DWH & BI in Banking @ ET 2 Nov 2004 Chandrasekhar

Treasury

International

BI and Data Warehousing: How will banks maximize their returns

out of them effectively?

V ChandrasekharBank of Baroda

InternationalTreasury

Agenda• Banking Scenario• Overview

– OLTP– DSS– EDWH– BI

• Indian Banking Needs• Issues in Indian Banks• Critical Success Factors


Banking Scenario• Globalisation• Competition• Reduced Margins• Increased complexity• Problems of Size• M&A• Regulatory Controls - Risk Management• Agility


Data But No Information • Data .. Data every where but not a bit useful• Inaccuracy • Incomplete• Delays • Fragmented

All human knowledge is captured and stored way back in 2000 and is doubling

every 18 months


Need

• DSS• BI


The BI architecture

The three main categories:1. Operational Insight & Intelligence

2. Strategic Insight & Intelligence

3. Special Situations Insight & Intelligence


OLTP Systems• For Automating Business Transactions• Enable bookkeeping • Functional silos – retail, corporate, treasury

etc – they are production data bases• No value for historical data• Data is diverse and complex• User access is complex• User access slows business operations• OLTP systems are not designed for data

analysis


DSS

• Not just individual functional information but cross functional

• Integration – relationships between data elements

• Adhoc Queries• Analytical Queries • Multi-Dimensional Views


Data mart (vs. Data Warehouse)

• Functional vs.. Centralised • LOB vs. Enterprise • Restrictive process orientation • No historical data• Departmental• Specialised• Local • Integrate independent data marts in to EDWH • Create independent data marts from EDWH


Data Marts

• No framework• Data quality• Incompleteness of data• High costs of development and running –

Manpower, Skills, Systems, Complexity• Redundant efforts


What is a Data Warehouse?What is a Data Warehouse?

• The classic 1993 definition by Bill Inmon, “father of data warehousing”– A data warehouse is a:

• subject oriented• integrated• non-volatile• time variant

– collection of data in support of management’s decisions.


• Mostly for performance reasons, a data warehouse is:– held in a separate database from the

operational database, – and usually on a separate machine.

• Perhaps more important reasons are:– navigation, ease of use, relationship with

business areas

Data Warehouse ImplementationData Warehouse Implementation

3 of 3


OLTP Vs DWH• Archived and summarized as opposed to current• Organized by subject as opposed to application• Static until refreshed as opposed to dynamic• Simplified for analysis as opposed to complex for

computation• Accessed and manipulated as opposed to updated• Unstructured for analysis as opposed to structured

for repetitive processing• Data warehouse provides on-line analytical

processing, (OLAP), as opposed to on-line transaction processing, (OLTP).

• One record at a time vs. massive records access• Sub-second response times vs. hours


DWH• Complex ad-hoc queries are submitted and

executed rapidly because the data is stored in a consistent format

• Queries don’t interfere with ongoing operations because the system is dedicated to serving as a data warehouse

• Data can be organized by useful categories such as customer or product because the data is consolidated from multiple sources.

• The data warehouse is a single source of consolidated data, which provides an enterprise-wide view of the business.


EDWH

• Never a big bang approach• Have a framework• Start small• Iterate• Architecture driven by business and not

technology• EDWH is part of corporate strategy


EDWH Model• Users (Transaction Generators )• Txn data• ODS• ETL• EDWH• Populate Departmental warehouses • Corporate / Departmental Business users


