Metrics for Assessing the ROI of Your Data Strategy
There’s no argument: Having a well-defined data strategy is crucial for any business’s success. But even the best fintech data analytics strategy will fall flat without a way of measuring its return on investment (ROI). Measuring the ROI of data is not as straightforward as measuring, say, a marketing campaign, because of its many indirect influences on a business.
To measure the data success, some key metrics must be determined. These metrics should be shared and understood across the business, by staff and stakeholders, so that there can be a clear view on whether the data strategy is working or if adjustments need to be made. In this article you will learn which metrics you can use, and the benefits of doing so.
Defining Your Goals
First, before defining metrics in data analytics, it is important to know your goals so that metrics can be formulated to meet these. What are the objectives that you want to achieve and how will your data inform these objectives? What will determine its success? To give you an example, you may wish to use business intelligence services to improve customer retention, boost your company revenue, bring down your costs, or elevate innovation. After this, you should determine the key performance indicators (KPIs) that will assist you in monitoring and evaluating your results. For example, you might use average order value, customer retention rate or profit margin.
Next, get to know your current state and baseline. How is data currently being used in your business? What are the strengths of your data? How could it be improved?
Finally, you should pick key metrics that align with your goals and objectives, some of which are outlined below.
1. Data Quality and Accuracy
Bad data will cost your business in time and money. Quality data that is accurate, complete, consistent, and uniformly understood is vital for ensuring the data is reliable and therefore usable. Good data quality leads to better decision-making and elevated operational efficiency.
2. Data Accessibility
Another metric you can use is data accessibility. You can measure the ease with which stakeholders can access and utilise data. You can assess metrics like availability rate and average data retrieval time across different departments. Better accessibility ensures that strategies and decisions can be made quickly with the support of easily accessible data.
3. Cost Efficiency
Collecting, storing and managing data costs money, and these costs must be understood and monitored. You can analyse these costs and compare them to the benefits gained from better, quicker decision-making and operations. Some examples of this are cost per gigabyte, cost per analytics process, and cost per user.
4. Data Software Integration and Interoperability
Interoperability refers to data being created and transferred accurately and uniformly across locations. As part of your ROI analysis, you can determine how well your data strategy integrates with existing systems and how effectively it communicates with other tools and platforms. You might measure integration time, success rate of integrations, and reduction in data silos. Take a look at the work we did with Kone, who was losing time and adding costs with administrative duplications. By implementing API data integration software with 3rd party platform providers (CAFM/BMS) to automate data transfer, we were able to help mitigate these problems and improve workflow efficiency.
5. Business Processes
You can monitor the efficiency of certain business processes during any data-driven campaign, such as reduced cycle times, increased productivity, and efficiency gains in different business operations. Doing this helps you to quantify the positive effects on your organisation as part of your ROI evaluation.
6. Predictive Analytics
You can determine the reliability of predictive analytics models derived from your data strategy. Measure metrics like error rates, and compare successful predictions against actual outcomes. Better accuracy will indicate the effectiveness of your data strategy in providing valuable insights.
7. Revenue Generation
Revenue can be quantified easily by using metrics like an increase in sales, improved market share, new leads, and the development of future business opportunities. These numbers help you to understand data-driven decisions in relation to monetary gains.
8. Risk Mitigation and Compliance
As part of your ROI analysis, you can assess how well your data strategy contributes to risk mitigation and compliance with industry regulations. You can measure data security, violations, attacks and improvements in security outcomes as insight into the strategy’s effectiveness in safeguarding sensitive information.
To conclude, evaluating the ROI of your data strategy is critical to ensure that your investment in data initiatives brings your business tangible benefits. By determining these metrics, you can adjust your data strategy, maximise its effectiveness, and gain better outcomes for your business.
We can help. Get in touch to talk to us about how certain metrics can be used to measure your data strategy.