Alteryx Use Cases

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Vodafone NZ Drives Digital Transformation Through Remarkable Simplicity

Alteryx
Alteryx
 
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Overview of Use Case

Vodafone New Zealand is a telecom service provider of mobile networks, online television, broadband, and other related services, as well as retails mobile phones and accessories. We’re in an incredibly fast paced environment, where technology is changing; customers demand more and more; and competitors throw more and more at us each day. Our colleagues needed less complexity in the way they manage these demands. They wanted simplicity because that would equal more freedom and more time for them to learn, improve and pay it forward to our customers. 

 

Describe the business challenge or problem you needed to solve 

The changes in the industry is driving digital first and simplicity for our customers, which in return puts a lot of demand in the requirements of the services we need to provide. New service locations, increased traffic demand and better faster services require accurate modelling, delivery of Smart Capex network builds and efficient ways of monitoring these complex networks.

 

It goes without saying that these demands from both the consumer and the network provides heaps of data, all coming from different sources in various data types, frequency and relationships. We’ve noticed a couple of different ways our people extract, transform and report using the different data sources, everyone holding fast to the way they find it best to do it.

 

This worked well when the demand was low, network complexity was manageable and changes to the model came through every now and then. However, to ensure that we can respond to our customer’s needs we had to simplify this process and be pro-actively prepared for change.

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Describe your working solution

We had a predicament, some really expert telecommunication and statistical minded colleagues who got stuck using Excel and never took the leap towards SQL, Python or some form of a way to get past the never ending vlookup intensive processing requirement of Excel. Then we have some colleagues who were really skilled at scripting, but looked to the technology experts in the team as they are just not there yet to build the analytical models by themselves. We always ended up with a lengthy process to build a productionised model as we had to ensure that both understand the technology and/or the code.

 

So with Alteryx we found the simplification we needed, where we could bring these two worlds together and for once we could see the possibility of these two different skilled colleagues working more efficiently in solving the problem as they would now understand the workflow and code they are looking at.

 

Alteryx allows us to respond quickly to our business needs and therefore changing and keeping track of our modelling changes. Improving our overall network modelling and performance monitoring automation capability – simplify things to make it faster and easier for our engineers and business to make the right decisions.

 

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The process needed to be easy, self-paced and practical. We had to take problems close to home, part of our daily work so that we can reap the benefits from day one. It was all about breaking down those self-doubts about coding and today’s data science magic and collectively building each other up to the benefit of ourselves, our colleagues and our customers. To make the transformation effective we wanted to make the learning structured, give the employees the gratification that they have achieved something and give the business something back in return for the time they’ve allocated to the training (not that we don’t encourage training). 


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  1. Identify use cases

We reached out to the teams from different domains, requesting them to pick one low complexity and highly repetitive task they have to do as part of their own or their colleagues work.

  1. Alteryx Support

This is where Alteryx really came to the party, arranging designer trial licenses for every user and giving them access to different levels of training through the Alteryx Discovery Program. Users could then identify the training they needed to accomplish the use case they’ve chosen and practice what they’ve learned developing something which were close to home before the knowledge gets lost.

  1. Scrum Setup

Ensure we provide a structured process to provide support, identify blockers and provide guidance on how they should approach solving their use cases. Setting up a Scrum board with each use case having its own ELT structured set of tasks, guided the users through the process and helped us to identify blockers and assign these to experienced users in the team. We had Alteryx support also pitching in when needed, helping us when our experienced users themselves got stuck.

  1. Reflect Deploy

This is where the team still had gaps either in training or finalising their use cases. We also spent the time to collect the required information we needed to go to the business for additional licenses and get the support we needed to productionise the use cases developed.

 

Describe the benefits you have achieved

We’ve learned to pull our individual data analytics strengths together, respond faster to the changes around us and deliver more with less time on our hands.

People
The investment in our people stimulated the collaboration and knowledge sharing, especially since the Alteryx ELT process is self-explanatory and easy to follow. It empowered the users to automate their own and their colleagues BAU tasks, where a simple process provided insight to the rest of the team, saving them time going through tedious Excel modelling and analysis of different web reports.

Process Gains
We’ve now made our process super easy to ensure we can deliver digital first solutions that improve our customer experience. Having our team focus on the higher valued tasks and leaving the repetitive tasks for Alteryx to handle.

Response times
Our response time improved in getting the answers that the business need, with easy to follow and repeatable analytics. The start of COVID-19 completely changed the traffic patterns as a result of more people working from home. The fast response in our network modelling, rollouts and automated monitoring was required as we move through the various lock down changes.

 

 

 

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