What you’ll find in this article
- Why the demand for data analysts has skyrocketed
- A definition of what Data Analytics is
- Jobs that require Data Analytics knowledge
- The difference between Data Analytics and Data Science
- Why you should start learning Data Analytics
- How to get started today
- Additional resources
Data is the world’s most valuable resource
In 2017, The Economist published an article titled ‘The world’s most valuable resource is no longer oil, but data’. Today, data is gold. With the right insights, companies have the power to optimise performance and outperform competitors.
Today, every company wants to be ‘data-driven’. However, to truly be able to take advantage of the wealth of data at their fingertips, they need data analysts. That’s why the demand for data analysts has skyrocketed, catapulting this career to the top of recruiters’ ‘must find’ wishlists.
The World Economic Forum’s ‘Future of Jobs Report’ found that 85% of companies were likely or very likely to expand their adoption of big data analytics by 2022. This means more companies will be looking for, not only data analysts but also talent with data analytics skills.
Whether you’re thinking of a career change, or you’re simply wondering how data analytics skills could help you get ahead in your career, here are some of the basics you need to know:
What is Data Analytics?
We can think of data like clues to a complicated puzzle.
Much like a detective, data analysts use raw data to identify trends, extract meaningful insights. By doing so, they try to answer a company’s most pressing questions. This information allows companies to understand past performance, eliminate bottlenecks, avoid risk, optimise processes, predict future performance and more.
For example, some typical questions a data analyst might seek to answer are:
- How did our sales team perform this year?
- Which investments would entail the least amount of risk?
- How could we optimise our supply chain?
- Where are the bottlenecks in our logistics strategy?
- How much is our revenue forecasted to grow this time next year?
- What targets should we set to improve our growth rate?
Wondering exactly how a data analyst does this? Here are just a few of the most common responsibilities of a data analyst:
- Evaluating what type of data is available
- Collecting data through various sources
- Organizing it into spreadsheets or statistical software
- Cleaning it – or ensuring there’s no missing data, duplications or errors
- Analyzing the information to identify trends and correlations
- Visualizing the data by creating statistical models
- Translating these insights into actionable recommendations to help a company understand what needs to be done
Jobs that require Data Analytics knowledge
The great thing is that data analytics can be applied to almost any type of business, industry or field. Here are a few examples of the wide range of job opportunities for data analysts out there:
These data analysts specialise in data coming from one specific department. How did the sales team perform over the last quarter? What can we expect in terms of sales for the next quarter? What can we do to increase those numbers even more? How did landing page B or marketing campaign C perform? How can we use social media data to double our number of followers? These are just a few questions marketing and sales analysts might be answering.
Risk analysts dive into investment portfolios, stocks and other assets. They set up predictive forecasting and they advise financial institutions on the best way to allocate resources and grow/manage wealth. Analysing data plays a crucial role in this.
While gaining customer and competitor insights is essential, nothing’s more important than having an engaged workforce. HR analytics is a fast-growing field. Here, a company’s people data is used to improve the employee experience within an organization. That’s done by finding ways to boost engagement, reduce turnover and make better salary distribution decisions. The data used includes everything from recruitment statistics to salary and turnover.
Law data intelligence
The application of technology like AI and machine learning to law is bringing about some exciting new possibilities. Data analytics is easing the work of legal teams with a multitude of things: research and analyse large amounts of legal texts, create computational models of legal arguments and even predict the outcomes of cases.
However, Data Analytics is still an emerging field. Therefore, there are still many debates going on around many data-related topics. Think of the ethical use of personal data, potential biases in algorithms and predictive policing. These debates definitely make this a fascinating field to watch.
Supply chain expert
Data analysts are helping make supply chains faster, more accurate and transparent. What does this mean in practice? More accurate forecasting enables companies to anticipate fluctuations in consumer demand and, thereby, make more accurate shipping and logistical decisions. Take Amazon as an example who came up with its own system for ‘predictive shipping’. It’s used to predict which products can be shipped to a logistical region before consumers even click the purchase button. Supply chain clouds are helping to integrate data from suppliers, service providers and other stakeholders, making the entire process more transparent. This is really paving the way for more ethical supply chain practices.
Health care data specialist
Hospitals, medical offices and care facilities are also benefiting from the data explosion. Data Analysts in this field are taking a deep dive into different data sources. The results help healthcare workers streamline their work and improve the patient experience. More and more processes are being automated, saving medical staff much needed time and resources. Predictive analytics is also being applied to reduce delays, improve response times and provide more personalised care for patients in need.
Data Analytics and Data Science are not the same
One mistake people often make is using the terms ‘data science’ and ‘data analytics’ interchangeably. While both data scientists and data analysts work with data, they use it in very different ways. As we now know, data analysts collect and analyse existing data to provide insights. They find answers to some of the most important questions companies are asking.
The most important part of a data scientist’s job, on the other hand, is different. They would typically come up with new questions companies could be asking to improve performance. They answer these questions by designing and building new statistical models and developing new algorithms. The same goes for experimenting with prototypes, predictive models and building custom analyses.
Take Airbnb as an example. At one point the company was faced many different types of rental options, locations and huge demand. It was its team of data scientists who then created new dynamic pricing models that would help renters set competitive prices. This couldn’t have been done by simply analyzing the raw data that was available. Instead, their team needed to build a specific model that could capture, connect and process different data points in a way that would give them the desired result.
While both rely on data mining, statistics and automation, data science typically requires deeper coding skills. These include extensive knowledge of machine learning, Hadoop and Java.
Why you should start learning Data Analytics
Even if you’re not considering becoming a full-time data analyst, analytics skills can help you get ahead in your career. More and more recruiters are looking for talented professionals who also have an understanding of data analytics.
This is especially true for managerial and decision-making roles. Businesses want people in top-jobs who understand how data analytics works. Leaders are supposed to know how they can use data-driven insights and how to communicate these insights to the rest of the team. Even if they’re not working as a data analyst themselves.
Think: Chief Executive Officer, Chief Data Officer, Director of IT, Human Resources Manager, Financial Manager and Marketing Manager.
According to PriceWaterhouseCoopers, “The immediate payoff for raising the analytics IQ in these roles is greater productivity and operational efficiency.”
So if you’re aiming to climb the career ladder, data analytics skills could be the answer.
How to get started
If you are highly disciplined and have time to sift through the jungle of information: there are a number of openly available resources online. However, if you like to learn from professionals in a structured environment, taking a course can be an ideal solution.
Growth Tribe’s 12-week live online course will get you up to speed with all you need to know about data analytics. Our expert trainers will help you:
- Master the most commonly used tools in the industry including Python, SQL, Tableau, Excel, Dataiku, etc.
- Gain hands-on experience with data analytics processes, data cleaning, data business cases, querying & organising data, data visualisation, and the basics of machine learning
- Get 1:1 coaching sessions and feedback while working on real-life data projects
- Receive a Growth Tribe certification and portfolio to showcase your work to potential employers