What if we treated all science like data science?

Often when working with clients, they ask me to meet with the data science team and determine what they need. They can’t understand the lack of progress in any initiative and why the team, despite their experience and skills, is not able to provide any insight into their organization.

Usually, within the first few minutes of the initial meeting with a data science team, the issue is clear.

Data science teams can’t produce digital initiatives results without the proper tools

In our new viewpoint, “Getting unstuck: Traction for transformation.” we discuss some of the challenges organizations face when trying to see results from digital initiatives. One common factor that hampers innovation across industries is the less-than-ideal use of data. Moreover, as some experts have said, successful digital transformation requires data transformation.

Where are the results? - an analogy

Imagine hiring a scientist specializing in molecular chemistry. They were at the top of their class from the most prestigious university, and you know that they are fully capable of making the latest discovery and can’t wait to see what amazing formula they create. They are excited, too, as they put on their white lab coat for their first day of work. “Let me show you to the lab!” you say as you gleefully walk them to their laboratory. “Well, here it is! I’ll check on you in a month.”

The chemist opens the door, flips on the light, and their face immediately drops. The room is a disaster. All the equipment is behind locked cabinets for which they don’t have a key. Even though there are jars of chemicals, none of the jars are labeled and it’s not clear how long they have been sitting out on the counter. The chemicals they really need are nowhere to be found, and after calling around, they find out that those chemicals are on backorder and can’t be delivered for months.

A month later, you walk back into the lab expectantly, ready for the latest new chemical formula that will send your sales soaring. “I have nothing,” says the chemist. “Nothing?” you say. “You’re the top chemist in the country!”

The takeaway

Would you expect the chemist to produce anything of substance after a month? They don’t have the foundation of what they need to do their job. Even if they did manage to mix something together that worked, they couldn’t explain what chemicals they used or the formula to repeat those results.

Data science teams need a solid foundation to glean results

Most organizations hire a data scientist and put them in the same situation as the poor chemist in the story above. The data scientists don’t have access to the data they need, which makes it impossible to get results.

The data that they can access has no associated documentation, and the age, relationships, and relevance of the data are unclear. The data is also spread around the organization, causing them to go on a scavenger hunt through different departments. Even if they are able to build an algorithm or machine learning model, it’s difficult to explain or repeat the outcome.

3 things data scientists need to be able to do their job:

1. Data engineering team to help gather and catalog data
2. Tools to source, identify, and promote production
3. A trusted partner – like CGI – to provide strategy and implementation for their data science initiatives like data engineering, analysis, and data governance practice

Done right, data strategy and implementation support can be the catalyst (chemistry pun intended) for real transformation. Let the experts at CGI help get your business the tools needed to get your  data science team set up for success.

Learn more about the role of data in digital transformation and the digital value chain.