Data is ubiquitous and is ever increasing with the proliferation of technological devices like smartphones, smartwatches, sensors, fit bits, etc. According to Social Media Today, it has been estimated that human beings produce 2.5 quintillion bytes of data every day, that is, each human and such massive amounts of data contain incredibly vital information which, when mined, stored, processed, and analyzed, could lead to valuable knowledge discovery that could positively inform an organization’s decision-making process. Today we’ll discuss the data analyst job description, salary, and duties.
Who Is A Data Analyst?
A data analyst gathers or collates data from multiple sources, cleans and prepares the data for analysis, peruses and analyzes the data (asking questions of it with the aid of statistical tools), looking for underlying trends and relationships, then communicates his/her findings as a recommendation to aid business decisions and optimize organizational processes. They do this through the creation of reports and dashboards using state-of-the-art business intelligence tools. The task of collecting, cleaning (processing), and analyzing the data is done by a data analyst and requires great skill. The explosion in data production in every field or industry has increased the need for individuals with this skillset. Some examples of such industries include but are not limited to:
- Banking activities
- Telecommunication companies
- Healthcare data
- Insurance companies
- Other financial institutions like hedge funds, asset management firms, investment banks, etc.
- Governmental and non-governmental organizations
- Digital marketing
- Retailing business
Even big tech companies like Google, Amazon, and Microsoft have not been exempted from this explosion. They produce a remarkable amount of data and need people who can extract it and make sense of it, allowing them to offer better and more personalized services to their vast customer base. Personalized marketing, as mentioned above, is probably one area where data analytics has dug its roots. Companies can now offer personalized services and goods to customers because it recommends google searches or movie recommendations on Netflix or Amazon’s offerings on their e-stores.
It is clear to see that data, as they say, is the new oil, and companies are rightly positioning themselves to be able to extract every single value from it. They will need people like you (because if you’re here reading this, I’m guessing you’re interested in becoming a data analyst) to be able to harness this power hidden within their vast amounts of data.
Job Description And Duties Of A Data Analyst:
As you’ve guessed by now, the data analyst job can be as fun as it is challenging, that treasure trove of information waiting to be uncovered, broken down, and used for some truly impactful work, but what does the job of a data analyst entail:
- First and foremost, a data analyst must be skilled at acquiring data from primary sources like an already established company’s database system or secondary sources like surveys or web scraping and identifying possible new sources from which the organization can collect more useful data. Whatever the source, an analyst must know how to acquire the data needed for various analyses.
- I was tempted to put this one above, but it is not entirely down to just the data analyst. However, depending on the organization, a data analyst may be tasked with the responsibility of creating/developing and maintaining data or database systems and solutions as well as developing and maintaining high-quality ETL pipelines (ETL is simply the process of Extracting data, Transforming it into a form that’s fit for use, and Loading the data into a chosen tool for analysis).
- Another important task, as touched on above, is that a data analyst must be skilled at processing data in a format that is fit for analysis. This process usually involves several tasks like imputing missing data, correcting errors in spellings, columns, or rows, deleting duplicates, and so on. It involves getting the data into a format that will give you the best chance at meaningful analysis.
- Now that the data is clean, the next step for the analyst is to analyze the data. That means spotting meaningful trends, relationships, modeling the data, making forecasts, and so on. He/she does this with various statistical tools like Microsoft Excel, SPSS, etc.
- The data is cleaned and analyzed. Now the analyst has to present this information in an aesthetically pleasing form and communicate his/her finding in the clearest way possible, giving recommendations that will hopefully help the relevant stakeholders to make key decisions.
These are the typical responsibilities or the job description of a data analyst, and they largely remain the same from organization to organization. Of course, different companies will require different things and will have different names for the role, but the job description of an analyst largely remains the same; get data, prepare the data, analyze the data, report/present your findings, and make recommendations that will better inform the decision-making process of the relevant stakeholders.
