This article is part of an Essential Guide, our editor-selected collection of our best articles, videos and other content on this topic. Explore more in this guide:
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- Evaluating Analytics as a Service in the enterprise
- Big data appliances give enterprises more data analysis options
- Amazon, GE, Pivotal collaborate on the Internet of Things
- What are useful resources for a data-analysis newcomer?
- What are the benefits and downfalls of Analytics as a Service?
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What are the pros and cons of Analytics as a Service?
The key advantage of Analytics as a Service is that it allows users to focus on exploring and analyzing data, a high-value activity. Instead of having to set up servers, configure data analysis tools and script reports -- as enterprises would need to do with on-premises analytics options -- IT teams can spend their time exploring the data, formulating hypotheses and discussing insights with collaborators.
Analytics as a Service (AaaS) providers offer some combination of data analysis and reporting tools; extraction, transformation and loading (ETL) programs; and visualization tools. These tools also enable those with limited programming or statistical analysis skills to delve into data. This, however, could create some issues.
Relying on a black-box analysis can be risky. Take, for example, an AaaS tool that uses your data to build a model that classifies customers as either "likely to churn" or "not likely to churn." Such a classifier could be valuable and allow you to focus your retention efforts on the customers most likely to churn. But you can't put too much faith into a classifier without knowing how accurate it is, both with training data and actual data collected in production. Does the classifier generate too many false positives (i.e., loyal customers mistakenly categorized as "likely to churn") or false negatives (i.e., missed customers that do churn)?
When evaluating AaaS options, consider the cost and time required to transfer data to the AaaS provider. You also must assess the vendor's support for version control and metadata about data sets, models and analysis results. Those features will be important factors in long-term manageability.
About the author:
Dan Sullivan, M.Sc., is an author, systems architect and consultant with more than 20 years of IT experience. He has had engagements in advanced analytics, systems architecture, database design, enterprise security and business intelligence. He has worked in a broad range of industries, including financial services, manufacturing, pharmaceuticals, software development, government, retail and education. Dan has written extensively about topics that range from data warehousing, cloud computing and advanced analytics to security management, collaboration and text mining.