A quick glance through a list of data analyst job requirements on recruitment sites such as Indeed provide a glimpse of what businesses are looking for when it comes to data skills. As well as a competence in working within specific software, such as Excel, Power Bi and SAS, to manage data sets, businesses are also looking for analysis and visualisations for those with business and not necessarily data knowledge. They are also looking for analysts to liaise with agencies providing additional data.

In short, data analysts are expected to source and manage data sets, update tools and produce visualisations and reports to feed business decision makers. Fair enough but given that the average salary of a data analyst in the UK is around £47,000, according to CWJobs, that now seems like a lot of money to manage data and create reports to push up the chain. These skills are in short supply too. Last year a Royal Society report in the UK claimed that demand for workers with specialist data skills has more than tripled over five years (+231%) and has led to shortages.

So, this begs two questions: are businesses using their data skills to their full capability and are they in danger of losing those valuable data assets if they don’t?

The answer to both questions is ‘yes’ but the problem facing most organisations is knowing what to do about it. Software tools and siloed infrastructures have tended to dictate policy and procedure. That has led to the current situation where skilled data workers are having to do the leg work, pulling data from outdated tools to try and pull together some semblance of trend analysis. However, there is light at the end of the tunnel in the shape of augmented analytics  

According to analyst company Gartner, augmented analytics and augmented data management are two of the biggest data trends for 2020. Because augmented analytics automates finding and surfacing the most important insights or changes in the business to optimise decision making, it can do so in a fraction of the time compared to manual approaches.”

This means that business insights and data trends can be analysed by anyone in the business without the need for a data analyst to create the visualisations. With machine learning and AI managing data processes, self-configuring and self-tuning that data, the highly skilled technical analysts can be freed to focus on higher-value tasks. This will inevitably re-shape their roles. Data analysts roles will become industry-specific with deep sector expertise and a more comprehensive industry view as opposed to a generalist data view. As a result, we will begin to see the emergence of sector specific data analyst expertise, such as financial services or retail expertise, able to ask a better question and get a better answer in the context of industry-specific trends and analysis.

This trend for specialisation of job roles within analytics (e.g. healthcare specific data analyst, financial services specific data scientist etc.) will only increase but the value to businesses will surely be realised in its strategic decision making. Data analysts roles will become industry-specific and be able to deliver macro and micro trend analysis to granular levels, specific to the sectors within which they work. This will add incredible value to organisations looking for a competitive edge in increasingly disruptive markets. It will also engage analysts more. Stop them being bored and therefore go a long way to retaining these valuable but currently under-utilised skills.

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