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Data has forever disrupted how we interact with machines and communicate with individuals in the past decade. Globally, about 2.5 quintillion bytes of data are generated every day, made up of YouTube videos, emails, text messages and multimedia messages. Both small and large enterprises today have enormous volumes of data inflow. Most of this data is only as valuable as the insights that these businesses can glean from it. This is where data analysts become relevant. A data analyst analyses and interprets statistical data, transforming it into actionable insights that can aid in real-time business decision-making.
Businesses today are becoming more and more dependent on datasets and insights. Insights help key decision-makers to make data-driven decisions such as which markets they should venture into, which products they should produce more, which investments will pay off and the customers they should target. Data also helps businesses in identifying opportunity areas that must be addressed.
Consequently, data analysis is among the most in-demand roles in the world today. Data analysts are being headhunted by the largest businesses across the world. If you are interested in a career that will withstand the disruptions expected in the coming decade, this is an opportunity you wouldn’t want to miss.
A data analyst is an integral part of an organization. They have a lot of responsibilities, from solving challenges using data to finding insights that support decision-making. However, their primary responsibilities can be summed up in these points.
Data analysts are also responsible for communicating their findings to the stakeholders/team members. They do this using visualizations, such as charts, to make it easier for people to grasp the technical points.
In today’s world, data analytics is the key to the success of an organization. If an organization can successfully analyze the data and use it to strengthen its business activities, it can grow significantly.
There are many different types of data analytics. Here, we have discussed the four primary types of data analytics.
“What happened?”
That is the question descriptive analytics answers.
Descriptive analytics forms the basis of other types of analytics. In simple terms, it refers to interpreting the historical data gathered and using it to understand the different changes. It uses historical data to make comparisons.
The most common descriptive analytics include the number of users, year-over-year pricing changes, etc. Descriptive analytics involves using raw data to reach meaningful conclusions. It draws a picture of what happened and compares it with other similar periods to determine the differences. Nowadays, data analysts use descriptive analytics with predictive and prescriptive analytics.
“Why did this happen?”
That is the question diagnostic analytics sought to answer. It is one of the first steps in an organization’s data analysis. It is focused on identifying patterns by comparing coexisting trends. It chronicles what has already happened.
The most common diagnostic analytics techniques used involve data discovery, data mining, correlations, and drill-down. If you are trying to find the root cause of an issue, diagnostic analytics is the perfect tool.
“What might happen in the future?”
As the name suggests, predictive analytics predicts and forecasts future trends and events. The idea is to analyze historical data, understand the trends, and make informed predictions.
Predictive analytics uses machine learning, data mining techniques, and statistical modeling to make predictions. The benefits of prediction include improving operational efficiencies, reducing risks, and making better decisions. It is used for fraud detection, marketing, and human resources. Some popular predictive analytics tools include decision trees, regression, cluster models, and neural networks.
“What should we do next?”
Prescriptive analytics is concerned with the next step an organization should take. It accounts for all scenarios, available resources, and present performance to offer the possible next steps. Prescriptive analytics is the complete opposite of descriptive analytics.
Prescriptive analytics use machine learning and artificial technology to help organizations make data-backed decisions. When used properly, it can also help in fraud prevention, risk limiting, and goal achievement.
Data analysts use multiple tools in the course of their jobs. Here are the tools you must know if you are looking to join the field.
Microsoft Excel is one of the most popular tools data analysts use. It is a spreadsheet software mostly used for data wrangling and reporting. It has superb features, functions, and plug-ins that make it essential in almost every field.
The only cons of this tool are that it doesn’t run very well with big datasets. However, despite that, you must be familiar with all its formulas and features.
Python is an open-sourced programming language. It is highly versatile, easy to learn, and has thousands of free libraries, making it popular among data analysts. Libraries like Matplotlib are brilliant for data visualization and reporting, while NumPy is superb in streamlining computational tasks.
The only disadvantage of Python is that it is slower than other programming languages. But its benefits outweigh its cons, making it essential for all data analysts.
