Whether you produce an app, service, or experience, you’re definitely collecting a lot of data in the process. As data is becoming a defining characteristic in the modern business era, your company’s data stores are a powerful way for you to differentiate your product, add value for your users, drive revenue, and more.
Infusing insights via an analytics solution (vs building analytics yourself) can bring a number of benefits to your organization, from a reduced total cost of ownership to freeing up time for your developers to focus on core elements of your product. With an increasing number of companies and individuals understanding the value of using data and analytics to improve different aspects of their business, the ability to offer embedded reporting within your application or service can give you a competitive edge in an increasingly data-hungry world.
Here are some important things to consider before getting started with embedded analytics.
In-house or out-of-the-box: Buy vs build
Once you’ve decided to infuse insights into your product, the first thing you’ll want to consider is whether to buy existing embeddable software and integrate it in your own app or to develop an analytics platform in-house.
In a world of unlimited resources, you’d probably want to keep everything in-house to retain full control of your product and include the exact kind of functionality and UX you desire. However, there are only so many hours in the day, and the fact is that business intelligence and analytics development are not core competencies for most businesses. Building a full-fledged business intelligence platform could take years of R&D work and huge financial investments and still not give you the results you really want.
Want a more in-depth look at the do’s and don’ts of embedded analytics? Read our exclusive whitepaper now.
Building a simple visual interface may seem like a light lift at first, but it’s important to note that business intelligence is about more than just displaying fancy visualizations on the user’s screen: It handles joining multiple data sources, runs fast queries on large datasets, and empowers users to explore their data in a wide variety of ways.
Creating a robust BI system that can handle the demands of big or complex data will require immense resources (in terms of time and money) and might still fail to achieve the same level of functionality as an out-of-the-box solution.
Ease of implementation
Having said all that about building in-house, you also shouldn’t overlook the possible hidden costs and time-sinks that come with some embedded solutions.
Problems with integration between your own software and the analytics platform you’re using to infuse insights to it have the potential to greatly increase your costs and production time, with lots of back-and-forth between you and your BI provider.
Additionally, some BI software is so complex to implement and use that it requires extensive training on your end before the system is actually up and running, further extending your costs and time-to-market. (It’s also a major headache in its own right!)
In other words, choosing external embedded BI won’t necessarily guarantee you faster time-to-value. It’s important to try the software out for yourself, on your own data, before making the decision.
Defining your requirements
It seems like there are countless BI products on the market and to the untrained eye, they could all appear to be promising the same essential things.
However, closer inspection — which might actually require downloading a trial version of the software or requesting a proof-of-concept — will reveal substantial differences between the different offerings. For example, front-end tools such as data visualization software focus on dashboard reporting, whereas end-to-end tools also handle data preparation and have a built-in querying and analytics engine.
The type of tool you need depends on (among other factors) the volume, variety, and velocity of the data you plan to process. Other considerations include:
- Size: How much data will you need to handle? Hundreds of megabytes? Gigabytes? Terabytes? Some BI tools’ performance can suffer when handling large datasets.
- Reporting: Will it be enough to generate a few pre-determined reports, or will you want users to be able to generate custom queries and reports?
- Security: Which permissions will you be able to set, and how difficult will it be to do so? Can you set permissions on database, table and row levels?
- Data complexity: Is your data fairly organized and structured, or are you dealing with complex data coming from multiple sources?
Build for your future needs
Even after thoroughly defining your exact plans for your infusing insights into your app or service, don’t forget that business intelligence is, to a large extent, the realm of the uncertain. The amounts and types of data we collect today would have been incomprehensible a few years ago, and there’s no reason to believe they will remain identical a few years into the future.
To avoid the need to re-purchase, re-implement, and re-train your staff when you discover the solution you’ve chosen can no longer fully satisfy your requirements, make sure that whichever embedded analytics platform you choose will be scalable. Assume your datasets will grow and that your querying and reporting needs will also expand, and make sure that the software you integrate will be able to handle a larger workload down the line.
Learn more: Evaluation, implementation, building a support model
Download our free whitepaper: 5 Do’s and Dont’s of Embedded Analytics to get more essential tips on choosing between vendors, the implementation process and how to assure your customers get the best support in the process.
Eitan Sofer is a seasoned Sisenser, having spent the last 13 years building and shaping our core analytics product, focusing on user experience and platform engineering. Today, he runs the Embedded Analytics product line which powers thousands of customers and businesses, making them insights-driven. Eitan is also an avid music fan and surfer.