Background Recognizing an opportunity to expand their customer base, Red Oak Strategic’s client, a global CPG manufacturer, formed a private equity arm to help develop FinTech platforms in developing economies. This involved buying or building various applications to serve specific needs for distribution partners across several markets. The ventures were spread across multiple continents with
Time and again, across Red Oak Strategic’s corporate engagements and particularly in the Financial Services industry, our team has found that key stakeholders often lack insight and analysis into the data behind their operations. Be it client holdings, portfolio company performance or even their own deal pipelines, there is often a massive blind spot for
The pace of our modern world, and the impressive volume of data we collect on a daily basis, can be dizzying. Take for example, the hour-by-hour updates and colorful dashboards made by news outlets as they track the spread of novel coronavirus (Covid-19). Organizations need quick and consistent solutions for exploring, analyzing, and acting on
While it might be tempting to liven up a report or presentation with a few 3D graphs, two-dimensional representation is generally better when numbers are the primary information you want to communicate. Nevertheless, on occasions when numeric values aren’t the primary focus, and you’re more interested in showing the shape of the data, adding a
Interaction Design for Data Exploration Visualizations capable of launching detail views can add value to a data analyst’s user experience. Programming in this kind of interaction automates the creation of complementary charts and increases ease of exploration by linking varied views of the data in a logical way. This tutorial offers a quick example of
Introduction In Part 1, we built an application to geographically explore the 500 Cities Project dataset from the CDC. In this post, we will demonstrate other exploratory data analysis (EDA) techniques for exploring a new dataset. The analysis will be done with R packages data.table, ggplot2 and highcharter. In this post, you will learn how
Exploratory data analysis (EDA) is generally the first step in any data science project with the goal being to summarize the main features of the dataset. It helps the analyst gain a better understanding of the available data and often can unearth powerful insights. Data visualization is the most common technique in EDA. During this
Our team recently designed a dashboard using R Shiny Leaflet allowing users to select many locations at one go on an interactive map. We created the map using the package leaflet.extras, which enables users to draw shapes on R Shiny Leaflet maps. When combined with the package sp and a function called findLocations, the leaflet.extras drawing tool can