2022 [done]
Hey guys, happy new year! This blog aims to disclose initiatives, achievements and adventures we had during the last year but also to share our difficulties. That is why we would very much appreciate questions, concerns or suggestions you might have around how to grow a data tech company like DareData Engineering.
For those who have never heard about DareData, we are a high-end consulting “boutique” in the fields of data science and data engineering and we aim to become an inevitable reference in the field. We work differently: we are a network of exceptional freelance, professional consultants where the network happiness is absolute key - with that, we want to change the way consulting works by sharing success much more than it is normally done.
So, let's dive into it - this is how I organised this post: I’ve created headings that are in line with our Areas of Responsibility (AORs) so you can see what we have been working on (I am sure that you will notice here and there some sales pitch - I've done it on purpose as this year’s objective is to unfold our services offers, as you will see - so let’s start with that: Marketing and Sales).
Marketing and Sales
In 2022 our motto was “go international”. For that, we've been positioning as specialists in Data Science and Data engineering and that is going pretty amazingly. We are getting noticed and a big help is coming directly from the success of our projects and our clients, due to good work delivered. Our brand visibility also achieved very good numbers (mapped by our website data such as clicks and origin of visitors).
We also worked hard to sell to great international clients and make partnerships in a truly win-win model. Finding the right partners and selling abroad is unquestionably hard for a small company and building a reliable inbound pipeline is even harder. Despite that, we were able to find great new clients, with 35% of our total customer base being international. But honestly, we still have a lot to learn here: we need to clarify our offers and improve our sales pipeline despite the huge market noise in the data field. Seems like a fun challenge, though!
Hiring
We grew to around 40 data professionals from a good range of countries. We have been picky in our hiring pipeline since we need to deliver excellent technical work. Something we found out was that some people that went through our hiring challenge were almost passing it: they showed a great attitude, good soft skills, but their technical performance was not there yet. We had a deep thought about it and we’ve decided to create a "Foundations Pods" for free - in a sentence, the idea is to help these people improve in their technical skills with a mentor from our team (and paid by DareData) until they are ready to join, if they want to - there is no commitment to stay or be hired at the end.
The idea was based on something we were already doing with some clients, that we call a “Continuous Learning Program”. It is meant to provide a continuous learning journey to teams, starting with a strong Foundation Course, but it then bumps up to advanced and specialised courses, since good data science and engineering relies on continuous learning. We have 12 people going through pods right now and we are very excited about having them as part of the network any time now. We've also discovered that this can be an excellent way to improve customers' skills on Data Science and Engineering so, if you have a big data science / engineering team, you will probably love the method we've developed.
Culture
There is a lot to say about culture on DareData, but nothing new happened in 2022. Let me get you our manifesto in case you don't know it. A TLDR is not new for you: we're a company with an immense commitment to creating a culture of happiness-first for our network. A practical example of this is full transparency of contracts with clients and people - rates, margins, objectives, everything is shared between everybody inside the company. This transparency extends to our relationships with our clients as well, as we don't sell things that we are not specialised in.
We are keeping things this way.
Financials and Mission Control
Let's speak about money: we've been doubling our yearly turnover for the fourth year in a row since inception without external investment, and we are now above 2M€. It forced us to be lean while we mounted a clear growth strategy - it worked well: we’ve created an internal mechanism to control objectives with OKRs and budgets per AOR and we were able to keep internal costs low due to the remote-only policy and a lean attitude in general.
However, the cash pressure due to good growth is definitely a challenge. We have been thinking about external investment but we are trying to keep the commitment of only having working partners.
This topic is something to think about during 2023 together with how to build an advisory board and how to hire a professional CFO :) Let us know if you have insights on these topics.
But enough about cash and old problems: next year's strategy is way more fun.
2023… and beyond!
We're aiming to slow down our growth for the next year. The idea is to better nurture our network as well as differentiating our offers: we’ve been realising that we have to change from a general data service provider to specialised data services with extra value to the customer - and we do know how to do that. With this, we want to prepare the "boat" for the next 3 years of steady and healthy growth!
Well guys, in summary it is already pretty obvious that the current context already feels like we are sailing on a different ocean (speaking about boats...) - the company size is different, new clients start to inbound and we are becoming really good at specific contexts like building reliable data science POCs and deploying them, build reliable data engineering, working as enablers in data mesh environments and doing amazing data trainings. And although we're already acting in a very specific area like data science and data engineering, we are seeing the need to become even more recognized at specific problems we do well.
Finally personal word about the changing times: the last months changed the A.I. and machine learning landscape and the impact to the world is not clear yet. But I see this change as a promising moment to increase everyone's quality of life. We as humans, might have to act though: LLMs and other large models are making clear the importance of deeply thinking about regulation, code/model openness and environmental awareness.