How Your Data Strategy is Critical for Your Embedded Analytics Solution

Every organization brims with potentially valuable data points that can be analyzed for better decision-making.

data-analytics-analysis

How data analysis leads to better decision-making is now clear. Every organization brims with potentially valuable data points.

“Instead of being a secondary focus — completed by a separate team — data and analytics is shifting to a core function,” Gartner noted in February. “However, business leaders often underestimate the complexities of data and end up missing opportunities.”

The challenge isn’t getting the data itself. It’s presenting the data in insightful, user-friendly ways. 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 — is all about the user experience.

Having a customer-focused approach toward your embedded analytics solution ensures you’re constantly evolving to provide a tool that best works for them. You won’t immediately get the perfect solution and shouldn’t expect to. The process starts with knowing a customer’s current data architecture and providing a data strategy that meets the performance requirements of their users.

Here’s how to get started and the pitfalls to avoid:

Anticipate Your Customer’s Embedded Analytics Needs

Embedded analytics is now a customer expectation, so the smart approach is to develop the analytics with the specific customer in mind. Your first step, however, is determining who will own your application’s content because a host of considerations are at play depending on who is in control.

If your customer is the owner, move forward carefully. They will be interacting with the data themselves, building their own queries and creating their own content. How will your application present the data to them? What calculations can you add into the application to help them get what they need? What kind of experience will they have? Put yourself in their shoes. They might not be a data expert, but they will need to run meaningful queries. Anticipate their needs.

The likely easier approach is when much of what’s happening through the application is under your control. You control the queries against it. In this case, you can create a simpler data architecture because your team will be managing it.

Assess the Data Architecture

A sure pitfall in your approach is trying to nail the perfect solution right from the start. The challenges of data architecture in embedded analytics can be complex and evolving. Consider data architecture a journey rather than an end result.

You need a clear understanding of three key factors to begin:

  • What data structures do you have?
  • What data architecture are you using?
  • Is your data architecture designed for the types of analytic questions that your customers need to answer?

Again, focus on your customer’s needs. What questions do they have, and can your data architecture answer them? Do you need to simplify how the data appears to end-users? How else can you adapt the data architecture to meet their requirements?

Nearly 40% of IT and business professionals say complexity and usability issues with their business intelligence tool is a challenge, according to research by Enterprise Strategy Group.

Your solution needs to be highly useful for the customer. Take the time to assess what you have and what you need.

Prepare to Change Course

As with any journey, you may need to alter the course. You’ll need a good strategy for making changes to your architecture when necessary. Don’t let the need for change surprise you. Consider these factors early in the process:

  • What is your starting point? Although the starting point is a straightforward consideration, highly transactional systems such as CRMs (Customer Relationship Management) and HRMs (Human Resources Management) that record your organization’s daily transactions are important to note.
  • Where does the client want to go? Is the ultimate goal a solution for now, information similar to what you have at the starting point, or a solution for later, much more aggregated data and trends over time? Be prepared for the queries your customer ultimately may need.
  • How much load can the platform handle? Queries that lag or stumble are of little value. Understand what kinds of information you’ll be dealing with so you know whether the current platform can support it.

Embedded analytics leads to better decision-making for your organization. Getting started begins with assessing your current data architecture and keeping mindful of where you want to go. Remember, the path to success is a journey, and your destination is a solution that is user-friendly and fulfills your users’ needs now and later.

Charles Caldwell

Charles Caldwell is the vice president 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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Charles Caldwell

Charles Caldwell is the vice president 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.