1. Challenges in integrating various DBMS during ERP (SAP) implementation
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Vignesh Ravichandran
Abstract:-
Business process integration is the main objective of an ERP implementation. ERP systems focus primarily
on the operations that are performed within an organization and they integrate functional and cross-
functional business process of an organization into a single database (Batada & Rahman, 2011). Data
integration plays the crucial in the process of business integration. In this paper, the process of data
integration was explained. Also, various challenges in data integration and the data migration during ERP
implementation was discussed.
Data Integration:-
Many organizations store information on many ways but mostly in databases which are built on different
DBMS. They need a way to retrieve data from different sources. “Data integration is the process of
combining data residing at different sources, and providing the user with a unified view of these data
(Lenzerini, 2002).” It is crucial especially in large enterprises that own a multitude of data sources.
For example, let’s take a scenario in a telecommunication company. A customer calls the customer care
representative to get clarification of his/her last month bill. The representative should be able to access
the accounting department data to answer the question. Further, the customer asks about the new tariff
plans and special festival offer. For this the representative needs to access the data from sales and
marketing department. A good data integration system would let the customer support department to
view information from all the three sources in a unified way, leaving out any information that didn't apply
to the search. This forms the foundation for a good ERP system.
Database Migration or Integration technique:-
There are different techniques for data integration available in the market. (Paper, n.d.) It’s the
organization which needs to find which technique suits them better mainly based on their existing

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Database management systems, data types, amount of data and licensing. The general technique which
most of the companies follow is data warehouse. The data from various sources are integrated into a data
warehouse using ETL process.
First data is extracted from the heterogeneous
data sources. Then the data is transformed
(converting the datatype format, removing
the unnecessary attributes etc.) and finally
loaded into the data warehouse which is built
on a DBMS. Figure 1 Traditional Data warehouse Design, (“etl,” n.d.) )
Oracle’s Data Integrator, Informatica’s PowerCenter, SAP’s Business Objects and Talend are some of the
famous data integration tools.
Case Study:-
MCF Technology Solutions is a data integration services provider which uses Talend’s open source data
integration suite to address many of the data integration projects. According to the case study by (Sean
& Kevin, 2012) One of the clients of MCF Tech is Creatis Bank which is a specialist in loan consolidation.
Creatis was facing few challenges. The technical architecture of Creatis was continuously evolving. They
wanted to create a responsive information system which coordinates with various departments like
marketing, risks, sales, financial etc. They don’t want to compromise on the performance and security.
Outcome:-
MCF tech implemented the data integration project using Talend’s integration suite. The data warehouse
was almost completed and it handles all the financial activities and bank clients. The branch manager was
able to create a dashboard of their customers to find all the relevant information and the central

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controlling department globally consolidates all financial activities. The newly created system also abides
by the law compliance which were put in place by the government.
Challenges in Data Integration:-
Now we understood how Data Integration works. Below section elaborates various challenges.
Social challenges:-
Data integration is basically about making people to collaborate and share their data. It involves finding
the appropriate data, convincing the departments to share it and explaining them the consequences to
do so and convincing data owners that their concerns about data sharing (e.g., privacy, effects on the
performance of their systems) will be addressed. (Halevy & Ordille, 2006)
Breadth and scope of the data:-
The volume of most of the identified transactional sources are usually very high. Every organization
generate large amount of transactional records. Further, understanding the naming convention,
associated records and data types of the data is necessary. Once we understood that the next question is
what facts need to be migrated? What do we need to do to them so that they align in our new ERP data
structure? This is usually described as the “breadth and scope” of the migration, i.e. what facts are to be
migrated mention the breadth and how many of them answers the scope (“The Big Secret to Painless
Data Migration The Big Secret to Painless Data Migration,” n.d.)
Complexity of integration:-
In many ERP implementation projects, initially it is not even clear what it means to integrate data or how
combined sets of data can operate together. The situation becomes much more challenging during the
merger of two companies and therefore the need for a single system to handle their different stock option

