NuData Warehousing and CRM

8/3/2019 NuData Warehousing and CRM

1/25

DATA WAREHOUSING AND CRM


2/25

A producer wants to know.

Which are ourlowest/highest margin

customers ?

Who are my customersand what products

are they buying?

Which customers

are most likely to goto the competition ?

What impact willnew products/serviceshave on revenue

and margins?

What product prom-

-otions have the biggestimpact on revenue?

What is the mosteffective distributionchannel?


3/25

Data, Data everywhere yet ...

I cant find the data I need

data is scattered over the network many versions, subtle differences

I cant get the data I need

need an expert to get the data

I cant understand the data I found

available data poorly documented

I cant use the data I found

results are unexpected

data needs to be transformed

from one form to other


4/25

What is a Data Warehouse?

A single, complete andconsistent store of dataobtained from a variety of

different sources madeavailable to end users in awhat they can understandand use in a businesscontext.

[Barry Devlin]


5/25

What are the users saying...

Data should be integratedacross the enterprise

Summary data has a real value

to the organization

Historical data holds the key tounderstanding data over time

What-if capabilities arerequired


6/25

What is Data Warehousing?

A process of transforming

data into information andmaking it available to usersin a timely enough mannerto make a difference

[Forrester Research, April 1996]

Data

Information


7/25

Data: Good for Business

Data is a fact or multiple fact or a set ofvalues that is raw material stored in astructured manner.

With interpretation and human intelligenceapplied it becomes useful information.

A database is a set or collection of thesestructured files and managed by a DatabaseManagement System.

With DBMS one can access data in a varietyof ways.


8/25

Data structure

DS important when deciding what is important toyou in the data nugget.

Example online surveys

Entities and attributes:Entities: Data defined by a common group ofcharacteristics that are

of interest to the business. It could be a person, place, thing, concept, event.

Attributes Are descriptors attached to entities.

In RDBMs entity is a table and the attributes as acolumn in a table.


9/25

CRUD matrix

Developing a CRUD matrix , once the entitiesand attributes are defined , is the first step inlinking business and data.

Create, Retrieve, Update and Delete

Creates 360 degree view of customer

It plays two vital roles:

When functions are mapped to the data usefulrelationship show on the matrix

Missing business processes or data entities areuncovered as are data and process redundancies.


10/25

Metadata

Data about data

Primary purpose is to be able to describe andcommunicate business and technicalinformation to persons within the organization.

These are classes that are used to group the datain organized and understandable fashion.

For example Last name, first name phone no. ismetadata. Greenberg, Paul,571-213-6988 is data.


11/25

Data Quality

Identify good data, bad data

Good data means accurate and non

redundant data. Bad data is inaccurate and repetitive data.

Can clog up your system, waste time andslow down system.

Maintaining quality of data not easy enoughespecially when you have hundreds ofgigabytes or even terabytes of data.


12/25

Seven characteristics of Data Quality

Accuracy: Data represent reality or a verifiablesource

Integrity: Is the structure of the data andrelationships among entities and attributesmaintained consistently.

Consistency: Are data elements consistently definedand understood.

Completeness: Is all the necessary data present

Validity: Do all values fall within acceptable ranges

defined. Timeliness: Is data available when needed.

Accessibility: Is data easily accessible

Customer satisfaction is ensured if quality data is available.


13/25

Data Models

Conceptual data models: Is the first thinking about the data in the early phases of

system development. Data requirement are scoped from a business standpoint. This stage the CRUD matrix is developed.

Logical data model: Follows the conceptual data model Technical theories of data architecture are used here. Normalization is an example to prevent redundant data.

No database is created Physical data models:

Is the mapping of database design data groupings, intophysical database areas, files, records, elements, fields, andkey adhering to constraints of the software.


14/25

Datawarehouse

An enterprise wide data collection that is organizedaround subjects , collected from multiple sourcesand centrally merged into a coherent body over

time. Datawarehouse have time period associated with it.

Various functions include: Data can be extracted from operational database.

It is processed and cleaned to eliminate incorrect andredundant data or add missing data.

Then loaded on RDBMS like oracle 9i and DB2 andanalytical operations are run using analytical tools like SASor OLAP


15/25

Possible Problems Architectural and human issues can turn a database into a place for

repossession and dismantling. 80% of time is spent on extracting, cleaning, loading no time for

application.

Incompatibilities in the system that are feeding the datawarehouses.

Data not being captured turns out to be important

Query and reporting tools that are easy to use everyone actually use andreports request overload.

Conflicting business rules among the users-same calculations performeddifferently.

Data homogenization

Heavy overhead

Security not assignable without process driven approach Lack of customer management against over concerns with resource

optimization.

