A Data Journey Starting with Discovery
How I went from construction to a data role in arbitration, back to construction, and then finally landed a permanent role as a data lead.
Introduction
A few months ago, I took on the role of Data Analytics Lead at my company KBR, here in Australia. Following that appointment, I started to reflect on my journey over the last few years, over how someone who was a project engineer on a construction site was now working in data analysis and business strategy.
During that reflection, I thought that although my path might have been winding and unconventional, there were several key aspects of my journey that were instrumental in getting me to where I am today, and I feel could be relevant to people on a similar journey.
Although many of the following points overlap, I am going to look at the following:
- Accidentally falling into a data related role
- Lessons learned in a high-pressure legal role
- Developing my skills and knowledge
- Leveraging social media
- Combining domain knowledge with data analysis and visualisation to improve business processes
- Seeking and taking opportunities
Here’s my story (and some lessons)….
One door closes and another opens
It actually started with being made redundant by my company, KBR, in late 2018. That’s right, I lost my job.
I was a civil engineer, and had been working in engineering design and construction for around 13 years in locations all over the world. In late 2018 I finished a 7 year stint as a project engineer and package manager on one of the world’s largest LNG projects here in Australia.
The last 4 of those 7 years involved working on site 6 days a week, with a 5.30am start every morning. So, even though I was disappointed to be made redundant, the thought of having some time off to relax, sleep in, take the kids to school in the morning and help my wife out around the house appealed to me.
After about a month, the extremely subtle hints that my wife was dropping suggested that the novelty of having me hanging around the house all day had worn off!
So, my thoughts naturally turned to what I should do next. Should I go back into engineering design, project management, or even site work?
Just as I had been dusting off my old cV, KBR called to ask if I would like to re-join the project (yes, really…). However, this role would be a little different to anything I had done before.
The accidental Discovery
The LNG project was nearing completion, and in that industry that can mean only one thing….it’s time for litigation and for lawyers to start planning their retirement funds!!
I was asked to come back to assist on a major arbitration dispute related to an alleged breach of contract on the power plant portion of the contract. This arbitration alone was estimated to be for the not so insignificant sum of around US$1.9bn.
I had previously worked in the contracting offices in Japan, engineering offices in Perth, in addition to being on site, which meant that I had a broad exposure and good understanding of the systems that were used by each joint venture partner for engineering, procurement, document, quality and contractual management.
And so, I joined the legal and e-discovery team to take a lead in identifying and recording project record types that may have been responsive to requests made through the discovery process. This was definitely not something I had ever planned for in my career, but I am always up for a new challenge if it involves working with great people.
But what is discovery and disclosure?
For those that may not be familiar with the process, a short, simplified description would be that each side of a dispute would make a justifiable and targeted request for documents related to their claim. The other side would then need to make best endeavours to search, locate and disclose documents that would be responsive to that request, all within a set constrained timeframe.
Project scope
The scope of the potential records was huge. When you have such large projects spanning almost 10 years, with design offices in Japan, Australia and the USA, fabrication in China, Thailand and the Philippines, and upwards of 8,000 people at peak on site in Australia every day, the amount of information that is generated is almost unthinkable.
For example, one system that manages drawing reviews may have had up to one million registered documents, with each document containing around 300 pieces of metadata. That is without considering the content of the documents themselves. Further, when you consider there may be 10–20 databases or systems that contain similar volumes of information, not to mention servers and work laptops, it is not surprising that this was the first time I heard someone talking in terms of “petabytes” of data, which I obviously had to go away and google!
Lessons learned in a high pressure role
We had a dedicated specialist e-discovery team who were looking after data retrieval and ingestion into proprietary platforms like Brainspace and Relativity.
I wasn’t needed for that aspect, but they needed someone with project knowledge who would either know or be able to research where particular types of documents may be stored, for example, 3D design models, technical queries or meeting minutes.
I soon settled in, and before we knew it, we were mapping document types to databases and opposing requests, and managing the identification, tagging, review and disclosure of well over a million documents, the majority over a period of around 6 months.
Without going into the detailed aspects of all my work, I thought I would share the key lessons I learned that, although not very technical in nature, I believe are particularly applicable to a career in data.
Lesson 1 — Accuracy and attention to detail
As mentioned, the amount of data and information was vast, and the lawyers and counsel were extremely demanding and unforgiving on both sides (maybe even worse on my own side).
