How to Become a Superhero Data Analyst and get that Edge in the Workplace?

The way organizations make decisions has been evolving. Before the introduction of Business Intelligence, the only options were gut instinct, loudest voice, and best argument. With the rapid progress in data-rich environment, the natural outcome is growing demand for Data Scientist. The rapid expansion of available data, and the tools to access and make use of the data at scale, are enabling fundamental changes to the way organizations make decisions.

Data Science interestingly is not an exact science but an ART of turning data into actionable insights. This is accomplished through complex business solutions translated by Data Scientists. Performing Data Science requires the extraction of timely, actionable information from diverse data sources to drive recommendations.

Examples of business solutions include answers to questions such as:

Which of my products should I advertise more heavily to increase profit?
How can I reduce business risk by optimising allocation of resources?
How can I increase ROI on a brand portfolio?

The key to answering these questions is: understand the data you have and what the data inductively tells you.

The business environment today is fast paced, volatile and competitive. If you are a new kid on the block, looking towards a rewarding and successful career in Data Analytics, you will need to constantly strive to have an edge in the workplace. Here are some tips on how you can do just that: –

1) Keep the Learning Curve Growing Constantly: In an increasingly data-driven, volatile and hugely competitive business environment one needs to always be one step ahead. Data Scientists who are able to keep pace with the evolution of data technology will be rewarded while those that do not will be challenged. There are countless new, faster and open source technologies and tools developing to enhance data analytics capabilities. The key is to be at par and keep the learning curve growing constantly.

2) Embrace diversity: Try to engage and connect to publicly available sources of data that may have relevance to your domain area. Try to solve competitions on Kaggle or dive deep into kdnuggets during your free hours. That time will be time worth spent and certainly adds value to your CV if your favourite past time is to play with numbers.

3) Unstructured Data: There are plenty of data scientists who are experienced in working with structured datasets. Learning how to manipulate and work with unstructured BIG data may give a data scientist edge over others.

4) Creative Analysis: Data Analysis requires the greatest effort. The Data Scientist actually builds the analytics that create value from data. Analytics in this context is an iterative application of specialized and scalable computational resources and tools to provide relevant insights from exponentially growing data. Data Scientist’s key role is to enable real-time understanding of risks and opportunities by evaluating data at hand in creative ways.

5) Domain Expertise: Domain Knowledge is most critical for any data scientist to prove his value. As much as the usage of sophisticated technology is key in driving business solutions, having a deep understanding of ones domain is the most critical factor in the true analytics part of a project. A data scientist must therefore invest considerable amount of time in building domain expertise in one or multiple domains such as retail, finance, telecom, health and so on.

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Analytics is the way to go!