As stated above, different industries will have different requirements of their analyst or, more aptly put. There are subtle or more specific tasks that a data analyst would be expected to accomplish depending on the industry in which they operate and having a variety of names across multiple organizations depending on the nature of their jobs. Some examples of such roles include but are not limited to:
- The business analyst who analyzes business data and spots trends that could give the business an edge over its competitors
- Credit analyst who analyzes and models credit/lending risk as well as performs credit monitoring
- Product analyst whose job is to optimize production process for different company products and give strategical advice to relevant stakeholders on the best pricing strategy for different company products
- Social media analytics involves but isn’t limited to analyzing social media trends regarding a product and mining social media sites to analyze the sentiment of users about certain company products or services and offer more personalized services to individual customers.
There are many more, but by now, it’s clear to see that not only is the job of a data analyst now more important than ever before, it has also disrupted every single industry meaning that the opportunities to get in are plenteous and fulfilling.
Data Analytics Use Cases:
So far, we’ve seen that data analytics has disrupted multiple industries or fields. We want to look at a few that, depending on the interests or current leaning or area of expertise of an individual looking to get a job as a data analyst, could be very informative.
- Banking Industry: The banking industry is awash with vast amounts of customer data, from basic and personal information like names, age, occupation to credit card details, transaction details, and more. The banks use this information to offer better services that are well-tailored to individual customers. This could lead to better customer satisfaction and hence retention. Another area where we’ve seen banks and other major industries use data to improve customer satisfaction is in automated customer service systems. By gathering large amounts of data and analyzing it, banks can know the most frequently asked questions and develop systems that can automatically handle these requests a lot faster, freeing human representatives to resolve more novel cases. This reduces the time customers have to wait on a call to address simple, frequently occurring problems, thereby hastening response times and improving customer satisfaction. Another popular application where a data analyst’s role in analyzing data and spotting trends has proven vital is in fraud detection. As I stated previously, banks store massive amounts and a variety of customer-related information like credit cards and transaction details. This means the banks have in their possession a history of a customer’s transactions. Using various techniques, a customer’s wild deviation from their regular transactions could be flagged as suspicious and taken for further investigation.
- Marketing: Much like the banking industry, the marketing field (digital inclusive) greatly benefits from using analytics in various ways. One of which is an essential application, customer segmentation. By segmenting your customers by different criteria, such as age, economic status, purchasing habits, and other demographic data, you’ll know how to accurately target different sections of your customer base, providing more personalized services that will improve customer satisfaction retention. Another pretty useful application, and a popular one at that, is churn prediction. This is more on the predictive analytics side, but this aims to predict or rather classify, based on certain criteria and available data, customers that are likely to stay on and continue patronizing your establishment or those that may leave (churn). This is remarkably important as it helps an organization decide what measures to take, including offering incentives through discounts or sales to ensure said customers don’t leave.
- Healthcare: Without a shadow of a doubt, health care has been one of the biggest benefactors of recent advancements in technology, especially the boom in data analytics. Due to the vast amounts of historical data, vastly better decisions can be taken regarding the best means of healthcare delivery to individual patients and determine which patients are at risk for certain medical conditions. Due to the rise of predictive analytics, data analysts can do as much as predict the likelihood of a patients’ re-admission based on available data that could reveal a patient’s disposition to certain conditions. A recent example would be analytics to predict patients who were most likely to be re-admitted following recovery from covid-19. This could help healthcare professionals prescribe more tailored solutions to such a patient to mitigate such a risk.
- Manufacturing Industry: Another industry experiencing the benefits from this boom in analytics is the manufacturing industry. Before now, managers could barely keep up with the sheer amount of data being produced during the manufacturing process and couldn’t tap into its many riches, which, without a doubt, limited growth potential. This is no longer the case with the advancements in analytics. Manufacturing industries can now collect and analyze the vast amount of data produced and have seen its benefit in areas like supply chain management, earlier detection of valuable out-of-stock materials, and predicting or forecasting demand for certain goods hence improving inventory management as well as early prediction of possible faults in production equipment which is important as it allows manufacturers to be proactive in maintaining key equipment and preventing their breakdown which could lead to a massive disruption in production activities.
