Data science should deliver business value.

When considering whether to invest in a data science project, itʼs useful to be clear about the kinds of value the project will bring.

Itʼs easy to fall into the trap of only valuing activity that can be measured in dollars. This misses a lot of real value, and can lead to the deprioritization of essential work that canʼt be easily connected to finances.

Not everything valuable is measurable.

I've identified six types of value that data science projects can generate:

1. Money
2. Customer experience
3. Employee experience
4. Risk mitigation
5. Legal compliance
6. Strategic alignment

You have a fraud detection model that analyzes online behavior to predict when a login attempt is likely fraudulent. If you improve the model's sensitivity without increasing false positives, you can decrease losses due to fraud without making things more difficult for non-fraudulent customers. This is measurable in dollars.

You build a customer-facing chatbot for your site that answers questions and can even perform some actions. This might increase conversion rate or average transaction value, but gauging how much is attributable to the chatbot is difficult and will undervalue the work. A way to gauge the non-monetary value of the chatbot would be to ask customers for feedback (Net Promoter Score, thumbs up/down). Positive customer experience is itself valuable, even if it canʼt be converted neatly into dollars and cents

You create a search engine that helps employees find particular datasets. You could try to measure the value in terms of (time saved) x (average wage), but this misses a lot of the value. How would you quantify money saved through better retention because the search engine makes analysts less frustrated? The chain of reasoning from measurable action to measurable financial impact is long, and the accuracy of each link is low, so financial estimates could vary wildly. But if it does improve employee experience, that itself is valuable

Your Internal Audit team is tasked with ensuring the chain of custody for your products brought back in for repair. The process includes taking photos of products on the way in and on the way out. You developed computer vision models to gauge the quality of those images. Are they in focus? Are they zoomed in? If you detect low-quality images, Internal Audit can arrange for training or better equipment to improve the process, increasing the reliability of the chain of custody, reducing risk.

Compliance with privacy laws like the California Consumer Privacy Act and the EUʼs GDPR comes at a cost, but the financial benefits are difficult to calculate. If the average lawsuit against companies in your industry arising from practices that donʼt comply is $10M, do we say compliance reduces our risk by half? three-quarters? Precision is out the window. But valuing compliance is meaningful on its own.

Your Chief Strategy Officer has intel that it's time to strike in a new geographic area. You build a model to recommend sites for company locations in the new area. Each location can generate financial value, but the presence in that area may have larger strategic value.

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Having multiple ways to gauge the business value of a project means that you can't prioritize with:

projects_df.sort_values(by='business_value', ascending=False)

So what use is this?

When discussing where to invest data science resources, frame the discussion so that the real value of the work can be fairly considered.