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Evaluating business intelligence in the cloud

Evaluating business intelligence in the cloud

By working on consulting projects related to cloud BI implementations, I have seen firsthand some organizations that are too willing to adopt solutions and leverage cloud based on brand awareness or what their friends and former colleagues at other companies are adopting, and not on a detailed evaluation of their business and technical needs.

Organizations need to not only look at what their business issues are now, but also what they hope to achieve in the longer term to ensure the solution they choose provides the viability and extensibility they require to change and grow with their needs. This requires looking beyond brand or general requirements and towards strategic data driven BI initiatives.

Here are they key considerations to consider before implementing your own cloud BI plans:

Selecting cloud for the sake of cloud is not a valid strategy. Organizations require the identification of their end goal and evaluate the best fit for their organization. With cloud becoming more widely adopted in the market in general, capabilities are more robust and can be comparable to on-premises analytics solutions.

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Organizations should identify whether acquiring new or additional hardware and paying for support and licensing long-term is more beneficial than paying a subscription fee and turning BI consumption into an operational expense. Capex projects will cost more initially, but their maintenance may be lower, while opex expenditures in the cloud may cost less to implement but can be more expensive depending on the scope and expansion of the project.

Where does data currently reside?

For organizations already leveraging data in the cloud, a cloud BI solution may be a natural transition. For instance, any companies leveraging cloud-based operational solutions choose to expand their use to include analytics. Other organizations may have to integrate data from on-premises solutions, which may require other different efforts of data integration.;

 



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