Source: analyticsinsight.net
It is incredibly tough for a lot of enterprises to make more informed decisions if the ROI behind data science models is less than the investment put behind it. A survey conducted by Deloitte says that a staggering 67% of executives are not comfortable accessing or using data pipelines from their projects for strategic decision making. Instead, they prefer to stick to their legacy systems than know how to use data analytics. The worst part is- shielding away from data science investments, the most common excuse?
Data analytics does not have enough Return over Investment (ROI). But is that true?
Return on investment is used as performance measurement and evaluation metric. Expressed mathematically as a ratio or a percentage,
ROI= (Gains – Cost of Investment)/Cost of Investment
There are several ways to calculate the ROI behind a data science project and ML algorithm, but how do you measure a qualitative term in a mathematical equation? So, how does an enterprise deduce the matrices to measure Data Science project success or an ML algorithm accuracy?
Measuring the success behind Data Science and ML algorithms-
• Investment Expenses
How much investment goes behind building an Data Science project and an ML algorithm? Investment expenses are all about forecasting these values as accurately as possible. That’s where the C-Suite brainwork begins. With the understanding of how much has to be spent to launch a new product or extend the functionality of a working system, enterprises can focus on the investment behind data science projects with the potential financial return that justifies the risks. This feasibility study of a data science project is directly connected with the return on investment (ROI) evaluation.
• Opportunity Cost
This simply means the decision-makers must think of how they could have deployed the investments if they didn’t invest it in a data science project.
• Cost of Running an ML Algorithm
Cost of running an ML algorithm means the transaction costs including the time spent on building, testing, and deploying a data science project or an ML model-based solution. Development costs include expenses on IT infrastructure and employee compensation, required man-hours, and maintenance costs.
• Time Estimates
The C-suite must determine how much time an investment behind building a data science project will start to pay off. The factor of time is useful when comparing two or more projects with the same expected ROI to be realized under the same circumstances.
• Inflation vs Returns
While calculating the ROI from Data Science projects and ML algorithms, the enterprise must take the underlying inflation into account to calculate and compare excess return over inflation or actual returns vs nominal returns to get a realistic ROI projection.
Adapting to a Data Science Strategy
According to Gartner, by next year 90% of big companies would hire a Chief Data Officer, a promising role that was almost non-existent a few years ago. Of late, the term C-Suite is gaining a lot of importance – but what does it mean? C-Suite gets its name from a series of titles of top-level executives whose job profile name starts with the letter C, like Chief Executive Officer, Chief Financial Officer, Chief Operating Officer and Chief Information Officer. The recent addition of CDO to the C-Suite has been channelized to develop a holistic strategy towards managing data science projects and unveil new trends and measure the ROI behind a data strategy that the enterprise has attempted to tab for years.
In a nutshell, boosting ROI from data science projects and ML algorithms is crucial for business success but the best way to trigger it would be by getting a bird’s eye view of an organisation’s data science strategy, which will help in predicting success accurately and thus help it to strategize ROI-supported decisions.