In our last article on embedded analytics, we covered the definition of embedded analytics, how it differs from traditional business intelligence (BI), and provided some digestible examples of how this type of analytics is being used today. Now we will explore some of the advantages to help determine if embedded analytics could benefit your organization.
Companies of all sizes, backgrounds, and industries are beginning to use embedded analytics. The organizations that are able to successfully extract tangible business value share one commonality: a data-driven culture. It’s essential that all teams understand the value of using data to support key decision-making and are encouraged to do so.
Historically, only BI teams full of data analysts and data scientists have been able to interpret and present an organization’s backend data into clear insights. Today, embedded analytics allows cross-functional teams to have access to readable visualizations that help with everyday decision making.
Take a look at your organization as a whole and consider which departments cannot currently access analysis. For example:
If multiple leaders in an organization are struggling to answer these types of questions, it’s likely that a data-driven culture is not yet fully developed. However, if various departments are looking for meaningful analysis to make data-backed decisions, a company already has the foundation on which this type of culture can be built.
The largest benefit of embedded analytics is that reports are made available to a larger audience of every-day workers rather than solely to teams that are technically trained in analytics. Visualizations can be rendered in an employee’s typical workflow rather than needing to log into a business intelligence application like Power BI.
Additionally, many embedded analytics providers have functionality that allows alerts and report schedules to be created—automatically sending data to both internal and external stakeholders that previously had no access to this information.
Here’s a great example: For years, supply chain analysis has used historical order data to help with demand planning. While traditional business intelligence may help with high-level forecasting for the year, embedded analytics can help those further down the supply chain receive real-time order notifications that can help prevent issues with out-of-stock products. This does not require logging into a dashboard but still provides the everyday worker with a tangible piece of analytics-based data.
Today, it’s more important than ever for data to be obtained easily. Given the impacts of the COVID-19 pandemic and increased number of companies that have remote employees, embedded analytics can also help ensure teams have access to the data needed to perform their jobs from anywhere in the world.
Customer Retention & Revenue Growth
Embedding analytics within an existing product can provide additional value to customers and generate new revenue streams for a business. A company that sells online tickets is likely to have a wealth of data on previous sales, including the busiest time-of-day for transactions. If this organization provided its users with embedded reports that display statistics on ticket sales, it is likely that their customers would be able to learn from the data and sell tickets more effectively in the future. This provides a stickier relationship between the two organizations and is likely to lead to increased customer retention.
Additionally, these reports can be made available via an increased subscription price which can help recover the cost of investing in embedded analytics as well as provide upsell opportunities that have discernable business value.
If your company is looking to prioritize a data-driven culture, make reporting accessible to more people, and monetize existing backend data to provide additional value to customers—embedded analytics may be a fit for your organization.
This is part two of our embed series. In part three, we’ll get a bit more tactical and dive into the details on how to embed analytics with Power BI.
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