Data as a Service (DaaS) - A Recipe to Expedite Information Delivery (DaaS >> Beyond Data) Lakshmi Randall

Transcription

1 Data as a Service (DaaS) - A Recipe to Expedite Information Delivery (DaaS >> Beyond Data) Lakshmi Randall Industry Analyst - Information Management & Analytics Unabashed.io Copyright 2016 Lakshmi Randall All Rights Reserved.

2 Isn t there a better way? Copyright 2016 Lakshmi Randall All Rights Reserved. 2

3 What is Data as a Service? Definition: Data as a Service (DaaS) is a style of information architecture geared towards transformation of raw data into meaningful data assets, and timely delivery of these data assets on demand via a standard connectivity protocol. DaaS architecture can perform one, or any combination of functions such as collection, ingestion, integration, enrichment, curation, contextualization, aggregation and analysis of internal and external data involving any structure or lack thereof. Copyright 2016 Lakshmi Randall All Rights Reserved. 3

4 Copyright 2016 Lakshmi Randall All Rights Reserved. 4

5 Scalable platform is needed to deliver highly customized data assets DaaS can leverage combinations of SaaS and PaaS as implementation models Copyright 2016 Lakshmi Randall All Rights Reserved. 5

6 Challenges Addressed by DaaS Strategic decisions depend on multiple perspectives (social, economical,..) which require combining internal and external data Focus on business outcomes rather than on developing and managing intricate network of data Size, Structural Complexity, and Frequency matter Too many Data Silos in the Enterprise Need reusable and meaningful data assets to share How can I discover data assets? Is there a data catalog? Can data be made available for self-service? Need to expedite delivery of purpose-built analytics Need purpose-built analytics with end-to-end visibility Copyright 2016 Lakshmi Randall All Rights Reserved. 6

7 Challenge: Strategic decisions depend on multiple perspectives (social, economical,..) and thus require combining internal and external data External data assets Open Data Research Findings Data Collectors & Aggregators Data Providers Data Marketplaces DaaS Enablers(offerings) Collect, aggregate, curate and Share external data assets Public Sector Entities Oracle Blukai Datafiniti IBM D&B Reltio Qlik Quandl DaaS Outcomes Share Communal Data Inject external context Infuse different perspectives Facilitate Data Monetization Exploit/Consume External Data assets Copyright 2016 Lakshmi Randall All Rights Reserved.

8 Challenge: Enterprises need solutions to cope with Data Silos DaaS approaches to connecting data silos Data Services Data Virtualization Data Cataloging & Publishing Solutions Data Preparation DaaS Enablers (offerings) Integrate, prepare, orchestrate, govern and share Denodo Cisco Informatica Tamr DaaS Outcomes Enable Interand Intra- Enterprise Sharing Copyright 2016 Lakshmi Randall All Rights Reserved. 8

9 Challenge: Need to expedite delivery of purpose-built analytics Data assets representing end product of analyses (KPIs, Metrics, etc.) SaaS based Analytics DaaS Enablers (offerings) Collect, Integrate, prepare, analyze, orchestrate, govern and share GoodData IBM Watson Analytics DaaS Outcomes Expedite time-tomarket of packaged and domain analytical solutions. Copyright 2016 Lakshmi Randall All Rights Reserved. 9

10 DaaS Use cases/ Areas of Impact Agility On-demand Access Publish/ Subscribe Model Data Monetization Self-service New Business Models Enhanced Decision Making Distributed Analytics New Channels of Data Unique Data Assets Enabling Extended Information Ecosystem Inter- and Intra- Enterprise Sharing Copyright 2016 Lakshmi Randall All Rights Reserved. 10

11 DaaS concerns to be addressed Data licensing and ownership Non-resilient technical architectures Potential impact from Business model changes. Potential to encourage retention and expansion of information silos. Potential for inconsistent metadata and master data due to on-the-fly rationalization. Potential to lose historical context when used in place of an EDW Copyright 2016 Lakshmi Randall All Rights Reserved. 11

12 Recapping a few key points on DaaS Business-centric Enable re-use of data assets Broad consumption Data/Information is the end-product Not suitable for one-off use cases Integrated Governance Specialized data assets Complexity abstracted for end users Copyright 2016 Lakshmi Randall All Rights Reserved. 12