Towards the end of 2016, the data analytics jobs market was booming, with some companies struggling to fill open positions. As data analytics is just beginning to gain mainstream momentum, more companies will be looking to beef up their workforce with qualified workers. With a projected 73% growth rate in job demand by 2022, now is a perfect time to jump into the data analytics field and start learning all you can about this growing industry.
Information technology is in a state of constant flux and change. As such, IT professionals must always be on the lookout for new trends and technologies to stay ahead of the competition. Data analytics is one trend that has been gaining momentum as it pertains to the gathering, cleaning, analyzing, and interpreting data in order to make informed decisions about business performance. In the past few years, there has been an explosion in data analytics jobs all across the IT landscape with demand showing no signs of slowing down anytime soon.
Companies are using data analytics to improve the customer experience, prepare for unexpected outcomes, and mitigate risk. This increased focus on data analytics has been fueled by cost savings and other benefits such as:
- Gaining a more complete understanding of a business’s customers
- Improving operational efficiency and reducing costs
- Decreasing human error
As companies continue to use data analytics as a method for improving performance, the job market will continue to grow at an exponential rate around the world. A recent study from the University of Massachusetts Amherst Department of Economics projected a 73% increase in data analytics jobs by the year 2022.
While this sounds great for those looking to jump into the game, it is important to note that these growth rates are conservative. As more companies utilize data analytics, more high-demand jobs will be created in the process. There are many entry-level opportunities as well as a number of positions for experienced professionals in fields like data science, machine learning, and artificial intelligence.
Skills required for Data Analytics
Data analytics jobs require a number of specialized skills, depending on the position and industry. For example, you might be interviewing for an entry-level data analyst role at an advertising company. If this is the case, you are probably being asked to perform basic data manipulation and analysis functions such as cleaning data, analyzing it, interpreting it using statistics and charts in order to make predictions about future outcomes or results.
You also may be required to integrate your findings into content written for a blog or e-book. In this case, the data analyst may be asked to incorporate data analysis findings into a larger piece of writing while making it easy for the reader to integrate those findings into their own research.
However, if you are interviewing for an entry-level job as a data scientist at a pharmaceutical company, you may be asked to take on more advanced tasks that would include utilizing machine learning and artificial intelligence to build your own algorithms. You would be building software that draws upon data to make predictions.
If you are in this field, you should have an excellent grasp of any programming languages as well as the ability to implement different techniques. They might be asking for technology proficiency as well as the ability to communicate clearly and effectively using Microsoft Word, Excel, Powerpoint, or other office tools. Your data analytics skillset may be asked to include problem-solving skills, project management capabilities, and analytical instincts.
The ability to think critically and creatively is also a critical data analytics skill. The best data analysts are able to think outside of the box and have the ability to see problems from a different perspective. You will be required to be extremely organized with your time and you must be able to meet deadlines while keeping your employers happy.
If you don’t have any experience in data analytics, now is the perfect time to start. There are many free resources available to help new entrants into this field learn about data analytics. From online courses like MOOCs (massive open online courses) to guides such as skill-based certifications, there are plenty of ways to get started on a path towards a career in data science or analytics.
Data Analyst Vs Data Scientist
As mentioned above, the field of data analytics is a subset of the larger field known as data science. While both fields are often used interchangeably, there are some key differences between them.
Data science is identified by the use of coding in both theory and practice. Data scientists also work with multiple sources and types of data and are able to focus on specific aspects of the problem at hand. Data science involves using statistical graphs and charts as well as other forms of communication to communicate findings to different audiences. Data scientists also work with different kinds of data, such as text, images, audio, and video.
Data analytics is identified by the use of statistical methods in both theory and practice. Data analysts are required to do basic data cleaning and manipulation including formulating descriptive statistics. They also may be responsible for data visualization using charts as well as other forms of communication to communicate findings to different audiences.
Read more about Data Analytics Vs Data Science here!
10 Data Analytics Job Types & Responsibilities
There are many roles that require the candidate to know analytical techniques and tools in various capacities.
- Business Intelligence Analyst:
Data analysts who work for organizations that deal with business intelligence are responsible for analyzing data to uncover patterns and trends in order to provide actionable insights to decision-makers. This might include identifying which customers are struggling in the market, or which products are the biggest sellers. Business Intelligence Analysts do not need a great deal of knowledge in math, although they should understand statistics well enough to be able to recall various statistical measures and formulas.
- Data Warehouse Developer:
In some companies, data warehouse developers work with data analysts to make sense of complex datasets. Their job is to extract and transform data and then load it into a database. They also are responsible for creating database reports for managers and executives. Data warehouse developers should have a very strong background in computer science. They must be able to design database systems that can handle the volume of information that needs to be stored.
- Data Scientist-
A data scientist is a highly specialized job. This type of job usually requires a PhD. A data scientist uses data to make predictions, which allows them to engineer solutions to the complex problems they encounter in the real world. These problems might include how to improve an online dating site or how to keep retail business from dropping off during the holiday shopping season. Data scientists must be able to communicate effectively with customers, colleagues, and bosses in order to explain their findings.
- Business Analyst-
Business analysts are responsible for finding ways to improve the efficiency of a company’s internal operations. They might be responsible for helping with inventory management, or they might be responsible for the intake of new employees. They might also be responsible for finding ways to make an organization more efficient through redesigning its processes.
Statisticians are another type of data analyst. Their jobs are similar to those of business analysts, except they use statistical analysis techniques to gather and present their findings. They use quantitative reasoning skills to interpret the numerical data they find in order to make conclusions.
- Marketing Analysts-
The primary responsibility of a market analyst is to find ways to increase revenues for a company. Market analysts use data to determine how the customer buying habits of their organization can be altered. They might work with other departments within the company, such as merchandising and sales, in order to change the way products are marketed. Marketing analysts should be very familiar with marketing concepts and theories in order to find areas of weakness in a company’s selling strategy.
- Operations Analysts-
The primary responsibility of an operations analyst is to help streamline a company’s internal processes. Operations analysts use data to determine how operations can be made more efficient and effective. They might work with a variety of departments within the company, or they might focus their work in just one area, such as the production department.
- IT Systems Analyst-
IT systems analysts are responsible for analyzing how well an organization’s computer network is working, and then recommend ways to improve operations. They gather data by gathering information from the IT department of the company. Systems analysts might also use data to determine if a new system is necessary in order to solve a current problem.
- Financial Analysts-
Financial analysts use data to help determine if a company should buy another company, or if the current investments are paying off. They also use data to determine if a company is operating efficiently. Financial analysts might use financial charts to determine the profitability of different products and locations.
- Project Management-
The use of data in project management is essential- especially if you’re managing multiple projects. Project managers usually have a spreadsheet to plan their tasks and resources. They also need to analyze the data so they can anticipate any potential issues and communicate them to decision-makers. Data analysis helps project managers prioritize the project and know what needs improvement.
The most important skill in Analytics
Along with core technical skills, a good analyst should also have the ability to convey findings effectively to not-so-technical business colleagues. It is important to simplify your analysis and communicate data-driven insights in a simple and effective manner.
Great analysts have excellent communication skills. They are able to communicate effectively with key audiences such as management, engineers, and product/market teams using reports, presentations, meetings, and other communication methods. A good analyst must be able to document their analysis process so that they can be assured that their logic is sound.
No-Code Business Intelligence tools have helped in the shift in focus from knowing how to code or writing complex SQL queries to actually gathering key insights and presenting them in easy-to-consume formats such that it is easy for anyone and everyone to understand data.
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