Name: Charles Joseph
Title: Founder & Data Governance Consultant
Company: DataZed
1 sentence company description: DataZed is a consultancy that works with organizations to improve their data quality, data governance and data strategy
Years in Position: Over 10 years
1-2 Previous Roles & Companies: Worked as a consultant with general insurers, Lloyd’s market firms, brokers and reinsurers, including Beazley Group and RSA
B2B, B2C or B2B2C: B2B
How can people connect with you? LinkedIn: https://www.linkedin.com/company/datazed/
Motto/Favorite Quotation/Inspiration: “If not now, when?”
Fun Fact: I was once an extra on a BBC drama about the war in Bosnia. My “role” was to watch a football match while drinking a beer. I was a natural.
In 1 or 2 short sentences, what makes a great data innovator? The strongest innovators are those who build on firm foundations. It’s easy to do “blue sky”, but if your input data is no good, then your output data will be no good.
Summary:
Charles specializes in data governance, data quality and data strategy for the insurance industry. He has worked with general insurers, Lloyd’s market firms, brokers and reinsurers in the UK and abroad.
He is passionate about helping people to extract value from their data, so they can increase revenues, decrease costs or mitigate risks.
Charles does not like heavy processes or excessive documentation. He’s pragmatic and focuses on tangible benefits. At one client, he was able to reduce the number of data controls by 40% while still improving the data quality.
Charles, what was your route into all things data?
I have been working with data for around 15 years. Early in my career at Deloitte, I had an internal client where I got involved in finding and cleansing corporate data. Looking back, it was a DQ and MDM role, but I was focused on fixing data without actually understanding that there was an art and science behind it. I then joined Beazley in the insurance sector as a data quality manager and thought it would be much the same as what I had been doing before, but then quickly realized it wasn’t at all. I was lucky to have great colleagues who helped me to transition.
The role was created to deal with an insurance regulation called Solvency II, which had a very large data component to it. I very quickly learned about concepts such as spreadsheet controls, data quality, data lineage, data dictionaries, all these things which we now take for granted. We later expanded our remit to business areas which were not directly impacted by Solvency II, and I subsequently learned from here to be pragmatic and show business colleagues that it wasn’t just a case of adhering to regulatory policies for the sake of it, but they would get value out of them.
If there was a “data industry” when I started out, it was niche, but it is very much apparent these days. I have always had an interest in data, models and analytics. For example, if you look at COVID-19, people are using data in all manner of ways to try and understand more about the pandemic. Data has become much more commonplace in every day matters such as news and sport. People are becoming more data literate. As people want to make sense of data, there is inherently a greater demand for data quality, data governance and master data management.
Then you founded DataZed and became a consultant, correct?
Yes, I’d been thinking about it for a while and finally decided to take the plunge while on paternity leave; starting the business in 2017. I was really keen to start applying what I had learned in different environments. I started with a high-street retailer which was going through the enormous challenge of connecting its customer data from millions of records sitting on legacy systems.
I then returned to the insurance industry with RSA, a large FTSE-listed insurance company, where the CDO was transforming the data function and I got to lead the Data Governance organization. This period covered the preparations for GDPR going live in May 2018.
After that, I was at a start-up within an established insurance broker that had set up a new division to take care of data, analytics and tech in general. With the backing of the company, we had a lot of opportunity to do what we needed to do, which involved setting up the framework of what the insurance broker will be doing with data over the coming years. Colleagues across the group saw value in what we were doing. They realized that insurers in general need to understand their customers much better.
What attracted you to the insurance industry?
It’s a fascinating sector. It’s not just about car or home insurance. Lloyd’s market firms are insuring everything from celebrities to spacecraft to ransom. And data is behind it all.
What are the main challenges facing the insurance industry when it comes to data?
What makes the insurance industry so interesting is also what makes it challenging. Digitizing complex underwriting processes is going to be hard – the complexity is why the risk has come to Lloyd’s in the first place. However, there is plenty of room to improve data processes, especially in the smaller businesses where manual activity in Excel is still dominant.
Rather than challenges when it comes to data, the mindset should be of opportunity. Of knowing how and what to do with it to drive great customer experiences.
Let’s take a simple example. You lose your mobile phone and want to claim on your phone insurance. When you call to let them know it’s stolen, most firms still force the policy holder to run through a series of the most fundamental questions (name, address, DOB) when they should almost already have this information to hand.
Collecting that information costs money – the call center, the colleagues, then systems, the claims managers reviewing the data. This all adds cost to the premium at the end of the day, and higher premiums drive away customers.
Automating the data collection helps matters. Taking away the need to collect it – because you already have it – is better still.
But let’s take it a stage further. Why can’t a savvy and responsive insurance firm connect the dots, understand the issue and, having used some anti-fraud logic, go ahead and mail out a new phone or a voucher to the client? This could automate the claims process and make it as painless to the customer as possible.
