How to Translate Data Science to Business Value: A Blueprint
To deliver on their potential, data science projects need to be aligned with business goals and stakeholders. Keegan Hines shares his blueprint.
John P. Mello Jr.
To be effective in a business setting, data science projects must focus less on the science involved and more on the business. Doing data science in an enterprise requires careful alignment with corporate goals and values, said Keegan Hines, vice president of machine learning at Arthur AI, a platform that monitors the productivity of machine learning models.
In his Tech Talk session for AI Exchange, âTranslating Data Science to Business Value,â Hines said there are both technical and corporate challenges to translating data science into business value. Technological challenges include model reproducibility, model deployment, and model monitoring. On the corporate side, organizational challenges and an us-versus-them attitude can make it difficult to bring the full potential of data science to bear on the needs of the business.
Make Sure the Data Science Project Is Relevant to the Business
This may sound obvious, but very often data scientists will start thinking about algorithms, optimizations, and metricsâand lose sight of the fundamental question of whether theyâre working on something that matters to the business.
Thatâs why, when designing a project, data scientists need to corral folks from both the technical and business sides of the company, Hines said. This will help create a shared context to ensure that the data scientists are cooking up something that addresses an important problem facing the company.
Involve All Stakeholders in the Project from Its Inception
ďťżAcceptance of a data science project in a business environment depends on stakeholder participation. Data scientists may be excited about the techniques in a project they believe will transform a business. But they shouldnât present it as a fait accompli to the people who will have to work with and implement the projectâessentially telling them that the way theyâve been doing things for years is all wrong. That approach is more likely to invite failure for the project.
So even if a team of data scientists gets the âwhatâ of a project right, theyâre still courting failure if they donât get the âhowâ right, too. The tech can be there, and the business impact can be there, but the inclusivity must be there, as well. âWe need to get everyone engaged together in a collaborative way to prevent organizational friction,â Hines said.
To set up a data science project for success requires not only everyone working together from the get-go, but also structuring the project so everyone is set up to share the victory at the end of it. âThere should not be any us-versus-them mentality,â Hines said. âWe need shared wins and shared commitment from both sides.â
Establish the Metrics Needed for Success
Ask data scientists about the performance of their model, and theyâll likely start talking about such things as ROC and AUCâperformance measures used in machine learning. Those measures arenât very helpful when trying to sell a project to business leaders who are unfamiliar with them.
To do that, data scientists need to focus on the key performance indicators (KPIs) that are meaningful to the business. The KPIs should be easy to measure. Moreover, project designers need to make sure they have the data to show that the model is having an impact on the KPIs.
For example, if an organization has a fraud-detection model, a data scientist can measure the efficacy of that model with metrics such as precision and recall. But those metrics mean little to the business. What itâs interested inâthe KPIs that are most important to itâare reducing fraud losses and keeping them down and whether or not the machine learning system is doing that.
Monitor the Model Continuously to Ensure Itâs Functioning As Intended
After a system is deployed, the people who developed it may move on to other projects, leaving it to run by itself with very little supervision. In that situation, performance will eventually degrade. And when that happens, investigators may find that the conditions in place when the system was implemented have changed.
For example, a data input may have changed and the model may need updating. Perhaps the way data is encoded was altered and no one downstream was told about it.
The point is, you need to have best practices in place around monitoring and governing the system to ensure that, once itâs live, it will continue to run smoothly. Moreover, thatâs something that needs to be considered during the design process, so that when the project is done, the tools to perform effective monitoring will already be in place.
Continuous monitoring serves another purpose for developers of nearly every machine learning project. It allows them to justify the project at any time because they can be sure itâs performing as designed and that conditions havenât changed that could affect its performance.
âIn deploying machine learning systems, like any kind of technology system, we have to think about ongoing governance, ongoing maintenance, ongoing monitoring, and bringing best practices to that kind of a project as you would for any other kind of software,â Hines said.
Focus on the Business Goals
Data science has the potential to transform business, but projects incorporating the technology need to be designed with business goals in mind. In addition, these projects need input from the stakeholders affected by them if theyâre to gain any traction in a business. Finally, once implemented, projects need continuous care and feeding to maintain optimum performance.
