Teradata® Analytics for SAP® Solutions A Better Way to Build a Data Warehouse with Data Extracted from SAP Solutions

Sponsored by:

By Neil Raden

Hired Brains Research LLC

March 2014

© 2014, Hired Brains Research LLC. No portion of this report may be reproduced or stored without written permission.

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Table of Contents

Executive Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3

Evolving Data Warehouse Requirements . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4

Iteration One: The Data Warehouse . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4

Iteration Two: The Customer/Product Data Warehouse . . . . . . . . . . . . . . . . . . . . 4

Iteration Three: The Big Data Data Warehouse . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4

SAP and Data Warehousing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5

Why Use an Enterprise Data Warehouse Today? . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6

Extreme Scalability . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6

Analytical Performance . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6

Integrated Data Warehouse . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6

Self-service BI . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6

Embedded Analytics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6

Reduce Complexity of BI Architecture . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7

Pervasive BI . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7

Teradata has a Better Solution for SAP Data Warehousing . . . . . . . . . . . . . . . . . . . . . . 8

Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .10

Endnotes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11

About the Author . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11

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Executive Summary

SAP has a vast functional reach through its suite of software

solutions.1 No other software company offers support for such a

comprehensive range of business processes and information flows.

However, all of that capability comes with a price – complexity.

SAP is the largest provider of enterprise resource planning (ERP)

software, equips its clients with a massive system designed to

integrate practically every function within an organization.

To do this, the SAP system is comprised of thousands of tables,

complicated relationships, many levels of abstraction and a vast

layer of application logic. Making sense of the data and under-

standing its semantics independently of its application layers

is nearly impossible by examining only the physical database

instances. Many of the crucial business rules are buried in code

that is invisible. Unlike simpler systems, building a data ware-

house around data from SAP® ERP solutions is a difficult task.

As a result, reporting on and drawing insight from the data captured

and managed within SAP solutions is challenging. Many organ-

izations using SAP ERP and SAP® CRM, two of its major applica-

tions, find the process of applying this data to analytical purposes

time-consuming and expensive. The internal data structures of

SAP solutions are designed for performing their direct functions,

not to facilitate reporting and analytics. Devising reports from

SAP tables directly requires knowledge of four separate domains,

all of which are very sophisticated: the underlying reference

model of SAP ERP solutions, which is vast, and spans many

different modules; the ability to develop code in the proprietary

language, ABAP,™ understanding the function of the application

interfaces (Business Application Programming Interface (BAPI)

in SAP parlance and there are more than 10,000 of them); and the

functional knowledge of the business processes themselves.

SAP developed its own data warehouse product, now referred to

as SAP® Business Information Warehouse (SAP BW), to satisfy the

needs of their customers that weren’t met with SAP ERP or SAP

CRM. Most SAP customers adopted SAP BW, but many found it

only satisfied a portion of their needs. A common drawback in

an application vendor-supplied data warehouse solution is the

need for integration of data from other sources, such as internal

applications from other vendors and external data from data and

service providers, social networks, customers, suppliers and other

critical participants in the operation of the organization. In fact,

many large organizations have multiple instances of SAP data

warehouse, each with their own implementation of SAP BW. SAP

customers typically have large investments in separate data ware-

houses and data. This is especially true where there is a substantial

amount of non-SAP data involved. In those cases, organizations

are more likely to opt for analytical solutions that can access and

process from multiple sources, including SAP solutions.

Teradata® Analytics for SAP® Solutions is specifically designed

for analyzing SAP ERP data. It includes the flexibility to pres-

ent and integrate data from multiple SAP systems and non-SAP

systems. It has been designed to be rapidly implemented and can

be extended to match changing business requirements. Teradata

Analytics for SAP Solutions gives business users an enterprise

view enabling them to make insightful decisions quickly and

gain a competitive advantage.

Teradata provides an effective solution for organizations that

use SAP by removing the complexity of building an integrated

data warehouse, directly from SAP. The purpose of this paper is to

describe the current needs for data warehousing, why SAP BW is

an incomplete or even unnecessary choice today, and how Teradata

Analytics for SAP Solutions provides a superior option.

