Return on Data” ROD to evaluate data-driven financing decision

Like ROI “Return on Investment”, “Return on Data” ROD is to evaluate data-driven financing decision.

The ROD metric is modelled on the classic ROI formula, which is a very popular metric because of its versatility and simplicity. Essentially a ROD examination is an easy way to gage the potential of a data analytics investment.

The whole point of data and analytics is to provide value for users and the provider company. Focusing on value rather than data prioritizes critical business outcomes and ensures that the step-by-step approach yields wins earlier and keeps momentum going longer. So Do not focus on data, focus on value:.

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Return on Data Formula

ROD = (Gain from Data — Cost of Data) / Cost of Data

Gain from data: Proceeds obtained from the better usage of data. Values may be grouped into the following categories:

· Business impact benefits

o Shorter sales cycles

o Competitive advantage

o Improved revenue per customer

o Increased volume of sales

· Operational benefits

o Employee time savings

o Avoid hiring of additional resources

· Technology and Institutional benefits

o IT maintenance cost reduction

o Consulting cost savings

Cost of data: Costs incurred by the required multi-level infrastructure of data. It may be grouped into the following areas:

· cost of operating the data centres, including the hardware, software and manpower costs of doing so

· operational costs including the deployment and upkeep of the data infrastructure, etc.

· costs of investing in data analytics solutions and of operating these including software, hardware, people, external resources, etc.

When Data Creates Competitive Advantage

To determine to what degree a competitive advantage provided by data-enabled learning is sustainable, we should answer the following questions:

1. How much value is added by data relative to the stand-alone value of the offering?

a. Accuracy of business deliverables matters

b. E.g. the testing data is essential to improving accuracy (Safety products, damages test device)

2. How quickly does the marginal value of data-enabled learning drop off?

a. Accuracy of business deliverables matters

b. More data increases accuracy significantly at margin

3. How fast does the relevance of the user data depreciate?

a. If the data becomes obsolete quickly, then all other things being equal, it will be easier for a rival to enter the market, because it doesn’t need to match the incumbent’s years of learning from data.

4. Is the data proprietary — meaning it cannot be purchased from other sources, easily copied, or reverse-engineered?

5. How hard is it to imitate product improvements that are based on customer data?

6. Does the data from one user help improve the product for the same user or for others?

a. Generalization matters (e.g. Meta-learning + Few-Shot learning)

b. Human in the loop matters (e.g. active learning)

7. How fast can the insights from user data be incorporated into products?

Note:

Data won’t build strong competitive advantage UNLESS the value added by data is high and lasting, the data is proprietary and leads to product improvements that are hard to copy, or the data-enabled learning creates network effects.

Trends Shaping Your Ability to Maximize ROD

· Distributed Digital Infrastructure

· Rise of Artificial Intelligence (AI) and Machine Learning (ML)

· Corporate Data Responsibility

Ref.

1- The One Digital Metric That Matters — Return on Data

2- RETURN ON DATA file:///C:/Users/CINPC0075/Desktop/ROD.pdf