The GDW architecture

App1

CRM

App3

App4

App4

Local DataMart

LocalDataMart

LocalDataMart

Batch ETL

Batch ETL

Batch ETL

Batch ETL

Real-Time

Messaging

Batch ETL

Batch ETL

Batch ETL

HRISHR

DataMart

Batch ETL Batch ETL

GlobalData

Warehouse

App2

Batch ETL


Data Warehouse Architecture


Business Intelligence is a Corporate Activity

Performance Management, Financial AnalysisBalanced Scorecard

Data Mining, Cause & Effect Analysis

Business Modeling & Simulation

Planning, Budgeting & Trend Analysis

OLAP & Spreadsheet Analysis

Operational & Administrative Transactions

Forecasting

Statistical Analysis

Performance Indication

Strategic Analysis

Information Extraction

Data Analysis

Data Collection

Query & Reporting Tools


BI PROCESS

• Transactional systems• Processes • Integration• Consolidation• Intelligence • Strategy modification• Implementation


Financial Services – Some Statistics

• 20 % of the customers represent 160% of the profits• A 5% increase in retention can produce an increase in

profits between 25-80%• A customer with one relationship with a bank has a 35%

chance of leaving• A customer with 3 or more relationships has an 85%

change of being with the Financial Institution for 3-5 years.• A shift in the customer mix from 30%(A’s) – 30%(B’s) –

40%(C’s) to 40-30-30 can often raise overall profits by as much as 60%


Banking Business Focus

• Account Centric• Product Centric• Market Centric• Customer Centric


Bankers Needs• Customer Profiling • Channel Profiling• Profitability Analysis• Performance reporting• Transfer pricing• ALM• Risk Management ( Credit Risk, Market Risk,

Operational Risk)• Wealth / Portfolio Management• HRIS


Financial Fraud

• Money laundering• Syndicated frauds – credit card cloning• Internal fraud – staff fraud• Merchant fraud• Challenges in identifying fraudulent

transaction before it is too late and preventing them


Indian Banking Scenario• Banks are struggling to stop the steady decline in

their margins• In India, many banks are still dependent on “interest

income”, but the growth and profits is in “fee-based income”

• Most banks have large customer bases, which could be gold mines, but have not been adequately tapped

• To create Customer value, banks need to think along 3 dimensions– Relationship numbers– Relationship profitability– Relationship duration


Banking Scenario

• Branch Systems• Size and Spread• Silo Applications• No / Limited Networking• No Centralised Repository• No Centralised Controls


Bank - Managing Relationships

• Manage all aspects of the customer relationships• Have 360 degree view of customer• Identify and retain the most profitable customers• Identify cross sell opportunities• Attract the right customers from the competition• Measure product and business profitability• Track customer preferences• Design new products


Banking DWH Uses• Enterprise-wide risk management and compliance

reporting for the bank group and both corporate and retail business divisions– Loan Analysis– Credit Risk– Market Risk– Operational Risk

• HR Management • Channel Management• Segmentation• Product / LOB Performance Measurement• MIS & Reporting


EDWH• Data warehousing provides data quickly and

in a format that greatly enhances the decision making process.

• The data warehouse allows financial institutions to exploit the potential of information previously locked in legacy systems ( which is currently inaccessible to the business user) or across current silo systems

• Massiveness of Data that needs mining• Complex views / relationships


Data

• In-house G/L• Silo Applications• External Data Sources• DWH Update

– Batch– Real-time


Metadata Major Categories– Technical metadata

• Information about data sources• Transformation descriptions• Warehouse object and data structure definitions for data targets• The rules used to perform data cleanup and data enhancement• Data mapping operations when capturing data from source systems and

applying it to the target warehouse database• Access authorization, backup history,data access, etc.

– Business metadata• Subject areas and information object type, including queries, reports,

images, video, and/or audio clips.• Internet home pages• Other information to support all data warehousing components.• Data warehouse operational information, e.g., data history, ownership,

extract audit trail, usage data


Data Mining• The process of extracting valid, previously

unknown, comprehensible, and actionable information from large databases and using it to make crucial business decisions

• Involves analysis of data and use of software techniques for finding hidden and unexpected patterns and relationships in sets of data.

• Patterns and relationships are identified by examining the underlying rules and features in the data.

• Most accurate and reliable results require large volumes of data


Data Mining• Retail / Marketing

– Identifying buying patterns of customers.– Predicting response to mailing campaigns.

