When penning his aphorism directed at politics, “Those who cannot remember the past are condemned to repeat it” (The Life of Reason), it is quite certain George Santayana had no idea how well it would apply to the building of artificial intelligence. The ability to use data to understand what has occurred takes a long time to master. You must first understand the historical events and the relationships between the data you have collected. Once you can use data to explain events, it is possible to learn to predict the future with sufficient reliability. Finally, you may integrate the solution into an artificial intelligence that can automatically react to situations. The following is my journey of growth from developing reports to an AI development.
From the rear-view mirror to wanting to see the future
My first supervisor asked me what I wanted to do when I grew up. I slipped out the hottest buzzword at the time, “business intelligence”, without actually knowing what it really meant. Yet today, I am still on that road of analytics. I started with coding and reporting.
I became a consultant very quickly. At first, I was in charge of reporting to one customer, and eventually to a group of customers in one business sector. As organisations learned to make data-driven decisions, their results improved, increasing the demand for new data. A team formed around me. The buzz was there and so were the sales, as representatives from the supported businesses wanted to increasingly use data to understand what was going on around them.
For me, looking in the rear-view mirror was not enough. I was soon eager to learn how to predict what would happen tomorrow.
Be humble – there is always more to learn
I kept staring at data through my crystal ball without understanding any of it. I knew how to combine and visualise data, but this was of no use – I needed something more. Looking for answers, I started going over my statistics studies and dug into machine learning. The road ahead seemed long, and I realised I would need the help of a partner.
I worked my way to one of Finland’s top data houses – Solita. It was clear to me I had a lot to learn, but I had no idea what a wild ride it would be. Every technique I had learned proved to have been child’s play. As the modern technology terms of “big data”, “DevOps”, “data science” and “the cloud” became concrete, they revealed how far off I was – and still am – from the spearhead of the industry. This is thanks to colleagues, each more skilled than the last, who would complete projects with relaxed confidence and great results. Best of all was how they all had time to teach me their best practices while doing their own work.
The pieces click
It has been more than a year now, and the pieces have finally clicked in my head. Taught by those wiser than me and using hundreds of hours of my free time to practice, I grew to be a builder of big-world data platforms, able to use data as a raw material for analytics – or at least able to start doing that and ring up an expert when I need to do something actually demanding.
It also took me a long time to realise that all steps on the journey towards mature analytics are equally necessary and valuable. The need to visualise data for that initial understanding has not gone anywhere. No smart advanced analytics solutions can be created without huge volumes of high-quality data. The key is using the right tools, and the choice depends on how well the business to be modernised is understood through data.
The impatient automate themselves to learn new things
A burning desire to face new challenges and raise your productivity leads you to automate your own work. For this reason, I have started using the Solita Agile Data Engine to automate the repeating steps of our data work while supplementing my skills through the AI expertise training programme launched by Solita.
Besides the work itself, the best way to learn is to teach others. To learn more of the journey of growth in analytics, I mentor recently graduated Solita employees and help the top Finnish companies recognise and develop their data capability. If you are interested in how you can develop your data capability or use data to grow your business, get in touch! Below you can also find my tips on how to enter the world of analytics and get your dream job. It may be closer than you think.
Top five tips for the career of your dreams:
- Learn the technological basics for a data scientist. Udemy has good learning materials on Machine Learning, Deep Learning and Artificial Intelligence.
- Make your own practice projects! You can find suggestions in Kaggle, well known in data scientist circles.
- Make a demo of your practice project and network with other data scientists who can mentor you as you pursue your career.
- Present your demo to potential new “customers” and ask them how they could benefit from better utilising data. If the ideas are not coming, tease your customer’s thinking with a few examples of potential opportunities.
- Do not stop after you get your first job – keep learning!
Olli Lindroos works for Solita’s Agile Data team. He is a passionate student and proponent of data-driven culture, with an interest in technology in all of its forms, whether it’s about the user experience, technical implementation or business strategy opportunities. Olli describes himself as a dad and a nerd, as well as a food and drink aficionado. In his free time, Olli likes to try out all the newest trends as a consumer and building the IoT equipment he needs by himself. Olli is on Twitter as @ollilind.