the data that it has been trained on.
  • Bias – ChatGPT and other machine learning models can exhibit bias in their responses if they are trained on biased or unrepresentative data.

Sometimes, for some use cases and some projects, these limitations will be acceptable…sometimes they won’t be. So, we need to consider these limitations before use, and certainly before making decisions.

Curious to try ChatGPT for yourself? Check it out at


In DATA DATA DATA, I want to share the most important lessons learnt that I’ve realised in the last 5+ years since I set up Data3 and started working with SMEs on their data challenges and data opportunities.

This time, it’s – start with what you’ve got.

We all want the latest toys, don’t we? In the data world, it’s the same. We want exciting things like AI and machine learning models and data science. We want to do cool shit. We don’t want to do data cleansing or data preparation or fill in all the gaps in our incomplete datasets. No! It’s the same with tools and technology – we want the best-in-class tools for our businesses, we don’t want the things we already have.

But, often, it makes sense to start with what you’ve already got. That’s why we always start with the 1st step of our 4-step process – the Data Discovery phase – which includes an audit and critical review of:

  • Data sources
  • Apps, platforms, and tools in use
  • Cloud infrastructure
  • Technology partners
  • Dashboards, reports & trackers

The reason we do this is that it’s often better to build on what you’ve already got, rather than starting from scratch. It also ends up cheaper and quicker most of the time too.

Think about it…

  • Already got AWS set up for cloud storage? Why switch to Azure or GCP?
  • Already got Azure set up for cloud storage? Why switch to AWS or GCP?
  • Already got Power BI for data visualisations & automated reporting? Why switch to Tableau?
  • Already got Tableau for data visualisations & automated reporting? Why switch to Power BI?
  • And so on…

I’m not saying you should never switch tech solutions…but there needs to be a compelling reason where the benefits exceed the costs, right?

Perhaps you have nothing available now except Excel or Google Sheets? You can still start pulling together the types of reports you need to run your business or team, even if it doesn’t look pretty or if it’s a manual process.

So, challenge yourself today – are you starting with what you’ve got? Or are you delaying your data transformation by waiting for the best new tech toy to play with?


I LOVE seeing examples of good/bad/funny data visualisations, data wins, data mistakes, and more. And these two blew my mind this month…

#1 This is a graph of tweets that contains the word “datavis” sourced from 1500 users over a 7-day, 2-hour, 0-minute time period…wow!

Graph of tweets that contains the word datavis

#2 This is a challenger network graph for Twitter which visualises tweets, topics (hash-tags) and users, as a network, by linking tweets and users ((connecting a tweet with its author, or with a user with its mentions)…WOW!

Image showing tweets, topics and users as a network

Have you seen any examples of data craziness you want to share? Please drop me a line.


In previous issues of DATA DATA DATA, I’ve shared the most frequent questions, the team and I get asked – Microsoft or AWS? Power BI or Tableau? Off-the-shelf or DIY? Recruit in-house or use a data consultancy? Data engineer or a data scientist? Data lake, database, or data warehouse? And dashboard vs insight report?

This time, it’s to monetise data or not?

When you think of data monetisation, you might think of something sinister like selling you customer data to some dodgy outfit who will bombard them with marketing mailers and emails. Now, that’s not good and probably breaches your customer terms & conditions, impacts your reputation, and you might end up with a GDPR fine. And no-one wants any of that! But data monetisation can be good for business, good for customers and compliant.

When we work on data monetisation projects, there are some common themes that come up time and again:

  1. NEW PRODUCTS & SERVICES – you could be sitting on a data goldmine. The data you hold could be used to generate valuable insight and uncover hidden patterns and trends for people. You could create brand new products & services. Exactly what type of products & services, will depend on your data and your market, but products & services to consider include:
  • Benchmarks – enable people to compare their usage/performance to everyone else
  • Forecasts – predict future trends form past performance
  • Indices – create a new index for your market
  • Datafeeds – sell access to your data
  • And more…

2. ANONYMISED & AGGREGATED DATA – to derive value from your data, you may not need to monetise the raw data, as there could be significant value in aggregated and anonymised data. Think about:

  • Market Trend Reports – use combined data to show what’s happening on a macro level
  • Comparisons – enable someone to compare their usage/performance vs the average of all your customers
  • Recommendations – use insight on someone’s purchase/usage habits, to recommend solutions that other customers like them have valued
  • And more…

3. DIRECT vs INDIRECT DATA MONETISATION – your data could be used to create brand new revenue streams or it could be used to improve your business results:

  • Direct data monetisation includes selling data solutions for a fee
  • Indirect data monetisation includes using data solutions to improve your competitive position in your market, improve acquisition volumes, improve conversion rates, improve retention or improve customer engagement

So, when deciding whether to monetise your data or not, think big and think differently – could your anonymised and aggregated data be used to find new insight that someone might value, whether that’s current customers or a whole new market?

In future issues of DATA DATA DATA, I’ll cover other frequent questions that I’ve come across when talking data with our clients…anyone want to guess what they are?!


As this issue is living in the world of ChatGPT and AI, let’s start with a sobering, if motivating, quote about AI replacing all our jobs…

Post saying - AI Will not replace you

As someone who’s not at all a football fan, unless I’m in a pub during a World Cup, this football-related quote surprises me as much as it will anyone who knows me…

Football post premier league explain data driven

Find out more about these Moneyball-esque football data stories here.

In future issues, I’ll share more quotes I hear, quotes I read, quotes you tell me about, and more – what’s your favourite data-related quote? Let me know and we’ll find a data-related prize for the best one.

DATA FREEBIES                                       

In this issue, I want to share the super helpful blogs the BoomBoard team have created at Blog Post – BoomBoard. If you’re a business owner, or a business leader in a small business, you’ll want to read these to help you with your data transformation, particularly prioritising the key metrics that really matter.

Blogs displayed on the BoomBoard Website

In future issues, I’ll share links to super useful, completely FREE videos, guides, templates, and similar…so, there are loads more freebies to come.

Thank you for reading…I’ll be back soon with more data news in 2023.

And, if you’re in data hell, I’m data Hels!

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