May we show
you a few things

How we helped a PE firm make a no-go decision

Client: Mid-market Private Equity Firm

Industry: Automotive Repair Shops

The firm had high-level research that was informative but imprecise and needed to identify regional differences and the level of fragmentation in the market. We provided them with a bottom-up view that would enable them to properly assess the potential value of the target on a zip code by zip code basis.

How we helped a hedge fund create a new alternative data source

Client: Value-Focused Hedge Fund

Industry: E-Commerce Event Management Platform

The fund wanted to quantify the impact of the acquisition on retention of the target’s customers. Additionally, they wanted to build a granular view of the acquiree’s customer base and revenue trajectory. We conducted a data harvesting exercise to compile the relevant data and constructed proxy measures that were strongly correlated to what the fund wanted to learn. Finally, we created an interactive dashboard so the team could monitor the performance of the company over time.

How We Helped a Manufacturing Firm Improve Operations

Client: Global Diversified Industrials Firm

Industry: Transportation

For a particularly challenging engineering use case, we built a model of the lifespans of interrelated components to make maintenance cycle recommendations. To understand the impact of those operational changes, we built a simulation of the flow of components over time to make predictions about potential cost savings. Through our work, we achieved a reduction in the amount of inventory used in repairs and a 12% increase in the average component lifespan.

How We Helped a Shipping Company Tackle an Almost Too-Big-to-Compute Problem

Client: Large Shipping Company

Industry: Transportation

We refined a firm’s shipping strategy to optimize between a fast, but expensive option, and a slow and cheap one. The problem was too complex to tackle computationally. We developed a network strategy using operations management algorithms to identify time efficient and cost effective paths that would not compromise service quality. Our operational plans provided ~20% operating expense savings over the original strategy.

How We Helped the Internal Marketing Team of a Large CPG Prepare for a Board Presentation

Client: Fortune 500 Firm

Industry: Consumer Packaged Goods

We helped this team to test and refine the allocation of its marketing and distribution spend under various scenarios. With our solution, they were able to continue monitoring the performance of distribution partners against expected KPIs and to tailor spending to a retailer- and geographic-specific level in a systematic way.

Helping a Firm Meet a Tight Regulatory Deadline

Client: Fortune 500 Firm

Industry: Industrials

The firm needed to search through a library of low-quality pdf and image documents and select all documents that contained a set of terms to address a regulatory compliance issue. We built an OCR (optical character recognition) system and created a searchable database to match the extracted text to each document. The firm was able to perform the term searches automatically rather than manually. At a later date, they deployed our OCR solution more generally across other sets of legacy documents.

Taking a Cloud of Words and Giving Them Meaning

Firm: Fortune 1000 Locomotive Company

Industry: Transportation

This firm wanted to extract insights from unstructured text entered by technicians during a maintenance event. The data came from different sources, was written in different languages, filled with acronyms and abbreviations, and contained typos and misspellings. The company wanted to take this cloud of words and boil each record down to a one- to two-word concept. We built an NLP model that increased accuracy by 24 percentage points, required fewer computational resources, and was deployed for real-time use.

Need to optimize logistics or match inventory? RRDS has experience solving these tough problems using specialized algorithms like Dijkstra and Yen’s. Our team focuses on network design and optimal control using both applied-theory and data-driven approaches.

One example was when we worked with a shipping company to build a dual network strategy that had to balance a fast but costly solution with a slow and cheaper one. The complexity tested the limits of available computational power, but we were able to find an approximation that was both accurate and scalable.

Looking to decrease costs and increase efficiency with specialized predictive analytics? Working side-by-side with subject matter experts, we can quickly identify whether a particular component or process would be a good candidate for unsupervised or semi-supervised learning.

In one case, we built a new method based on changepoint detection to help a leading industrials company identify potentially troublesome units earlier in their lifecycle, thus reducing costs and return maintenance visits.

Are you making use of the richest pool of data in history? We have the tools and capability to build fully and partially-automated solutions to harvest primary data directly from the internet.

After the Sandvig v. Sessions ruling that web scraping is allowable regardless of TOS language, we are now able to enrich your existing data with new sources of information. Clients have used web-scraped data to investigate correlations between their internal KPIs and weather, macroeconomic factors, competitor behavior, and customer preferences.

We have experience scraping multiple sources and cleaning and aggregating the results into analyzable form. One of our toughest scraping problems required a semi-automated approach that paired our software systems with human judgement to map out a particularly fragmented and rapidly evolving industry.

Want analytics that will work even at scale? We work hard to ensure the solutions we provide are well-tested and robust. We have R and Python developers on staff who follow software engineering best practices including regular refactoring, active versioning, and rigorous unit testing.

We typically use open source solutions because we find those environments more dynamic and adaptable. However, we have experience with the top commercial packages as well, depending on client preference. Our work has been used in Fortune 500 companies like GE, Kraft, and Ingersoll Rand.

Already have database infrastructure in place? Working with your internal IT and data engineering teams, we can make a plan of action for how best to work within your existing infrastructure or make improvements to facilitate scale and speed. We’re equally comfortable with NoSQL and relational databases.

Need to convey results in concise and actionable manner? We can develop visualization and reporting tools that will make it easy to interpret and utilize the analytics that we’ve built. We have created dashboards in Tableau and made bespoke web-based visualizations using Javascript frameworks like D3, Chartjs, and React. For those most comfortable in Powerpoint, we can also develop a polished template and programmatically pre-formatted outputs.

Translating Business Problems into Data Science Solutions

Unsure if data science is the answer? We can help you analyze your problem and quickly determine the best approach to solve it. We have data scientists who come from the investment world and who specialize in bringing technical products to market. Our work has led directly to patent pending solutions and in some cases, has had direct bottom line impact for clients.

We have worked with firms who are new to data science and are looking to build a systematic approach to rolling out analytics. We have also worked with early stage ventures and portfolio companies to assess their internal data and brainstorm avenues for monetizing and productizing it.

Not a data scientist but curious about how machine learning and data science could help you? In early 2018, we developed a framework to help non-technical business and line managers successfully navigate data science initiatives. Our framework expands the typical strategic planning concepts beyond strategy and tactics to include a stakeholder assessment and a mapping of operational capabilities.

In our experience, taking time to consider the larger environment in which advanced analytics will operate can mean the difference between solutions that wither on the vine and those that ultimately get deployed. As a team, we know what it takes to successfully translate business questions and use cases into a working data science approach.

We are mathematicians at heart. We founded RRDS in 2017 because we see the beauty and elegance in providing well-crafted solutions to very challenging problems.

Over time, we recognized an opportunity to help private equity firms and their portfolio companies benefit from data science and machine learning. We believe that at this moment, a vast opportunity to transform the private equity investing process exists. With the general proliferation of data, the advancement in computational methods, and an increasing interest in using these tools, data science can and will transform the way in which investors do deals.

Our mission is to make advanced analytics accessible and available to non-data scientists. In more broad terms, we shine a light on the business questions you might have been unwilling or unable to try to answer in the past.

We believe in the power of data science to illuminate and inform, to move from disaggregated bits of datum to truly actionable insight.

We do this by sharing our skills, expertise, attention to detail, and obsession with rigor and practicality with like-minded professionals who are equally demanding of themselves and their teams.