Data Warehouse Modernisation:
from Passive Data Warehouse to Live Analytical Ecosystem
Today, with most people connected to the Internet, the power of the customer is almost limitless. They can browse your competitors’ Web sites. They can compare prices, view sentiment about your business, and switch loyalty in a single click any-time, anywhere all from a mobile device. In addition, social media has given customers a voice to express opinion and sentiment about products and brands and to create social networks by attracting followers, and following others. For many CEOs, customer retention, loyalty, service and growth are top of their agenda. Therefore, they want access to new data to enrich what they already know about customers.
In addition, COOs are adding telemetry to capture new data to optimize operations. Yet at the same time, regulations like GDPR, KYC, MiFiD are everywhere, making governance and risk management also a priority.
Given these new requirements, many companies running traditional Data Warehouses and Data Marts are realizing that just recording historical transaction activity is not enough. The pace of change is quickening, business is demanding lower latency data, the backlog of changes to Data Warehouses and Data Marts is growing rapidly while testing remains slow and complicated. Also with business unit autonomy, new technology available on the Cloud, and pent up demand for Machine Learning everywhere, shadow IT is springing up in business units fracturing the analytical effort and building new analytical silos that are not integrated with Data Warehouses. With so much pressure to remain competitive, how then do you modernize your analytical setup, to improve governance and agility, bring in new data, re-use data assets, modernize your Data Warehouse to easily accommodating change, lower data latency and integrate with other analytical workloads to provide a new modern Logical Data Warehouse for the digital enterprise?
This new 2-day seminar looks at the business case as to why you need to do this, discusses the tools and techniques needed to capture new data types, establish a data pipeline to produce re-usable data assets, modernize your Data Warehouse and bring together the data and analytics needed to accelerate time to value, deliver new insights to foster growth, reduce costs, improve effectiveness and enable competitive advantage.
What you will learn
- Understand why Data Warehouse modernisation is needed to help improve decision making and competitiveness
- Have the ingredients to know how to modernise your Data Warehouse to improve agility, reduce cost of ownership, facilitate easy maintenance
- Understand modern data modelling techniques and how to reduce the number of data stores in a Data Warehouse without losing information
- Understand how to exploit Cloud Computing at lower cost
- Understand how to reduce data latency
- Know how to migrate from a waterfall-based Data Warehouse and Data Marts to a lean, modern logical Data Warehouse with virtual Data Marts that integrates easily with other analytical systems
- Know how to use data virtualisation to simplify access to a more comprehensive set of insights available on multiple analytical platforms running analytics on different types of data for precise evidence-based decision making
- Understand the role of a Modern Data Warehouse in a data-driven enterprise
- The traditional Data Warehouse and why it needs modernised
- Modern Data Warehouse Requirements
- Modern Data Modelling Techniques for Agile Data Warehousing
- Modernising your ETL Processing
- Accelerating ETL Processing using a multi-purpose Data Lake & Data Catalog
- Rapid Data Warehouse development using Data Warehouse automation
- Building a Modern Data Warehouse in a Cloud Computing Environment
- Simplifying Data Access: Creating Virtual Data Marts and a Logical Data Warehouse Architecture to integrate Big Data with your Data Warehouse