The sums of money were enormous, pressure was intense, and big reputations were on the line both for multinational companies and lawyers alike. So, the accuracy of any information you provide needs to be 100% correct, otherwise your mistake could be very costly, and embarrassing for everyone on your team.
On occasion, opposing expert witnesses would submit statements talking about documentation and potential deficiencies. One of my roles was to review those statements for accuracy against our records and, if there was disagreement, submit a response, and occasionally a sworn affidavit, highlighting any issues relating to accuracy or context.
When you are swearing on and signing legal documents about the correctness of the data you are submitting, you soon learn about ensuring accuracy by checking and rechecking your numbers, and then checking again!
Although as an engineer I was always conscious of accuracy and correctness in my work, this particular experience really drummed into me the need for checking your data, models and calculations, and that how any inaccuracies will damage both your work and personal reputation.
As I moved into other roles, accuracy and attention to detail is always at the front of my mind, and something I think anyone who is working in data needs to take seriously.
Lesson 2 — Research and critical thinking in developing narratives
A key lesson for anyone moving into data analytics is to understand the role of research and analysis in helping to develop a narrative in your work, particularly in explanatory analysis.
Working with lawyers on such a large case, I learned at first hand the importance of research and how it helps in the development of a narrative, which I guess is akin to the subject of “storytelling” that you hear in the data world.
In the legal world there are usually two opposing views and when they turn their research work into pleadings or statement of claim, there will be a structure and theme developed to produce a stylised narrative that is tailored to the needs of a particular audience. This is supported by aspects of the law, enormous amounts of reading and research, and further demonstrated by key documents and accounts of what happened.
This is mirrored in data and business intelligence, where a narrative is created with an end user in mind, and is supported by a combination of research and data analysis.
Sitting in workshops and meetings and listening to some world leaders in construction law developing such structured narratives, I was often asked to perform research to answer certain questions. I was fascinated as to how they planned and constructed very complex and intricate issues into a coherent narrative that could be understood by people who were not subject matter experts.
It was something I have tried to apply to my work. If you ever read my blog posts or showcase portfolio, you will see I always put an emphasis on the planning and research stages of an analysis and setting a structure. Hopefully it is also evident in my work that I try to create a structured and focused narrative, theme and flow in my reports.
As an aside, there are a few great resources for anyone startering in this area, including “storytelling with data” by Cole Nussbaumer Knaflic (books and podcasts are highly recommended), and the “Thinking Like an Analyst” course run by Maven.
Lesson 3 — Develop your credibility and protect your reputation
Without credibility you will go nowhere fast.
People need to be able to trust what you produce, and that depends on building, maintaining and protecting your standards of accuracy and attention to detail.
Normally, e-discovery and document disclosure are boring secondary issues in arbitration that lawyers don’t want to pay too much attention to. It’s just nerds finding documents and handing them over, right?
But it became a major issue on our case. There were insinuations that the other side may wish to delay the hearing, and were looking for excuses to do so. That is where disclosure came to be almost “weaponised”, as the implication of the other side not having access to all relevant requested information could be grounds for delay in a hearing, and those delays could cost us time and a lot of money.
Arbitrations have periodic meetings called Case Management Conferences (CMCs). These are between opposing legal counsel and the Tribunal, who are a panel of mostly retired senior judges. As our case involved multinational companies, these were often held all over the world, in London, Singapore, Hong Kong and Sydney.
Disclosure “issues” started to be raised at these CMCs, and there was typically no one on our counsel team adequately briefed to be able to counter any claims being made during these briefings. As the issues started to get more “airtime”, cause some red-faces and increase the likelihood of delay, I started to fly out monthly to attend and assist legal and counsel teams.
My role would be to listen to claims being made by opposing legal teams and effectively act as a “fact-checker”. This is where the combination of my knowledge of the project records and the document disclosure power query models I developed came into play. I was able to quickly check disclosure records almost in real time and provide that feedback to our counsel who could then respond or dispute the claim and try and minimise any impact.
Performing these roles under pressure over a prolonged period and providing accurate data-backed responses soon helped me develop my credibility and earn a reputation at all levels as being the “numbers guy” that people could rely on.
When the case finally went to a 6 week hearing in Sydney in 2022, that developed reputation and credibility meant I was requested by the counsel team to be there in person each day to fulfil a similar role.
