Data Science

Many businesses struggle to make productive use of the vast amounts of data they now collect.  Some of it is familiar data related to supply and demand, even though the scale may have increased thanks to the proliferation of products and the globalization of supply chains.  Newer data sources include information about individual clicks on a company’s website and natural language processing to interpret the sentiment of comments made on social media.  It all amounts to what many refer to as “big data.” 

To most, “big data” really means “too big to comprehend” or simply “too complicated to study in a spreadsheet.”  They’re not wrong!  But there are wonderfully effective techniques that can be used to organize the data and then to glean insight from it.  This is the realm of the data scientist.

The team at End-to-End Analytics includes experienced data scientists well equipped to help you with problems like these:


Big Data

Data Architecture

Before a firm can exploit big data opportunities, it first has to gather and store that data.  And this requires defining the data architecture.

Unfortunately, many firms pay insufficient attention to this first step, and they end up with the wrong data stored in the wrong way, often adding excessive cost.  To avoid this situation we work with clients to:

  • Evaluate different potential uses for big data and the value of each.

  • Use that analysis to determine what data to capture and what not to capture.

  • Determine at what level of granularity to capture the data.

  • Design the right data structures, balancing data volumes against speed of access.

  • Automate data cleansing procedures so that data irregularities do not undermine subsequent data analysis steps.


Big Data

Machine Learning

Recent years have seen a remarkable democratization of machine learning.  With the advent of powerful open-source tools such as R, anyone with a dataset can now apply a sophisticated machine learning algorithm to it.

Unfortunately, there is a world of difference between simply running a complex algorithm and actually deriving business value from the results.  The latter requires real expertise – both technical and business expertise.

Our machine learning experts work with clients to:

  • Frame the business problem in a way to make it amenable to a machine learning approach.

  • Select the right algorithm for the problem.

  • Transform the data to construct meaningful variables (a step often known as “feature engineering”).

  • Interpret the model results, diagnose any problems, and iterate until the predictive power of the model is maximized.

  • Turn the model output into actionable business insights.

  • Move from insights to tangible recommendations.


Big Data

Internet of Things

An increasingly important source of big data is the so-called “Internet of Things” (IoT) – the vast web of interconnected devices that arises when everyday objects acquire internet connectivity.  Applications occur in both consumer and industrial spaces.

Data provided by the IoT can improve the efficiency and effectiveness of almost any business process.  For example, our big data experts work with clients to:

  • Quantify failure probability over time for equipment based on state information from sensors embedded in key components.

  • Improve maintenance schedules using these failure probability profiles, accelerating maintenance for at-risk units and avoiding unnecessary interventions for others.

  • Optimize routing of vehicles based on IoT data about vehicle loads, locations, and congestion.

  • Improve inventory replenishment, taking advantage of IoT data on inventory levels and locations.

  • Enhance customer service by using IoT data to remotely diagnose technical problems.