Information Governance as a Key Enabler of Today’s Big Data Strategies
Allie (Grassie) Harris
Allie (Grassie) Harris is the Director of Information Governance for Ontario Lottery and Gaming and is responsible for the creation and implementation of organizational Information Governance strategy. Allie’s prior roles include Bank of Montreal’s Data Governance & Analytics team as the Senior Director, Data Governance, and progressive positions at the Canadian Imperial Bank of Commerce (CIBC) in Enterprise Process Management and Analytics, and data management, governance and analytics for the Wholesale Banking area.
She sits on ISO/TC 68, WG6 (LEI), co-convenes the ISO/TC68 Standards Advisory Group, and sits on the Securities Industry and Financial Markets Association (SIFMA) Steering Committee for Legal Entity Identification. She was part of the 2011 industry delegation to the Financial Stability Board meeting on LEI in Basel, where agreement was reached on the creation of a Global LEI standard. Allie has been involved in standards for many years, from health informatics, semantics and messaging, HL7 Canada Secretariat, and is the Vice-President of the Information Resource Management Association of Canada.
Richard Beatch, Ph.D
Richard Beatch, Ph.D., Semantic and Metadata architect for Bloomberg LP, based in Princeton, NJ. In this capacity he serves as the Ontologist behind the development of the Financial Instrument Global Identifier (FIGI) which is an open standard, unique identifier of financial instruments which is now an OMG Standard. Richard is a member of the Architecture Board and Board of Directors of the Object Management Group in addition to serving his native country, Canada, on the Standards Council of Canada. As an extension of these rolls, Richard works on various global ISO committees. He holds a Ph.D. in Ontology and has worked extensively in roles ranging from Knowledge Architect to various senior management roles at multiple companies. For much of the past decade, his work has focused on financial data and the development of semantics models aimed at optimizing the efficacy of that data.
Title: Information Governance as a Key Enabler of Today’s Big Data Strategies
Information Governance is a key enabler of today’s big data strategies. As we move past data meaning being imposed by columns and rows, and as our data grows ever more complex, traditional data management methodologies, including Customer Relationship Management systems or Product Management systems can simply fracture and proliferate data.
Ontologies are “a set of concepts and categories in a subject area or domain that shows their properties and the relations between them” or more simply, a way of defining a thing, and that thing’s relationships with other things. But before you can use a ontology to accelerate your data strategy – you must have the structures in place to ensure that the process of defining the data is governed!
Who in your organization has the right to define what is meant by “net sales”? Who can define “the customer” and their attributes? Allie Harris and Richard Beatch will take you on a whirlwind tour of “Data Governance 101”, where you will learn the basics of:
- Understanding your data “domains”
- Roles and accountabilities – Stewards, owners and custodians
- Metadata and data definitions – the “how” and the “why” of understanding the data and its importance to creating ontologies
As you reflect on a governance effort in your organization, you quickly realize the need to integrate data from multiple sources. Further, as organizations grow, the need for data integration across silos and between divisions looms large. This is where standards can provide a platform for data integration. If we are to integrate data from such diverse sources in an efficient manner, our life is made easier if the data is natively structured and described in a manner that permits easy integration.
Traditionally, standards organizations produced standards documents that were designed for humans (very patient humans) to read. The rise of technologies shifts this paradigm. By providing a standard manner by which meaning can be expressed in a machine-readable format, the door is opened for scalable data integration. From a standards perspective, however, the critical element is that standards that can be expressed in terms be consistently expressed so as to be machine readable and genuinely interoperable.