Making data literacy within Virgin O2 a core competency

Tarush Aggarwal (00:00):
Hi, everyone. Welcome to another episode of People of Data, where we get to highlight the wonderful people of data. These are our data leaders from various industries, geographies, and disciplines. Discover how companies are using data, their achievements, and the challenges they face.

Today on the show, we have Bhagya Reddy, who has 20 years of experience. She's the Executive Director of Data Intelligence and Data University at Virgin Media, the UK's largest mobile network. Beyond her corporate role, Bhagya is the visionary behind her startup, Training in Data, which empowers individuals, particularly women and people returning to work, to excel in the data domain. She's also passionate about mentorship and speaking engagements, where she talks about fostering a diverse and inclusive data ecosystem. Welcome to the show, Bhagya. So good to have you here.

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Bhagya Reddy (00:52):
Thank you, Tarush. Thank you so much. I'm so excited to be here.

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Tarush Aggarwal (00:56):
Let's get right into it. What does Virgin Media do, and how does data play a role in fundamentally what Virgin does and how Virgin makes money?

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Bhagya Reddy (01:08):
Yeah, sure. I'll talk about it. Virgin Media O2 is a UK-based telecommunications company formed through a merger between Virgin Media and O2 in 2021. It provides broadband, TV, mobile, landlines, and other telecom-related services. Combining the strengths of Virgin Media's cable network and O2's mobile network, it became a big brand.

The company aims to deliver seamless connectivity and innovative services to its customers. It offers high-speed internet and fiber. Right now, the merger has enhanced its capability to invest in 5G and a full fiber network.

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Tarush Aggarwal (01:55):
Awesome. How does data play a day-to-day part in this sort of Virgin Media O2 story?

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Bhagya Reddy (02:03):
Data plays a crucial role in helping Virgin Media O2 improve its services and operations in several key ways. I'll highlight five of them.

First, Customer Insights and Personalization: It's all about analyzing customer data. Virgin Media O2 gains insights into user behavior, preferences, and usage patterns, such as when a customer walks into the store, whether they ask for a mobile plan or broadband. This allows for personalized marketing, tailored service offerings, and improved customer experience. For example, if a customer is unsure whether to buy an iPhone or Android, we help them understand the options based on their data.

Second, Network Optimization: Data analytics helps monitor and optimize network performance. Understanding traffic and usage patterns allows us to enhance network reliability, manage congestion, and plan capacity upgrades to ensure consistent service quality.

Third, Product Development: Data-driven insights enable the company to innovate and develop new products and services that meet and evolve with every customer's needs. Whether it's for a college student or a professional, understanding market trends and customer feedback helps refine our offerings and stay competitive.

Fourth, Customer Support: Leveraging data in customer service improves response times and resolution rates. Predictive analytics can identify potential issues and enable proactive support and maintenance before customers raise complaints.

Lastly, Operational Efficiency: Data analytics streamlines internal processes by identifying inefficiencies and optimizing resource allocation, leading to cost savings, better decision-making, and overall improved operational performance.

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Tarush Aggarwal (05:08):
That makes a ton of sense. Like the obvious question is, you know, all of these are probably in line with every other telecom company in the world. What does Virgin Media O2 do better, or what is your competitive advantage over the next player who's doing the same?

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Bhagya Reddy (05:26):
I'm sure everyone is now working agile, whereas truly agile is not everywhere. It's like having a problem now and solving it in the next hour or determining the SLA for it. We do track each and every minute detail as well. For example, when a customer walks in, how much time they are taking in the till, and what complaints they have raised. Analyzing the data and reflecting based on the data is what we are doing. Especially having broadband and mobile in the same place is really beneficial.

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Tarush Aggarwal (06:06):
So basically, what you're saying is by consolidation—not just a single business unit, but having multiple—and by fundamentally being the largest provider, you're investing in the entire journey. That makes a ton of sense. Well, what's the sort of culture like in Virgin Media O2 on a day-to-day basis?

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Bhagya Reddy (06:24):
Data encourages diversity quite a lot, especially in leadership roles, having women and diverse profiles. It matters a lot, even working with neurosurgeons or anyone who is physically challenged. We have colleagues and we work together well and accommodate each other's needs.

Virgin Media works in a very hybrid model. When I say hybrid, it's really hybrid. We come to the office only when it's really needed, not every single day. That gives flexibility. Yes, we have onboarding sessions going on. It's exciting—monthly, we have onboarding sessions for new members where we all come in, say hello, and discuss what we do.

