Big Data Conference
For many companies today, investment in data and analytics is now mainstream. It is high priority in the board room and business stakeholder expectations from Artificial Intelligence are high especially to improve Customer Experience. Therefore investment is happening with multiple different departments and business units now funding their own data and analytical projects. Data Science initiatives have emerged everywhere across the enterprise and Cloud adoption is growing very rapidly for many of these analytical projects. The result is lots of activity especially on the Cloud.
Data Lakes have emerged on the Cloud using Cloud storage instead of Hadoop.
Cloud based Data Warehouses are being built and existing Data Warehouses are being migrated.
Data Scientists are using self service data preparation automated Machine Learning to speed up model development and trained models are slowly being deployed to serve up predictions on demand.
However, the result of all these independent data and analytical initiatives is that silos are emerging. New data flows are being created that IT are unaware of and a lot of new data is flowing into the enterprise from edge devices, ungoverned downloads and customer facing mobile applications connected to enterpise applications on multiple Clouds as well as the Data Centre.
Two major problems are emerging.
The first is that data sources are growing, and the data being captured is spreading out across Data Centre and multiple Clouds creating a distributed landscape. This makes data harder to find, harder to integrate and harder to govern.
The second problem is that although there is a lot of activity and investment going on in data and analytics, much of it is fractured and not integrated.
- So how do you sort this out?
- How do you create an AI strategy to pull together independent projects across multiple business units?
- How do you use data and AI to improve Customer Experience?
- How do you industrialise what you are building to shorten time to value?
- Also, how do you govern your data across a distributed data landscape to get your it under control?
- How do you create a data architecture to operate across edge, multiple Clouds and the Data Centre?
- Can you build Data Warehouses more rapidly on the Cloud and how do you integrate your Cloud Data Warehouse with your Data Lake?
- Finally, is building predictive models enough?
- How can you go beyond predictive and prescriptive analytics to become a self-learning enterprise?
All of this will be answered and more at this year’s Italian Big Data Conference.
- Creating an AI Strategy for Maximum Business Success
- The impact of the Cloud on Data Architecture for Data Driven Enterprise
- Enabling value driven Data Governance in a distributed data landscape
- Enterprise DataOps – Industrialising and Automating Pipeline Development for Data and Analytics
- Next Generation AI – Transitioning to the Continuous Self-Learning Enterprise
- Component Based Development of an Agile Data Warehouse
- Integrating your Data Lake and Data Warehouse on the Cloud
- Data Governance automation using Artificial Intelligence
- The Customer Data Platform – Improving Customer Experience Using Data and AI