Skills And Competencies Of A Data Analyst:
This is probably one of the most frequently asked questions and ‘probably’ the part of the article you’ve been waiting for, second only to the salary, of course. So without much ado, let’s get to it. This question’s answer is in 3 parts; hard (technical) skills, softer skills, and competencies.
The hard/technical skills: These are moderately easy to pick up and could take you roughly 3-4 months, maybe less, to get a handle on and start applying for jobs. They include:
- SQL, which is the standard language used for querying relational databases. In simpler language, company data mostly resides in a database, and you as an analyst need to have the ability to extract the data necessary for your analysis. SQL helps you accomplish that. It is a pretty easy and intuitive language to learn, and with constant practice, you could be up and running with it in a couple of months.
- Strong foundation in statistics and mathematics. As an analyst, you need to be comfortable handling numbers as well as drawing insights from them. Statistics helps you build the intuition necessary to spot vital trends and relationships and interpret them.
- Statistical software applications like Excel and SPSS are also vital tools for analysts to carry out data wrangling (processing/cleaning) and apply statistical formulas to quantify their findings or observations from the data.
- Business intelligence tools like Tableau, Microsoft’s Power BI and Qlikview are vital tools in a data analyst’s toolbox as it helps them create stunning, aesthetically pleasing visualization for their presentations.
- Programming languages like R and Python are also desirable but are not mandatory. Mind you. You could still take up the challenge and broaden your skillset, maybe even increase your pay, by learning one of them. Python, in particular, is a pretty easy language to pick up.
Now, for the soft skills. These are important because as a data analyst, you’ll have to interact with different players from different teams, with different types or levels of expertise. You’ll need these skills to ensure smooth communication and understanding. They include but are not limited to:
- Good communication skills are important. You’ll need them when relaying your findings, especially to a less technical crowd.
- Effective time management and organization skills to keep to deadlines and manage multiple projects at the same time.
- Good leadership skills
- A good data analyst must be a self-starter; he/she must have a proactive approach to problem-solving
- Critical and logical thinking skills.
Looking at the items above may be daunting but all of those can be learned, google and YouTube should be among your best friends in these times.
Competencies refer to what exactly it is that you’re good at. Basically, what do you bring to the table apart from those aforementioned individual tools? How have you leveraged those tools in your projects and study? Competencies are usually similar to what is listed in job descriptions;
- Database querying and maintenance
- Data visualization
- Data preparation and cleaning
- Exploratory data analysis
- Business acumen;
Business acumen or domain expertise is an essential but underrated skill for a data analyst. A working understanding of the business processes will give you a more intuitive (easy and natural) understanding of the data, making it easier to spot trends than if you had a mere superficial knowledge of the organization’s business process.
It might seem like a lot, but it isn’t, it’s doable, and most importantly, the job opportunities, scope for growth, and impact make it very worthwhile. Just ensure you pace yourself to avoid getting overwhelmed and, most importantly, work on projects, small ones (or big ones), because they will help you apply your knowledge. Resist the temptation to get stuck just watching tutorials. Take the next step after learning and apply your skills to real-world problems, as that is the only way to ensure the knowledge sticks with you. It also shows prospective employers that you can do those things you list on your CV.
Salary Of A Data Analyst:
Now for the moment, we’ve all been waiting for. You’re not going to work for free after all. So, how well are data analysts paid? The simple answer is, very well. In the USA, according to job listings on some popular job boards like ‘indeed,’ the average salary, per annum, of a data analyst is $ 75,152, excluding possible bonuses. This is a very healthy compensation if you ask me. So you get paid well to work on exciting, challenging, and rewarding work with great flexibility as the role allows for remote work (like a lot of jobs nowadays, especially IT jobs).
I’m pleased to see that you made it this far, but we’ve, unfortunately, come to our last bus stop for now. A quick recap; a data analyst is one whose job is to source data, prepare it, analyze it, and present his/her findings as recommendations to aid the business’ decision-making processes, optimize an organization’s operation, offer better services to customers, and so much more. Without a shadow of a doubt, it is a highly challenging yet rewarding field.
Also read How to Become a Data Analyst in 2021