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Tarush Aggarwal (07:19):
Are you calling in from the office today?

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Bhagya Reddy (07:34):
Yeah, it's exciting to be in sometimes when you want to do gatherings or parties or even just getting to know each other. Those kinds of times generally work best.

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Tarush Aggarwal (07:43):
Yeah.

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Bhagya Reddy (07:46):
So that's it.

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Tarush Aggarwal (07:46):
And how does the culture map into your team, you know, at Virgin Media O2, which is famously known for its diversity and openness? How does this map into the data team and more so into your leadership style and management?

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Bhagya Reddy (07:56):
Yep.

Yeah, sure. I'm a very open and honest person generally. If something is not working, it's all about saying, "No, it doesn't work. Let's change it." I'm very proactive in terms of challenging as well. If somebody challenges me, saying, "You cannot do it," or "This takes six months," I'm happy to take up the challenge and prove it wrong or successful.

My team is very supportive and encourages each other. They know I like direct feedback, and it's always open and honest. We work as a team rather than in a manager-peer relationship. It's more like colleagues working together, and that's what I encourage.

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Tarush Aggarwal (08:58):
Nice. Talking about the team, what does the team look like today? How big is the team? What's the breakup? And in terms of your data ecosystem, what does the data stack look like?

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Bhagya Reddy (09:10):
The data stack is GCP, and we use DBT as an ETL tool, specifically DBT Cloud. We're moving towards GCP quite a lot. The migration has been completed, and now we are in the journey of decommissioning the BI systems. Right now, we're using Tableau as one of our BI tools.

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Tarush Aggarwal (09:17):
Use DBT Cloud or DBT Enterprise?

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Bhagya Reddy (09:38):
Yes, for the self-service analytics for us. And Atlan is one of the tools we're using for data cataloging, data governance, and data lineage. Data observability and data quality are also in progress at the moment.

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Tarush Aggarwal (09:47):
Amazing. Are you also using Aspen or other tools for observability?

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Bhagya Reddy (09:55):
There are other tools as well which we are exploring at the moment. We have Abnishio and are looking into Monte Carlo, a new tool from Salesforce, as well.

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Tarush Aggarwal (10:08):
Amazing. And you mentioned you are deep provisioning some BI tool. Which one are you moving away from?

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Bhagya Reddy (10:14):
We're moving away from OBI, Bevo, MicroStrategy, and other similar tools, consolidating everything into one tool for a streamlined process.

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Tarush Aggarwal (10:25):
And that's gonna be Tableau?

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Bhagya Reddy (10:35):
Yes, that's right.

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Tarush Aggarwal (10:35):
And you use a separate ingestion tool to get data into GCP, like Airbyte, Fivetran, or Hevo?

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Bhagya Reddy (10:50):
We use DBT at the moment for getting the data into GCP. Yeah, that's right.

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Tarush Aggarwal (10:55):
I've got to see if building your pipelines over there. Very exciting. Awesome. And what does the data team look like?

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Bhagya Reddy (11:02):
Okay, the company overall is around 250 to 300 people on any given day. The centralized team, which I head up, is about 15 people. We're a mix of data analysts, engineers, and data scientists, including ML and MLOps. We have a few people outside the centralized data team in finance, pricing, compliance, and other areas, but we work really closely with them. Most individuals in the business, regardless of their department, have basic analytical skills. The central team is responsible for building all the infrastructure and maintaining the tech stack, including Airflow, GCP, DBT, and Looker as our BI tool.

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Tarush Aggarwal (09:26):
Yeah. Nice. When you mentioned GCP, you more specifically using BigQuery as your warehouse? That works really well with BigQuery and Looker. When you say Looker, I'm assuming the sort of commercial version of Looker, not Looker Studio. Awesome.

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Emily Loh (09:35):
That's correct. Yes.

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Tarush Aggarwal (09:35):
Exactly.

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Emily Loh (09:35):
That's right. Exactly. Yeah. So we're, you know, we've done as much as we can to actually use Looker as our self-service analytics platform. Of course, the platform is the entire thing, but Looker is the face of it essentially. And we have been pretty successful at pretty much enabling everyone to self-service most of their day-to-day needs. And this is super important because, like I said, we have 15 people out of the entire company who are on the centralized team.

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Tarush Aggarwal (09:57):
Yes.

