Information Architecture at the Enterprise Level Results of the 2013 DATAVERSITY Survey

Transcription

1 Information Architecture at the Enterprise Level Results of the 2013 DATAVERSITY Survey David Loshin Knowledge Integrity, Inc.

2 Methodology 28 questions divided into General demographics Current and future information architecture implementation Definition of information architecture Data modeling and metadata Data integration Master data management Data virtualization 2

3 Response 205 participants, average number of respondents was 140 Cohort largely consisted of data management professionals Data and/or Information Architecture: 63.3% (124) Information/Data Governance: 31.1% (61) Business Intelligence and/or Analytics: 20.9% (41) Industries represented: Insurance (16.1%) Finance (10.2%) Technology (8.8%) Consulting (8.3%) Education (8.3%) Healthcare (8.3%) Banking (6.3%) Retail (4.9%) Energy (3.9%) 3

4 Definitions of Data Architecture Terms used synonymously include Data Architecture (DA), Information Architecture, Enterprise Information Architecture (EIA), Enterprise Information Management (EIM), Enterprise Data Architecture, Enterprise Architecture (EA) Provided definitions for information architecture can be organized into these subgroups: Policy and requirements management Interoperability Data management and metadata management standards Data life cycle management Data layouts and models Uncategorized Do you have a formal definition of Data Architecture within your organization? Yes 36% No 64% 4

5 Components of a Data Architecture Data modeling Metadata management Data integration Master data management Data virtualization 5

6 Current and Future Implementations What existing practices should be included in the IA? Data Modeling: 76.1% Data Warehousing: 64.9% Naming Standards: 62.1% Database Design: 61.6% 6

7 Current and Future Implementations, Continued What is most important to include in the IA? What should not? Data Virtualization: 18.6% Data Virtualization: 58.9% Database Design: 11.9% Data Governance: 54.6% Data Storage: 10.4% Data Services/SOA: 54.5% Business Intelligence: 10.1% Data Retention: 52.3% 7

8 Data Modeling Positive results: Complicating factors: 42% use the data model as the starting point for application development Only 75% answered these questions Approximately 50% have begun developing an enterprise data model 8

9 Metadata Encouraging to see increasing use of diverse tool sets for capturing and managing metadata 9

10 Data Integration Data integration has a broadening landscape Yet Over 33% of the respondents perform 50% or more of their data integration manually 10

11 Master Data Management MDM is a maturing practice Most frequent master domains? Customer 58% report not having a golden copy as part of the master data architecture Product 11

12 Data Virtualization Growing recognition Main factors driving use: Real-time or on-demand access to data Reduce replication of data Time to market/agility 12

13 Considerations Despite the apparent need for information architecture, it is still difficult to pin down what it really means to practitioners There is growing awareness of the value of a coordinated approach to dovetailing the use of tools to support the information management practice Increasing maturity in modeling and metadata has visible benefits in reducing total cost of information management Techniques previously seen as auxiliary such as master data management and data virtualization are increasingly seen as core components of information architecture 13

14 Questions and Open Discussion If you have questions, comments, or suggestions, please contact me David Loshin