In today’s digital age, data is omnipresent. We, humans, are generators of massive amounts of data each time we interact with any device, instrument or fellow humans. Data is a gold mine of priceless information for companies, provided they are aware of how the data can be used. 

Analyzing data has come a long way from the times of pyramid building in Egypt to machines that count data. Data analytics has had a dramatic growth from 17% in 2015 to 59% in 2018! According to a report by Market Research Future released in February 2019, global data analytics is expected to touch a market value of $77.64 billion by 2023 with a CAGR of 30.8 % 

However, today the industry requires candidates who can extract relevant information through hoards of data, similar to finding a needle in a haystack. It doesn’t stop here. Data Analytics demands more attention. Here is where Data analysts come into the frame. 

Who is a Data Analyst? 

Data Analysts are the ones who collect and process data, and perform statistical operations with the know-how of statistics, mathematics, data visualization and communication to name a few. 

Through the collection and organization of data, the information is used for problem-solving and/or providing answers to your questions.

In a way, Data analysts are professionals who combine analysis along with technology that will help end-users achieve business objectives by making use of the processed data to its maximum. 

Data Analyst’s Responsibilities 

The responsibilities of a Data Analyst vary with the level and area of expertise. Let’s look at some of the areas where a Data Analyst can contribute:

1. Work in tandem with the management, aiming at determining the business needs and goals of the organization; helping build strategic data architecture

2.  Sourcing, maintaining and cleaning the data. This helps to identify process improvement opportunities

3.  Interpretation of data –  pinpoint trends, correlations and patterns in complex data sets. Prepare and provide reports to management with clear data visualization 

How Data Analytics has evolved with time

In the last two decades, there has been a huge amount of progress in the areas of Information & Technology. It was in the late ‘50s that the discipline of Business analytics emerged. It was then that special tools were designed which could identify patterns. At the turn of the century, the Internet and social media giants began uncovering, collecting and analyzing a new type of data- big data. Today, the third era — Analytics 3.0 has evolved and expanded across industries and sectors, catering to Artificial Intelligence and Machine Learning. This will deliver results and insights that are real-time. 

Here are a few skills that are required for data analytics:

Critical Thinking and Problem Solving

Data Analysts of the highest order experiment, test hypotheses and think creatively about problems. The key attributes of a good data analyst are interpreting data with curiosity and creativity. In the present times when software takes over a growing number of tasks, data analysts might have to depend on their individual ability to apply manual judgment to business challenges. 

Given that Data Analysts are required to analyze and interpret large amounts of data, knowledge of statistics and statistical packages such as Excel, SAS, etc come handy.

Communication 

Communication skills are vital for anyone who wants to share their findings with their management, team or end-users. If a data analyst working through raw data lacks in communication skills, the data itself is worthless. Hence, data analysts must be proficient with report writing and presenting findings. 

Today, courtesy of communication skills, company and management heads are assisted by data analysts for informed decision-making.

Technical Expertise 

The organization of data is not as simple as ‘clicking’ a button. There are times when the use of technology may backfire and may not run perfectly. Shortcomings in database and code mean that it falls on the data analysts to look out for the problem and also troubleshoot to have a smooth and efficient data capture. A good Data Analyst must be armed with strong knowledge of programming (JSON,R, ETL Frameworks and Python) and reporting and visualization packages such as Business Objects, Tableau or Qlik.

Attention to detail 

Data analysts work with a humongous amount of data. Attention to detail plays a key role during times when processes that capture and sort data are being built or designed as per the case.  Expertise in analytical and data mining skills with the ability to collect, organize, analyze and disseminate significant amounts of information with attention to detail and accuracy plays a pivotal role.

Machine Learning and Artificial Intelligence 

Machine Learning is an ever-growing space and intelligent systems will not only be used for detecting the patterns in the data but also be upgraded to improve other tasks.  A Data Analyst has to focus on continuous learning to stay abreast of changing times and make use of Artificial Intelligence tools and approaches for solving problems in real-time. 

There is a huge surge for learning frameworks and Artificial Intelligence to bag senior analyst positions and those with the ability to program, have strong chances to team with data scientists and work on developing new machine learning solutions. 

As organizations continue to invest huge sums in analytics, which in a way supports digital transformations, it is essential that they keep a tab on the developments and adopt skills that bring whooping success in the years to come. 

Now that you are familiar with the requisite skills, it’s time to focus on your development areas and sharpen the ones you already have. Many data analysis courses are available in the market. Check out Springboard’s Data Analytics Career Track, a comprehensive and unique course covering key aspects of data analysis such as SQL, MS Excel, Python, Microsoft Power BI, and other advanced analysis techniques. This is provided in partnership with Microsoft.  

With the right course, tools, or certifications, you can accelerate your career in data analytics. 

So what are you waiting for? Pull up your socks, roll up your sleeves and get going now!