Implementing analytics at your company is a multi-team job. In Building Bridges, we focus on helping end users, app builders, and data experts select and roll out analytics platforms easily and efficiently.

Data is the new currency. As a hot commodity, it makes sense that product managers want to ensure that their application is delivering detailed information for their end users.

In order to infuse customer-facing insights into their product, service, or experience effectively, product managers (PMs) will need to build rapport with their companies’ data engineers. Depending on the organizational structure, these data experts may work outside of the realm of the software and product development team as a part of a central data team. Creating a strong connection with the data experts and getting their buy-in is critical in building a solid embedded analytics solution. This is even more important when they are part of a separate central data team.

Step #1: Proving the value of analytics for customers 

This first step is the most crucial for product managers: convincing the organization at large that infusing analytics into products should be part of the company’s story. This conversation may happen with upper-level executives in addition to the product development and engineering team.

Many organizations understand the importance of accessible dashboards, visualizations, and drilldown menus for their end-user. However, this value may not be immediately apparent to everyone at every company. The PM needs to sell the importance of analytics in the app and can do this using several arguments:

  • Customers want it! If customers are clamoring for more insights, bring their voice into the boardroom. After all, NPS is top of mind for many organizations.
  • Competitors are doing it! Your competitors may already have implemented a data strategy for their consumers. Your company needs to catch up or die.
  • Data is today’s differentiator. If your competition still hasn’t become attuned to the importance of data, weave together a narrative about how data access has impacted other industries. Show the success of companies that implemented a data strategy early on and the decline of businesses that failed to do so.
  • Plot the expected ROI of an embedded analytics offering. You know that data can result in a better experience for your customers, so offer an ROI analysis of this feature that they need.

While this conversation may be more with the higher-level executive team, a savvy product manager will connect with the data engineers on the product team to provide backup to the C-suite. This is where it’s vital to take time to connect with those individuals on your team and to build a coalition of highly respected, technical contributors who can back you up.

And as you develop this relationship with your data engineers and product developers, you can segue the conversation to whether you should buy or build your analytics solution.

Step #2: The eternal debate — buying vs. building your analytics

You’ve made connections with the data engineers on your team, and they’re ready to back you up on the importance of having an analytics solution to infuse insights into your offering. However, the conversation will quickly turn to how this solution should be implemented.

Seasoned product managers know that their team’s resources should be devoted to core product features and differentiators — to that end PMs should begin lobbying for an off-the-shelf analytics solution that can infuse insights into workflows and customer-facing applications. Here are points that a product manager can bring up to emphasize the importance of purchasing a solution.

  • Focus on the core product. Most internal development teams dislike building dashboards — instead, most developers and engineers want to focus on enhancing the feature sets of the core product. Aim to engage the development and engineering team’s desire to do meaningful work by offering a better option for providing end user dashboards and data visualizations.
  • Analyze the total cost of ownership of building vs. buying. The product development and engineering team may not have as deep of expertise regarding embedding analytics as the analytics gurus at a third-party company. PMs should examine the total cost of ownership of building the embedded analytics solution, providing troubleshooting/maintenance of the tech, and offering customer support on it versus the value of buying it.
  • Get to market faster by buying an analytics platform. When it comes to software development, speed to market is often an important differentiator. By purchasing an embedded analytics solution that can be implemented lightning quick into your product, your team will be able to go to market much faster, giving you a leg up on the competition.

Step #3: Understanding your development and engineering requirements

Now that you have made your case for buying an analytics solution, it’s time to listen to the requirements of your technical team. They will want to make sure that the embedded analytics solution has the following:

·  Security based on roles and access levels. The team will want to ensure that the analytics platform is secure and can expose the right data to the right person. Additionally, the complete dataset should be stored in a secure manner.

·  On-premises or cloud hosting options. Depending on industry, compliance, company strategy, or any combination of those variables, the development and engineering team may have a specific requirement or point of view around where the analytics solution should be hosted.

·  Look and feel of the white-labeled solution. User experience professionals often fear that a third-party solution will stick out like a sore thumb from the rest of the bespoke application. Understanding their requirements for the customer experience will be key in delivering a seamless in-app analytics experience.

Code-driven solution vs. no-code graphical user interface dashboards or both.  Different product engineers have different requirements for an embedded analytics solution. Some want a code-driven option where they can model data and visualizations with advanced coding languages like SQL, R, and Python. Others may want the capability to build dashboards with an easy-to-use, no-code or low-code interface to focus on other more pressing tasks and enable fast time-to-market. The best option typically is one where you can do both and get the best of both worlds: no-code dashboards that enable self-service and data democratization with customers plus code-driven capabilities for handling more intense and customized modeling. Understanding the requirements for your team is paramount — versatile platforms like Sisense offer both code-driven and no-code analytics options.

The analytics solution for product teams

If you’re looking to take your application to the next level by infusing insights into your customer-facing product, service, or experience, Sisense makes it easy. Recognized as a visionary by Gartner for analytics and business intelligence platforms, Sisense knows that making a difference in the lives of customers is most important. See how Orion implemented Sisense and doubled its business by providing its customers dynamic insights. However you’re looking to change your users’ lives and win your market with insights right where users need them most, the power to build it could be in your hands.

Shruthi Panicker is a Sr. Technical Product Marketing Manager with Sisense. She focuses on how Sisense can be leveraged to build successful embedded analytics solutions covering Sisense’s embedding and customization capabilities, developer experience initiative and cloud-native architecture. She holds a BS in Computer Science as well as an MBA and has over a decade of experience in the technology world.

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