Do you know how much your house is worth? Or that Tesla you just bought; how much money is that saving you by using electricity? Perhaps, you also know how much your data is worth. No? Let me help you quantify it!
Data is being captured at every step of our digital journeys and it’s also being monetised at a large scale. But not every piece of data has an associated value. In fact, very little actual data valuation is carried out.
Why is that?
It’s because, there isn’t one simple framework for doing this. It’s neither like a property that appreciates in value over time and nor like a car that depreciates in value.
So, how do you value this behemoth?
#1 Asset’ising the data in line with business areas
#2 Use-case & risk-based valuation
Let’s dive into each of these:
This is also called the consumption-based approach and here is a great article on this.
This is in line with how a lot of data governance model currently work. It’s simply chunking up the data in line with key business areas / model, like customer, sales, accounts, billing etc.
Once the data is divided up, the business area is now held accountable for the management of the data.
Now, this is where things differ from the typical data governance model. Management of the data is not simply accountability end to end, but also means valuation of that data.
Each data asset would have set of metrics associated with it. Things like:
Some further questions are asked on the cost of maintaining this data. This isn’t just IT infrastructure costs, but costs for data stewards and owners in the business. The cost for improving the quality of the data etc.
When the questions above are answered, it’s easily evident which data asset across the enterprise is more valuable. This also helps doubling down on data asset that have the potential to drive more value for the business.
Some common sense should also be applied to this approach, as not all data assets are equally as critical. Accordingly, a weighting factor can be applied to boost the metrics.
This is a proactive approach, where even before use-cases have been understood, a valuation is applied.
This is a more reactive approach. And here is a great article on this approach.
This solely bases the value of the data on how it is being used right now.
So, the questions that would be asked are instead:
Now that the use case related questions have been answered, the second part of the equation is applied.
And as above, the same factors on the cost of maintaining the data is applied. Remember, having more data brings additional regulatory and reputational risk. So, a balance needs to be struck between data capturing, its maintenance and its risk.
Some additional factors, especially for data consent can be applied. If a customer has consented for their information to be used and now that demographic of customer fits the particular use case; then clearly this data is a lot more valuable.
By answering the above, you can value the data based on the use-cases and risks of that data. This also helps pinpoint data that is being captured, is of high risk and not being used at all. An opportunity to save some costs by removing this useless data from the organisation.
There isn’t a one size fits all approach. Each organisation may value their data differently based on its business model. A tech giant like Google’s entire business is dependent on good quality data whereas some traditional organisations may not rely on it as much.
Having said all that, it’s imperative to understand that the world is going digital faster than ever before with a multitude of challenges brought in by COVID. An organisation not valuing or using the data it has to get a competitive edge is soon going to be left behind.
If you’re still reading this, I hope you’ve found some value in this blog post.
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Check out my other blog on Data Privacy