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Evolving Data Warehouse Requirements

Data warehousing (DW) and business intelligence (BI) progressed

through a series of iterations from a fairly simple premise twenty-

five years ago to an essential part of enterprise information

technology today. Originally, DW was thought of as a collection

point of data to serve the needs of programmers struggling to

keep up with a report backlog. In time, the data warehouse was

seen as a mechanism for supporting the expanding role of users,

analysts and application builders. Today, DW/BI provide data

management and presentation of vast quantities of data, from

both internal and external sources, with support for advanced

analytics, real-time decision making and even crossover initia-

tives such as master data management.

Data warehouses are, in general, in their third iteration. SAP BW

was designed with first and second iteration thinking. Today’s

requirements for reporting and analytics demand platforms that

are far more scalable, more manageable, provide lower TCO, are

more open and agile and are more powerful and more capable

than those of ten and even five years ago.2 A brief discussion of

the iterations follows:

Iteration One: The Data WarehouseThe original designs for data warehouses were based on data vol-

umes and loads and concurrency dramatically lower than those

we see today. This “managing from scarcity” approach minimized

physical resources by limiting function and use. Source data

was drawn only from the organization itself, aggregated at the

monthly, geographic and product level and was provided at wide

intervals, such as monthly or quarterly. Integration of external

data was rare. The primary role was to provide source for report-

ing by IT.

Iteration Two: The Customer/Product Data WarehouseOnce sales data expanded to the customer level, and the periodicity

expanded to daily, a data warehouse, without expanding its sub-

ject area, could grow by an order of magnitude. A 50Gb database

could easily exceed 500Gb or even reach the unimaginable level

of a terabyte.

Many data warehouses were hosted on the general purpose,

merchant relational database from IBM, Oracle and Microsoft,

but they lacked adequate capabilities for reporting and analysis

needed for organizations. Load processes were slow, and queries

performance was poor. Teradata data warehouses were available

and thriving as an integrated data warehouse with the high speed

analytic processing, however Teradata lacked interoperability

with SAP data. As a result, reporting and analytic on SAP data

was bound by data marts, non-integrated Operational Data Stores,

query metering and restricted access.

Iteration Three: The Big Data Data WarehousePhysical and functional requirements are expanding rapidly.

Requirements are generated from both the advance of technol-

ogy and the rising expectations within organizations to do more,

faster3 to handle a great deal more data, rely on vastly superior

hardware platforms, routinely incorporate external data, and

even more unstructured data, especially from the Internet. Best

practices already call for the ability to not just report data, but to

analyze it, and even make predictions from it as well as disburs-

ing BI in many forms to a much wider audience. While speed and

concurrency are now table stakes, flexibility, harmonization and

dynamic performance are key features.

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SAP and Data Warehousing

To deal with the need for more reporting and analytics demanded

by the SAP customer base, SAP initially released SAP Business

Information Warehouse (SAP BW) (BIW, later shortened to

“BW”) as an extension to the SAP solution. As SAP BW evolved

over time, its functionality expanded rapidly, only as a tool

for data from SAP solutions, not as a data warehouse to fulfill

their needs.

The initial release of SAP BW was based on an early iteration of

DW thinking, and those initial design decisions still prevail; the

current SAP data warehousing suffers from performance, usability

and scalability problems.

Performance, usability and scalability are critical components of

a data warehouse, but not exclusively. SAP BW does not address

the underlying problem of data harmonization: the content of

SAP BW is typically limited to data from the SAP system and

the numerous “data cubes” are independent, not harmonized

with each other. In addition, SAP data is not harmonized with

non-SAP data sources. Many organizations are forced to build a

separate data warehouse to integrate all of the other needed data

not part of SAP, leaving two choices. Moving SAP BW data into

the DW, which has been difficult and generally too slow, or mov-

ing all of the enterprise data into SAP BW, which has proven to

be an unpopular choice.