Developing my skills and knowledge
Taking a step back to look at my development, as I delved into world of e-discovery and data disclosure, I realised that I would need something more than being able to perform an INDEX:MATCH in excel!
So in parallel to learning the above work life lessons, I embarked on some skill and knowledge development which helped me automate and streamline many of my processes, and create tabular models in power query to connect details on many of our datasets and records of disclosure.
So, from initially discovering a certain Mr Chris Dutton on a series of pivot table and power query courses on Udemy through to becoming an accredited data analyst and somewhat of a dataviz expert, I thought I would share some of the advice and lessons I learned along the way; not just in technical tools, but understanding how to approach analysis and effectively communicate the outcomes.
Lesson 1 — Focus and Schedule
I probably don’t need to tell you there is a lot of competing content out there. A lot of tools, languages, trends, influencers and people selling the dream of $200k jobs from “mastering data analytics in 48 hours”. I could have become overwhelmed and wasted time jumping from tool to tool.
I was lucky enough that after taking those courses with Mr Dutton, I started looking for more of his work and stumbled across the hallowed ground that is the Maven Analytics platform.
I was signed up almost straight away, and for me, one of the key benefits was the development planner. Through a series of questions, it looked at my needs, goals and timeframe and set up a targeted, achievable and realistic schedule that I could work to in parallel to my ongoing work commitments and provide a good foundation for me to further develop.
“Realistic” and “achievable” are key here. We all have lives, family, friends, hobbies, study and/or jobs that we need to balance. Development should be paced to avoid overloading on information or burnout. Although I burned through some of the excel courses at first, I allowed time to pace myself and ingest what I was learning when it came to Power BI and Tableau, and gave myself a chance to use it in the real world, either in work or on a data challenge.
Although it crossed my mind a lot, I thought it was important to try and not compare my own progress against that of others. Everyone has different starting points, skills and interests, so it’s always best just to focus on what is in front of you. You would be amazed what an average of 6 or 7 hours a week can achieve over a year or two (taking breaks here and there).
Of course, just having a well laid out schedule won’t make you learn. I was lucky that the Maven courses are engaging, comprehensive and well-paced. I’ve tried several training platforms, and many have their own advantages in terms of breadth of content. But in my opinion, Maven genuinely sets the standard both in their courses and they way their team interact with and help out the community.
I promise they didn’t pay me to say that 😀, and I know many, many people who agree.
Lesson 2 — Beyond the technical tools
Learning tools like Power BI or Tableau is one thing, but without knowing how people think and react, or how to effectively communicate with an audience or end user, those tools can become next to useless. I am sure you have seen plenty of dashboards that look nice on the surface, but when you look at them more closely, they are disorganised and don’t tell you anything at all.
As I became more exposed to data visualisation and narratives, I started to become interested in subjects like design theory, journalism, communication and human psychology. Reading articles and books from authors like
- Alberto Cairo
- Nancy Duarte
- Dona M. Wong
- Stephen Few
really provided that extra level of insight into how we, as humans, consume visual and spoken information.
Beyond that, I took an interest in emerging trends in the resources and energy industry I work in. I started reading about topics like the energy transition, the future of critical minerals, government policies and geopolitical trends. This has started to bear fruit as I now get to work in business development on these subjects in my company, which I touch on a little later.
I think it is important that if you are working in an industry, or wish to work in an industry, then you should make an effort to keep up to date with latest developments.
Lesson 3 — Mixing up mediums
One more thing I learned was that you don’t need to be sitting at a desk and clicking your mouse to be developing your skills or knowledge.
You can learn pretty much anywhere.
I find exercise, particularly running, a good way to clear the mind and think of new ideas. Sometimes I like the silence, but other times I would listen to conversational podcasts on data like Andy Kriebel’s dual axis podcast. I still find Cole Nussbaumer Knaflic’s storytelling with data podcast a great resource for many reasons, and have listened to several of the episodes multiple times (especially anything with Alberto Cairo).
When people ping me on LinkedIn to ask for tips on what they should learn, I always point to this podcast. It’s free and has so much great advice gleaned from her books, talks and videos.
As mentioned above, I also enjoy reading about topics like visual theory or human psychology, and always have a book in my bag to pull out on the daily commute if I have 20 minutes. And if I don’t feel like reading, I can always see what the latest offerings are on Youtube.
I should point out that it’s not to say that I am studying, listening and reading 24/7 (although some days it feels like it). It’s more that I find it helps to break up and vary the methods in which I consume information to keep things interesting.
