Our new Solutions Spotlight series will focus on client outcomes, use case solutions, impactful new technology and best practices across data and analytics. We’ll cover strategy, governance, user adoption, finding ROI quickly, architecture, agile development, tips and tricks, and more.

As part of these series, in our latest webinar we talked about creating real ROI with data science, and (spoiler alert) we do this by focusing on the business value that our initiatives are going to bring. Let’s explore more!

Adding in the Data Science

We started talking about the percentage in which companies have adopted AI globally. In Australia for example, 24% of companies have deployed AI and 44% are exploring. This leaves us with 32% of companies which potentially haven’t started exploring yet the benefits that AI and ML can bring to the organisation. There might be several reasons for this, such as companies not having the right skillset in house or them having other pressing priorities to focus on. That said, it is important to understand the benefit that these technologies can bring and, moreover, to have a framework to deliver value to the business in an agile way.

This framework starts with being able to identify the different use cases and prioritise in terms of value to the business and effort of implementation. It is recommended to start with the highest value and lowest effort use cases to showcase early on the benefits of using ML:

Increasing ROI

As part of this framework to shorten time to value, we talked about one of the current challenges of organisations aspiring to do data science: every data analyst and data scientist might be using their own preferred tools and languages, which makes it difficult for the organisation to enable team collaboration and having a sustainable way of productionalising and monitoring the models later on. This is where our technology partner Dataiku comes in.

On one side, Dataiku enables people from all skill levels and working styles through their unique combination of visual recipes for the low-code/no-code audience, all while catering to the people that prefer coding with their code recipes, notebooks and code environments. On the other side, Dataiku enables the full machine learning project lifecycle—from ideation and exploratory data analysis, to data preparation and model experimentation, to getting the selected models into production and monitoring them afterwards—all under one roof, which means streamlined processes that allows you to focus on more important things: delivering new use cases and capabilities to the business.

With the help of a framework, the right toolset and a partner that supports you from zero to ML excellence, you are on the right path to deliver real business value through data science:

Are you excited to explore more? Contact us to chat about how we can empower you to start your data science journey today.