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In our first post in this series, we started examining a critical aspect of interoperability: the intersection between Business Intelligence (BI) and data science platforms. The two approaches share a common goal: delivering rich interactive applications and dashboards that can be shared with others to improve their decision-making. However, this common purpose often leads to the tools (and the teams that support and use them) being seen as competitors for software budgets and executive mindshare in a large organization.

In the previous post, we reviewed two high-level approaches for combining these tools to deliver increased value to an organization: Using data science to either complement or augment self-service BI. That is, using the tools either side by side to tackle different use cases, or together to tackle a single analytic problem.

In this post, we’ll focus on the strengths and challenges of the two approaches, to help you identify which tool to use in different situations.

Strengths and Challenges of Self-Service BI

Self-Service BI tools, such as Tableau, PowerBI or Spotfire, are widely used because they allow business analysts to:

Despite the strengths of BI tools, they also present challenges that may not be obvious at first glance. BI tools:

Strengths and Challenges of Code-Friendly Data Science

When compared with self-service BI tools, open source data science tools using R and Python provide:

Despite these strengths, teams which adopt open source, code-friendly data science do encounter a number of challenges. Open source data science tools:

Table 1: Summary of the strengths and challenges of using Self-Service BI and open source data science tools.
Strengths Challenges
Self-service BI Tools
  • Explore and visualize data without coding skills
  • Share analyses and interactive dashboards
  • Do self-service reporting and scheduling
  • Support data-driven organizations
  • Are difficult to adapt and inspect
  • Are limited by their black box nature
  • Struggle with enriched or wide data
  • Create uncertain conclusions
  • Include limited data science and machine learning capabilities
  • Require skills that aren't easily transferred
Open Source Data Science Tools
  • Provide a wide range of open source capabilities
  • Unlock the benefits of code
  • Allow fully customizable data products
  • Have broad Interoperability
  • Create transferable skills and analyses
  • Tap a wider pool of potential talent
  • Necessitate coding in R or Python
  • May require package and environment management
  • Provide limited native deployment capabilities
  • Don't include enterprise security, scalability and cloud features

RStudio Tackles the Open Source Challenges

The challenges for open source data science summarized above are significant–and are the specific challenges that RStudio addresses.

Complement and Augment your BI Tools

Code-friendly data science with R and Python is powerful, and can be even more valuable when used in conjunction with self-service BI tools (as discussed in our first post).

The strengths and challenges above show that:

Organizations must consider this balance, between the barrier to entry and the complexity of the questions that need to be answered, when choosing an approach. In future blog posts, we will dive more deeply into this topic, explore specific integration points for BI and Data Science tools, and provide concrete recommendations.

We’re happy to help you explore these topics, 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.

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