What are the best ways to measure your data strategy's ROI?

To measure data strategy ROI, track cost savings, revenue growth, efficiency improvements, and customer satisfaction. Utilize metrics like customer acquisition cost, retention rates, and data-driven decision impact. Regularly analyze these factors to gauge the effectiveness and financial impact of your data strategy.

The right metrics and KPIs are paramount to measuring the Data Strategy's success. However, it's not a numbers game; it's about precision and relevance. Striking a balance is critical. Effective measurement encompasses various vital aspects, including risk and compliance, data quality, operational efficiency, business impact, usability, and stakeholder satisfaction. The focus should be on quality, not quantity, to avoid being bogged down by vanity metrics. The key takeaway is that metrics should align seamlessly with the organisation's objectives and overarching vision.

Measuring the ROI of your data strategy involves -~Defining clear objectives ~Identifying KPIs ~Establishing baselines ~Implementing the strategy ~Ongoing data collection, analysis, and ~ROI calculation. It's essential to quantify both tangible and intangible benefits, make adjustments based on results, and communicate findings to stakeholders. This iterative process ensures that your data strategy aligns with business goals and delivers value over time.

When calculating ROI of a data strategy, besides quantifiable monetary benefits, consider intangible benefits such as adherence to regulations, enhanced data quality, and improved decision-making. Organisations can use a combination of benchmarks, before-and-after comparisons, surveys, and qualitative assessments to measure/track their impact. 1. Compliance and Regulatory Adherence: A decrease in violations suggests better adherence to regulations facilitated by data strategy. 2. Reliability and Trust in Data: Fewer data errors and inconsistencies indicate improved confidence and trust in these data for decision-making. 3. Improved Decision-Making: Are decisions being made more quickly and accurately, with fewer instances of guesswork?

Measuring data strategy ROI is vital for proving its worth. Ways to calculate ROI include: Cost Savings : Reduced data storage and processing expenses Time Efficiency: Time saved in data handling and decision-making. Operational Efficiency : Fewer errors and optimized processes. ROI Metrics: Use relevant metrics like ROI, ROD, or CLV Benchmarking: Compare with industry benchmarks Long-term Value: Consider lasting impact on business success.

Check Current Usage: Look at how data is being used in your company. Do a Data Checkup: List and check your data for mistakes or missing info. Set a Starting Point: Compare your data handling to standards or competitors. Make Clear Pictures of Data: Create dashboards to show your data situation. Find Gaps: Identify what's missing or could be better in your data strategy. Write Down and Discuss: Document findings and share with key people for feedback.

Context matters. Communication without context can lead to misunderstandings and a disconnect between team members. It's crucial to provide context when communicating, so everyone has a clear understanding of the objectives, the reasons behind certain decisions, and how their contributions fit into the larger picture. This contextual understanding helps in aligning the efforts of data professionals, software engineers, and other stakeholders, making the collaboration more effective and the project more successful.

If all or part of your data is customer facing this is the easiest to factor since It is a KPI in your product strategy. For internal KPI monitoring, you could use tools like Alation or one of several Data Observability tools or Data Catalog tools that "gamify" the process they support and monitor. These tools also monitor data usage with their own insightful dashboards.

This ROI on the example doesn't make sense. ROI can sometimes be more complex to calculate depending on the specifics of the project, and other factors like time value of money and ongoing operational costs might need to be considered. There's also quantitative metrics and qualititave metrics. Not a simple math formula like this.

The best way is to document all the ways your data is used. Data users should be able to at least share how the data helps them day-to-day, while managers should be able to provide a monetary assessment of the effects - they might be operational efficiency, prevented downtime, economies of scale or whole new business opportunities. By simply adding the monetary utility for each group of data users, you get to the total gain. The TCO calculation is trickier - it should include fixed investment, opportunity costs (e.g. viability analysis; legal and compliance costs), operational costs (human capital, support, amortization). A mistake I've seen time and time again is businesses downplaying costs to maximize ROI.