5 New Year Data Resolutions
30 December 2019
Start the 20s off right – with a New Year’s resolution to get your organisation’s data in tip top shape. Here are some of the goals you might want to aim for…
1. Clean your data
Modern businesses collect data from many different data sources. It might be online surveys, point of sales information, internal HR systems or any number of other sources.
Invest in making sure that you’re collecting data in a standardised way (are your dates all formatted the same?) and you’re not missing any gaps (are you relying on someone manually updating a spreadsheet? Does that always happen?).
2. Break down data silos
Almost every organisation has silos of data – a membership database that doesn’t talk to a website CMS, or a point of sales system that doesn’t talk to a CRM. These data silos slow down your company – having to extract and match data from disparate systems takes time and means you’re not making decisions in real time.
This tends to happen naturally as companies grow, and it’s something that you need to be constantly aware of and pushing back against.
The three keys to breaking down data silos are to consolidate your data management systems, change your company’s culture so that the default position isn’t just to spin up a new database in isolation and finally to make a consolidated data layer a key part of your organisational strategy.
3. Take privacy and security seriously
The 2020s are going to see more bad actors trying to get unauthorised access to data, and consumers more concerned about protecting their privacy.
Now, more than ever, the privacy of your clients has to be always front of mind.
The Australian Government have published a very useful guide to data analytics and the Australian Privacy Principles. Read it.
4. Analyse the data you have
There is no point in cleaning your data and consolidating your data silos if you’re not doing anything with it.
Make sure you’re investing in data visualisation and analytics capabilities so you can find the hidden insights in your data and take your business to the next level.
5. Use your data to strengthen your business relationships
Are you tracking and analysing all your interactions with your customers? Do you have a clear and searchable record of all the touch points your clients have had with your organisation?
Make 2020 the year you invest in a next-generation CRM solution like SwiftFox, to get a 360 degree view of your customer relationships and really get the full value out of the data you are collecting.
How hot will it be on Christmas Day?
16 December 2019
Weather forecasting is one of the oldest forms of predictive analytics. One data source we often look at here at The Red Fox Group is weather data.
While we don’t have any trained meteorologists on staff (so please don’t ask for detailed forecasts!), we can look at how the weather may be impacting your business performance. Do your retail sales slump or outperform on rainy days? Do more people visit your website when they’re stuck inside on cold days?
But given Christmas is getting nearer, we thought we’d take a look at what to expect on Christmas Day.
In Australia the Bureau of Meteorology compiles and makes publicly available observation data from thousands of weather stations around the country (with 762 stations currently reporting temperature data). We’ve analysed historic data for the last hundred years for the state capitals, here is what we’ve found…
On an average Christmas Day the mainland state capitals tend to reach the mid-to-high twenties. Perth, Brisbane and to a lesser extent Adelaide tend to be warmer than Sydney and Melbourne. Hobart is noticeably cooler than any of them. If you’re on the mainland, then chances are it will be hotter than 25 degrees. But as the graph shows, there’s a lot of variation!
Melbourne has a higher record Christmas Day temperature than Sydney – but its coolest ever Christmas Day was also cooler than Sydney’s. As will surprise no one who’s been there, Melbourne’s temperature is simply more variable.
In the last century the hottest Christmas Day in any capital city was in Perth, which hit a scorching 42 degrees in 1968.
Unsurprisingly, the coolest maximum was in Hobart, which reached just 12.6 degrees in 1984.
On Christmas Day last year the hottest capital city was Adelaide, which hit 37.9 degrees.
We are not meteorologists. Don’t ask us what the temperature will be next Wednesday.
But we can use weather data to help understand and predict things. Such as the number of people attending sporting matches, visiting cultural institutions, or buying particular products.
Regardless of what the weather is like on Christmas Day, we here at The Red Fox Group wish you and your clients all the best for a great break.
The 5 biggest data news stories of 2019
10 December 2019
Moore’s Law states that computing power doubles every two years. This means that the field of data science is constantly evolving as computers become more powerful.
As 2019 draws to a close we thought we’d take a look at some of the biggest headlines of the year concerning data.
1. The rise of the Citizen Data Scientist
As more free or cheap data analytics tools hit the market, and governments become better at proactively releasing big data sets the last few years have seen the emergence of the concept of the Citizen Data Scientist – essentially someone who does data science as a hobby or in their spare time, often without any formal training.
Gartner (who originally created the term Citizen Data Scientist back in 2016) is predicting that next year we will reach a tipping point – citizen data scientists will surpass data scientists in the amount of advanced analytics produced.
This opens huge questions around accuracy and the value of trained skills.
2. Google’s Quantum Computing breakthrough
This year saw a major advance in computer science that will quickly change the face of big data and analytics.
They reportedly achieved quantum supremacy – where a functional quantum computer was able to perform a task faster than any existing classical computer.
This proves that the theory behind a quantum computer is viable and will only spur on additional investment in this cutting-edge field.
The increased computing power available will mean increased collection of data and more powerful artificial intelligence to analyse it.
Quantum computing is on the way. And it will change the world.
3. Consumer data privacy improvements
The fallout from the 2018 Cambridge Analytica scandal continues. Consumers are more aware than ever of risks to their own data privacy – here in Australia over 2.5 million citizens opted out of the Federal Government’s My Health Record digital health system by the time of the close off period in February of this year.
Back in February OpenAI announced that they had developed an artificial intelligence algorithm so powerful at natural language processing that it was able to write believable fake news. In a world where social media is filled with fake news, many people found this understandably unnerving.
Over the course of the year they performed a staged release of the technology and by July had released the GPT2 algorithm in full.
OpenAI claim they so far they have seen “no strong evidence of misuse” – but if it is so good, then there may be no way of seeing how it is already being used to spread misinformation.
You can explore a demo of the algorithm here.
5. The Apple Credit Card and algorithmic bias
Tech entrepreneur David Heinemeier Hansson made global news with a series of tweets claiming that his Goldman Sachs backed Apple Credit Card gave him a credit limit 20 times greater than his wife, despite the fact that the couples file tax returns jointly.
The @AppleCard is such a fucking sexist program. My wife and I filed joint tax returns, live in a community-property state, and have been married for a long time. Yet Apple’s black box algorithm thinks I deserve 20x the credit limit she does. No appeals work.— DHH (@dhh) November 7, 2019
This opened the door to questions around algorithmic bias.
Goldman Sachs denied accusations of bias and offered to reevaluate credit limits on a case-by-case basis.
This leads to some big questions. Could a credit limit algorithm be sexist? How much transparency should there be on algorithms and the decisions they make.