Which Businesses Benefit the Most From Data-Centric AI?
Data-centric AI can benefit any organization looking to better manage and leverage its data to make improved business decisions. Organizations with large data sets, such as financial services, healthcare, retail, and telecommunications, can benefit the most because they have more data to deal with and are looking for ways to optimize it.
The use cases are endless. It can be used for anything from predicting customer behavior to predicting the stock market. It is not limited by the constraints of human intelligence and can process much more information than a human being could. It can also learn from its mistakes and improve itself over time.
How Do We Measure the Value of Data-Centric AI?
Organizations can use a variety of methods to determine the value of data-centric AI. One of the most common ways to measure the value is by calculating the cost savings related to the optimization of existing processes. For example, if an organization implements data-centric AI solutions to streamline the process of generating reports for customers, then the organization can measure the value by calculating the cost savings of not manually creating the reports.
Alternatively, businesses could measure the impact of data-centric AI solutions from their top and/or bottom line, assigning value to the outcomes achieved alongside any larger qualitative goals. Data and analytics can measure the results of project initiatives, but the success of these projects often relies on the presence of champions within the organization. These internal champions keep their users engaged and help business leaders understand the overall impact of changes made within the organization. The steps below provide a simple way for organizations to measure the ROI or Return on Value (ROV) of data-centric AI. These steps are also applicable to a variety of organizational-level, technology-focused, digital transformation efforts.
Step 1: Frame the business problem and define measurable outcomes for success
Projects need clearly identified business problems in order to determine what the success of a project looks like. From there, it is important to know how to best measure success by utilizing the SMART goal framework, which stresses goals should be both measurable and time-specific. Organizational leadership should also decide if the goals are easily measurable based on the data currently available. Measurements are often established with little consideration of how outcomes themselves will be measured based on the data currently available.
Step 2: Measure outcomes indicated by connecting data and analytics
Not only is data critical to AI, data is important to the overall success of the entire organization, establishing analytics to measure the outcomes indicated in the previous step. This is critical to the success of the current business problem as well as any subsequent problems encountered. In order to scale in a way that’s measurable and intentional, businesses should ensure they take a data-centric approach to measuring value-driven outcomes.
Step 3: Determine a breakeven point (B/E) based on TCO as well as qualitative and quantitative values
Total Cost of Ownership (TCO) is the cost of any technology adoption and/or associated subscriptions. This value is often tracked for budgeting purposes and reported as part of critical decision-making. However, this TCO number should be presented alongside measurable outcomes to determine a breakeven point (B/E) for the organization’s financial outlook. Most companies focus on qualitative value from technology innovation, and they do not take the time to establish quantitative measures that business leaders can easily attribute revenue enablement activities to the budget allotted to TCO.
Following the steps above ensures that data-centric AI planning and decision-making is focused on the overall value associated with the specific outcomes identified by business stakeholders that are critical to organizational success. Additionally, we recommend starting with a proof of concept (POC) project to get initial investment from senior leadership towards a larger more concentrated data-centric AI effort. These projects can take anywhere from five to eight weeks depending on the size of the organization and scope of the project. A POC can enable your organization to quickly test assumptions and drive specific measurable outcomes that often make it easier to engage in further discussions around what is possible.