The article discusses different ways that data science teams can create value for organizations. It highlights four categories: metrics and measurement, AI/ML product or product features, strategic insights, and operational decision products. Understanding which category your team falls into can help you communicate your value proposition to stakeholders.
How Data Science Can Deliver Value
Classifying its value proposition can make it easier to communicate what your team does
Most tech workers would agree the current economic environment is not great. Many companies laid off staff earlier in the year including the company I work at. Tighter cash flow often means greater scrutiny of teams’ performance and value-add to the organization. So it becomes more important that you and your team can clearly articulate how you contribute to the business’s goals.
But there are different ways that teams — especially data science teams — can create value. They include metrics and measurement, product features, strategic insights, and operational decision products.
1) Metrics and Measurement
Do not underestimate the value of good metrics in a company. A data team responsible for metrics and measuring those metrics has a critical role in assessing how the business is performing. They help alert business leaders to declining performance which may require a pivot. They help define Key Performance Indicators so teams work towards the right goals. They create forecasts for planning and measuring performance. Essentially, they simplify the large complex nature of running a business into some key statistics to streamline a lot of decision-making. This is highly valuable work.
Additionally, metrics & measurement also encompass experimentation, a common responsibility of many data scientists. Did a new feature contribute to a lift in the metrics? Did the latest marketing campaign increase bookings? These analyses fall under this category.
2) AI/ML Product or Product Features
A second, common area of data science is building externally-facing product features. These features are often powered by machine learning, although this is not a requirement. This work creates a service/feature or improves the user experience for customers. An example would be a recommendation engine on a streaming platform that gives TV show suggestions. The deliverable is thus a working (and often a key requirement is scalable) algorithm that can be embedded in a product or website.
3) Strategic Insights
This is probably the vaguest area in terms of what is actually delivered, but strategic insights encompass research or analysis to guide business decisions. It can leverage data analytics, statistical modeling, simulation, etc. It can be simple to very advanced, but the deliverable is generally a recommendation. How should the business proceed and why? These projects are usually executed very closely with business stakeholders. They tend to focus on an ad-hoc question as opposed to regular decisions that need to be made (see next category).
4) Operational Decision Products
Finally, this category focuses on tools that are built to make internal business decisions. How can the business operate more efficiently by making better decisions and/or quicker decisions? For instance, building a tool to help the marketing department bid on a Google keyword is a problem that could be solved by an operational decision product.
The primary difference between these categories is how they contribute value. It’s critical to know your work’s value proposition and articulate that to stakeholders.
If you want to evolve your company with AI, stay competitive, and use it to your advantage, consider the following practical solutions:
Identify Automation Opportunities
Locate key customer interaction points that can benefit from AI.
Define KPIs
Ensure your AI endeavors have measurable impacts on business outcomes.
Select an AI Solution
Choose tools that align with your needs and provide customization.
Implement Gradually
Start with a pilot, gather data, and expand AI usage judiciously.
For AI KPI management advice, connect with us at hello@itinai.com. And for continuous insights into leveraging AI, stay tuned on our Telegram or Twitter.
Spotlight on a Practical AI Solution: AI Sales Bot
Consider the AI Sales Bot designed to automate customer engagement 24/7 and manage interactions across all customer journey stages.
Discover how AI can redefine your sales processes and customer engagement. Explore solutions at itinai.com.
