A smart data strategy will power your embedded analytics forward

Let’s get real about real-time data.

We are reminded daily how real-time data makes business decisions easier. Timing is essential. Yet still overlooked in the need for real-time data is operationalizing the data itself. Timely data is just part of the equation. Being able to glean actionable insights in a timely manner is the other part. According to research by Enterprise Strategy Group, 38 percent of IT and business professionals say complexity and usability issues with their business intelligence platform is a challenge.

Companies are yearning for an analytics solution that provides a better user experience. The expectation now is embedded analytics -- the visual, real-time data analysis that occurs within a user’s regular software platform without the need to toggle to another application. Embedded analytics is all about user experience. As such, data teams need to provide analytics to fit their users’ needs.

The perfect solution isn’t obtainable immediately. Rather, a customer-focused approach to embedding analytics within an application is a journey. Data teams can work with their users over time to refine what they need and what would work best to make informed decisions. But to get started, data teams must know a customer’s current data architecture and have a data strategy that is adaptable to suit future needs.

Anything less is, at best, a missed opportunity. At worst? A precursor to bad decision-making.

What’s under the hood of your embedded analytics

The first step toward incorporating embedded analytics is to survey what your customer has and what your customer needs. Where are the gaps between their expectations and what you can deliver?

Compare what data structures they have with what data structures you are using. The goal is to know whether the data architecture ultimately will provide the answers the customer needs.

You may need to simplify how the data appears to users. You may need to adapt the architecture to better suit the performance and other requirements of the customer.

But what you end up with must be highly usable -- clear and customizable to the novice as well as to the expert.

Who has the keys to the car?

Who will have ownership over the application’s content, the data team or the client?

When your team will be managing the data architecture, you can create a simpler data structure. You can control the queries. This is an easier approach because you are the one who largely is in control of the application.

The other approach, of course, is when customers build their own queries and generate their own content. You need to be mindful of what kind of experience they will have directly using the data. You also want to be mindful about how you present the data to them.

Think of what calculations or other steps you can add to the application to help them along.

The path forward may change. Change with it. 

Moving ahead, the critical thing to keep in mind is that you will want to have a strategy for making changes to the data architecture. Undoubtedly you will need to adapt.

Half of the IT and business professionals surveyed by Enterprise Strategy Group say they expect the need to add new features and functionality in their BI platforms within a year.

First, consider the architecture you are starting with. Pay particular attention to highly transactional databases such as enterprise resource planning (ERP), customer relationship management (CRM) or human resources management (HRM) because they record the organization’s daily transactions.

Looking ahead, consider the type of information your customer will want. If the application will be looking at much more aggregated data, your data strategy will need to anticipate the types of queries needed in the future.

Consider how much of a load the application can handle. You don’t want performance to lag once people start using your embedded analytics. Take steps to improve your infrastructure. When making changes to your data architecture, speed should be the primary consideration.

Life in the fast lane

Having a customer-focused approach toward embedded analytics ensures that your data team is constantly evolving to provide a solution that works for your customers. How you stand out is developing analytics to fit their needs.

Understand what your customer has in terms of data architecture and what your customer will need. Develop a data strategy. You want to give your customers not just a fast ride but a smooth one, too.

Photo Credit: Artem Samokhvalov/Shutterstock

Charles Caldwell is the VP of product management at Logi Analytics, which empowers the world's software teams with the most intuitive, developer-grade embedded analytics solutions. He has more than 20 years of experience in the analytics market, including more than 10 years of direct customer implementation experience. Charles writes and speaks extensively on analytics with an emphasis on in-app embedding, optimizing user experience, and using modern data sources.