An InetSoft BI Webinar: The Alphabet Soup of Data Integration Technologies

This is the transcript of a Webinar hosted by InetSoft on the topic of "The Alphabet Soup of Data Integration Technologies." The speaker is Abhishek Gupta, product manager at InetSoft.

Today we are going to talk about data integration, and attempt to sort out the alphabet soup of data integration technologies. We’ll learn the differences between ETL, EAI, EII, EIM, and explain how things like SOA help data integration. We’ll provide some insight into why companies choose one method of data integration over another?

While some terms like ETL or Extract, Transform and Load have been around for a while, other terms like EII, or Enterprise Information Integration, are fairly new. Even when you know what the abbreviation stands for, it doesn’t always shed much light onto what they do and how and why people choose different integration technologies. We are talking about data integration, and I think that most people know it will be great to have all their data sources highly integrated.

What is really forcing companies to take data integration seriously? There are a lot of business drivers now for companies to do data integration. Suddenly there are government regulations such as Sarbanes-Oxley which is putting a lot of pressure on the financial transparency demands and stockholders. There are industry regulations and initiatives across other industries such as HIPAA and Basel II Accord.

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There are intense competitive pressures, and there is increased interaction with customers, partners, and suppliers by enterprises today which is increasing demand for data. Our expectations regarding accessing information from personal experiences are raising the bar that the companies have to approach.

FedEx and UPS, for example, provide online tracking packages capabilities. Amazon and eBay are offering online order and status checking, and then for all of us accessing our banks and investment firms, trying to get 24x7 access to account and business transactions, all that puts great demands on data integration today for companies.

So I have this thought a lot when I am trying to do transactions like that, why can’t companies just mash all of their data together? What is so hard about data integration?

Well, that is what a lot of business folks are asking today when we ask them for large budgets. Getting integration is about taking data and turning it into useful business information. The data comes from all sorts of sources, and each source defines the data differently. There are different list of products, customers, and suppliers. Business measurements such as sales inventory and profits are also defined differently.

If you just mashed everything together, it wouldn’t match up, if you are mashing up apples and bananas. The hardest part is making the data consistent, comprehensive, relevant, and timely so that business folks can use it, and that is why data integration is tough.

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When it comes to successful data integration, it is not about the tools or a specific method as much as the process. Let’s talk about how companies should approach data integration projects.

Well, firstly what companies need to do is approach the data integration process from an enterprise perspective. This holistic approach means poking your head above the applications and determine what the business needs in regard to information. So think globally, and act globally. Set up an overall program and process in order to implement and introduce steps. That is the best approach to success.

Data integration doesn’t just involve tool selection. It involves peoples, processes, and politics. You need to establish, for example, data governance to enable business ownership towards reaching a consensus on data consistency information and requirements.