Introduction

Launching an embedded analytics project can be a difficult task for software development managers and product owners alike. Add onto that the urgency to quickly evaluate test and implement a 3rd party solution that’s becoming more the norm in agile development environments and you can easily see how proper planning can be overlooked. You would be surprised how many companies see problems and delays midway through an embedded analytics project because they didn’t align executive and customer expectations before kicking a project off.

It makes sense to try and get quick wins for your customers and embedding an analytics product can be a great way to do that.
To ensure you get the most value out of an embedded analytics project, we’ve put together an easy to follow 3-step guide to planning your embedded analytics project.

1. Define Data Insight Vision with Stakeholders

Deliverable: Full Requirements Document

The first and most important step in planning an embedded analytics project is to define the vision for data insights with key stakeholders, this includes both internal stakeholders and customers. A data insight vision is the ideal version of your customer’s and executive team’s insights experience.
The goal of these discussions is to bridge the gap between internal and customer requirements and should always focus on clearly defining the three key areas below:

• Business Requirements-  Questions around business requirements for an embedded analytics project should focus on the overall value customers will receive from additional insight, the future objectives of the business, and the financial measures of success for this project. Some example questions would be:
  • “Are we going to offer this reporting and analytics package as a part of our core offering? Or as an add-on?”
  • “How will the additional insights provided affect our customer’s perceived value of our product?”

• Technical Requirements- Questions around technical requirements should focus on potential deployment models, what hardware and software will be needed, and expectations for how an analytics product will embed to your product. Some examples would be:
  • “Will the reporting and analytics technology deploy on-premises or in the cloud?”
  • “What development resources are we able to allocate to deploying a third-party analytics solution?”
  • “How does our hardware footprint and operations change?”

• Roadmap Requirements- Questions around roadmap requirements should focus on how future features will rely on an embedded analytics product and thereby defining any future dashboard and reporting needs. Some examples would be:
  • “How will additional insights evolve customer use of our product?”
  • “Of my future roadmap items, which will use the additional insights created from embedding additional analytics in my product?”

2. Model the Future Application’s Insight Experience

Deliverable: A Mock-Up of Your Application’s Insight Experience

The second set of discussions should focus on modeling the future application’s insight experience, this includes how customers will interact with analytics within your software. From this, you can better understand current workflows and pain points to determine what and where new features will be most beneficial. Insight experience can be separated into the two categories below.

• UI/UX Experience: Modeling your expected UI and UX experience gives you great insight into the best way to integrate an embedded analytics product. This often relies on the ability to match the look and feel of your current application and will give you a better idea of what customization options will be needed.
• Features/Capabilities Usage: Modeling current and predicted usage of reporting and analytics capabilities can help give great insight into where new features can enhance other parts of your product.

3. Reporting and Analytics Assessment

Deliverable: Reporting and Analytics Needs Analysis

You’ve gathered your requirements and have modeled your ideal insights experience, now what? The next step of the planning process is to take stock of deliverables needed by project end and which of these should take place during evaluation of candidate offerings. This can include specific customer reports, dashboards, filters, queries, visualizations, etc. During an evaluation, many companies duplicate their frequently accessed reports to see the work effort involved as a starting point. They then expand beyond current capabilities to understand potential opportunities for added value or differentiation.

Summary

Selecting an embedded reporting and analytics technology can be challenging but it doesn’t have to be if you plan. By aligning executive and customer requirements, modeling your ideal insights experience, and assessing the reporting and analytics deliverables, you’ll be well on your way towards fulfilling your insights vision. More importantly, you will be and well on your way towards adding important new customer value to your existing product or service.

The post Embedded Analytics 101: Making Your Embedded Analytics Project Successful From Step One appeared first on JReport Blog.