Embedded analytics are a powerful tool for adding value and stickiness to your desktop and mobile apps. Many app developers are providing these features as standard in their tools. But should independent software vendors (ISVs), systems integrators (SIs) or companies building internal apps build analytics tools in-house, or should they buy them from specialist providers?
There are many considerations to the buy or build dilemma. One reason many developers want to build in-house is the limitation of support services from embedded analytics vendors. Although most providers offer support services, these are not normally included in the price of the package, and are offered at an additional cost. 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.
A second consideration is cost. Standard price entry points for embedded analytics can start at $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.
Creating analytics features in-house may be the best option when working on smaller projects with limited sets of requirements—especially if your development team has a relevant skill set and previous experience developing embedded analytics and data visualizations.
Lastly, one of the most convincing arguments for building in-house is that 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.
On the other hand, there are many benefits to buying rather than building. The main disadvantage of building in-house 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.
There is also a significant cost associated with building embedded analytics, which on average takes seven months to complete. The estimated average in-house cost can run as high as $350k (based on average U.S. salaries). This includes: four software developers for seven months, one QA professional for seven months, two UX/UI designers for six months and one data scientist for one month.
Your in-house team is generally responsible for supporting anything built in-house. With the buy option, support is 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% of the cost of software during its lifetime is tied to keeping it up and running. Maintenance costs can be significant.
The time it takes to bring a product to market is another consideration. 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 reduces your time to market.
Making a final decision on whether to buy or build is a complex task. But in the end, ask yourself:
As more organizations realize just how important understanding their data is, embedded analytics have become the key to harnessing that power. Whether you decide to allocate internal resources or hire a vendor, including powerful embedded analytics and visualizations is virtually a must since it helps you differentiate your app and keep users engaged.
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