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What Goes Into Deciding The ROI For Your Data Science Program
Data science is rapidly being embraced by organisations of all sizes and types and is driving significant business impact. By investing in infrastructure, people, and processes, organisations are increasingly becoming data-driven and are able to unearth insights like never before. Do you know, on an average, 40,000 search queries are performed every second on Google alone – imagine the amount of data that is being generated!
However, a data science program is like an enterprise system implementation – although benefits are hard to come by and take time to realise, when they do, they completely transform business outcomes. Most data scientists have trouble proving the ROI on their analytics and data initiatives. But that isn’t to say there is no ROI – there is, and it’s immense. However, unlike sales or profit margins that you can measure, the ROI of a data science program cannot be quantified – it is more qualitative and reflects in the productivity of employees and the efficiency of business processes.
The ROI of your data science program is directly related to how your organisation perceives it. If the data science program is considered an expense, then there is a high chance the reporting infrastructure, tools, and training are minimal. Furthermore, there is hardly an analytics roadmap, and each line of business is left on its own to use application-specific tools for reporting and analytics. The outcome – Time-consuming, costly, error-prone insights that result in a low ROI. However, if your organisation looks at data science as a tool to improve business operations, the analytics roadmap will serve as a guide to optimise operations by integrating data sources, analysing it, and enabling business users to access information more easily, answer questions faster, and gain new insight into organisational processes, markets, and industries. The outcome – Quick, efficient, real-time insights into business operations that result in a high ROI.
With every human being on the planet generating 1.7 megabytes of data every second of the day, the need for data science programs is more than essential. However, only 0.5% of global data is being used to influence business decisions. This is majorly due to the fact that calculating the ROI of any data science program is not straightforward. At the end of the day, organisations love metrics, and if you cannot prove the ROI, the investment is considered to be a waste. But that is far from the truth. If you are having difficulties in proving the ROI of your data science initiative, consider the following:
Although calculating ROI for most IT initiatives tends to be a straightforward task, the same cannot be said for a data science program since the benefits are difficult to quantify. Certainly, data science enables better decision-making, which does drive revenue, but how does one calculate the ROI? Begin by asking a few questions: Is your data science program enhancing employee productivity? Is it enabling them to carry out tasks faster? Are you generating more revenue? Is it helping you reduce costs? Is it aiding you in improving your competitive standing? In all probability, data science must be helping you achieve all this and more – the fact it, it is just difficult to justify.
ROI Dependencies
The ROI of your data science program is directly related to how your organisation perceives it. If the data science program is considered an expense, then there is a high chance the reporting infrastructure, tools, and training are minimal. Furthermore, there is hardly an analytics roadmap, and each line of business is left on its own to use application-specific tools for reporting and analytics. The outcome – Time-consuming, costly, error-prone insights that result in a low ROI. However, if your organisation looks at data science as a tool to improve business operations, the analytics roadmap will serve as a guide to optimise operations by integrating data sources, analysing it, and enabling business users to access information more easily, answer questions faster, and gain new insight into organisational processes, markets, and industries. The outcome – Quick, efficient, real-time insights into business operations that result in a high ROI.
Calculating ROI
With every human being on the planet generating 1.7 megabytes of data every second of the day, the need for data science programs is more than essential. However, only 0.5% of global data is being used to influence business decisions. This is majorly due to the fact that calculating the ROI of any data science program is not straightforward. At the end of the day, organisations love metrics, and if you cannot prove the ROI, the investment is considered to be a waste. But that is far from the truth. If you are having difficulties in proving the ROI of your data science initiative, consider the following:
- Define your business problems: The benefits of data science are many. But what are you trying to achieve through the initiative is crucial for measuring the ROI. Define your business problems and identify the goals you want to achieve through your data science initiative – clearly defining your goals is the first step to measuring the ROI for your initiative.
- Calculate productivity gains: Employees make up the most important resource in an organisation, so if they are able to perform tasks more efficiently, the ROI is apparent. Estimate the average cost per employee per hour and then calculate the hours of increased productivity.
- Assess the increase in revenue: One of the most plausible benefits of data science is in the generation of additional revenue for an organisation. If your data science program is enabling you to capture more customers or causing your existing customers to purchase more – the end result is the same – more revenue for your organisation. No matter what the reason, calculate the additional revenue it is helping you generate.
- Determine the reduction in costs: Data science also helps organisations save humongous costs – direct or indirect. If your data science initiative is helping you run a predictive maintenance program more efficiently, you can save costs by either purchasing new parts or replacing the entire machine in time thereby avoiding a catastrophe.
- Calculate your competitive standing: Most often, a data science program will help in differentiating your company from the competition and cause customers to choose your business over others. If you’re able to attract more customers, or if you’re able to improve the customer satisfaction rate, you can be sure to credit it to your data science program.
- Determine the reduction in time taken to do tasks: Data science initiatives have a direct bearing on the time taken to do tasks. By analysing humongous amounts of data in a fraction of a second, it enables organisations to complete more projects, reach to conclusions faster, and drive value sooner. Calculate the difference in average time taken by your employees to do a task with data science as opposed to without – you will be surprised at the results.
Data Science for Success
Although calculating ROI for most IT initiatives tends to be a straightforward task, the same cannot be said for a data science program since the benefits are difficult to quantify. Certainly, data science enables better decision-making, which does drive revenue, but how does one calculate the ROI? Begin by asking a few questions: Is your data science program enhancing employee productivity? Is it enabling them to carry out tasks faster? Are you generating more revenue? Is it helping you reduce costs? Is it aiding you in improving your competitive standing? In all probability, data science must be helping you achieve all this and more – the fact it, it is just difficult to justify.
