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Buying BI

Choosing an Analytics Platform:
3 Factors to Consider for Your Application

By Dean Yao | July 16, 2019
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Product managers and software development leaders are increasingly recognizing the benefits of adopting embedded analytics, as it helps them provide customers with valuable data insights and stay competitive in the market. While the benefits are readily apparent, choosing a solution from the dizzying array of products on the market can be difficult, even overwhelming.

>> Related: BI Buyer’s Guide <<

As you evaluate embedded analytics platforms, let these three key considerations be your guide:

#1. Architecture and Technology

In order to truly embrace the benefits of buying analytics technology, the data architecture of your embedded analytics solution should happily coexist with your existing technology environment. Otherwise, you risk further burdening already overtaxed developers and platform support staff.

In addition to freeing up resources for your core product development, an architecture-compatible solution will provide appropriate business intelligence security and reduce operational costs. It should also give you the ability to scale and support large user communities in a cost-efficient manner, support multi-tenancy for SaaS deployments, and the ability to run on various cloud hosting platforms with cloud data sources.

#2. Outputs and Development Process

The embedded analytics product you choose should efficiently produce all of the reports, dashboards, and other types of data visualizations your customers require, while allowing for easy customization. A platform that cannot deliver on this requirement will cause your developers just as many headaches as one that is incompatible due to architecture and technology.

Many engineering teams have struggled to keep up with customer requests for report creation and customization, and these ongoing requests impact core product development as well as time-to-market for each release. An embedded analytics platform that offers self-service reporting can reduce or eliminate this drain on development resources. Even if you’re not ready to offer self-service options to customers now, choose a solution that has this capability so you can address internal agility and market demands in the future.

#3. Pricing and Licensing Models

The cost of your embedded analytics solution should align with your existing product’s pricing and licensing, ideally providing revenue growth as well as cost savings.

Depending upon the analytics vendors you evaluate, there are a number of licensing models available, ranging from term-based licenses to annual subscriptions. Pricing plans are sometimes based on factors such as user counts or product revenue. Ensure that you have a clear view of current and potential go-to-market packaging and pricing for your core product before you decide on a platform provider.

To learn more about how to package and price embedded analytics, read our eBook >

 

About the Author

Dean is the senior director, product marketing at Logi Analytics. He brings over 15 years of experience in software marketing and product management at companies including Jinfonet (now a Logi Analytics company), Nimbula (acquired by Oracle), and VMware. He started his career in hardware virtualization research at Intel Research Labs. Dean earned his Ph.D. and M.S. in Computer Science from the University of Southern California, and a B.S. in EECS from UC Berkeley.

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