5 things you need to know about data analysis

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As businesses look to adapt their models, data, and analysis skills are increasingly prominent. Here are five areas to be aware of when analysing data.

Few professions have been left untouched by the unstoppable advance of modern-day data and the subsequent scramble to make sense of it, to unlock its secrets, and accountancy is at the forefront. But it’s not just a case of plug-in and go.

Why data analysis?

“Big data analysis is a tremendous asset for companies, from start-ups to international organisations,” says Matt Weston, managing director at Robert Half UK. “It’s a way for teams to help CFOs prepare for economic change, develop forecasts and models, examine customer trends, and gain competitive advantage. Right now, as businesses are looking to adapt their business models, the demand for data analysis skills is rising.”

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1. A hybrid skillset

Accountants are well-suited to data analysis, Barlow believes, given their analytical mindset and training, but it won’t suit those who simply want to sit behind a computer. It involves more communication – it’s about conveying potential scenarios and trends so that a business can then make the best decisions. 

Financial and data analytics skills are becoming a much bigger part of the world of finance and accounting, says Weston. “Over the last few months, financial planning and analysis, financial modelling, and financial/management accountants have all seen a rise in demand where data analysis has formed a deciding factor of the job description.”

Weston notes that while a key part of the role involves data retrieval, interpretation, and analysis, the perfect package of expertise includes:

  • Functional skills such as financial analysis and planning, budgeting, forecasting, operational analysis, and cost management.
  • Technical skills including data mining and extraction, statistical modelling and data analysis.
  • Soft skills like communication, decision analyses, strategic thinking, and process improvement.

2. Analytical mindset

Barlow foresees a time when accounting firms will compete with marketing agencies and other SME service providers all trying to analyse and explain their clients’ data. If accounting firms focus more on business partnering and business-wide data, they can then move away from simply reporting financial data to talking about “why something has happened” by bringing in operational data from across the business. 

For Barlow, there is a certain mindset that makes a good data analyst. It’s all well and good having the technical skills to extract and interrogate data, but if you’re not inquisitive, the technical skills will be for nothing. 

While business acumen is crucial, equally so is understanding a company’s business model, says Barlow. “There’s absolutely no point in doing any of the analysis if it doesn’t inform and help make better decisions.” 

3. The SME factor

Barlow – whose young firm proactively embraces all things digital, data, and analytical to provide clients with the super-rich and diverse business information that goes above and beyond what businesses used to expect from their accountants – suggests that the proliferation of data in SMEs is reshaping practices and finance functions.

Unless you’re a Big Four-esque multinational, you’re not likely to have mathematicians and engineers heading up dedicated data analysis teams. Thanks to more data flowing from new and relatively affordable programmes spanning all operational areas – marketing, sales, finance, and HR – big data is no longer the preserve of the big hitters. 

This means that modern accountants, while not necessarily needing to be arch data analysts, will benefit from having a data analysis string to their bow. As an example, in the simplest terms, accountants can just dump data into Excel and manipulate it. Barlow says that if he was presented with two accounting candidates of exceptional quality, but one had experience with a database management language such as SQL or a programming language such as Python, he wouldn’t hesitate to hire the SQL/Python person. 

4. Clean data or dirty visuals

If attention to detail is oft-touted as an important skill for an accountant, in data analysis it’s doubly so. A huge part of data analysis is the storytelling. This is increasingly done using data visualisation programmes, such as Power BI, Tableau, Klipfolio, and Looker, that provide innovative and simplified ways to explore and ultimately “visualise” data.

But for this to work effectively, you need complete and clean data because if it’s not then insights will be inaccurate and may lead to bad decisions. Yet again, this is where an accountant’s skillset is well-matched to data analysis. Accountants are rigorous with numbers – they understand that human error happens, so validity checks are bread and butter.

5. Demand for analysis skills

It’s highly likely that in the future, practices and finance functions will start to develop distinct data analysis teams with appropriately trained professionals. Until then, especially at SME level, it will fall on an accountant to take on such tasks. 

“As the digitisation of business accelerates, and so too access to big data, the demand for big data skills will grow in step,” says Weston. “There is a range of career options available to finance and accounting professionals who want stronger big data skills. By taking the initiative to improve your expertise, you’ll increase your chances in finding your next role or advancing from your current role.” 

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Neil Johnson is a freelance business journalist who contributes regularly to trade publications and member organisations, covering employability, recruitment, business trends and industrial analysis.

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