When considering a career in IT and wondering where to specialise, the idea of being a data analyst is one of the more exciting possibilities. But will you get stuck doing one job, or are there a variety of different and desirable career choices available?
In this article, we look at what roles are open to someone experienced with crunching the numbers and coming up with meaningful conclusions.
Data Analysis With Big Data
When you think about a data analyst, it’s natural to immediately link it to big data. There’s a substantial push for companies that have collected a mountain of customer and market data to figure out ways to find meaning in all those 1s and zeros.
Using statistical software to make the connection between different data sets and how they indicate new market trends or other shareable nuggets is a skill set that few possess. Indeed, companies are crying out for this kind of mind and struggle to locate suitable personnel.
To learn more about the qualifications and a career path for data analyst role, check out this blog post on Aston University Online. It’s an interesting read as they have put together a review of the career prospects for graduates of programs that cover this topic, such as their MSc Business Analytics online degree.
While there are relational database managers that administer the database and ensure it can perform SQL queries reasonably quickly, they’re far less adept at dealing with the data itself. That’s not typically within their role’s remit. Similarly, a database programmer isn’t equipped to cleanse data in a database either – they’re involved in letting people enter or amend information stored within it.
However, for someone familiar with datasets, database constructs, and how information is stored correctly, it’s another matter entirely.
Finding Value And Removing Unwanted Data
With data cleansing, usually, there is a large, disorganised database that’s been created and added to over several years. When a database isn’t structured in a clear, logical manner, it’s difficult to search. Also, if it’s cluttered with irrelevant or incorrect information, this muddies up the result of search queries being run on that database (garbage in, garbage out).
The role of a data cleanser is to sort through the mess, establish what is and is not valid information, and clean up the information stored. Records that are no longer required can also be deleted to reduce the database size for faster future record retrieval.
Modelling Via A Deep Dive Into The Data
For large companies, charities and government agencies, it’s essential to draw trends and conclusions from the data they’ve collected. Otherwise, it’s practically useless to them.
For instance, the loyalty card programs that supermarkets offer are not there to provide discounts or vouchers to customers and encourage their brand loyalty alone. With the inclusion of each customer’s home address or approximate location, their shopping habits, and the time they go shopping, it’s possible to draw a multitude of conclusions. Indeed, supermarkets create models based on this data to better understand shopping demand, patterns of behaviour, and more.
Similarly, data modelling has numerous applications for companies that use data analysts to sift through all the data collected and find meaningful patterns or reach conclusions substantiated by the known data points.
A fraud analyst is tasked with finding ways to locate people who are defrauding the business. Just as fraudsters get more intelligent in their attempts to make ill-gotten gains at a company’s expenses, companies must have their wits about them to look for the changing signs of problems.
With this kind of role, what an analyst is most looking for is patterns. It’s not usually a single transaction that is catchable right away. For that, filters are required to flag questionable aspects of a transaction or activity based on past evidence of how fraudsters acted. However, this doesn’t work as well when they derive new ways to get around these automated checks.
Using sophisticated patterns, it’s possible to flag activities that stick out. For an insurer, that might mean multiple claims which are similar in nature over an unrealistically short period. While that doesn’t indicate insurance fraud, it’s something to look at.
Similar patterns based on data analysis prove useful to create new rules to successfully flag other actions that didn’t look suspect before but do now.
For a data analyst, many of the roles in major UK cities offer a salary in the £30,000-40,000 range. However, expect to get more for roles that are more complex and challenging. There’s also considerable international demand for data science because of the growth in big data and the need to draw meaning from it.