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Don’t Make These Integration Strategy Mistakes

Check out these 5 errors that industry experts say you should factor in when strategizing and following through on enterprise data integration

Misidentifying Metadata Definitions Misidentifying Metadata Definitions

Metadata may not be the most “sexy” aspect of information systems or business problem solving, but it is crucial, according to data quality expert and author Peter R. Benson in a discussion with OCDQ blog writer and industry consultant Jim Harris. Finding ways to link terminology through “concept identifiers” retains communication and but avoids ambiguous tags that can get lost in an integration. “If you take a hard look at the financial crisis or cancer research you will indeed find the reason the challenges are so difficult to solve is in large part because of the limitations in our ability to communicate effectively and the lack of transparency that comes from poor data integration. So, metadata is really important,” Benson says.

Missing the “Machine” Behind the Business Missing the “Machine” Behind the Business

Information management expert and writer Malcolm Chisholm says it’s important to give data as much value as the tools and hardware you buy as part of an integration: “There is widespread recognition that data is a valuable resource and that the value needs to be unlocked from the data. Data integration and BI environments are being built to unlock this value. However, these data-centric applications are not like their process-centric forerunners. In particular, the data, which is the raw material the ‘machine’ processes, needs as much attention as the machine itself. This was never really the case in process-centric applications.”

Taking Document Architecture for Granted Taking Document Architecture for Granted

Integration strategy requires the proper enterprise architecture. However, InfoTech Research group reports that three-fourths of mid-sized organizations still don’t take into consideration all of their architecture during an integration roll-out. By missing this step, later integrations are “increasingly complex,” adding time and cost.

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Mistaking Data Needs with Latest Hype Mistaking Data Needs with Latest Hype

Has the talk about big data caught your CIO’s eye? What he or she really might be talking about is the need for an extensive integration or BI program, suggests Forrester’s Boris Evelson. Don’t let the next big thing overshadow the integration effort that could bring in more widespread business returns and solve the perceived problem in the first place.

Loading Based Solely on Cost Loading Based Solely on Cost

On its face, standard programming is the less expensive approach to load the data warehouse. You may be tied down to this method based on budget, but it also carries plenty of pitfalls in terms of workload and errors (you’re only as good as your weakest developer). ETL tools carry a big price tag and may not be the easiest to vouch for to the business side. For a start to decide between the two for your integration efforts, break coding and ETL options down by productivity, methodology and documentation rather than a cost-first approach.

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Read more case studies, thought leadership and news articles on integration here.

All photos used with permission from ThinkStock.

Data integration efforts can expose a lot of other underlying information issues, not to mention some groaning from IT officers. Even with more data coming from more sources than ever before, there are a few basic data integration principles that can keep your implementation from going haywire.

 

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