Data integration in the cloud


I feel like I'm singing a song called Data in the Sky – With Options! The cloud is forever in our minds these days as a lower cost option because it requires fewer resources to address our data needs. Cloud solutions are an increasing part of many organizations' budgets every year. sky, cloud, buildings

Whether enterprise data is on the cloud or in some computer center on RAC, issues around data integration still persist in most every organization. For example, data movement and integration are required in a cloud implementation just as they are in other implementations. And if our data volume is enormous, cloud may not be the answer. We need to look at the cloud as just one solution to help meet our data needs.

If you have multiple application systems that do the same thing or overlap in functionality, data integration is always an issue. Some options for organizations include:

  • Convert to only ONE software that has the functionality needed to run the day-to-day business – sounds great, but may not be feasible.
  • Sunset older systems – this requires no additional data or functionality to be added.
  • Cross your fingers that the business groups don’t run off and buy another tool with their own project budget money.

In my opinion, some sensitive and personal information should not be placed on the cloud. For instance, personal medical and insurance information, banking data, etc. Due to the sensitivity of this data, the enterprise may need to address data integration locally. We should consider data integration for the enterprise, not just for the data warehouse. Data integration can include the following:

  • Data quality tools – offer on-the-go (in process) quality checks with corrections to make sure that ONLY correct data is propagated throughout the enterprise.
  • Data profiling tools – help us find the data issues before they happen (can be placed within a process).
  • ETL/data movement tools – batch and real-time data movement must be taken into consideration.
  • Metadata management tools – this spans across all these tools to acquire a view of the enterprise through business rules, definitions and processes.
  • Data governance – required to protect and assess enterprise data needs.
white paper
Data integration paper

Each of these tools and techniques are part of an enterprise solution for data integration, and should be part of any robust data management platform.

SAS is a leader in Gartner Magic Quadrant for data integration tools for the fifth consecutive year.


About Author

Joyce Norris-Montanari

President of DBTech Solutions, Inc

Joyce Norris-Montanari, CBIP-CDMP, is president of DBTech Solutions, Inc. Joyce advises clients on all aspects of architectural integration, business intelligence and data management. Joyce advises clients about technology, including tools like ETL, profiling, database, quality and metadata. Joyce speaks frequently at data warehouse conferences and is a contributor to several trade publications. She co-authored Data Warehousing and E-Business (Wiley & Sons) with William H. Inmon and others. Joyce has managed and implemented data integrations, data warehouses and operational data stores in industries like education, pharmaceutical, restaurants, telecommunications, government, health care, financial, oil and gas, insurance, research and development and retail. She can be reached at

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