Keeping Up With Data #59
Data has value. There is no doubt about that. Opinions differ on what that value is and how to quantify it. Is it an accounting or economic exercise? Is it even possible to have a ‘generally accepted’ formula for data valuation? I think leveraging the economic value of data (by using it) will in time create larger demand for data. This will then drive supply and the market will converge to a value for data. Until then, I believe, the ‘how can I use it’ question will be more relevant than ‘for how much can I sell it’. Just like the numbers in the image above were estimated based on the value of having data on certain personal profiles.
The theme in the following reading list is value and complexities related to measuring and unlocking it.
- ThoughtSpot CFO on getting the technicalities — and the story — right in cloud transition: Many traditional businesses are using data and digital technologies to reinvent themselves as a SaaS. ThoughtSpot is far from a traditional business, but the lessons from their CFO, what a shift to a cloud-based SaaS business model means for the financial reporting is worth reading. Data and digital strategy can fail incredibly if they are not backed by investors and shareholders. Creating a narrative for a change in headline metrics is as important as the change itself. (CFO Dive)
- How to properly use data in your organization: An essay about various angles to data. It starts by acknowledging that “data enhances excellent product; data does not make great products”. But for sure it doesn’t hurt to use data to keep improving the operations, products, and services. There is just so many traps along the way: untamed growth of data, poor communication, or repetition to name a few. Plus, sometimes we are hoping to find an answer in data, when in fact we should just simply ask customers. Either way, we should always qualify data, and constantly keep improving its collection and processing. We ‘should be using the data the same way the Weather Channel does’. (Bowen @ Medium)
- Singing the Data Analytics Blues: There seems to be a lot of complains about data analytics. Partly because of very high expectations — it would be amazing to make decisions under certainty, right? However, there is not much certainty in the world of business. The three main issues with success of data analytics resonate with me strongly: (1) a lot of data analytics is just a hypothesis-free data mining; (2) the complexity of business is impossible to be fully captured in data; and (3) in business, data analytics can’t predict future. Suggested remedies? Never analyse without a theory, recognise complexity, work on your judgement, invest in genuine experimentation, and — most importantly — never believe that an answer is perfectly right because data analytics said so. (Roger Martin @ Medium)
We are in December — Christmas is around the corner, travel restrictions are getting tougher (no family visits this Christmas for us), it’s cold and snow outside. And every business is sprinting towards the winter break. It feels a bit like a Champs-Élysées stage in the Tour de France. Overall winners are already celebrating, the rest of us are trying to get some last results.