In previous posts, we’ve talked about the critical importance of interoperability, and how it helps organizations and data science teams get the most out of their analytic investments. We’ve focused recently on the ways that R and Python can be used together, and how RStudio’s products provide a single home for R and Python. For the next few posts, we will turn our attention to a different aspect of interoperability: the intersection between Business Intelligence (BI) and data science platforms.
Organizations, large and small, have taken various paths on the quest for better, more data-driven decision making. Historically, many large organizations were dependent on centralized IT-driven projects to develop reports and dashboards. As pressure has increased to become more agile in creating and delivering insights to improve how decisions are made, organizations typically adopt these approaches:
Both approaches allow the analytically-minded to draw on data from multiple data sources and to explore, visualize and understand that data in flexible and powerful ways. They also allow users to create rich interactive applications and dashboards that can be shared with others to improve their decision-making.
These common purposes and capabilities, ironically, often trap the teams that use and maintain these tools as organizational competitors for software budgets and executive mindshare. These very different approaches can end up delivering applications and dashboards that may (at first glance) appear very similar. The strengths, weaknesses and nuances of the two approaches can be obscured to decision makers, especially to executive budget holders.
However, this confusion obscures the distinct opportunities each type of tool provides and how using the tools together can deliver even more value to the organization.
In our next post, we will do a deeper dive into the strengths and challenges of self-service BI tools and code-oriented, open source data science — and what to consider when choosing an approach.
In talking with many different analytic teams at organizations that have successfully combined BI and Data Science, their strategies have typically fallen into two categories: Using data science to either complement or augment self-service BI.
In the complement approach, organizations use BI and data science tools side by side for:
A great example of this approach comes from Dr. Behrooz Hassani-Mahmooei, Principal Specialist and Director at the Strategic Intelligence and Insights Unit at Monash University:
“Tools such as PowerBI are very useful when you want to start from data and generate information. But when you have a specific decision that you are expected to inform, especially a strategic decision, you need tools that enable you to start from that decision and reverse engineer back to the data. That is where R and RStudio helped us as a competitive advantage, in what we call strategic analytics, where you need maximum flexibility and reproducibility as well as clarity for communication and translation.”
In the augment approach, organizations use BI and Data Science tools together to:
An example of the augment approach was highlighted in recent TrustRadius review, where an IT Analyst at a real estate company shared:
“If you are an R user and you have models or reports that you work with regularly, RStudio is a great solution. I also find it handy for building quick apps using
Flask-Adminfor user config tables that support Tableau or Power BI reports for budget tables, KPI targets, and metric targets.”
In our next posts, we will dive more deeply into the strengths and challenges of self-service BI tools and recommend specific approaches and points of integration to consider. For now, if you are part of a BI or Data Science team in an organization, I encourage you to reach out to your counterparts and explore how you can better tackle your common purpose of improving decision making in your organization.
We’re happy to help, so if you’d like to learn more about how RStudio products can help augment and complement your BI approaches, you can set up a meeting with our Customer Success team, or start a conversation with me on LinkedIn.
Many tools used routinely by software developers can also be useful to data scientists.
In this post, we explore possible challenges to putting Shiny in production and how to overcome them.