Leveraging social media
Even though I felt I was performing well in my job, and following a focused schedule to upskill, I would say that it’s arguable that the confidence I got to take that next step and actively pursue a career in data stemmed from my engagement with social media and related platforms.
It provided me with the following key benefits:
- challenged my boundaries and stretch my skills
- provided a platform to build a public facing portfolio of work
- enabled me to engage with experts and get their feedback
- gave opportunities to develop presentation and communication skills
- connected me with like-minded people around the world
- advertised my skills to colleagues and management within my own company
Benefit 1 — Challenge my boundaries
Social media and data platforms have a wide array of data viz challenges and workouts to help everyone test and stretch their skills and showcase them to a wide audience.
When I first took part in a Maven challenge back in 2021, I didn’t realise they would become a cornerstone in my development. It is certainly something I always recommend to those starting out — get out and participate — and even as you get more experienced it is still valuable. It is not often in a professional environment that you can fully let your creativity take hold and have full reign over your output.
I took part in the first one for the Olympics in 2021. Looking back, it was an overcrowded report, lots of color, logos, and trying to tell everything all at once.
But I soon learned to experiment with and apply the skills I was picking up in my job, reading and study, to start introducing structure, narrative, flow as well as color theory and Gestalt principles.
After the first effort, I always tried to treat the challenges as just that, a challenge. I would never just “knock out a report” or repeat something I had done before. I needed to do something that was new for me, either visually, in a different tool, or in how the data was being analysed or transformed.
This is where I researched and applied key techniques that you don’t always get in courses. For example
- applying normalised data values in the Telecoms Churn challenge
- creating Likert Scales to examine survey data in the Airlines challenge
- created running total calculations in power query to estimate table usage in the Pizza challenge
- experimented with Deneb to create custom visuals in the Northwind challenge.
As the challenges were posted on LinkedIn, and I began to be placed in the finals, I started to gain more connections and grow my network exponentially, and even surprisingly got a series of video reviews of my work by Carl Seidman.
Over the last two years, I have been lucky enough to be placed in the finals of 14 Maven challenges, mangaging to win 2, and even pick up Maven Hall of Fame for 2022 in the process, something I could not have envisioned when I first started taking part.
Beyond the challenges, I also took part (not every week!) in highly recommended project activities with the teams at:
- Makeover Monday
- Workout Wednesday (Power BI and Tableau)
- Viz for Social Good
- Enterprise DNA workouts
- storytelling with data
Public Facing Portfolio
I know I am like most people, in that it has not been possible to share reports or visuals I have created in work. Therefore, it can be difficult to put together a portfolio of work to demonstrate your skills.
I found that taking part in these challenges and online activities had the added benefit of creating an almost ready-made portfolio.
I started to create a portfolio of my work on my blog and had some visuals on my Tableau public profile, but then Maven came up with their own showcase platform providing somewhere for me to both embed my work and also provide commentary and context on my process and technique and get feedback from the community.
It’s definitely something I would recommend people take advantage of if you do not already have somewhere to showcase your work.
Expert Feedback
Many people have asked me for tips on how to develop a challenge entry that can compete for a finals place. One thing I always recommend is to watch the judging panel review videos that Maven create at the end of each challenge cycle.
They provide excellent critical feedback on what makes a great report in certain scenarios, whether that be an exploratory or explanatory report, a business dashboard or an infographic. They look at what information needs to be brought out, and the best techniques to visualise and communicate them. I even went back to watch the older videos in challenges I didn’t take part in.
As my network grew, I started to find myself chatting with fellow data analysts on LinkedIn. I was able to gain a few close associates who I was comfortable with both giving and receiving honest, open and critical feedback (There are many, but noting a special shout out to Marjolein Opsteegh and Jasmin Jusufbegovic). I found having this kind of “truth teller” was very beneficial, as often some people will be afraid to offend by being too honest!
I was also getting requests for reviews and feedback from fellow challenge competitors. It isn’t always possible to provide it, but I would make the effort to give honest feedback where I could because it’s one of the main ways of improving how you work and think.
I felt pride when I saw some of those people who I gave advice to get final spots.
On the Maven pizza challenge, I was getting quite a few requests, so thought it a good idea to hold live review session on YouTube. It was a great experience to be able to share thoughts and opinions on what works and what maybe doesn’t when communicating certain types of data.
