Why You Need a Data Strategy for Your Customer’s Embedded Analytics

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Embedded analytics has become more than a “nice to have” feature for an application. It’s an end-user expectation. But meeting that expectation requires far more than meeting the initial task at hand.

Rather, a customer-focused approach to embedding analytics within an application is a journey, and you need a smart data strategy to guide you along the way. You need to take some time with an assessment of the customer’s data architecture. You need to readjust as you move ahead based on what’s working and what isn’t. And as your data architecture evolves, you will need to ensure your solution meets your customer’s needs as well as the performance expectations of the customer’s users. User experience, as we know, is critical. Let’s get started.

Assess Your Data Architecture

Trying to get the perfect solution right from the start is foolhardy if not futile. Instead, first set out to understand what your customer has, what your customer needs and where the gaps are between their expectations and what you can deliver.

What data structures do they have? What data architecture are you using? Will that architecture provide the answers the customer needs?

If not, you need to simplify how the data appears to end-users and adapt the architecture to better suit the performance and other requirements of the customer.

The goal isn’t just a solution that will work. The goal is a solution that will work well, a solution that is highly usable.

The Query Question

A key consideration in developing your approach is whether you or your client will have ownership over the application’s content.

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

But if the customer is building their own queries and generating their own content, you need to be careful about how you present the data to them. What kind of experience will they have when interacting with the data themselves? What calculations can you add into the application to help them along their way?

A Data Strategy That Adapts

Those baseline assessments are a good starting point, but moving ahead you will need to adapt. And to adapt smoothly and successfully you’ll want to have a strategy for making changes to the architecture.

The aim is to overcome what data architect Martijn ten Napel describes as the disconnect between what a data architecture most often is, a technology-focused roadmap, and what organizations need, guidance on how to create a valuable and sustainable data landscape.  

Consider where you are starting from. Whether their database system is enterprise resource planning (ERP), customer relationship management (CRM) or human resources management (HRM) is especially relevant because those types of highly transactional databases record the organization’s daily transactions.

Consider what type of information your customer wants going forward. If the application will be looking at much more aggregated data and trends over time, your data strategy will need to anticipate the different types of queries needed in the future.

Consider how much of a load the data and queries will put upon the platform. You don’t want performance to lag once people start using your embedded analytics. Taking steps to improve your infrastructure can safeguard against that pitfall. Speed should be the primary consideration when making changes to your data architecture.

Embedded analytics is a “must have” feature for applications today. You need to take a customer-focused approach in creating those analytics by following a data strategy that recognizes the customer’s current needs while preparing for the inevitable changes to come.

About the Author

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.

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