When I graduated, I never really thought I would become one of those super cool dudettes behind a jazzy silver mac, playing around with data, but, well what can I say? Life had other plans for me. My first encounter with Google Analytics happened at the beginning of May this year, before which, I was totally clueless about this tool; and after which I had my entire career path switched. Coming from a hardcore cloud background, I was an absolute novice to the very intriguing world of digital analytics.
As a computer science graduate, I was aware that business intelligence tools existed, but did not get the chance to work with them ever. Fascinated by the power of data, I got my opportunity when preparing for my recruitment test for the position of Digital Analyst at MarketLytics. I was given a few resources to begin with; a demo account with access to Google’s Merchandising store, which is a playground for a beginner like me. Here, I had access to Google Store’s live data that I could toy around with and look at live reports that really help in the understanding of analytics for starters. Since then, there has been no turning back; the friendship between data and me began.
(Don’t take it seriously )
When I got into MarketLytics, I knew that I had loads to learn. I was surrounded by analytics geniuses and here I was; intimidated initially, but firmly believing it won’t be long before I would become one of them, thus, the process of exploring, learning, making mistakes and improvement began. I don’t want to brag but I was lucky to have a great mentor who guided me through everything and from there, things started to make sense. The best part of my work was; Monday’s were no longer dreadful.
I am this cat. This cat is me. We like Mondays now. Mondays are the best. 🙂
Analytics can help companies understand their users better and in return themselves better. It helps enhance their marketing strategies and understand how as a company, they can satisfy their customers in excellence; by predicting customer preference. With the help of analytics, it is easier to produce Key Performance Indicators (KPIs) and have an appropriate, consistent way to measure them. Critical questions that can change the fate of a business can be answered with the help of analytics.
But but but is analytics only limited to Google Analytics? Of course not! Tools such as amplitude, mixpanel and others with more or less the same core concept, all come under the Analytics umbrella, all of them analyzing and making effective use of the data whether from the website, a mobile device or a huge data set where important insights are required. Analytics can help shape the future of a business and give the business, a necessary push it needs.
For me, personally, analytics has been beneficial in allowing me to look beyond data, find meaningful information and making it understandable for businesses. It has allowed me to look at things from different perspectives, with so much to learn there’s always something new to explore with each project. Analytics is beneficial to all industries and can have startups and enterprise level business find the right direction for their growth. I perceive analytics, as a way of finding clues from raw data, deriving hypotheses from these clues and then testing those hypotheses and validating their accuracy; we can then derive valuable information with the right kind of data and techniques.
After looking into analytics and data for a while, with my very limited knowledge (hey I’m still a beginner), I’ve rounded up three most common paths that can be explored as a data enthusiast: Data Analyst, Data Engineer and and Data Sciences. I know you’re probably thinking about the difference between these (quite a mind reader I am :p). Well, so here’s that; Data Analyst analyze the already processed numeric data and help with the important decisions for a company. Data Engineers as the name suggests, engineer the entire process from designing, testing and maintaining the data warehouses or large quantity of data using SQL and other data transformation tools. Lastly, the Data Scientists; the cool dudes are those who apply stats and maths with Machine learning to teach the machine to answer complex questions form the data and derive insights that can help change the course of a decision.
Now with all these fancy data fields, one would be perplexed as to what’s best path for them (Currently going through the same) but like i said i’m still a beginner, I feel there’s so much more to explore and find out which path best suits me, which one I’ll enjoy the most and most importantly which one will keep the Monday blues away. For now, I feel that analytics is helping me look into different patterns in my daily life and think of ways about how I can turn this information to my benefit.