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packages. For an example, what do stock options and financial statements in one company even mean in
the context of a merged company? It illustrates the demands that may be imposed on the data
management systems to accommodate when such unexpected complexity occurs.(Halevy & Ordille, 2006)
Data Quality (DQ) Issues:-
Data Quality is defined as data that is perfect for use by data consumers (Xu, Nord, Brown, & Nord, 2002).
DQ issues have become increasingly prevalent in most organizations experiencing some level of DQ
problems within their firm. DQ is very much crucial to perform a forecast, analysis and for decision making.
When organizations are implementing an ERP high priority needs to be given for DQ issues.
Case study on Data Quality issues during ERP (SAP) Implementation:-
This study is about the ERP Implementation in a large government Transport-services Company derived
from the paper(Xu et al., 2002). It is a subsidiary company. But still it was financially consolidated with its
parent company. They identified the Data Quality issues which existed in their organization. They have
two financial goals:
1. To improve the accounting data’s integrity.
2. To refine the financial abilities of the organization
Though this company was huge it has just 25 employees in finance department. So most of their accounts
related work like accounts payable and accounts receivable they were outsourcing. When the parent
company decided to implement SAP R/3, they also divided the company into two. However, they still only
run the one SAP client. When there is new releases or upgrades they did as a joint exercise.
DQ Problem:
Before the ERP implementation they had difficulties in reporting. It was mainly due their
accounting methodology. The parent company reporting’s was in cash and this company’s reporting was

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in accrual term. So they were running two mirrored ledgers. One was cash ledger and another one was
accrual ledger. Because of this, they faced lot of challenges in ERP implementation. It took a while for
them to reconcile between these ledgers. From this, we can infer how the data quality is a big challenge
during ERP implementation and also companies preferred to implement an ERP to tackle their DQ issues.
Query processing:-
Performance is the key for customer satisfaction. Especially, in an ERP system the business users expects
the report or the response to be as soon as possible. Unlike a traditional database setting as shown in
figure 2, a data integration system cannot neatly divide its processing into a query optimization step
followed by a query execution step. (Halevy & Ordille, 2006)
Figure 2 Explain plan and execution step for a simple select query in a MYSQL database.
Because the context in which a data integration system operates is very dynamic and the optimizer has
much less information than the traditional setting. As a result, two things happen:
1) The optimizer may not have enough information to decide on a good plan, and
2) A plan that looks good at optimization time may be arbitrarily bad if the sources do not respond
exactly as expected. (Halevy & Ordille, 2006)
This can lead to severe performance related issues and the response time can affect badly.

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Conclusion:-
This gives an overview about the various challenges in data integration during ERP implementation.
Though it’s a very difficult step there are various researches going on in this field. For example, the in-
memory database started trending in a very short memory period of time. SAP introduced the new
technology SAP HANA smart data integration which they claim it supports integration across multiple data
sources into a single SAP Hana instance. (“About SAP Hana,” n.d.) But still these challenges will remain at
least for the next few years since a solid integration technique arrives and the companies understand the
need for data integration.
References:-
About SAP Hana. (n.d.). Retrieved from http://hana.sap.com/abouthana.html
Batada, I., & Rahman, A. (2011). Selection, Implementation and Post Production of an ERP System.
Proceedings of the European Conference on Information Management & Evaluation, 38–44.
Retrieved from
http://search.ebscohost.com/login.aspx?direct=true&db=bth&AN=60168222&lang=pt-
br&site=ehost-live
etl. (n.d.). Retrieved from http://trianzblog.com/wordpress/?p=203
Halevy, A., & Ordille, J. (2006). Data Integration : The Teenage Years. Artificial Intelligence, 41(1), 9–16.
doi:http://portal.acm.org/citation.cfm?id=1182635.1164130&coll=Portal&dl=GUIDE&CFID=388999
36&CFTOKEN=23237860
Lenzerini, M. (2002). Data Integration : A Theoretical Perspective. Proceedings of the Twenty-First ACM
SIGACT-SIGMOD-SIGART Symposium on Principles of Database Systems, 233–246.
doi:10.1145/543613.543644
Paper, A. W. (n.d.). Eight Styles of Data Integration.
Sean, M., & Kevin, T. (2012). What you need to know, (July), 60–67.
The Big Secret to Painless Data Migration The Big Secret to Painless Data Migration. (n.d.).
Xu, H., Nord, J. H., Brown, N., & Nord, G. D. (2002). Data quality issues in implementing an ERP.
Industrial Management & Data Systems, 102(1), 47–58. doi:10.1108/02635570210414668