Building an Enterprise Data Lake for Enterprise Data-as-a-Service
Most organizations today are dealing with multiple silos of information. These include Cloud and on-premises based transaction processing systems, multiple Data Warehouses, Data Marts, Reference Data Management (RDM) systems, Master Data Management (MDM) systems, Enterprise Content Management (ECM) systems and more recently Big Data NoSQL platforms such as Hadoop and other NoSQL databases. In addition the number of data sources is increasing dramatically especially from outside the enterprise. Given this situation it is not surprising that many companies have ended up managing information in silos with different tools being used to prepare and manage data across these systems with varying degrees of governance. In addition, it is not only IT that is now managing data. Business users are also getting involved with new self-service data wrangling tools. The question is, is this the only way to manage data? Is there another level that we can get reach to allow us to more easily manage and govern data across an increasingly complex data landscape?
This seminar looks at the business problems caused by poorly managed information. It looks at reference data, master data, transaction data, metrics, Big Data and unstructured content (e.g. documents, email, etc). It looks at the requirements to be able to define, govern, manage and share trusted high quality information in a hybrid computing environment. It also explores a new approach getting control of your data that includes participation from IT data architects, business users and IT developers. This includes creating and organising data in Reservoirs and introduces Data Refineries in an enterprise approach to managing data. It emphasises the need for a common collaborative process and common data services to govern and manage data.
Data Scientist Survey
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What you will learn
Attendees will learn how to define a strategy for enterprise information management and how to implement it within their organisation. They will also learn how to organise data in a Reservoir, the importance of an information catalog, data standardisation and business glossaries when defining data to be managed. They will learn an operating model for effective information governance, what technologies they need and an implementation methodology to get their data under control. They will learn how to apply this methodology to get master and reference data, Big Data, Data Warehouse data and unstructured data under control whether it be on-premise or in the Cloud.
- Strategy & Planning
- Methodology & Technologies
- EIM Implementation – Data Standardisation & the Business Glossary
- The Data Refinery Process
- Organizing the Data Lake
- Transitioning to Enterprise MDM – the Change Management Process
- Refining Big Data & Data for Data Warehouses
- Information Audit & Protection – the forgotten side of Data Governance