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Data and the Digital Future – A Summary

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Five speakers and 120 attendees walk into Wintec and Tauranga Art Gallery to talk about data and an inspiring digital future.

Sounds like the start of a joke but it isn’t – we are all amidst the biggest change in how we live and work. The Fourth Industrial Revolution, as the World Economic Forum has defined it.

A few key aspects of this evolution to keep in mind:

  • We don’t know what’s ahead and therefore we don’t know what opportunities lie in wait
  • As a result, open minds are essential
  • Prolific data will be generated and this will inform the future
  • AI will help us by feeding off this information for the benefit of ourselves, and our organisations
  • Practical ways to take advantage of this major change exist already

The purpose of this blog is to provide an overview of the topics discussed at our event at the end of September, but before I start I would like to thank all of you who gave us feedback.  Your comments are invaluable and will help us create an even better event next year.

The following four sections are summaries of the presentations by myself (Igor Matich), Cyrus Facciano (PwC), Andrew Nimick (NZME) and Justin Flitter (NewZealand.AI).If you’d like to jump to one, please use the links provided. I would also like to thank Ben Hunter for giving us a demonstration of the AWS platform. 

We need to look at our children to see what’s ahead – Igor Matich, Dynamo6

From communist Croatia to current day New Zealand in thirty years, and with the current pace of change there’s no knowing what will be here by 2050.

As a father of four one of the biggest things I notice is how our children are so comfortable with mobile technology with learning delivered digitally on any device.  This will have huge implications for our organisations because it’s such a big difference to how I learned.

A lot of us will feel that our IT infrastructure used to be simple – a box in the office that needed looking after.  And now that box doesn’t exist and while complexity seems to have arrived, its existence is full of opportunity.

Big change is not in the future anymore and has already been happening for a while now.  Organisations like Uber, AirBnB, Lemonade and Society One, have all created new business models; using digital technology to bring delightful experiences to customers.  While these examples are for well-known, international and large organisations, there’s no reason why this sort of transformation cannot also happen for the more modestly scaled New Zealand business.

The key to this is having the cloud as the foundation, and APIs as the digital glue that will bind the solutions.  More likely than not now, there will be a software as a service (SaaS) solution for your needs and using this, businesses can consider digital treasure chests or areas in operation that can be improved using digital technology.  This may not be a wholesale change but a small improvement that will add value.  Look for this treasure.  

Current topics of interest such as the Internet of Things and Artificial Intelligence is already becoming the norm rather than the exception.  However, the value that can be generated from these won’t eventuate unless your people and teams, and the culture of your organisation, become your first and most important consideration.  Without strength here, nothing else will add value.

Find Igor’s slides here.

Observations and Practical Realities about Data & Analytics in NZ in 2017 – Cyrus Facciano, PwC

For serial entrepreneur and author Ben Yoskovitz: analytics is the measurement of movement towards business goals.  

There are four types of analytics in data science:

  • Descriptive – what happened?
  • Diagnostics – why did it happen?
  • Predictive – what is likely to happen?
  • Prescriptive – what is the best action?

Currently, there are very few organisations that have reached the prescriptive stage but this is changing because of a realisation about the potential for growth, increasing demand, competitive differentiation and developing desirability from using data effectively.  Organisations are moving from leaning towards a conservative approach to data, to having more of an adventurous one, while also balancing this with risk.  The best approach is to apply a customer lens to current operational thinking and to look for the sweet spot where the two intersect.  

For data to be of maximum value it needs to be applied to an identified and agreed organisational objective, that cascades through an organisation to business units, and then project teams and contributors.  Within this, potentially there will be a wide variety of issues that can be addressed, only issues or opportunities which have the highest impact, are linked to purpose and are actionable should be progressed.  

One issue comes with people’s perception of the volume of data available to them – One issue comes with people’s perception of the volume of data available to them – they understand they have data, and they probably have more than they realise. However, when considering the key, high impact problems to be solved, they probably don’t have enough.

Building an analytics capability across a business can reveal nuggets of value by understanding the technology, talent and organisation, processes and governance, culture, business application and data itself.

Data is owned by everyone in the business and value can be drawn from it when applying both evidence, and intuition based on experience in a business.  There will be a mixture of old data and new data, which will often be unstructured.

The business world has been talking about big data for some years now but it’s still overwhelming for many – it doesn’t need to be, as long as an organisation starts small by analysing a limited, high impact, use case, to begin with, taking the lessons and then iterating.

