Not many people know this, but the First Order (the bad guys from the latest Star Wars films) once created a Data Science team. It all ended very badly, but based on some intel smuggled by a chippy R2 unit, we were able to piece together the story …
Analytics: Expectation vs Reality
Now, of course this is just (data) science fiction, but the basic plot will be familiar to many of you.
The marketing hype around AI and Data Science over the last few years has really raised the stakes in the analytics world. It’s easy to see why – if you’re a salesperson selling AI software for £1m, then you’re going to need to be bullish about how many millions it is going to make/save the customer.
The reality though is that Data Science can add enormous value to an organisation, but:
- It isn’t magic
- It won’t happen overnight
- It’s very difficult if the building blocks aren’t in place
- It’s more about culture and change than algorithms and tech
So, how do we deal with a situation where leaders (whether they be evil Sith overlords or just impatient executives) have inflated expectations about what is possible (and have possibly over-invested on that basis)?
Education is key
With so much buzz and hype around analytics, it’s unsurprising that leadership are bombarded with an array of confusing terminology and unrealistic promises. To counter that, it is important that Data Science teams look to educate the business and leadership on what these terms really mean. In particular, we need to educate the business on the “practical” application of data science, what the possibilities are, and the potential barriers to success that exist.
Create a repeatable process
Once we’ve educated the business about the possibilities of analytics, we need to create a repeatable delivery process that is understood from both analytic AND business perspectives. This moves the practice of analytics away from “moments of magic” producing anecdotal success to a process that is understandable, repeatable and produces consistent success. Within this, we can establish shared understanding about how we will prioritise effort, measure success, and overcome the barriers to delivering initiatives (e.g. data, people, change).
Having established the above, we must engage with the business and leadership using our new consistent language and approach. This will ensure the business understands the steps that are being carried out and the risk of success and failure. After all, if there’s no signal in your data you can’t conjure accuracy from nowhere – ensuring that your stakeholders understand this (without getting into the detail of accuracy measures) is an important enabler to engaging effectively with them.
Being in a situation where the value and possibilities of data science have been significantly over-estimated can be very challenging. The important thing is to educate the business, create a repeatable process for successful delivery and be consistent and clear about the realities and practicalities of applying data science.
Then again, if your executive sponsor starts wielding a Lightsaber – I’d get out quickly.