We know that most customers buy on price. If you’ve had to make a claim, you’ll also know that most customers renew based on claims experience.
We’re hearing a lot about disconnected customer experiences and how they lead to disconnected customers. Do you see this in the insurance industry?
It comes back to data. A few years back, you might hear of poor customer experiences from your friends and colleagues. Really poor experiences might make the money pages of the newspaper, or even a consumer-rights TV program such as BBC Watchdog.
Now we can access multiple opinions online with a couple of clicks. It’s no longer about the cheapest price, if a search on that brand brings up page after page of horror stories.
Therefore, there is an inherent need to give customers an outstanding customer experience and great service – real tangible value. When customers make a claim, you have to make it a painless experience; you cannot ask them to start from the beginning and tell you who they are again.
The problem is, for an insurance company to understand that a customer calling in to discuss his pet insurance is the same customer who has a current car insurance policy with them sounds very sensible and easy, but in reality, is very hard.
Most insurance firms still hold data in disparate systems that are not joined up and don’t “talk” to each other, let alone enable matching and merging the data. Also, both products call for different information: What an insurer may want to know about a customer’s pet that they will insure is different from insuring them for health or car or travel insurance.
And yet, if Insurers could leverage the data from disparate systems and get a holistic view of the customer, they could avoid such disconnected customer experiences.
How can insurance firms deliver outstanding customer experiences?
You have to know who your customer is; you need to connect the dots.
Responsive data management solutions, such as Reltio, allow insurers to bring all the information together and have it at their fingertips. They give them the assurance that the data is of high quality, it’s rapidly accessible. And as a data layer, they automate the match and merge capabilities and give the insurer to understand the customer better. And the more they know, the better they can price the insurance premiums, and the higher the level of service they can give them.
When a customer engages with the insurer to buy a certain insurance product, the insurer can type in their name and get an instantaneous lowdown on who the customer is, what other products they have bought, their claims history and all their contact data – while ensuring that they don’t see any data which they are not meant to see due to Data Privacy.
(Example – the team on a motor policy need to know about the policyholder’s health; the team selling phone insurance cover don’t).
Responsive data management solutions also help with customer communications. For instance, a customer may be interested in emails about looking after their pets, because they care deeply about their cats and dogs. On the other hand, car insurance is just a means-to-an-end, so they can legally drive their car, and they have no interest in emails about car maintenance. This sort of thing can become very complex, very quickly. But not so with responsive data management solutions such as Reltio, which can really simplify the challenge of managing communication preferences.
A lot of people are hoodwinked by AI and ML but fail to get the basics right first. Would you agree? If so, how does this play to the insurance industry?
Very much so. If I were to ask, “Do I invest in a responsive data management tool?” or “Do I invest in a data lake with a data scientist to play with it?”, the latter sounds so much better and more fun. But then you will end up with the situation where expensive data scientists spend a lot of time cleaning up data before they can do anything with it. Having the foresight to put your master data in order, which involves data governance and data quality, will pay dividends.
And it’s here that Reltio can help. By being a SaaS, cloud-native solution, Reltio does the heavy lifting for you. You just pay as you go and scale up when you have more records. You can continue to add in new data sources and it can help you match, merge and deduplicate and you’re off. And this would be extremely beneficial to insurers, so they can deliver outstanding customer experiences.
What 4 pieces of advice would you give an insurance professional who wants to get started with responsive data management, and deliver great connected customer experiences?
- Think of the customer experience and journey you intend to support. The business case needs to lead to a definable benefit, usually a monetary one. Then you can determine a holistic data strategy across master data, analytics, AI/ML and compliance. Make sure there is consistent customer data foundation across all departments and data technology that will support flexibility and agility.
- Saying that, it’s best to start small and win hearts-and-minds. Set aside some money for innovation, use this money to do PoCs and tests on small data sets. They might work, they might not, but it’s worth experimenting. Avoid advising that entire systems need to be replaced right away. Instead, take the MVP-approach: Start small and test. If it works, scale it up.
- Avoid the nay-sayers and the yes-butters. Don’t fall into the trap of letting co-workers tell you responsive data management is “too hard.” Instead, keep delivering value.
- Connect, network, and share. If you have an interest in MDM or responsive data management, ensure you connect with like-minded professionals. LinkedIn is great for this and you can even link to me! (Top tip – remember to add a note to the invite explaining why you want to connect). There are also groups such as Instech London and Lloyd’s Lab which foster innovation and can be a source of help. Networking is imperative; what worked in Edward Lloyd’s coffee house in the 1600s is still carried on today in 2020 in Starbucks (or, more recently, on Zoom, Hangouts and Teams).