Clients of SAP became a growing chorus for improvements in

the reporting process, both operational and analytical. SAP

responded with the first release of SAP NetWeaver® Business

Warehouse as a first iteration data warehouse, but with some

twists. Instead of an open data warehouse that could be accessed

by third-party tools, SAP created a whole BI application suite

with analytical templates, ETL routines to load and refresh them,

reporting and analytical tools, as well as the complement of main-

tenance and administration capabilities.

As a first iteration data warehouse, its concept was sound, but

it exhibited signs of performance problems from the outset,

particularly in the time it took to load the data and refresh all

the structures and especially when clients wished to modify

their analytical models. Though the data was physically housed

in relational tables, it appeared, through the various layers of

abstraction, as a series of multi-dimensional “cubes,” lacking

the flexibility of a true enterprise data warehouse built with a

relational database.

In subsequent releases, SAP expanded the functionality and took

aim at the performance problems, but as their customers moved

into third iteration needs, it became clear that the underlying

databases used were not up to the task. Current relational

database platforms for the SAP system from IBM, Oracle and

Microsoft are architecturally matched to the load and perfor-

mance characteristics of operational systems such as SAP R/3®

where there are millions of transactions to handle while main-

taining coherency between different modules. But in data

warehousing, where there are large batch data loads and unpre-

dictable query loads, not every relational database system is

capable of meeting the requirements.

The current SAP approach to data warehousing is to port SAP BW

to SAP® HANA’s in-memory computing platform. While it can

speed up some processes, it is an expensive proposition and does

not deal with the many shortcomings of SAP BW. Re-platforming

a solution on faster hardware rarely solves the problems of scale,

concurrency, inflexibility and lack of transparency. Access to SAP

BW would still require all of the proprietary tools SAP provides

for SAP BW on conventional relational databases, some of which

are not yet certified for SAP HANA.

Teradata now provides a better alternative by moving SAP and

non-SAP data directly into a Teradata Database, alleviating all

of the challenges of dealing with SAP BW, and resolving perfor-

mance, scalability and harmonization issues.

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Why Use an Enterprise Data Warehouse Today?

Iteration three of data warehousing allows for a broad range of

options, from a single enterprise data warehouse, to an ecosystem

of platforms performing specialized functions, provided they

operate in harmony to minimize duplication, provide extra-

ordinary service and are economically manageable over a long

period of time.

This requires extreme scalability, analytical performance, logical

location of processing and minimized data movement and most

importantly, the ability to handle the loads of data and use that

today’s businesses demand. In particular:

Extreme Scalability The amount of data (such as historical data or expanding pro-

duct portfolio data) and its sources (additional SAP and non-SAP

systems) are changing continuously so your data warehouse needs

to keep up. None of the currently supported databases of SAP

BW are able to scale to the volumes that third iteration operations

demand. SAP BW only scales out to tens of terabytes in a clustered

symmetric multiprocessing (SMP) environment, while a growing

range of rivals scale into the hundreds or thousands of terabytes,

to support mixed workloads with high user concurrency.

Analytical Performance High speed analytical performance is a requirement for data-driven

businesses that are proven to be more financially successful4 than

their competitors. The data warehouse must be designed to man-

age and perform mixed analytic workloads, not just transactional

processing. Every part of its architecture should align to support

data warehouse processing. It has been a well-known fact that it is

impossible for a single database kernel to excel at both transaction

and analytical processing. Oracle, IBM and Microsoft databases

are primarily transaction processing tools that require constant

tuning to deliver even barely adequate performance at analytical

tasks, especially at large scale and low latency requirements.

Integrated Data Warehouse Organizations employing SAP software, despite its broad reach

and functionality, still use other applications to complement it or

surround it. Because the subject areas of SAP BW do not com-

prise an entire enterprise data warehouse, many businesses rely

on SAP IT experts to manually extract data from SAP solutions

and integrate into the data warehouse. The data warehouse must

be designed to simplify the entire process from data extraction,

integration and loading with the goal of creating a single business

layer for analytics and reporting. Ideally, this process is driven

from a graphical user interface promoting self-service data access

and analysis by business users.

