BI and Data Science: Deliver Insights Through Embedded Analytics
Data Scientists are trailblazers. They look for value inside of data and seek to ask the right questions, disseminating insights to their stakeholders. In the world of business intelligence, those “on the ground” need more than just static reports. They need access to clear reproducible insights for exploration, feedback, and action, all in the right place at the right time. This may seem daunting but fortunately, BI and analytics have been tackling these challenges for some time. Embedded analytics integrates data analysis inside workflows, applications, and processes that people use every day, helping move the point of discovery to the point of decision.
In this post, we are going to dive into how the data scientist can integrate insights, increase adoption, and effectively empower end-users to make better decisions. With a code-first approach, data science is perfectly suited to rapidly integrate organizational insights with everyday systems. Our last post covered practical ways that BI and Data Science collaborate with data handoffs. Now let’s look further at how analysts, decision-makers, and end-users can benefit from “tightly tying the rope” between embedded analytics and data science in a secure, scalable, and flexible way.
For an enterprise, data security is regularly a top concern across the entire organization. Security must be front and center as you plan your path forward and coordinate sharing across stakeholders. Not everyone will likely require (or should have) access to the same data. This is where having a system in place that customizes security and permissions for various predetermined roles, often at the data and row-level, will be critical. You need to define which of your stakeholders can view and collaborate on various data products. For example, will only internal users have access, or will outside stakeholders and/or customers also be consuming information as a service? Will you need to integrate with existing services?
No matter the answer to these questions, considerable work will be involved to ensure that proper security is enforced and organizational standards are met. This is one of the major reasons that RStudio Connect is considered, to simplify the deployment of data products for multiple users, integrating directly with existing security protocols like LDAP/Active Directory, OAuth, PAM, SAML, and more.
As your user base grows, effective communication of results often requires access to the right tools for scheduling and alerts. Your team will likely need automated systems for updates and emails at critical times. No one wants to constantly monitor dashboards or receive non-relevant alerts. Having a system that helps you to administer alerts and scheduling will not only make your life easier but will make working and communicating across multiple teams and stakeholders over the long run more effective. Learn about how RStudio Connect makes this easy in our “Avoid Dashboard Fatigue” webinar here.
Embedded analytics runs on scalable platforms, particularly with software as a service (SaaS) to manage cost and capacity over time. As a data scientist, you can plug into these, allowing end-users to utilize models and increase adoption. The Plumber API (R-based) and Flask API (Python-based) both work alongside each other with RStudio Connect to provide the perfect combination of organizational access and integration.
