Measuring the effectiveness of your company's data-driven initiatives is vital. However, despite this progress, companies often fail to assess their data projects' ROI accurately.
The problem is that it's hard to quantify data and analytics projects' impact on improvements and critical business results, such as increased profits and reduced costs. Additionally, estimation is further complicated by the fact that it can be difficult to convert all the benefits such solutions deliver into one figure, particularly when these benefits are spread across different departments and teams.
ROI, or return on investment, is a financial metric that measures the profitability of an investment. There are several different ways to calculate ROI, but the most common formula is simply net income divided by total investment.
However, it can be tricky to represent the ROI of data and analytics projects through numbers alone. To calculate ROI, businesses must first establish their definition of "success" and identify all the direct and indirect ways in which data or the data department has contributed. Direct impact occurs when a project leads directly to an outcome. Indirect impact occurs when a project enables activities that lead to better outcomes or stimulates other activities that result in improved outcomes.
Success can take many forms, both tangible and intangible. Data can play a role in increasing sales, improving performance, reducing returns as well as enhancing customer satisfaction and corporate reputation. It is essential to consider all possible scenarios in which data-related projects (for example, data science projects) could be beneficial and lead to success.
As with any other IT project, data and analytics can deliver both quantifiable and non-quantifiable advantages. Let's examine them more closely:
One of the metrics that can be relatively easy to measure by data-driven initiatives is cost reduction. Automation and the creation of an analytical ecosystem which is easy to use typically result in a significant decrease in the amount of time users spend on mundane tasks. This increase in efficiency can be approximately quantified by conducting interviews with users to understand how much time will be saved. A rough estimate can then be calculated using a formula such as this:
monthly savings = hours saved in 1 month * the total number of users * approximate hourly employee cost (full cost and not only employee rate)
Additionally, the ROI of data can be estimated through infrastructure and licensing cost reductions. For example, switching to on-demand cloud computing can drastically decrease infrastructure costs. According to a KPMG report, organizations could save 10-20% of their annual IT budget by migrating some or all activities to the cloud. It would free up more than 30% of the budget on infrastructure (mainly data centres and networks). Exact savings figures can be extracted from the cloud vendor's bill.
The minimization of support costs can also be used to measure the ROI of data-driven solutions. Simplifying the analytical ecosystem will also most likely decrease overall support time and cost. A data-powered customer service chatbot can help reduce support operating costs and enhance customer experience.
Calculating ROI from direct data monetization is fairly easy. It is typically a straightforward process that relies on income from selling the data. That is if your main goal is generating a new revenue stream and not, for example, expanding your service offering to strengthen your relationship with clients.
There are various types of data that can be monetized - from unrefined sensor information to observations gleaned by analytics departments. The kind of data that can be transformed into a product differs considerably for every industry. For more information on using data to generate revenue, read our article "Data Monetization: Turning Data into Profit-Driving Assets."
It can be challenging to capture data and analytics projects' effects on company revenue. The measurement heavily depends on the nature and number of revenue streams, as more than one initiative can contribute to revenue growth.
In other words, data-driven decision-making supported by data analytics can be assessed regarding its impact on individual projects. For example, using machine learning for customer segmentation may help focus advertising efforts on the most promising prospects, reducing marketing costs and increasing sales revenues.
In the automotive sector, predictive maintenance to optimize vehicle lifespan and performance will likely lead to reduced maintenance expenditure and increased numbers of new customers attracted by this improved offering.
Data and analytics initiatives can decrease the time a product takes to be developed and ready for use by the public or within a company, as well as improve the processes' completion speed. Not only does your product stay on the market longer, generating revenue, but sometimes getting your product to market faster gives you an edge over your competitors and helps you gain a larger market share at an early stage.
Saving user time mentioned above can result in those users starting work on new initiatives sooner and delivering them earlier, which will have a quantifiable impact.
For example, if a data scientist dedicates an average of 10 hours per week to data cleaning and can reduce that time commitment to just 1 hour through new data and analytics initiatives, it would free up time for work on more complicated and innovative tasks. However, the time saved by these types of initiatives is not always limited to data teams; businesses may need to consider how everyone in the organization will be affected to realize the full potential consequences of each project.
This factor is calculated based on estimated losses that could occur if third-party access to critical business data occurs and others.
This factor should be considered in terms of the potential loss to the business if it delays adoption or the initiative fails. A lack of investment in data initiatives or solutions can increase the likelihood of a data breach or fines for regulatory non-compliance (such as with GDPR).
When a new compliance framework such as GDPR appears, there are usually few discussions on whether the company should comply. However, if putting it into numbers is necessary, we can use the same approach as with security.
Even though it can be pretty challenging, it is essential to calculate the return on investment of data and analytics projects for two main reasons:
A few key factors to consider when calculating ROI for data and analytics projects include, but are not limited to: direct data monetization, income boost, and faster time to market. By considering all the relevant factors, organizations can get a clear picture of the ROI of data and analytics projects. This information can help to decide which projects to pursue and how to allocate resources.
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