4 Key Differences Between BI and Embedded Analytics

Introduction

In a previous post, we discussed the evolution of the business intelligence (BI) industry. There are many changes to BI software that are leading to the rise of Embedded Analytics. Now, let’s go deeper and look at the important differences between BI and Embedded Analytics.

#1 Developer Friendliness

With the explosion of SaaS companies, comes the massive growth in demand for developers. After all, you don’t have a SaaS product without software engineers. But these are not IT admins and database administrators. These are the team members responsible for carrying out the product vision.

As a result of the immense competition in most industries, product and company leaders expect them to be efficient and deliver as quickly as possible. Traditional BI software was never meant for this developer audience.

Embedded BI software is an attempt at serving SaaS companies, but it’s still the same BI system that was, and still is, difficult to embed within a multi-tenant, SaaS application. After all, large enterprises using BI for internal usage want fewer developers, so they design their tools for self-service. The ideal offering for a SaaS application is to allow their customers to build custom dashboards, tailored to their business needs.

In a SaaS world though, that means hundreds to thousands of customers each with their own business needs. The traditional BI leaders focused on embedded dashboards only, not the creation process. With these tools, developers have no chance to create advanced experiences for end-users. The newer class of Embedded BI solutions tried to go a step further and allow their dashboard builders to be embedded.

But having been in the industry for over two decades, I can tell you it was never easy to implement and most companies spent far too many developer resources to make it work. It’s a square peg in a round hole, even today. Embedded Analytics solutions aim to serve this audience directly by building the solution from the ground up for developers.

That means having an API-first approach to an analytics solution with no-code widgets that deliver real value in terms of time and cost savings.

#2 Costs

Speaking of costs, have you ever tried to license a system based on user licenses to a SaaS platform that cannot predict how many users need access? I didn’t think so and good luck if you want to try. This is one area where the differences really shine through.

Traditional BI systems are selling you server licenses and user licenses. Sounds great for an internal project when IT is the primary buyer and can stand up servers as they need.

Cloud hosting has made it easier to access servers on-demand, but how are you going to predict usage across a platform that has 500+ customer tenants on it?

Embedded Analytics is aligned with SaaS application costs and moves from the difficult-to-predict user model to a licensing model based on value. And that value is directly tied to the amount of data being created on your SaaS application that requires analysis and action.

It’s much easier to look at historical data creation to predict future growth needs without worrying about users.

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#3 Architecture

Without getting too technical on this topic, software architecture is another key difference. Traditional BI solutions are server-based systems that were never meant to scale with cloud platforms like AWS. Their design is for individual server deployments.

Sure, you can cluster a few servers together, but that’s not harnessing the power of the cloud architecture. These systems are stuck in the past, regardless of what their marketing department tells you. The only way out is a full redesign from the ground up and that takes years of work that companies are not willing to invest in. This is one of those areas that directly triggers an evolution of technology.

Embedded Analytics aligns with SaaS application hosting principles and that means a cloud-first strategy from the beginning. AWS has made tremendous strides in serverless technology that makes it not only easy to manage but incredibly scalable and cost-efficient.

Why would you want your development team to build with technologies that fight that trend?

#4 Data Readiness

Lastly, the requirements to ingest data into a traditional BI are rigid. Again, looking at when these systems were initially built, SQL databases were the only data structure that could be used. So regardless of how data is created, it must be forced into this relational structure.

The growth of NoSQL-based systems like MongoDB and ElasticSearch alone illustrates the shift in SaaS application data management. NoSQL deals with semi-structured data, but another class of unstructured data comes from solutions providing services like text or image analysis.

BI systems are basically of no use without significant data manipulation. Data warehousing that typically accompanies most BI solutions is rigid, limiting the performance and utility of SaaS companies.

Remember, BI companies do not target SaaS companies and their multi-tenant, B2B2B use cases. Embedded Analytics, however, is targeting SaaS companies, and that influences everything about this solution from the ground up.

To wrap up, none of these key differences get into feature comparisons between systems. Every system has a pie chart. The real reasons to move on from traditional BI systems though lie in these four areas.

As company and product leaders, you align your SaaS product to your customers’ needs. And when time is money, the only question is what are you doing to save your team’s time?

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