High maintenance system.


16/25

Reasons for Failure

Design: bad architecture, data drivenmethodology rather than business drivenplan

Technical: Ignoring the obvious issues relatedto query volumes and network traffic

Procedural: using methodology which

doesnt have prototypes, proof of concept

Sociological: he who controls data controlsthe world.


17/25

Real Time Databases

Customers run their own universe.

Hence companies need as current data as

possible to make appropriate decisions. Data is perishable commodity : the older it

gets, the less relevant it is.


18/25

States of Data

Data marts: focus on a single subset ofenterprise data.

Datamarts are smaller and use software to

summarize , store and analyze data that maybe useful to you someday.

Operational Data Store: analogous to short

term memory. They are interim areas that is used to store

continuously updated recent data gatheredthrough the course of a business day.


19/25

States of Data contd

Corporate Information Factory: it combines theproducer and consumers of information into a singlearchitecture.

CIF uses datawarehouses or ODS as the assemblypoint for data captured from operational systemsand business processes of the subject company.

To maintain CIF effectively four operations andadministration function needs to be implemented.

System management Data acquisition management

Service management

Change management


20/25

States of Data

Data vaults: are linked directly to the businessprocesses that move the company.

Functional areas are indentified such asfinancial or marketing, then the uniquesubject areas within units such as billing orcampaign management, then then topic

areas such as invoicing or direct mail.


21/25

Capture CustomerData and Measure

Results

The Customer


Results

Take Action toEnrich the Customer

Relationship Capture CustomerData and Measure

Results

Build and ManageCustomer Value


Results


Results

CaptureCustomer Dataand Measure

Results

Store Data, Mineand Make

InformationAccessible

The CRMDynamic

Customer Relationship Management is a ongoing, dynamic learning process for an organization

Customer Relationship Management

Process

The building blocks of CRM allow an organization to manage this cycle and use

the knowledge on customers to enhance the Life Time value of the customer

portfolio.

No organization has perfect information on its customers. Knowledge of

customers is continuously enhanced through the CRM dynamic.

l i b h d


22/25

Implementing CRM must be approached

from an Integrated Perspective

All areas must be implemented, to some degree, to effectively manage the customerrelationship. When pieces are implemented in isolation, the benefits are less than

overwhelming.


Results

The Customer


Results

Take Action toEnrich the Customer

Relationship Capture CustomerData and Measure

Results

Build and ManageCustomer Value


Results

Capture Customer

Data and MeasureResults

CaptureCustomer Data

and MeasureResults

Store Data, Mineand Make

informationAccessible

CRMwithout anIntegratedApproach

A data warehouse full of data withoutthe tools to extract knowledge isnothing more than expensiveinventory.Sophisticated mining tools onlyproduce results only as good as thedata they mine.

Developing insights on how to improve the value of thecustomer relationship without having the infrastructure totake action has no impact on the bottom line. In addition,there is no opportunity to test the theoretical analysis.

Implementing new technologies withoutthe knowledge on how to enrich therelationship is likely to yield a return belowthe cost of the capital expenditure.

Taking action to improve therelationship without measuring theresults provides no evidence ofsuccess or failure and limits theopportunity for learning.

Capturing gigabytes of customer datain disparate operational systems thatare next to impossible to access mayrender the data useless.


23/25

Deployment and Support

The Building Blocks of CRM

e-Business

Sales ProcessAutomation

Call Centres

EnablingTechnologies

DataWarehousing

MetaData

Data Cleansing

OLAP

EIS

DataCapture

External

Databases

MarketResearch

Customer TouchPoint Integration

People

Organization

KnowledgeManagement

StatisticalModeling

Data Mining

CustomerProfitability

Segmentation

The building blocks of CRM are the things that need to be in place for an effective CustomerRelationship management program


24/25

Data Capture and Warehouse

What Data do we capture on Customers?

CustomerBehaviour

Product Portfolio Householding

UsageProfile

Migrationin

Usage

Loyalty/

Switching

CustomerInteractions

Acquisition

Information

InboundContact

Outbound

Contact

Base Data

Segments Profitability Life Time Value

CustomerProfile

Demographics/

Firmgraphics

Attitudes

Product/Service

Preferences

Intentions

ExternalData

Geo-

demographics

Campaign

History

Derived Data

The Customer Data Model

Census


25/25

Data Warehouse for Decision

Support & OLAP

Putting Information technology to help the

knowledge worker make faster and better

decisions

Which of my customers are most likely to go to the

competition?

What product promotions have the biggest impact on

revenue? How did the share price of software companies

correlate with profits over last 10 years?

NuData Warehousing and CRM

Documents

Transcript of NuData Warehousing and CRM