C. G. Lambert: My Data Analyst Career Journey

Hi Chris, please introduce yourself to our site’s readers.

Hi, I’m C. G. Lambert. I’m currently Chief Analytics Officer at Clamp which is a consultancy which specializes in improving Analytics organizations. We look at process, culture and systems to figure out how to maximize the impact of Analytics. It’s fun because it's not just limited to technical recommendations like which database they’re using: we deep dive into the stakeholders, their personalities and relationships to see where the blockers are and give concrete steps that clients can take to drive meaningful change into their function.

Can you share with us how you entered the data analytics industry, and tell us about your first data analyst role?

I stumbled into it sideways: I came from a webdev background. My experience with database driven websites was helpful when I got a job in Forensic Data Analysis at EY, and then I applied for a job with TripAdvisor as an Senior Analyst. Trip had been looking for someone with Big Data experience but nobody was in the market with every skill they were looking for. They had been looking for someone for nine months when I came along. While I didn’t have any Hadoop or Linux, I knew data and I had a passion for travel. So that was good enough. The rest I learned on the job.

What is it that you've personally found most exciting about being a data analyst throughout your career?

At the peak of my career it was answering questions that stakeholders had not asked yet and giving them advice on what to do next as a result of that. When you can align the work you do with what the business needs, there's a real sense of achievement and a sense of camaraderie. Everybody is on the same page striving for the same objectives.

Over the course of your career, you started in the banking sector, moved onto a developer role, and then found your footing in the data analytics space, where you quickly grew from a business analyst role, into more senior and leadership data manager roles, eventually starting up your own portfolio of companies. Talk us through this journey.

A lot of what I talk about in my book is recognition of the work environment you find yourself in. Knowing what opportunities you can expect from your employer goes a long way in deciding how long to stay, and when to start looking outside the organisation. At Trip they purposefully kept the headcount low, which was great for forcing the business to only focus on the most important thing. But it had the unfortunate side effect of constraining careers - the only way forward would be if someone won the lottery, retired or died. So when I was ready for my next step, I knew it would have to be outside the firm.

What would you say analysts who want to grow into leadership roles, must know and do to stand out and rise through the ranks?

Again, there are whole chapters on this in my book, but the hardest lesson I learned was that you can’t just sit in the back office and churn out reports. The most efficient way of getting ahead is to be as close as you can be to your stakeholders so that you understand the business as well as they do. This is incredibly difficult as an analyst, both because of the typical analyst’s personality but also because you will usually have to fight against the organisation to get as close to the business as is needed.

And when you do finally get that leadership role, you may find that you have serious Imposter Syndrome. You may feel like you are being thrown very different challenges (which is fine: it’s what you wanted!), but are not being given the support or skills you need to meet those challenges. This is the biggest cause of burnout in new managers. Getting through the first year as a manager is an achievement and there are some really useful tips in the book on how to manage this and how to banish Imposter Syndrome.

Throughout your career, what did you learn about using data insights to make an impact in various organisations and industries? What would you say all your roles had in-common - i.e core, and on the other hand, did you have an experience where you really had to get outside the box and the usual path?

Learning where the Analytics role fits into the business is really important because you establish just how you are going to show that you are driving business value and justify your salary, your bonus and any promotion opportunities. For example, at HSBC I worked in Analytics in the Model Risk Management area. So it was fundamentally about avoiding any of the sanctions or fines that could be levied by any of the regulators in jurisdictions that HSBC operated in.  

Supporting the Sales team at TripAdvisor on the other hand was great - the reporting and guidance we gave the clients allowed them to spend more money with us and Sales had the best parties.

So it's important to know what constitutes “impact” so that you can maximise it. One way is to look at the sorts of things that are rewarded, who gets promoted for what, what efforts are lauded.

I guess all my roles have had one thing in common. Everybody thinks that they know what they need to know. As a manager, stakeholders will usually have an idea of the sorts of things they want your team to analyse. As an Individual Contributor your manager or your stakeholder will have a (hopefully) well defined problem that they are going to get you to look at. But all of the biggest impactful analyses that I have been involved in have all come from work that I have done on my own initiative. The danger of that is that you go off on a wild goose chase and present something that nobody has asked for and has no value. If you do it right though, you show that you understand an area of the business well and know what will move the needle for them.

