#235: Maximising the productivity of the data-led enterprise with UNSW, EG Australia and Compare the Market

This week we bring you a special episode of the Data Futurology podcast, featuring the keynote panel from our OpsWorld conference earlier this year featuring guests at different levels of data maturity. They shared their stories of the journey to enabling and unlocking the true value of data self-service.. 

The panel featured Kate Carruthers, Chief Data & Insights Officer, UNSW Sydney. She shared the university's experience, which has had a mature data environment for several years. At the other end of the table was Conor O'Neill, Head of Data Science, Compare The Market. He represented an organisation that is rapidly addressing a lack of data maturity across the organisation.

The third person on the panel was Arvee Manaog, Head of Enterprise Systems, Data & Information Management, and Integration, EG Australia. She shared insights on how to effectively get organisation-wide buy-in, and then effectively educate all stakeholders on how to effectively use self-service.

The panel was wide-ranging, starting off with a discussion around best practices in data self-service, before moving on to an in-depth summary of how to effectively approach self-service from each level of data maturity.

There was also a robust Q & A session at the end of the panel. Through the robust audience questions, the panellists discussed strategies for ensuring data trustworthiness in self-service. They also discussed how ROI is best measured with self-service data practices.

Businesses of all sizes that want to maximise data value should look at effective self-service approaches. This panel provides invaluable insights into both getting started and continuing to innovate once the data environment has been fully modernised and transformed.

Enjoy the show! 

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“Some people are very not data literate at all, you know. Some people just want the number. So that's fine. We need to make sure we're building things for the actual people that are using them.”

—  Conor O'Neill, Head of Data Science, Compare The Market

WHAT WE DISCUSSED

2:07: Felipe introduces the three panelists.

3:19: Carruthers explains UNSW’s perspective around best practices in data self-service.

6:23:  Manaog explains the challenges of secure self-service in EG Australia.

10:38: Manaog explains the initial steps EG Australia took to get started on the data self-service journey.

14:40: O’Neill describes some self-service approaches he's seen work well.

19:50: Carruthers describes how UNSW has kept engagement with DevOps-created dashboards and models high across the organisation.

22:50: The panel takes audience questions, with the first being “How do we influence and motivate data silo owners to share for indirect enterprise outcomes?”

27:07: How can a mature data organisation bring together data literacy and digital literacy across users?

28:11: For a less mature data organisation, how can data leads ensure data trustworthiness in self-service?

30:14:  There are trade-offs involved in self-service models. How can those be managed in the pursuit of a self-service culture?

35:38: What are the most effective techniques for measuring ROI with self-service data practices?

EPISODE HIGHLIGHTS

• Carruthers: “What we've found is that we've got people in the business who think they know what they're doing but they don’t, and we really need to protect them from themselves. We've got all the data in our data lakes fully protected and we're implementing a data governance tool that will allow data consumers to access data on the fly.”

• Carruthers: “An interesting thing we've started to do is talk about data protection. Rather than having a separate conversation about cybersecurity, data governance, privacy, risk management records, and archives, we're bundling all that under the notion of secure data protection, because they're really all risk functions that look to protect data.”

• Manaog: “We’re using DataIQ. And it actually helps because it's easier for users. I got a good adoption rate for that because it’s possible to do drag and drop, there are recipes and users don't need to code. They can easily do their analysis, create their workflows and then come to the hub and say, can you productionise this?”

• O’Neill: “In one model, we're doing a hub and spoke approach, where we have champions placed within the business units. We are working with those champions to ensure that we understand how they're using the report. It’s not just what they want to see. But in practice, what are they doing with it?”

• O’Neill: “Some people are very not data literate at all, you know, I have a data science background, I'm always wanting to drill into things and explore them in many different ways. That is not true for everyone, which is something I've had to learn. Some people just want the number. So that's fine. We need to make sure we're building things for the actual people that are using them.”

• Carruthers: “In 2018, we decided to shift away from our legacy platform and move into the cloud. We just didn't lift and shift. We talked to every single user and found out what they were doing with the data and reports. And we built what they wanted. And some of our reports have pretty much everybody in the organisation looking at them.”

• Carruthers: “I knew we'd had a really big breakthrough when one of the Deputy Vice Chancellors who always wanted his employees to be able to print reports out in PDF started looking at them on his phone. But he came to understand that he could look at it whenever he wanted to.”

• O’Neill: “The business teams that are responsible for the products that generate the data, are ultimately responsible for the quality of that data. This also goes into interpretation, so if there's uncertainty about what something is meant to mean, we're the facilitators of what the business defines for how a source should be used, and what it's meant to mean.”

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