As an aside — a little advice to those who seek this type of feedback — write a brief summary of what you have done and what you were hoping to achieve, as it gives the reviewer something to base their feedback on. They will be more likely to respond.
Writing and Presentation Skills
Engaging in social media platforms like LinkedIn and Twitter gave me the opportunity to practice my writing and presentation skills. It was a gradual approach over a period of months and years, where the more I engaged through writing and making videos, the further opportunities seemed to open up with other people and groups on social media.
Initially, I learned that if you are posting your latest challenge, or offering some advice or technical solution, you need to think about how you post, the language and structure. I found myself reading tips from copy writers on how to hook and engage an audience.
After some trial and error, I soon started to see benefit in terms of the engagement. But it is not simply about writing a good hook, if people take the time to comment or engage with you, you need to also take that time to acknowledge and have a conversation.
I soon started to extend my writing on the strategy and processes I was using in my work, and I naturally gravitated towards creating a blog at dg-analysis.com.
I was writing both for myself and for people in my network; it would create a record for me to refer back to, and also hopefully help others on a similar journey. I was getting positive feedback from both my peers and people I looked up to, which encouraged me to do more.
I soon transferred from the written word to the spoken word, creating YouTube videos on techniques I was using to create my challenges and particular types of viz. I am not necessarily a natural speaker, so it was good practice for me. I don’t get time to make as many videos as I would like, but it is definitely something I have enjoyed doing.
After this exposure, I started getting some invitations to talk about and host challenges.
For Workout Wednesday, in 2022 I was given the honour of hosting a challenge and creating the walkthrough video for a Likert Scale analysis.
Around the same time, I was also invited to take part on the Enterprise DNA Industry Summit as I was someone involved in mining and resources, and I gave a live presentation on visualising the energy transition.
Catch the attention of your own company
Finally, posting content on LinkedIn is also a great place to advertise yourself internally within your company. Although people in my company knew I was working in arbitration and disclosure, they wouldn’t be aware of any of my abilities in data visualisation or creating reports.
The more I was creating and gaining exposure and recognition, the more people would approach me or contact me inside my company asking for assistance or advice.
And when I gained the Maven Hall of Fame earlier this year, I ended up having several write ups and video calls on the subject, which further improved my profile inside the company.
In the next section I will talk about creating and taking opportunities in the work I was doing in my company to fully transition into a data role, but it was really the leveraging of social media that helped me instigate and gain traction.
Using experience and knowledge to improve business processes
After working for a few years in arbitration, soon my thoughts turned to my next move. What would I do next? Should I go back to construction, or should I think about a career in disclosure and data analysis in arbitration, or maybe something else?
As discussed below, I went back to engineering and construction, but with an eye on seeking opportunities to apply data analysis and visualisation to improve processes.
Lesson 1 — Using domain knowledge
Making a career change at anytime can be quite daunting and a major risk, especially if you have been working for a number of years in a particular industry. There was self-doubt on my part. Have I had relative success in arbitration only because I had a solid understanding of the project and context from my work experience, or did I have a talent for analysis?
The risk of potentially jumping both industry and career type seemed a little too much for me. As part of my reflection on what I had been doing for the previous few years, I saw great potential for harnessing data within the construction industry to improve the way we work and help decision makers.
I had thought about how we could report on and visualise quality, health and safety stats, document management, engineering progress, etc.
So I made a plan to go back into the construction industry and look for ways I could combine my domain knowledge in engineering and construction with the analysis and visualisation skills I had acquired in the last few years.
If it worked, then great (which it did!). If it didn’t then I would still be working in a role or industry I had significant experience in. As such, the risk of my decision was mitigated and I was comfortable with the approach.
Lesson 2— Starting small
My new role was as a package manager on a critical minerals resource project and started in late 2021. While performing my standard duties, I was looking for the opportunities where I could harness the data we were collecting to help in the management of the project.
I found the key lesson here was to start of small and simple. My advice to anyone is to talk to your colleagues and bosses and ask about:
- what everyday processes or tasks take up their time
- what tasks they wish they didn’t have to do
- whether there is there any data associated with these tasks or processes that is being collected
- whether there are any particular insights or decisions that they make based on this process
If these situations exist and you can start solving these, and improve team productivity, more people will start noticing, asking questions and coming to you for help.
As an example, we had a consultant with multiple engineers in different countries performing work for our company. They submitted weekly hours for each person in a spreadsheet. It wasn’t easy for management to see at a glance how many hours each person was working on a weekly basis and see how that was trending over longer periods of time and against forecasts and budgets.