Self-service BIOne of the significant costs of running SAP BW comes from the

requirement for expert consultants to design, implement and

maintain nearly every aspect of the SAP BW implementation. In

contrast, best practices recommends a self-service BI environment,

where once established by IT, business users are empowered to

easily access and refresh data from their SAP solutions and quickly

generate and run their own reports without day-to-day IT support.

Embedded AnalyticsStudies have shown that advanced analytics, including statistical

modeling, predictive modeling, data mining and optimization are

closely correlated with competitiveness. Unfortunately, delivering

advanced analytics is still a time-consuming, largely manual job

performed by highly skilled (and highly compensated) profession-

als. Data warehousing and advanced analytics have never been

very closely tied, thus modelers use a data warehouse as a data

source for creating their own separate databases for analysis.

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Teradata® Analytics for SAP® Solutions is specifically designed for analyzing SAP ERP

data. It includes the flexibility to present and integrate data from multiple SAP systems

and non-SAP systems.

Advanced analytics best practices leverage in-database analytic

capabilities, where the data warehouse vendor along with

third-party analytic tool provider engineers solutions enabling

analytic methods to execute within the data warehouse. This

immediately eliminates work and makes more efficient use of

resources, both human and machine, but in the longer term, will

enable statistical models to operate seamlessly with the data

warehouse, facilitating a whole new set of hybrid operational/

analytical/predictive processes.

Reduce Complexity of BI ArchitectureWhile it is already possible to move non-SAP data into SAP BW

and SAP BW data into other external structures to provide a

more complete view of enterprise performance and analytics, it is

currently tedious and slow. Best-in-class solutions bring all of the

data into a single platform and simplify the design and the effort

to maintain the implementation. The integrated data warehouse

is the foundation of your BI architecture, so it makes sense to

bring in SAP data into your existing BI architecture than to try to

recreate a new BI foundation.

Pervasive BI BI today goes far beyond data and reporting. BI is becoming pro-

active, real time, operational, integrated with business processes,

and is extending beyond the boundaries of the organization. It

was able to do this by providing simple, personal analytical tools

on an as-needed basis with a minimal footprint and cost. The key

element of pervasive BI, where BI is used both openly and hidden

in other processes in many operations of a business, is seamless

and almost effortless integration between operational systems and

reporting/analytical views of the business. “Seamless” implies,

very low latency update and enhancement powered by metadata-

driven semantics (to its credit, one of SAP BW’s early innovations

was to provide a data warehouses with pre-built connectors).

The real benefits of the enterprise data warehouse is to provide

the means to mobilize the data resources from the SAP systems to

meet the service level agreements (SLAs) without the complexity

and cost of SAP solution implementations.

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Teradata has a Better Solution for SAP Data Warehousing

Teradata’s approach to data warehousing with SAP ERP data

leverages the Teradata Integrated Data Warehouse, proven tech-

nology designed for high-speed analytics on massive volumes of

data as the core analytic infrastructure. Teradata Analytics for

SAP Solutions employs an architecture to simplify the process of

creating a data warehouse using ANY data from SAP solutions

such as SAP ERP, SAP BW or others SAP modules. Not only does

Teradata provide the massive scalability to grow with your busi-

ness, but Teradata Intelligence Memory also delivers the speed of

in-memory processing.

This is a simplified model of the Teradata Analytics for SAP

Solutions architecture. SAP data (principally ERP data, as SAP

BW data is derived from this source, but it is possible to extract

the data from SAP BW as well, particularly those elements that

serve reporting and are not present in the ERP data). The data

extractors of Teradata Analytics for SAP Solutions are able to cut

through the layers of abstraction and indirection in the SAP model

and bring data into a staging area where data can be harmonized

and merged from multiple SAP instances and other non-SAP data,

including your existing data warehouses and data marts.

In fact SAP solutions are so complex that the average SAP solution

has more than 25,000 tables of data. The ability to identify the

business rules and relevant data for analytical purposes is one way

Teradata Analytics for SAP Solutions reduces complexity.

In addition Teradata Analytics for SAP Solutions builds the core

enterprise model and virtual presentation layer, commonly referred

to as the business semantic layer giving the extracted data mean-

ingful names. This empowers business users to create their own

reports and dashboards without IT support structures. Simply

dragging and dropping the clearly named data into their reports.

