We spoke to Hays’ James Milligan to find out about some of the biggest job trends within the analytics industry.
The whole data space calls for many skilled professionals across a variety of areas. And as the advances in AI and machine learning call for more data analytics, certain roles have become particularly essential.
Hays’ James Milligan said Data is a critical focus for C-level executives in many of organisations right now. “A typical data strategy would involve leveraging data to make informed business decisions and gain a competitive edge,” he said. “Companies use data to identify trends, patterns, and insights that can be used to improve business processes and drive growth.”
So, what are some of the components of data strategy driving demand for these roles? Milligan said governance, management, analytics, visualisation and machine learning all play key roles in the heightened need for these skills.
Companies need to establish data governance policies and procedures to ensure that data is accurate, complete, and reliable. They need to ensure they have people who can help collect, store and manage data in a way that is secure, efficient and accessible.
Data analytics is used to identify trends and patterns that can be used to improve business processes and drive growth, while visualisation is used to communicate complex insights to stakeholders. Finally, machine learning algorithms would be used to build predictive models and identify patterns in data. These needs inform the roles that are most in demand right now.
5. BI developer
A business intelligence (BI) developer bridges the gap between business strategy and technical implementation. Milligan said that these professionals work with stakeholders to understand business needs and create BI interfaces and dashboards to make reports that the wider organisation can use.
“In terms of technical skills, you’ll be comfortable working with BI platforms and databases,” he said. “Soft skills are important, too. The right candidate will be a problem solver with attention to detail who can collaborate effectively.”
4. Data architect
An architect’s role is to design the framework by which organisations collect and store data – in particular, the flow of data through the business needs to be as simplified and efficient as possible.
“You’ll need expertise in data modelling and database administration, while an understanding of security is also beneficial,” said Milligan.
3. Data scientist
A data scientist takes large amounts of unstructured data from numerous sources. They then use technologies such as AI and machine learning algorithms to make sense of it all, before delivering solutions to the business.
Milligan said programming skills are important for any data scientist – in particular Python, R, SQL, Perl (5) and C/C++. “A data scientist will also need to know how to use analytics tools such as SAS and Hive,” he said. “Beyond experience with these tools or languages, a strong foundation in maths or statistics is useful, as well as the ability to communicate your findings coherently.
2. Data engineer
Engineers typically come in at the start of the data process. The role involves cleaning and storing the data so it’s accessible to other stakeholders in the business. According to Milligan, the cleaning part is important because data scientists can take insights from the right information. This is why this role also featured high up on the overall in-demand tech jobs earlier this year.
“In order to store data effectively, you’ll need to be fluent in languages such as SQL and NoSQL,” said Milligan. “Proficiency in data modelling and using tools to process data is also a must.”
1. Data analyst
The number one role in this space is, unsurprisingly, data analysts, as they interpret the data that has been stored and processed before analysing it to spot trends. These professionals inform the wider business of these trends so that they can solve problems. There’s a lot of responsibility involved, which is these roles are so important right now.
Again, SQL is important, as is experience in data visualisation tools such as Power BI, according to Milligan. “After that, communication is key – it’s vital that your audience, who might not share your knowledge, are able to understand the message.”
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