Data scientists are expensive to hire and often come with a diverse range of skills including coding, statistical analysis tools like R and business experience in a range of different areas.

Yet this idealised background isn’t necessary to do data science and gather insights and perspective from your data. Your organisational already has at least one old-school data scientist — Accountants, Financial Controllers and Finance Professionals are all data scientists.

Accounts can and should be at the forefront of organisational data science. They have the best access to critical and validated financial data. They are often in a position of authority and access. And many within the organisation will see them as trusted with traditionally confidential data which may be difficult to share cross-functionally.

In recent years, Finance Professionals have seen an almost complete disruption and automation of their field. With online tools like Xero and Freshbooks almost anyone can invoice, automatically reconcile and evaluate their financial performance. There are even hybrid solutions which offer on-demand finance professionals to help with tax filings, and more complex, situational calculations.

Organisations and Finance Professionals can utilise this disruption as an opportunity to leverage their skills and grow professionally into related areas (statistics and basic scripting). They are in the best position to be the champions of data control, security, privacy and science within their organisations.

Two things prevent most finance teams from making this transition: organisational support and personal exposure.

First, organisations (and their leaders), need to actively encourage and support finance teams to make the transition to data insight teams. This means encouraging, mandating and allocating budget to automating and modernizing finance processes that are often manual or duplicated across multiple redundant systems. These initiatives should be mandated by management and owned by the finance teams. Those teams given the right direction and budget are best equipped to implement streamlined systems. The priority of an modern, fully-automated and streamlined system needs to be made clear as part of this mandate to ensure that the finance team doesn’t use this as an opportunity to double down on existing systems.

Second, individuals need to take initiative to explore the areas of data science closest to their own expertise — especially the areas related to math, statistics and business advisory. By focusing on the “low hanging fruit” that relates to their own expertise, they can get comfortable with the different approaches and see the large overlaps between finance and data science. Over time and with growing confidence, Finance Professionals will be able to add more value to the wider business and leverage the data insights to support recommendations.

With this approach organisations and individuals can gain more value and insight without investing in an expensive and uncomfortable data science organisation.