What are some of the key things you would recommend people focus on?

It is easy to focus on technical excellence. To do the courses. To collect trainings. Showing these certificates on your CV can be seen as progress to being a good Analyst. And to a certain extent that is necessary. You need to be able to use the tools. But if I can leave readers with one piece of advice it would be this: focus on actual business impact. Learn the business. Sit with your stakeholders. Speak their language. Find out their pain points. And learn about the dollar impact of any of the pieces of work that you’ve done. And put those in the CV. That shows people that you have a strong focus on how your work is used and how it improves the business.

Can you share what prompted you to write the book about your analytics career? What are some of the key messages that you’re hoping to pass along to readers?

I was moving more into consulting and thought about how to give back to the industry, so I thought I would write a book about the questions that I had been asked the most in the teams that I had run. It’s important to have an opinion but I wanted to make sure that I was not just presenting unexamined opinions, so I interviewed fifty managers and senior managers to see if some of my more out there ideas were shared. And that validated my ideas, added a few more and allowed me to broaden the range of interesting anecdotes that I could weave through my book.

Something that a lot of people are wondering and asking about - What recommendations would you give to someone who is looking to join the data industry and get their first full-time data analyst position?

Again, great question, and a whole chapter in my book on it (Chapter 6: Breaking In). Fundamentally you need to show that you can use the tools that they use for the role, and if you can’t point to real world experience then you really need a portfolio of analyses that you have done for yourself. If I wanted to break into Analytics and had no work experience but an interest in Ice Hockey, I would download a whole bunch of Ice Hockey data and do a bunch of analytics and dashboards on that data. The questions I would have from my interest would drive me to ask insight questions of the data. And that would hopefully come through in the results. The most frustrating thing I have been asked is “where do I get data”? The world is swimming in data - it’s raining data, go outside with a bucket and collect some! A google search will give you a lot of results, websites supporting data science competitions like Kaggle is another place. And if you need to scrape a website for the data, that is another skill that will look good on a CV. (My lawyer insists that I mention you should always comply with terms and conditions of use for websites)

Anything you'd like to highlight, or add? Something that not specified above but you hear a lot, or would be helpful for people to know?

It’s really important to take responsibility for your career. To know where you want to go and by when, to have some hard conversations with your bosses and let them know your expectations, to find out if those expectations are reasonable and what they can do to support your goals. And then deliver bucketloads of business value. Every job is a marriage of convenience. It is never “family”. Family love each other unconditionally. As soon as you stop delivering on what you promised, whether that is getting paid on time or opportunities for advancement, the marriage is no longer convenient. It is frustrating to hear people talk about a promotion they’ve been promised for two, three or four years. Inertia is not your friend. Take responsibility. If you’re happy doing what you are doing for the pay you are receiving then great. But if not, do something about it. If you can have honest conversations with your boss then that's a good start. But sometimes the level of trust that that requires is more than they have earned. Which surely would push you one way or the other in a career decision, right?

An extra one: How do you see the increased availability of AI tools such as ChatGPT, Bard etc, impacting the typical data analyst role? Are you using AI tools to augment your  thinking, analysis and overall work?

I have a whole section on Unpopular Ideas (three chapters of them!) and I have a bit to say about AI in there. But I’ll leave you with this: businesses have been working very hard to generate, cleanse and collate data so that they can do Analytics on it and generate business value. It’s very exciting playing with AI because it promises to do exciting things with analytics. But the current state of data with all the efforts to date is pretty dire: data lakes are called swamps because everything is dirty. If you can’t rely on your data, then bolting a sex new LLM on it will not lead to great insights.

And with the hype around AI you have both the hallucinations as well as good AI on bad data to contend with. But that will make you sound like a luddite, so learn as much as you need to. But never bet your career on AI results!

This has been great, thank you again, Chris. Where could people connect with you?

You can check out all my books at my personal website at cglambert.com. If you like the book, please do leave a review and if you don’t like it then connect with me on LinkedIn and tell me what you didn’t like about it.  

Website: cglambert.com

LinkedIn: linkedin.com/in/christopherlambert