Today, cloud providers enable both small companies and enterprises appreciate cost-savings, scalability, and ongoing server support, moving their applications or parts of them to the cloud. However, data integration still remains among the challenges cloud providers and their customers face. According to a number of cloud experts (David Linthicum among others), this occurs due to the fact that data integration in the context of the cloud has not yet been clearly understood and elaborated.

• Sadly, but data integration seems to be an afterthought for many cloud providers; they do not consider the need companies have for synchronizing data in the cloud with the on-premises sources.
• Another issue is that there are still no common standards for data integration between different clouds. Companies have different business goals and they may use the services from different cloud providers. So, today, we speak about not just cloud-to-on-premises integration, but cloud-to-cloud integration, as well. Since there are a number of different cloud platforms available today, providers are expected to consider cross-platform integration as soon as possible.

So, in the context of the cloud, data integration means the possibility to integrate and manage data across all on-premises and cloud-based systems a company utilizes. Cloud provider should consider these options and give its customers such a possibility.

Today, well-established data integration process is necessary for a sound business. Business information is a valuable asset; companies’ decision-makers depend greatly on the data they receive, its quality, value, and actuality. As the amounts of data companies work with grow exponentially, the requirements to ETL systems get more complex. Today, ETL providers face some new challenges along with traditional data integration issues:

Scalability. ETL systems need to be able to process large volumes of data that intend to keep growing. Moreover, today’s business reality requires getting more data in less time. So, scalable ETL is a must.

Operability. A large company’s IT system comprises multiple disparate sources of business-critical data, such as databases, CRM systems, etc. These days, ETL tool should have connectivity to all those systems. Ah, moreover, data integration between all data sources often requires complex transformations to make the data fit the formats common for this or that system.

Real-time data integration. This requirement is being heard more and more often. The need for real-time data demands from ETL systems the ability to process extract-transform-load operations and gather all the data in a standard, homogeneous environment in a really short period of time.

Finally, the Cloud. As cloud offerings get mature and provide some beneficial solutions (especially for small and mid-sized business), companies choose to move parts of their applications to the cloud. Providing the connectivity to cloud systems is a today’s ETL challenge, as well.

It’s a sad fact, but many organizations realize the poor quality of the data in their databases, only when it comes to database integration. Data quality issues are among the common reasons for data integration failure.

This neglecting attitude to data quality lies in the fact that companies often don’t understand how much data quality impacts business processes. Thus, each data source or database a company uses may have its own rules and standards for data quality. The issues, however, evolve as soon as the database integration started in order to get a unified look at, for example, company’s customers’ data.

Those issues may come out of the difference of data fields, for example, or data formats, so the same contact may be represented differently in different databases. Thus when it comes to database integration, it can’t be performed correctly due to those differences, which may lead to data duplication, and many more data quality issues. In fact, in the result of integrating several databases of poor quality, a company gets one big database of poor quality. This means that database integration was in vain, as it failed to achieve its main goal of providing the company with a general view of business data, while the integration expenses were significant.

Unfortunately, data quality technology does not always allow organizations to fix poor data. So, it’s much wiser to implement company-wide standards for data quality to prevent the appearance of data quality issues associated with integration of data from heterogeneous sources, then to perform data cleansing and other data quality procedures afterward.

Today, organizations face increasing data integration challenges. The amounts of data grow progressively, demanding for new levels of data protection, and making data migration even more complex.

At the same time, business demands access to a real-time information and quality data to make right business decisions. There are plenty of technologies that are able to address data integration challenges, though some of them get old-fashioned, some continue to mature, etc. Thus according to Forrester Research, MDM and data quality services continue to mature, while ETL (extract, transform, and load) and data replication “have reached the Equilibrium phase,” and some technologies are moving to a decline.

But that doesn’t mean that a technology or tools which are in the top of the list today, may become a successful solution for data integration challenges. Lots of organizations regard data integration as mostly a technological process, not taking into account how it impacts organization’s long-term plans and the success (or failure) of business. However, successful data integration is mostly about strategy. And when a strategy is defined, the choice of tools get’s much easier, and there’s no risk that a chosen technology won’t cope with the task.

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.