O C T O B E R 1 8 , 2 0 1 6 S A N F R A N C I S C O B A Y A R E A , C A

#DenodoDataFest

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Accelerating Self Service BI via a Logical Data Warehouse

Mark Blanchette

VP, Business Technology and Data ManagementOctober 2016

Agenda:

1. Introduction

2. Seacoast Use Case

3. Logical Data Warehouse Components

4. Accelerating Self-Service BI

5. Summary & Q&A

3

About Seacoast Bank [NASDAQ: SBCF]Attractive Geography; Deep local Roots; Benefiting from Investments in Digital Transformation and Commercial Loan Platform, and Strategic Acquisitions

Seacoast Use Case

Background• Historically, centralized reporting

• Majority of the data came from the “core”; hosted data warehouse environment

• Reporting, mostly batch, with little interaction with the live data. Output excel, .pdf files

Business Drivers• Business self-service: “Democratization of the data”

• Unification of information assets

• Enablement and speed to onboard of 3rd party and on-premisedata with the hosted environment

• Lessen Seacoast’s dependency on external vendors for maintaining our information

Project Approach

Socialize

•Discuss with Executives

•Obtain financial support

Selection

•POC with Vendors

Engineer

•Design Solution

•Model Data

Build

•Infrastructure

•Logical views

•Report templates

Pilot

• Business report users

•Verify data

Deploy

•Deploy solution to production

What is a Logical Data Warehouse (LDW)?

According to denodo:

A logical data warehouse is a data system that follows the ideas of a traditional Enterprise Data Warehouse (star or snowflake schemas) and includes , in addition to one (or more) core DWs, data from external sources.

LDW provides a single view of all the data, without having to transport it from its original silo, thus easing consumption of the information.

8Presentation Title Here 8

Data Virtualization

Repository Management

Metadata Management

Taxonomy/ontology

Performance Monitoring

LDW From a Component Perspective

Service Level Agreements

Solution Overview

• Virtualize data using Denodo and presented it as datamarts to reporting

tools

• Physically move/cache data, when needed, for performance reasons

• Utilize SAS Visual Analytics for Enterprise Reporting, interactive reports,

and analytical reporting

Hosted Data Warehouse

Application Data

Store (ADS)

Dimensional

Data

Warehouse

(DDW)

Data Virtualization “NetFlix Streaming”

Physically Move Data “DVD”

Data Sources Transport & Storage Layer

Data Marts -

Physical

(MySql)

Virtualized

ViewsVisual Tools

Operational

Reports

Ad-Hoc

Queries

Presentation Layer

Web

ServicesM

eta

-Data

Other sources of Data

Loan

Origination

Systems

Other

Application

Data

Data

Defin

itions

Mobile

Visuals

Reporting

Ad Hoc Tools

LDW Architecture Metadata Mgmt.

Data Virtualization

Integration Self Service BI

Taxonomy/ontology, performamce SLA’s

Repository Mgmt.

Datamart Creation

• Leveraged the dimensional model (star and snow-flaked schema) from the hosted environment

• Created 9 datamarts covering the main business subject areas of the bank

• Created over 30 derived (virtual) views segmented into the business datamarts

• Build once , deploy everywhere

• Views loaded into SAS Visual Analytics, where they are available in the in-memory model for reporting and analytics

Derived (Virtual) Views

Enterprise Data Dictionary• Created a custom web based application for managing

business terms• Linked with views to provide business friendly names in SAS Visual

Analytics

• Used for both development and self-service BI

• Terms can be custom tagged, classified by security classification, assigned to a data owner.

Metadata Linkage• Created a process to link data dictionary term names to

derived views. Business “friendly” terms

• As data stewards, update, add terms, they can be integrated as part of the end user interface

• Terms on reports can be easily searched in the dictionary

Physical Column Names Business Term Names

Self Service BI

• Sources of data can be consolidated in the logical data warehouse and be made easily available to reporting engine

• Webinar was developed to guide business users through the basics

• Easy to use web based reporting and analytics platform – SAS Visual Analytics

• Departmental datamarts that are optimized for reporting

• Report templates, that replace one or more static reports, yet deliver dynamic content

• Reduce and eventually eliminate ad-hoc type service requests

Report Example

Logical Data Warehouse Benefits

50%

Less time vs traditional data warehouse approaches

3 Hrs. VS 3 Days

Sourcing data for BI vs traditional ETL methods

Data from different technologies/sources can be easily combined

At least for Seacoast, a LDW will help free up resources to work on other Enterprise projects.

Additional Benefits of Denodo VDP

• Real time web services• Utilized web services during the project to expose data to custom

applications.

• Data governance • Data lineage, impact analysis

• ITPilot : • Capture data from websites for including into derived views.

Summary

• Using a Logical Data Warehouse (LDW) can significantly shorten the time to source/model data vs traditional approaches.

• With a LDW, IT can quickly deliver datamarts and views of the data that are optimized for reporting.

• Lines of business, analysts and data scientists can focus on decision making rather than how to source the data.

• Creating reports that allow for dynamically selecting the data, will allow for higher degree of reusability and focus on just the data needed.

Panel

M O D E R A T E D B Y :

Mark Blanchette

VP Data Warehouse, Seacoast Bank

Praveen Saluja

Director of BI, Fastaff

Chandra Siv

General Manager and Head – Data & Analytics Solutions, Mindtree

Lakshmi Randall

Head of Product Marketing, Denodo