The most important advice is to start small.

Find Cyrus’s slides here.

Data lakes, getting wet, getting dirty – Andrew Nimick, NZME

The volume of data necessary for effective analytics is critical and a good analogy is a need for data lakes and not puddles.

For NZME and Grab One all the data they generate is a mix of structured and unstructured – and there’s a lot of it.  We didn’t know the questions we needed to ask in order to find answers and so we just needed to get involved and get our hands dirty and immerse ourselves in the data.  Once you do this you find data and also identify what’s missing.

For Grab One, we didn’t have any data about our email campaigns.  We were just sending emails out about all offers to wide audiences and for some time it worked, but eventually, it began to be treated as spam and disregarded.  

We needed to change our approach so we started to pull supporting data in, then built and applied a relevancy engine and lifted our game, so now if someone buys a certain item, the next email they receive will reflect their previous buying patterns or search history.  This wouldn’t have happened if we didn’t immerse ourselves in the data, and analyse until we had an answer.

As a result of this, we now have a programme called Project Mangrove that looks at how data can propagate ideas.  Our data is put on AWS Cloudera and since doing this, the team as a whole has learned a huge amount about data governance.  

Another way of looking at it is a little pig hunting truffles in the forest.  We are sniffing, snorting and burrowing until we find golden truffles of useful information that can deliver value to our audience.  One result could be delivering breaking news, relevant to different people because of different interests. You have to be able to experiment and learn, follow a hunch and prove or disprove it then build it out and turn it into a product.

If you really want to know how a data lake works, get a big slide and zoom down into it with a big splash.  If you do this, you will find data and be able to deliver value simply because you have started to play with it.

Find Andrew’s slides here.

New Zealand.AI – a platform to inform, support and showcase Kiwi businesses using AI – Justin Flitter

AI is a relatively new subject but interest in it is growing rapidly as we begin to understand its use and the value it can bring to people’s lives and businesses.

As a result, it’s important to have forums focused on engaging with the business community, both technical and non-technical, to help them design and build new AI-powered applications, services and products.

The AI journey for any business follows a similar approach to any software development, journeying from start to the discovery phase, then learning, building, developing and finally commercialising. In New Zealand, there is a growing network of partners providing support for any business at each stage of this journey and for each of the six types of artificial intelligence:

  • Machine learning – algorithms that can learn from and make predictions on data
  • Automation – robotic process automation (RPA) or the use of software with AI
  • Computer vision – capturing and analysing visual information using a camera, analogue-to-digital conversion and digital signal processing
  • Conversational AI – a chatbot or a computer programme designed to convincingly simulate how a human would behave as a conversational partner
  • Robotics – used for tasks that are difficult for humans to perform or perform consistently
  • Natural language generation (NLP) – the processing of human language by a computer programme

AI is accessible today and there are many businesses adopting the technology to develop competitive advantage including:

  • Imagr – a retrofit shopping cart product with Computer Vision to scan products eliminating the checkout process
  • FaceMe – a digital employee solution meaning employees can be recruited with pre-learnt skills picked up from other jobs and roles
  • PrescisionAI – using robotics and Computer Vision to scan rows of plants, and machine learning to predict the harvest yield
  • Centrality – creating connected experiences using Blockchain and machine learning

The most important point to highlight is that AI is accessible now but it requires:

  • AI strategy – McKinsey has highlighted that 30% of AI adopters are achieving revenue increases
  • Leadership – successful adopters all have strong leadership support for the new technology
  • Capability – it’s important to look for opportunities to develop partnerships to grow capability
  • Live in the cloud – invest in the cloud and develop a data strategy first
  • Start small – identify a specific business problem or process that’s easy to automate and will allow the collection of data

Find Justin’s slides here.

Bringing it all together

We enjoyed our two events in Hamilton and Tauranga enormously, and we very much appreciated people spending time out of their day to come and listen and hopefully learn. You can view the photos on our Facebook page.

For me the most important points were:

  • Life and work is changing for good and it’s important to be involved
  • When it comes to data, think large volume – you will probably need more than you think
  • If you are taking the first step, start small, tackle one project and then see what you learn
  • AI is here to stay, so throw yourself in
  • There are very practical solutions and applications available through AWS, as demonstrated by Ben Hunter

Thank you again for coming – we will definitely stay in touch as we want to keep this important conversation going. If this sparked some ideas on how you can leverage technology drop us a line.