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Emily Loh (10:11):
Um, I think something that's very typical of most young data teams is that you start to field all these requests, right? I think everyone has that experience in data where, especially when you're starting out, you're just answering questions all day long from stakeholders. So we're actually trying to expand our self-service platform to make sure that we're not the blockers as well. Because, again, 15 people are across multiple time zones in the world. So we stretch mostly from LA, where I guess you are right now actually, the west coast of the US all the way to Asia. And you can imagine, you know, someone's waiting for an answer. Most people are sleeping at some time, so that can be really risky in terms of who is able to access what information when.

Speaking of self-service though, I think it's somewhat controversial because we are rethinking, if I'd be completely honest, aspects of the self-service platform. I think this is also typical in the journey of data and small data teams because, yeah, you need it, right? It's absolutely necessary to enable the team to do more high-impact, like more needle-moving work. At the same time, especially, you know, we're not a huge company, 250 to 300 people, but we are large enough where we don't have eyes everywhere. It's actually impossible. So there have been cases, again, which is not unusual and it's expected where, you know, the level of analytical ability amongst different stakeholders is not created equal.

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Tarush Aggarwal (11:36):
Yeah, of course.

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Emily Loh (11:51):
Our metrics layer is always changing and the definitions are always changing, which makes it very hard for end-users to really know what they're looking at. And then, of course, you know, go for metrics.

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Tarush Aggarwal (12:00):
Is that for you? Sorry, was just, you know, in the metrics layer, you know, using Looker, which has got LookML, which is awesome. Has that really become the metric and semantic layer?

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Emily Loh (12:09):
Yeah, for us it has been mostly because it's easier to maintain like a singular layer within LookML. We also are very strict about our governance in terms of LookML and what we have in our transformation layer. So DBT and LookML are almost exactly the same. There are a few nuances, but we try to keep that the same just because, yeah, otherwise someone changes something in DBT.

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Tarush Aggarwal (12:28):
Yeah, got.

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Emily Loh (12:37):
And then, you know, someone looking at Looker and it's not the same. We're very, very strict about those elements of our transformation process because of that reason, definitely.

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Tarush Aggarwal (12:42):
Got.

Yeah, you have. What you're saying is essentially, you know, you don't use LookML just from a modeling perspective. There's no modeling, in fact, in it. You're really using it more in the semantics of like, you know, what you want customers to be able to do with that data, not so much on what the definition is.

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Emily Loh (13:06):
Exactly. Yeah. And I think there's also some tension here, right, between the way analysts and scientists use that same semantic layer, which we do, versus a more layman, let's just put it that way, user. And you might need more data and more granular data for sure in Looker for the purposes of analytics when we're driving deep, when we're doing model building, where we're really doing exploratory work.

And that doesn't always translate really well to other types of end-users. So that's why I'm saying that we're trying to rethink our approach to self-service because, at the heart of it, is, it is a great, you know, kind of initiative is definitely a necessity to have, but we have to have a lot more intent, I think, in terms of, you know, evangelizing what self-service actually is. Think the initial instance is just like, okay, just make it available, the semantic layer is there, it's easy peasy, you know, and because of our governance, have faith that it is showing accuracy at least. But interpretability really differs between different stakeholders and that can be really problematic if you don't really control over it, have control over.

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Tarush Aggarwal (14:24):
Yeah, you know, we usually kind of get into like what's working well and like, you know, what are you proud of? You know, since we're on the topic, you know, one of the questions we ask on the show is, you know, what is one of the challenges you're facing and, you know, what are you doing to solve it? Is this the big challenge? And, you know, the evolution which you sort of spoke about from self-service and everyone can use it. But that's a stage which many companies never even get to, right? Like they

All these desperate data teams. So they have all of these separate data sources. They have multiple sources of truth. So, you know, just the concept of self-service for many of these organizations has been a myth. So the fact that you're already there is, you know, already super interesting. But sort of going into one of the big challenges, what would you say that is today?

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Emily Loh (15:04):
That's true.

Yeah, I think it's one of the challenges, although I think it's a challenge that's under a larger umbrella about what the data team or actually, I won't even say the team. I would say the data function is supposed to add to an organization.

Having worked in a variety of organizations, it can differ so wildly. And you really have to decide, you know, like, where do we want to go? What kind of impact do we want to have?

There's also, I would say, an identity problem, right? Because when you're an analyst, and this is going back to, think, I don't know if we touched upon it, but, you know, nowadays there's usually a debate between, what is a scientist versus what is an analyst? What is an engineer versus what is an analytics engineer? And so on and so forth. I don't think it really matters that much at the end of the day. Where it does matter is where we have struggled with this at MoonPay actually.