• Banking – Detecting patterns of CC fraud– Identifying loyal customers

• Insurance– Claims analysis.– Predicting which customers will buy new policies.


Data Mining Operations

• Predictive modelling• Database segmentation• Link analysis• Deviation detection


Data

• How to extract the data• Cleaning the data• Validating the data• Aggregating the data• Integrating the data• Transforming the data


Data

• Incorrect data• Missing data• Lack of sufficient data detail• Insufficient Security and Privacy


The two BI “mega-problems”• Misalignment between BI/DW solution and

organizational structure/culture– Primarily, but not exclusively, in enterprise-wide

initiatives– Highly centralized? Totally decentralized? Loosely

coupled? It all depends!

• Focusing on the wrong architecture first– Both are important, but the BI architecture should come

first and guide the DW architecture…and be appropriately aligned with the structure/culture


Data Quality• Data Quality Principles - accurate and consistent data between

trading partners in the consumer products industry • Business not IT owns the data• Managing Data as a Product• Treating Data as an Asset• Optimise Data not Process• Data Quality Best Practices• Keep Data Structures Simple• Use Standard Names and Definitions• Developing Meaningful Definitions• Classification and Report Structures are treated separately• Labels used must be consistent and unique• The Data-Information-Knowledge Continuum


Data Quality• Improve data quality management in your organisation• Address challenges faced during implementation of

data quality systems• Look at the available technology options and choosing

the right device• Create a strategic vision for data quality management• Leverage from best case examples of effective data

quality management strategies• Streamline your data quality process with the help of

data management• Seek the consultancy of hands-on experience from data

quality management experts


Data Quality• Ensure your organisation is geared for a data quality

management strategy• Optimise your core data entity through tools and quality

systems• Improve ROI by aligning data quality systems with

business strategies• Implement value-added data quality processes to your

organisation• Learn to effectively master data quality management to

increase your organisation's ROI on EDWH


BI Strategies

• Comprehensive BI strategy and architecture• Short-duration BI “mini-strategy” offerings• Executive education briefings• Staff training in BI best practices• Assessments of existing BI environments and

implementations• BI project risk assessment, quality assurance

(QA) planning, and project plan/methodology review


IT Issues

• Technology Islands• Incomplete information is more

damaging than no information• Has the “modern era” (since 1990-

1991) of BI and DW been successful or not?


Sources

• Legacy data access• Multiplicity of sources• Storage needs• Integration issues• Multi-dimensional analysis

Technology Gap between where data is stored and where it is used


Implementation Issues

• Unclear Business Requirements• Multiple Data Sources• ETL• Modeling• Integration• View


Problems of Data Warehousing• Underestimation of resources for data loading• Hidden problems with source systems• Required data not captured• Increased end-user demands• Data homogenization• High demand for resources• Data ownership• High maintenance• Data Refresh Cycle• Long duration projects• Complexity of integration


BI Issues• Inflexible reports and queries• Tools that are too difficult to use• Data not available timely enough• Too difficult to add new data sources• Lack of user-oriented metadata• Lack of system-oriented metadata• Inability to intermix ERP, CRM, and other data• “Deskbound” analytics


Critical Steps to Success

• Business Sponsorship• User Commitment• Data Warehouse Experience• Data Quality Problems• Inability of Users to Easily Analyze the

Data


BI• Sales• Operations• Finance• HR• Customer service• Supplier relations• Agility – not in SW but in people• Helps in refocus organisations scarce

resources


Technology• MS DOS etc ushered in an era of

affordable computing• RDBMS, BI , WFL etc came down to

desktops from glass cages• Ubiquitous • Empowerment of customers and

employees• Self Service• Software as Utility

Treasury

International

Thank You

DWH & BI in Banking @ ET 2 Nov 2004 Chandrasekhar

Documents

Transcript of DWH & BI in Banking @ ET 2 Nov 2004 Chandrasekhar