I created a simple Power BI report detailing these trends and metrics that could be automatically refreshed weekly as the data came in. It was able to be used straight away in meetings with the consultants. We could immediately discuss workloads and utilisation rates for each person, and see how we were trending against forecast time and costs, and make decisions on whether adjustments were needed.
I built on that momentum, developing mini projects to help out my own team initially. However, once it got noticed in meetings, I started helping other teams and management. Before I knew it, I was involved in developing finance reports, commercial agreements, social media statistics, as well as procurement and health and safety.
Some teams needed to be won over to utilising tools like Power BI, but these automated reports and visuals were able to be referenced in presentations and monthly reports and reduce the effort and time required by each respective team.
Lesson 3 — Learn to say no
It might seem a little counter-intuitive, but not every problem requires a dashboard or report. Being enthusiastic to help meant that I had a few failures where I made a report that either no one used as there was no real defined end use for the report, didn’t offer “as much flexibility as excel”, or was unworkable as data was either sparse or not regularly collected.
After those failures, I learned to make sure I was asking the right questions, about the requirements and the available data, and learn to say no, or offer a potential (often simpler) alternative.
To paraphrase an old saying, “just because you have a hammer, every problem is not necessarily a nail.”
Lesson 4 — Expand influence and take opportunities
Creating these tools and reports is great to start with, but I was still coupling this with my main job. How could I take it to the next level and allow me to work exclusively in data analytics and business intelligence?
The good thing about the things I was doing both in work and on social media platforms meant that I was getting noticed by more senior management and leaders in the business.
Such people are learning about the power of data and how it can be used to help in developing strategies, direct a process or assist in making smart choices and decisions.
It only took one such leader to approach me and have a discussion about developing a strategy for identifying and targeting future work in the mining and resource sector in a smarter, data driven way. He asked if I would like to work with him to develop a strategy that integrated data in helping produce an outcome.
It was an opportunity too good to pass up, even if I was spending my own time working on it.
Mining and resources are the backbone to the Australian economy. There are often hundreds of future projects on the cards, but only a small percentage of them are feasible due to a multitude of factors. Our goal is to be able to provide engineering, management and construction support to project owners. The problem was, how do you identify that small set of projects that are likely to proceed and fit in with our overall business strategy?
Together we worked (on the side) for around 9 months developing that initial business development strategy. We had to research and analyse where the world was being directed in the near future for mineral demand based on technology changes, energy transition, geopolitics and government policies.
Then we looked at which trends aligned with our business practices, preferred client profile, and where we could add value from our expertise and technology know-how.
Finally, we then examined data on all future projects and project owners on the horizon and, based on feedback from technical lead workshops, I developed a model to provide a series of multi-leveled scoring criteria to help identify those projects that meet the “sweet spot”, and we believe are most likely to proceed.
As a business, we could then rank and identify those clients early and start conversations on whether we would be able to assist in developing and executing their project. Previously, it may have taken a small team of people to do such market intelligence research, or else you might rely just on networking or connections, and even then it would be difficult to get a full picture.
This process wasn’t intended to fully replace more detailed due diligence or remove the benefits of networking but was intended to help remove the “noise” of hundreds of projects, and provide a more targeted set of projects that the business development leads could focus on.
Needless to say, once the results were produced and shown, other business sectors became interested as to whether we could develop similar methods or tools to complement their business development.
I believe taking this opportunity and executing it was the final catalyst to give me the confidence and ability to move into a data lead role within the company earlier this year.
I finally had that combination of skills, business knowledge, self confidence and confidence and support of my peers and management to make that change.
So if you are working in a company that isn’t yet fully harnessing the power of data, seek those people with influence and start conversations about ways that data could help in their decision making. Who knows what opportunities are waiting?
Conclusion
My transition into data in terms of the pathway or timeframe may be completely different to your own or those you know, but it shows that it is possible from many different angles.
Essentially, I discovered an interest and passion in data analysis and visualisation, and I intently developed it through study and continual practice and application through learning platforms, social media and within my day job.
I was able to carve a path for myself and make an impact within my company that allowed me to take that next step.
The journey from construction to data analytics was challenging but ultimately rewarding, and has allowed me to contribute to decision-making processes and drive positive change within my own company, and will hopefully only grow from here.