Teradata Analytic for SAP Solutions offers organizations an

integrated, end-to-end, Teradata IDW based solution for extract-

ing data from SAP ERP/ECC systems. Building an integrated data

warehouse for reporting, business intelligence and analytics. This

software solution is extremely flexible, easy and rapidly imple-

mented because it is built on an open framework. It can be used

with leading ETL and BI tools and is proven to reduce Total cost

of Ownership (TCO).

Figure 1. This diagram illustrates the Teradata Analytics for SAP Solutions end-to-end, from data extraction, loading, transformation, integration and analysis.

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Teradata Analytics for SAP Solutions delivers benefits:

• Optimizes end-to-end processing within the Teradata data

warehouse delivering extreme scalability.

• Integrates multiple SAP and non-SAP data sources to provide

single business layer for cross-functional analysis.

• Leverages Teradata Database’s in-database analytics to provide

deep data mining and predictive analytics.

• Delivers results in days, not months because integration is

prebuilt and driven by an easy-to-use interface.

• Includes prebuilt extract, transform, and load (ETL) data flows

implemented in data integration software for self-service data

access.

• Provides prebuilt SAP data mappings, change data capture

logic, and data models to extract data from SAP financial,

logistics, manufacturing, human resources and sales software

modules.

The Teradata Analytics for SAP Solutions breaks down the silos

that SAP solutions can create, allowing business users to view data

from across business modules. For example, a common report is a

list of top 10 customers by revenue (see Figure 2). Alone this report

does not provide the business with a great deal of insight. Teradata

Analytics for SAP Solutions integrates modules and directly link

to customer revenue to accounts receivable data, so business users

can see patterns of behavior, such as top customer who persistently

pays late or not at all (see Figure 3). Analysis on integrated data

gives business users useful insights which could be used to iden-

tify and resolve issues. Teradata Analytics for SAP Solution also

enables SAP data to be integrated with other company data on the

EDW, facilitating the ability to run predictive analytics so business

strategies and tactics can be changed to make the company more

efficient. Ultimately Teradata Analytics for SAP Solutions provides

a holistic view of the company which is vital to drive knowledge

and competitive advantage.

Teradata has a professional services organization numbering in

the thousands, worldwide, who are trained in and focused on data

warehousing, big data analytics and BI. They have deep experi-

ence in many vertical industry applications and their ability to

deploy gives Teradata Analytic for SAP Solutions a large boost in

ability to deliver expertise to the SAP customer base. Organiza-

tions can leverage Teradata PS expertise to realize results in a

matter of days.

Figure 3. Integrated view of top 10 customers by Revenue and Payment History provides more insights of the customer value.

Figure 2. Report of the top 10 customer by Revenue based on silo’d data provides a limited view.

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Manufacturing Plant Maintenance Control

Plant, property, and equipment are an organization’s tangible

assets with long-term commitments. These assets are usually

funded with long-term cash sources, either long-term loans or

by shareholder investment. While information on these assets is

relatively straightforward, the operational maintenance

schedules are often complex, stored in external systems and

managed by multiple resources. Dedicated technical managers

must organize multiple maintenance schedules for each asset.

Teradata Analytics for SAP® Solutions provides these technical

managers with the analytic capabilities to maximize the

production availability and minimize the maintenance costs for

each asset.

Integrating data across multiple SAP ERP modules within a single system

Analyst may ask, “What is the correlation between number of

breakdown events and produced output for a given period?”

To answer this question, you need breakdown event count from

the SAP ERP Production Maintenance (PM) module and

aggregate production volume SAP ERP Production Planning (PP)

module. By combining the data from multiple SAP ERP modules

into a single business view, organizations can better understand

how process breakdown events impact production volumes to

optimize maintenance budgets.

