Data Mesh – Opportunity and Challenges for Enterprise Data Management
Over the last couple of years, the data mesh approach has emerged as a new framework to help solve many of the challenges that have plagued organizations as the number and diversity of their data sources has increased. Integrating those sources to satisfy an ever growing set of data consumers and use cases has become increasingly difficult. Data mesh proposes a domain-centric distributed ownership and architecture to serve “data as a product” with federated governance. While this offers substantial benefits for data agility it often requires a significant transformation in a company’s enterprise data management. This session discusses the benefits and challenges of the data mesh approach and describes a Snowflake sample architecture for a data mesh implementation.
CDO successes through governed collaboration – working with business functions and IT
Looking at Masterdata Management, Process Management and Data Governance - I will share a number of concrete cases and lessons learnded from my past few years as a data leader.
A Business-first Approach to Building Data Governance Programs
Traditional data governance initiatives fail by focusing too heavily on policies, compliance, and enforcement, which quickly lose business interest and support. This leaves CDOs and governance leaders having to continually make the case for data governance to secure business adoption. We will share the a lean, business-first data governance approach that connects key initiatives to governance capabilities and quickly delivers business value for the long-term.
Data for AI and Machine Learning - Data Strategy: Governance, Preparation, and Ethical Use of Data for AI/ML
Many organizations are rapidly recognizing the emerging practicality of integrated analytics, artificial intelligence, and machine learning (AI/ML) for simplifying business processes and optimizing business applications. Yet despite the growing fascination with the opportunities for data scientists to employ AI and ML techniques to available data sets, many analysts are seemingly unaware of the need for a comprehensive, governed data strategy to ensure the suitability of the data used for analysis. In this talk we will discuss four governance aspects of a data strategy that help prepare your data for AI/ML deployment, namely:
- Data preparation: standard practices for data standardization and transformation to support analysis and enable feature extraction.
- Data quality: practices for ensuring conformance to defined data quality expectations.
- Data protection: methods and techniques for defining and compliance with policies preventing unauthorized data use.
- Ethical controls: oversight ensuring against inherent data biases to reduce exploitative use, prevent negative stereotyping, and increase inclusion and equity of analytical algorithms.
Umiemy skalować platformy danych. Czy umiesz to samo zrobić ze swoją Organizacją? / Scaling Data Platforms is solved. How to scale your Organisation to match?
Organisations are researching approaches for managing their data assets at scale. At the same time they are reviewing the structure of the organisation and the skills needed to accelerate time from raw data, to data organised as a product. By deploying a Data Management platform such as CDP, it is possible to secure and govern the platform as a whole, while using experiences to enable the lines of business with self-service analytical tools. This provides teams with access to data insights at the right time, supported by a culture of making data-driven decisions leading to an overall improved customer experience.
WORKSHOP I: Super powers of the graph databases
Description:
Do the graph databases stand as a panaceum for data and relations storage? What are their actual powers and how to use these powers optimally? Main features of the graph databases on the example from market leader - Neo4J.
Detailed agenda:
- What is the data context (relations between data)? Graph theory..
- Methods of storing and analysing the context information.
- Why relational databases are less effective and less useful in storing and analysing the relations?
- Graph databases - idea and architecture and key properties; practical exercises on a ready made environment.
- Building the applications by means of the graph databases and their selected functionalities.
- Comparison of the relational and graph databases on chosen examples.
- Summing up, discussion, Q&A
WORKSHOP IV: Information Risk Management for Data Professionals
Description:
As organizations mature their ingestion, integration, persistence, and analysis of data, they are gaining an awareness of the risks of ungoverned data and unprotected information. Concerns about identifying, classifying, and then protecting sensitive data resources are driven by good business practices and customer satisfaction as well as regulatory compliance and guarding against a widening array of risks impacting information value, including threats originating from both outside and within the organization.
Understanding information risk is the prerequisite to managing those risks, and in this tutorial we explore data governance techniques to guard against negative impacts of information risks. We identify different classes of information risks (including impacts of poor data quality, effects of inadvertent unauthorized data access, and accidental exposure) and discuss approaches to information risk assessment, categorization, and management. In turn, we consider data governance practices and procedures for managing information risk and establishing controls and mitigation strategies.
Benefits for participant:
Attendees will learn about Information risk assessment and management:
- Different types of information risk
- How regulations scope and define what is considered to be protected information
- How data assets are assessed and classified in terms of their levels of sensitivity
- Specifying and enforcing compliance with data policies
Agenda:
- Introduction - what is “Information Risk,” where does it fit in overall IT risk management, and what are some categories of information risk associated with data access, usage, quality, and protection
- Data sensitivity – In this section we discuss how “private” data is just one classification of a more general set of categories of sensitivity requiring different levels of oversight and control.
- Information Risk Assessment, where we discuss approaches for scanning the organization’s data landscape, classifying data assets according to information risk categories, and documenting this knowledge in a data catalog.
- Information Policies and Data Controls, where we dissect the concept of an information policy and how it translates into data governance policies.