- Data Science
- Machine Learning
With more and more organizations recognizing Data Science and Machine Learning as a business imperative, it’s important to consider how to drive from experiments and proofs of concept to deliver repeated business value through operationalized analytics.
Many companies are already investing heavily in using data science approaches to understand their business better. Others are in the early stages of learning, and keen to reap benefits quickly. Whatever your position, it’s vital to consider how the insights generated by data science and machine learning teams will be shared with the business decision-makers. Operationalizing your analytics solutions ensures your teams constantly have the most up-to-date insights, based on the best interpretation of the data.
In this recorded webcast, Thorogood Consultants Liz McCreesh and Andrew Kennedy draw on examples of real deployments at enterprise customers to show how they’ve combined best-in-breed analytics engines such as Databricks and Azure with end-user dashboards and reports to deliver business value by driving action.
In this session, we look at questions such as:
- What does it take to create an operationalized analytics solution that unlocks insights that improve key decisions, time and again?
- How can I maximise on my data science and machine learning investments to deliver value on a long-term basis?
- What does it look like to shift focus from individual experiments to delivering user-focused solutions that have an identifiable impact?
Is it for you?
- Are you struggling to make insights from data science widely available within your organization?
- Have you faced issues in moving from a successful proof of concept or pilot project to a high-value enterprise level solution?
- Do you have questions regarding which technologies best suit your needs, both now and in the future?