Data Challenges

Are you ready to face your data deamons?

Limited insights

Do business opportunities remain uncaptured because modern and cutting-edge technology cannot be deployed like AI (Artificial Intelligence) or predictive analysis?

Situation: Need for advanced analytics everywhere

Data analysis is one of the most valuable practices for gaining a clear understanding of your business performance in today’s competitive market. To stay ahead of the competition and gain these important insights, it is necessary that you can make optimal use of the available analytical technologies on the market.

Challenge: Finding the right data science use cases and getting enough clean data

A common challenge nowadays is that there are a lot of ideas about data science. Many use cases might pop up in your organisation. Selecting the right use case to start with does not only require AI skills, but mostly business knowledge and knowledge of your organisation. It requires good understanding of the value of the use case AND the complexity of implementing it. Both from a data readiness perspective, as well as from a model perspective.

The bulk of work in AI projects usually sits in getting enough data ready for advanced analytics, like ML and statistical models. This is referred to as data preprocessing.

Solution: Advanced analytics & test lab

Sparkle has a team of business data scientists with a unique skill set combination: combining technical AI/ML knowledge with sound business knowledge. This will facilitate the selection of the right use case.

Data preprocessing work can be reduced significantly with a modern data architecture.

A modern data architecture is often cloud native and includes a test lab for data science experiments. The test lab can use structured and unstructured data from a data lake or any other data source within the platform. There is often additional data used in the data lab that’s not yet available in the organization’s data platform. If the experiments are successful and are also expected to return value on a recurrent basis, then the provisioning of this additional data sets is industrialized in the data platform and the AI models are deployed and monitored in production.

Do contact us to discuss how we can help you overcome your data challenges.

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