Integrating data across multiple SAP ERP systems

Teradata Analytics for SAP Solutions also integrates data from

multiple SAP® systems across the organization to answer a

broader set of questions. For example a business that

manufactures the same product in separate facilities will use

separate ERP systems to manage each facility. Although the

process and bill of materials are the same, total cost of good

manufactured can be different due to a number of local factors

such as labor cost, logistical conditions and machinery. Since

data is managed from different SAP ERP systems, data must be

manually extracted and integrated from the different systems

to answer the simple question: “How do cost of manufactured

goods from one facility compare to another?” Teradata Analytics

for SAP Solutions simplifies data integration to answer questions

from an enterprise view.

Integrating SAP and non-SAP data

An integrated data warehouse provides a holistic view of your

business beyond functional operational segments. Following

the manufacturing examples, an analyst may ask, “What is the

relationship between physical condition in a plant and the

number of breakdown and cost? To answer this question, the

analyst must extract breakdown and cost information from the

SAP ERP modules and integrate this data with geographic,

temperature variance, and plant maintenance data that directly

impacts the physical conditions of equipment in the plant.

The real value of an IDW environment is the ability to combine

the production schedules, the actual production output with

planned maintenance events and any other external

information to get a 360 degree view of the entire operations in

order to better manage the asset. Combining this data gives the

ability to impact the sales organizations and external third

parties who would use this data to move additional products

and services of the organization.

EB-6949 > 0314 > PAGE 11 OF 12

Teradata Analytics for SAP Solutions employs an architecture to

simplify the process of creating a data warehouse using

ANY data from SAP solutions such as SAP ERP, SAP BW or others SAP modules.

Conclusion

Organizations today need to leverage every bit of information

they can manage, in the forms that suit them, when they need

it. Packaged data warehousing with SAP solutions was a success

only within the limits of how SAP defined data warehousing.

Today, the term “data warehouse” encompasses a wide variety of

architectures, methodologies and solutions. The one problem that

is never completely solved is how to capture and harmonize data.

That problem is now greater than ever.

Teradata Analytics for SAP Solutions gives SAP customers high

speed, simplified and useful application. Those customers will

also be served by a very large professional services organization

from Teradata Corporation that is focused on one and only one

pursuit – data warehousing and BI.

EB-6949 > 0314 > PAGE 12 OF 12

All rights reserved. No portion of this report may be reproduced or stored in any form without prior written permission. Teradata and the Teradata logo are registered trademarks of Teradata Corporation and/or its affiliates in the U.S. and worldwide. SAP, R3, SAP NetWeaver, ABAP, and BAPI are the trademark(s) or registered trademark(s) of SAP AG in Germany and in several other countries.

© 2014, Hired Brains, Inc. www.hiredbrains.com

About the Author

Neil Raden, based in Santa Fe, NM, is an

industry analyst and active consultant,

widely published author and speaker and

the founder of Hired Brains, Inc., http://

www.hiredbrains.com. Hired Brains

provides consulting, systems integration

and implementation services in Data

Warehousing, Business Intelligence,

“big data”, Decision Automation and

Advanced Analytics for clients worldwide. Hired Brains Research

provides consulting, market research, product marketing and

advisory services to the software industry.

Neil was a contributing author to one of the first (1995) books

on designing data warehouses and he is more recently the

co-author with James Taylor of Smart (Enough) Systems: How to

Deliver Competitive Advantage by Automating Hidden Decisions,

Prentice-Hall, 2007. He welcomes your comments at nraden@

hiredbrains.com or at his blog at Competing on Decisions at

hiredbrains.wordpress.com.

Endnotes

1. For the sake of brevity, this paper refers primarily to the SAP

ERP and SAP CRM applications, not its entire Business Suite

and other products and services.

2. These iterations are general in nature and meant solely to

illustrate the evolution of data warehousing the expansion of

its requirements. Data warehouse development, or maturity,

happened at different times for different organizations.

Iteration One, for example, actually began almost 30 years ago,

and iteration Two was first apparent about 1995. Iteration Three

first appeared as a trend approximately 5-10 years ago.

3. Actually, external pressure from Search an ‘E-business” created

demand for all sorts of new data sources now commonly

referred to as big data.

4. According to research by Economist Intelligence Unit http://

www.cio.com/article/730457/Data_Driven_Companies_

Outperform_Competitors_Financially