First
Finance manages the balance sheet, so if someone knows how to really put a number on the value of data, its Finance. This kind of value resides in the intangible assets class. But before you can go that way, first you make an inventory of the different data assets in your company. This can for instance be a database with a set of properties of your customers. This allows business partners or potential acquirers to identify upsell and cross sell opportunities. Or datasets describing how your audience is engaging with your e-commerce site. Allowing to optimize for conversion or develop a new product or service based on the shopping behavior on your web shop.
Of course, Finance needs input from sales and marketing to get into the nitty gritty of these datasets to assign a monetary value. But why not put finance in the lead to make sure the valuation rules are clear and up to accounting standards? Now the value of your data can be represented in a valuation of the company: in the sale of the company, or in the stock price.
Second
People with a knack for Finance are process and detail oriented. They want to know what's behind every euro or dollar that goes in and out of the company. Granted, in large cooperations this is not workable, but it's a mindset to wanting to understand what's behind the numbers. If you look at data beyond the financial data with this mindset, you will also want to know where it's coming from and what processes to review to improve a certain KPI.
Third
Last but not least, let's be honest: a company has to be profitable to be able to fulfill its mission. To reliably track and predict this profitability, the link between operations and finances has to be crystal clear. Finance can take the lead in making the financial repercussions of business decisions transparent. By doing so, you create a valuable integrated dataset where other more advanced data science exercises can plug in to.
