Data migration is a complex process, and it differs greatly from other IT projects. Good sound approaches should be introduced when data migration strategy is being planned. Success of data migration depends on many things, each detail is important. But there are certain conditions that affect the complexity of data migration process, and thus demand for even more attention.

One of such challenging conditions is moving from a current vendor to a new one, which may mean another type of applications and systems, different data formats, etc. This may, probably, demand the use of data migration tools different from those, a company had utilized.

Another condition that complicates data migration is moving from physical environment to virtualized one, such as the Cloud. Though the end result of the shift is going to be great, a much greater effort is needed to overcome difficulties, connected with the process (lack of interoperability between different cloud and physical platforms, security provisions, access to the data, etc.)

So, as new possibilities for storing, accessing, and working with data appear, promising significant decrease in expenses and resources, organizations will adopt them. However, certain things and conditions that may complicate the process of data migration should be taken into account, and companies should provide for them.

CRM is a great solution to effectively manage company’s customer data. However, to ensure efficiency, get the most out of CRM system, and avoid CRM failure, special attention should by paid to the data that is being migrated to a CRM, and how it is being migrated. There are challenges that may affect the process of data migration:

• Migration from heterogeneous sources. Data migration from various sources to a CRM is a challenge, so it should be approached with care and strategy. Migrating the data just as is leads to a CRM failure, as you simply take siloed data and move it to another system (the data still remains siloed). So, before data migration takes place, there is a work that should be done to ensure the quality of the data that is targeted to a CRM.
• Data mapping. To migrate data, one should map the source and the target. That’s clear. The problem is, that in different sources data fields possessing equal information, may be named differently (for example, “Username” and “Account”), or vice versa, equally named fields may stand for different values (“Name” may stand for a first name solely, or for the full name, including first name and surname). That’s why it’s very important to map fields in a data source with appropriate fields in a CRM application.

It’s highly probable that before data migration is started, data will need to be transformed, in order to make it appropriate for a new CRM system. Surely, it should be cleaned from duplications, incomplete records, outdated records, etc. Only when data is prepared, clean and in the appropriate format it may be migrated to a CRM.

There is a wide variety of ETL tools available on the market, starting with solutions with minimum functionality and ending with tools that help solve complex tasks. There is also a choice between proprietary offerings and open source ETL tools, Web based and desktop solutions. Selecting an ETL tool requires some effort. When choosing an ETL tool for a particular company, a lot of things should be taken into consideration, including currently used data management processes, technologies utilized, IT staff available, etc.

Thus, to evaluate ETL tools and make a decision in favor of a certain offering, a set of questions should be answered, such as:

• The operating systems supported by an ETL tool,
• The volume of data the tool is able to handle in a given period of time,
• Data sources and data formats the ETL tool supports, etc.
• There is also significant to find out on what conditions maintenance and support are provided (paid, free of charge, etc.)

Besides, company’s requirements to data integration should be analyzed, and compared with the functionality that different ETL tools provide before making a decision and purchase a tool. Thus, a company may avoid paying for the functionality they are not going to use.

“The data, and integration strategies around the data, is something that most figure is there, will be there, and requires very little thinking and planning.” – This is what I’ve read today at ebizQ.

This again supports the idea that data, though being “the biggest companies’ value”, is still often being neglected. And this results in data integration and quality issues, providing inconsistent data and ruining the entire idea of data integration as a way to provide a clear view on enterprise data. The data that is important for business decisions.

In fact, very often when it comes to designing and developing the architecture, all the attention is focused on technical side of the process. Thus data integration strategies become an afterthought making it difficult to meet business requirements. So, the message is that provisions for data integration should be made at the level of the development of enterprise systems architecture.

Lately, there have been talks about the need for a union between data integration and data quality solutions, as no integration initiative can be called successful without the quality of the data provided.

Today, the functionality that provides data quality becomes a requirement for data integration tools. However, data quality still remains a challenge. According to Gartner, only 25% of organizations will proactively and comprehensively include data quality processes and competencies in their data integration work in 2010. This might mean that a large number of enterprises will have to struggle with incomplete, inconsistent and inaccurate data, as their main focus is on the mechanics of data integration process with no connection to data quality.

Technology providers apply more efforts to combine data integration and data quality processes into one data management process. Steps have already been made toward the consolidation of technology offerings, instead of using distinct data integration and data quality solutions. The benefits of this consolidation are quite obvious – a consistent and complete view of the business data, and as a result, the ability to change business processes and models faster to gain the market.

Small and mid-sized companies have already appreciated the opportunities for business that cloud offers, such as simplified the work with applications, the possibility to cut hardware costs, management costs, etc. However, large enterprises, though showing interest in cloud offerings, remain less active in cloud adoption. The reason is that the concept of cloud is relatively young, and there are challenges cloud adopters are likely to face.

Data integration between cloud applications and in-house apps and systems is, probably, one of the biggest challenges. Simply moving data to the cloud will not bring any value, as without integrating it with the on-premise data an enterprise will have just two separate environments containing unsynchronized data. There are several factors that complicate data integration with the cloud:

• Interoperability is a pain for cloud platforms. They emerge fast, and interoperability is not taken into account. This complicated data integration initiative for enterprises, as the choice of the tools will depend on the platform (so if more than one platform chosen, there’s problem).
• Another thing is that cloud data integration requires rather new approaches and data integration mechanisms. Thus there will be a demand for new data integration tools, as well.
• Moreover, different cloud platforms have different levels of security. As data integration between the Cloud and on-premise applications means moving sensitive data across them (and the amount of data will be increasing), there should be developed security standards to protect that data in transit.

The above complexities keep large enterprises from adopting cloud offerings as willingly as small and mid-sized companies do.