And it is wrapped up somewhat into this self-service analytics question, if you will. And it is, who do we want to be? And what role do we want to play? And how do we actually end up achieving that? That's the biggest challenge, I would say. Because when you're in a company, so I've been in MoonPay for two and a half years, it is super easy to...

Believe that, okay, people are coming to me with requests, that is my job. I answer those requests. It takes a very strong person to say, "No, that is not our job. This is our job instead." So now we're moving a lot more towards understanding the role that data plays isn't necessarily to provide information just broadly.

Right? It is, in some sense, of course, but there has to be, like I said, intent behind it. Because talking about the digital, like the data footprint, our data footprint has gotten so big.

That I'm kind of like, we need to reel that in because it's basically a state of entropy where you're just like, okay, it's going to chaos really soon. And it's better than I've seen in a lot of other companies, which is great. But we're also not in the business of being like, okay, we're better than others. So we're good. It's not the case. We still have a lot to go. And so I think that data we're now operating on this model of data is meant to give operational clarity.

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Tarush Aggarwal (17:17):
Yeah. Yeah.

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Emily Loh (17:42):
To the entire business. And that's from every step. And first of all, to ourselves, actually, as the leader of the data team and leader of the data function, there are some times, admittedly, when I'm like, have no idea what's going on over there or why we decided to do that thing. And if I don't know as a leader, how do I tell the people to know? How do I know? How do I expect?

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Tarush Aggarwal (18:02):
Yeah.

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Emily Loh (18:06):
People on the ground to know either, you know, these engineers working on these really cool things to one end, you know. And so that's, think where data plays a very critical role in helping just understand, which is different than information, I think, right? Because information is information and it can be taken at face value and you can think that it means what it means. But then when you speak to someone else in the same organization, you might even sit next to them.

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Tarush Aggarwal (18:11):
Yeah.

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Emily Loh (18:35):
Yeah. You realize like, no, they don't think the same thing as I do at all. So, you know, as a data function, you really need to kind of align everybody around those things and make sure that everyone is on the same train going forward because at a startup, especially, you can think you're on the same train, but actually you're on a different planet. And that can be detrimental to the business.

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Tarush Aggarwal (18:59):
I love the honesty and transparency and highlighting some of the practical day-to-day challenges. What is one achievement which you're particularly proud of where data has made an impact in the business sense, helping MoonPay do what they want to do, which is be that bridge between crypto and typical

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Emily Loh (19:20):
Yeah, for sure. So I think it's coming off of the same challenge that we're trying to solve, right? Exactly, how do you think about operational clarity? What does that even mean? One thing that we did on the team, one of my analysts created an opportunity sizing framework, and that has really helped rally the business in all sorts of ways around these really cool things we all want to do.

And we don't often have a mindset of, "It's cool." But is it good now? Is the market ready for it? Do we have enough resources to put behind it?

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Tarush Aggarwal (19:53):
Yeah.

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Emily Loh (19:58):
What does success look like? And again, all these things we all presume because we're part of the same culture and part of the same company that we all think the same, but the nuance really makes a difference. So the opportunity sizing framework, along with the prioritization framework that we've also put in place, really helps everybody understand the why of what we're doing. For example, when we're implementing a new payment method,

This has happened quite a lot, which is important for us because we're a payments company. So we want to make sure that we're diversifying our offerings to all of our customers. But the way that we go about integrating something like that can differ wildly depending on what we're trying to achieve. Are we trying to achieve more engagement? Are we trying to grow new users? Are we trying to improve transaction success? All of those things are factors, and there cannot be a presumption, which we have done in the past, that it is all understood because it's not. And so we're trying now to really understand whether these are initiatives that are worth doing today or, you know, what the scope could be

depending on what we're actually trying to achieve in an explicit manner.

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Tarush Aggarwal (21:18):
Yeah, wow, that is a really thoughtful answer because it again goes into the why. And I think one of the challenges data teams have had, and I speak about this quite publicly, is that it's becoming difficult to quantify the ROI. If the answer is just decision support, we're helping you make better decisions, that's great. But

As a CFO writing the check at the end of the day, it's hard to like, what happens if we do 20% less of this or like 30% more of this. So I love the framework which you have around, you know, the impact framework. That is incredible. Emily, this has been incredible. We're super, super excited about what MoonPay has to do and thank you for being on the show.

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Emily Loh (22:04):
Thank you so much for having me. Yeah, it's been a great conversation. So I've had a great fun.

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