Data & Analytics Summit 2018
by Mike Ferguson, Lawrence Corr , Rick van der Lans, Jen Underwood, Daragh O' Brien download a PDF brochure
For many years companies have been extracting operational transaction data from OLTP systems into classic Data Warehouses for query, reporting and analysis to help them make decisions in a relatively passive way. However, in recent years things have started to change quite dramatically with organisations now demanding more data and the use of analytics in almost every part of the business.
There are several reasons for this. One of the most important is the fact that the mobile device has made prospects and customers very powerful. Everywhere you go, people are looking down at a mobile device that is almost always connected to the Internet. The result is that loyalty has become cheap and companies are now having to work harder to keep their customers by collecting as much data as possible to understand them better and using it to personalize products and services to meet their needs.
At the same time, Digital Transformation is changing the way companies operate. Prior to digitalization customers, suppliers and partners interacted with employees who then used applications to transact business. In the new digital enterprise customers, suppliers and partners are interacting with organizations via self-service mobile applications and on-line computer systems. Therefore, the chance to engage them is very limited. Also companies are deploying sensors in operations to collect data to enable them to improve efficiency and reduce cost.
The bottom line is that Data and Analytics have shifted to the centre of the enterprise. Data is now coming in everywhere and the need for analytics is in every part of the business.
New NoSQL analytical platforms have emerged, we have gone from batch based data extraction, a Data Warehouse and passive analysis to a more complex world of multiple analytical systems and workloads. In addition, there is massive pressure to manage, govern and integrate data and across this systems and to simplify access to delivering integrated actionable insights into to every part of the business.
- How can this be achieved?
- How can you do this in a hybrid computing environment when you have to manage data across multiple Cloud and on-premises systems?
- How can you improve agility while also ensuring data is governed to remain compliant with new EU legislation like GDPR?
- How do you leverage new technologies like deep learning, Artificial Intelligence and Advanced Analytics while simplifying access so that self-service BI users can take advantage of it all?
In this, Enterprise Data and Analytics Summit 2018, we address these issues. We will look at the technologies, techniques and architectures needed and how to integrate analytics and insights across analytical systems to deliver competitive advantage.
- Building an Intelligent Enterprise in a Hybrid Computing Environment
- Governing Data Across Multiple Data Stores - The Critical Importance of an Information Catalog
- Understanding the GDPR
- Big SQL Solutions for Big Data Systems
- Machine Learning in the Enterprise
- Data Modelstorming: From Business Models to Analytical Models
- Practical Analytics Innovation
- Dimensional Design Pattern Recognition and Usage: Advanced Star Schema Modeling
- Integrated Data and Analytics or Unmanaged Silos?
- Data Management in a Cloud Computing Environment
- Unifying Data Lakes, Data Marketplaces, and Data Warehouses