Why all of your staff should be trainee data analysts

Businesses are keen to hire data specialists – but they don't even have to. Nick Jewell of Alteryx explains why every member of your staff should be a data analyst.

The importance of data in running and growing an effective business has been unequivocally recognised. Now the question is, where are the data workers who can make this happen?

Data-led organisations take account of the world around them as it is, and not as they think it is. Having an analytical mindset and encouraging all staff (not just data specialists) to base all proposals on the very best data available should be a priority in 2018.

Organisations are starting to understand that business value is going to come from the more sophisticated analytics that bring smarter insights and are just now making their way into the mainstream.

The jump to these higher-value analytics really throws the issue of analytic talent shortage into sharp relief.

Employers keen on data skillset

Data skills have never been in such high demand. Last year Glassdoor announced that data scientist was the best job in America, based on pay, career advancement and treatment in the workplace. The only problem seems to be that there aren’t enough data scientists to go around – globally.

So what if everyone in the business could play the role of data analyst? Specialised skills are great, but sometimes you don’t need a data expert, you need someone who knows how to ask the right questions of their own data.

Building an analytical culture and empowering workers with the right tools will lead to better outcomes for your business.

Here are a few key things to remember.

Everyone can be a data analyst

Whilst there is a continuum for those who ‘do analysis’ and there will always be a place for specialists, the people within the business do their job every day have the knowledge to do their jobs better, given the right data and the right tools.

For the business analyst doing their job day in, day out, those levers come in the form of data. Knowledge workers will often do analysis as part of their job, and an increasing number are doing ‘advanced’ analytics – including spatial and predictive.

Gartner predicts that by 2019 the analytics output of business users with self-service capabilities will surpass that of professional data scientists.

Analytical work is more rewarding

Problem solving is how humans feel accomplishment. Overcoming business challenges should be enjoyable, and when line of business employees use data everyday they immediately become line of business analysts. Solving problems is what humans do, and where they can’t, there’s no thrill.

Analytics should give everyone in the business the ability to have an ‘aha’ moment every day.

Sharing is more than caring

Jobs performed in isolation risk being repeated by the next person (and the next person after that). The usual tools used are the generalist applications like Excel or Access, and they get saved down in the department. With no visibility, nothing is shared between teams, and the wheel gets reinvented annually.

Duplication is another way of saying lost productivity. A platform that all staff can use to share insights and leverage value multiple times from one piece of ‘heavy lifting’ allows an organisation to retain knowledge and hard work to let ‘hard work work harder’.

Those organisations who are aware of their data assets, and opening them up across the business, will naturally become insights-driven where it’s easy and natural to answer questions. And inevitably, where answers are findable, the insights are easily embedded back into the company’s business models in a virtuous cycle of learning, improving and optimising.

People asking clever questions become cleverer

The hardest part of the analytics process is often figuring out the right questions to ask. Deep, functional knowledge is required to get at really meaningful questions.

It’s largely ineffective to expect a centrally-based analytics team to understand the pressing issues within each department across an organisation. The people with the functional knowledge of the business are out in the business.

Understanding so much, they will find that answers lead to new questions, and the hunt for excellence continues.

Here, let me do it

The growth of self-service analytics solutions, from tools to entire platforms for the enterprise, has meant that coding is no longer a roadblock in the way of anyone who wants to interact with data.

There are code-free, and indeed code-friendly, options abound now. Good thing too, considering how in-demand coders are, just like data scientists.

Legacy systems used to assume a level of technical proficiency of the user, but the latest generation of data solutions delivers a user-friendly interface – often drag-and-drop – that removes the need for coding.

With that potential hurdle addressed, the entire hiring dynamic shifts. These self-service tools enable businesses and recruiters to look at a far wider pool of candidates for any role, allowing them to consider factors outside of specific coding languages – and reducing the need for two-year graduate degrees.

Fortitude: never surrender

Not asking questions is one of the biggest fundamental losses within a company when expertise is siloed and people no longer have the understanding on the context of what they’re working on. Empowering everyday employees to become data science experts based on self-service solutions helps them to carry on making discoveries.

Gartner’s recent survey of more than 3,000 CIOs shows that CIOs ranked analytics and business intelligence (BI) as the top differentiating technology for their organisations. It attracts the most new investment and is also considered the most strategic technology area by top-performing CIOs.

That’s how analytics makes an impact: by rapidly rewarding success and growing exponentially into a new way of working that’s entirely beneficial, and more than a little addictive.

Further reading on data analysts

Nick Jewell is a technology evangelist at Alteryx

Related Topics

Data

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