A great article recently published by Eric Weber articulates why people consider that treating data as a product is a hard task - and raises 5 very good points!
And two of those have specifically resonated with us.
The fact that data does not bring value by itself
Too often, there has been this belief that just by assigning people to make "something" out of available data, there will be value creation.
But it remains key to start from a business problem and then identify the right data and the right approach to actually address it!
The fact that quantifying the value from data is hard
Value creation from data is often indirect and as such not always measurable in terms of direct economic impact. Direct value creation can be measured in terms of incremental sales, improved margins, etc., while indirect value creation relies on risks reductions, improved decision intelligence, improved work environment, reduced environmental impact, etc.
In order to assess value, we need to think in terms of both quantitative and qualitative benefits, and factor in the costs and risks implied by relying on data.
To sum it up, approaching data as a product is thinking about adoption from the very start: making sure the team collaborates with target users on solving the right problem, that they are actually ready to use is and adapt their operating model/processes if needed, and that the results are at the right level of quality and risks are well managed. What we usually synthesize as the 3Us of data & analytics products.
We encourage you to consider your data & analytics investments as products and apply the same principles to manage your portfolio of data & analytics assets as you would do for any product investments. A great recipe to achieve direct or indirect monetization, and manage your data assets as a core capital of your organization.
Ready to discuss further on optimizing your value creation with data & analytics? Contact us!