Microsoft Power BI is a business analytics tool for data visualization, predictive analytics, and many other things. It was initially introduced as an Excel plug-in but quickly became a standalone tool. Power BI allows data analysts to create interactive reports and dashboards. It seamlessly connects with Excel, SQL servers, text files, and other useful tools. The tool is regularly updated to keep up with the changing requirements.
One of the disadvantages of Power BI is that it has a clunk user interface and has limited data in the free version.
Tableau is a data visualization tool mostly used for creating interactive dashboards and worksheets. You don’t need to have coding experience to use Tableau. It is incredibly easy to use and has great visualizations. It can be used on mobile as well.
Tableau, however, lacks data pre-processing and cannot be used for building complex calculations. Despite this, Tableau’s excellent visualizations make it popular among data analysts.
Jupyter Notebook is an open-sourced interactive authoring software. It is used for sharing codes, presenting results, and developing tutorials. You can also use Jupyter Notebook to create interactive documents with live code, visualizations, and narrative texts.
It supports more than 40 languages and can be integrated with big data analysis tools easily. However, there are better options for collaboration, and you have to provide all extra assets since the tool is not self-contained.
Apache Spark is a data processing framework for big data and machine learning. It helps data analysts process huge data sets quickly and accurately. It is easy to use and exceptionally fast. Other tools like Hadoop cannot compare with Spark in terms of speed. However, since it lacks a file management system, it is typically integrated with Hadoop.
SAS is a statistical software suite used for business intelligence and predictive analysis. It includes reporting, data mining, and customer profiling. SAS is robust and versatile, and suitable for big corporations. SAS is an expensive software that requires different levels of expertise. New and better modules are regularly added as per customer demands in SAS.
Are you looking to take up a data analyst role in your career? Take a look at some responsibilities that this role comes with.
To become a successful data analyst, technical experience alone is not enough. Earlier, requirements included an undergraduate degree in an analytical or statistical subject like statistics, mathematics or economics. Even today, students with this background have an edge over the competition. Alternatively, you could also choose a postgraduate degree or a skill-based certification. Skill-based certifications have become immensely popular these days as they cover all data analyst roles and responsibilities.
In addition to your technical skills, you also need soft skills like:
Data analyst is just one of the roles in the vast data analytics industry. Take a look at the top job roles that use data analytics. The salaries provided have been shared from LinkedIn (USA) and Glassdoor (India).
Data Analyst - USD 41,600 - 93,600 / Rs. 3,00,000 - 12,00,000
Analytics Manager - USD 78,000 - 150,000 / Rs. 9,00,000 - 31,00,000
Business Analyst - USD 50,000 - 97,200 / Rs. 3,00,000 - 15,00,000
Data Architect - USD 80,200 - 170,000 / Rs. 11,00,000 - 34,00,000
Data Engineer - USD 65,000 - 140,000 / Rs. 4,00,000 - 19,00,000
Database Administrator - USD 48,000 - 120,000 / Rs. 3,00,000 - 15,00,000
Data Manager - USD 41,600 - 144,000 / Rs. 3,00,000 - 30,00,000
Statistician - USD 55,000 - 128,000 / Rs. 3,00,000 - 19,00,000
Research Analyst - USD 41,600 - 85,000 / Rs. 2,00,000 - 9,00,000
Research Scientist - USD 60,000 - 160,000 / Rs. 4,00,000 - 28,00,000
All of the figures given above provide the approximate salary range that is accepted as the standard across most data analytics companies. They vary based on location, experience, organisation and skills among other factors. There are huge opportunities for growth and easy ways to reach new heights through several role-based certification exams.
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Several roles require the same or similar skill sets. Three such roles are a data analyst, business analyst and data scientist. However, their primary differences can be seen in how each role uses data.
In small data companies and new startups, data analysts generally take up some of the responsibilities that usually are assigned to data scientists. This includes decision-making or predictive modelling, for example.
Also Read: Data Analyst Salary in United States
We are now in the new normal, where most consumers have moved online and data is generated from a multitude of sources. Fill the gap in the market and expand your horizons by enrolling in a Data Analytics certification course today.
Michael Warne is a tech blogger and IT Certification Trainer at Koenig Solutions. She has an experience of 5 years in the industry, and has worked for top-notch IT companies. She is an IT career consultant for students who pursue various types of IT certifications.
