For app development houses whose tools collect data that could be useful for the end user to make decisions with, there is little excuse not to use embedded analytics and visualizations. However, is it better to build the data analytics machine yourself or buy a bolt-on solution from a third party?
Why should you build?
- Small, fast projects: Creating analytics features in-house can be the best option when working on smaller projects with limited sets of requirements — especially if the development team in question has a relevant skill set and previous experience of developing embedded analytics and data visualizations.
- Total control: One of the most convincing arguments for building is that it lets product managers remain fully in control over every aspect of their application: not just its functionality but the look and feel as well. By keeping all aspects of development in-house, product teams can control branding, user experience, and functionality. The loss of this control is one of the main disadvantages of buying.
- Limitations of support services: Although most providers offer support services, these are not normally included in the price of the package, and are instead an add-on. However, in the majority of cases, such support services are in fact effectively a requirement to ensure the visualization is tailored to the use case and look and feel of the app.
- Cost predictability: Standard price entry points for embedded analytics start at anywhere from $30K to $75K per year. However, behind the upfront pricing structure, there are often multiple levels of service, as well as limits on usage and number of applications the embedded analytics can be used in. This can make pricing far less predictable.
Why should you buy?
- Focus on core product: The main disadvantage of the “build” approach is that developers have to switch their focus away from working on the core product to create complex embedded analytics features. Buying saves time and money over-training a development team that may lack previous embedded analytics experience and eliminates the need for training where internal resources are simply not available.
- High cost to build: There is a significant cost associated with building embedded analytics, which on average takes seven months to complete. The estimated average cost is as much as $350k (based on average U.S. salaries).
- This includes:
- 4 software developers for 7 months
- 1 QA professional for 7 months
- 2 UX/UI designers for 6 months
- 1 data scientist for 1 month
- This includes:
- In-house support: Anything built in-house will have to be supported in-house. With the buy option, support will be provided by the third party, via the cloud, and ISVs will not have to allocate resources to fixing issues if and when they occur. As much as 90%1 of the cost of software during its lifetime is tied to keeping it up and running. Maintenance costs can be significant.
- Faster time to market: With average build-it-yourself times taking seven months or more, many product teams decide to buy a bolt-on analytics solution due to the need to release a product as quickly as possible. In a fiercely competitive SaaS market, and with CEOs demanding quick turnaround, buying a pre-built, off-the-shelf solution drastically improves time to market.
That is why we are offering Reveal Embedded, as a means to help your customers easily visualize their data generated within your app. Still can’t decide if you should you build or should you buy? We’ve created a whitepaper to help you make up your mind.
Drive insights and better business decisions by adding powerful analytics to your next app with Reveal Embedded.Categories: Embedded Analytics