Want data science talent? Look to your Finance team.
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.
Finance and data
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 across functions.
The disruption of finance
Finance has been completely disrupted by automation in recent years. With online tools like Zero 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 a very strong position to be the champions of data control, security, privacy and science within their organisations.
Making the transition
Two things prevent most finance teams from making this transition: organisational support and personal exposure.
First, organisations need to actively encourage and support finance teams to make the transition to data insight teams. This means encouraging, mandating and allocating budget for automating and modernizing the finance processes that are often manual or duplicated across multiple redundant 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 functions. By focusing on the “low hanging fruit” that relates to their own expertise, they can get comfortable with the different approaches and see the overlap between finance and data science.
