Managerial Insights
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Managerial Insights



Filling Predictive Modeling Gaps with Anomaly Detection
posted by Mosaic Data Science

Anomaly detection can be deployed alongside supervised machine learning models to fill an important gap in both of these use cases. Anomaly detection automates the process of determining whether the data that is currently being observed differs in a statistically meaningful and potentially operationally meaningful sense from typical data observed historically. This goes beyond simple thresholding of data. Anomaly detection models can look across multiple sensor streams to identify multi-dimensional patterns over time that are not typically seen.

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Data is Everywhere!
posted by Mosaic Data Science

In the course of several recent data science projects, I’ve been examining data providers external to Mosaic. It’s certainly not the most exciting topic, but questions often to seem arise that are structured something like “If only we knew [X], then we could do [something awesome]” Trying to make progress on these projects has led me to chase down some data. Here are a few notes on various lessons and providers that may be useful for others.

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Data Science in Manufacturing
posted by Mosaic Data Science

Manufacturing holds multiple predictive analytics and data science opportunities. With the rise of the Internet of Things (IoT) and data collection technologies becoming more accessible, manufacturing companies have a wealth of data to mine. Companies can use predictive analysis and optimization algorithms on these data sets to apply data-driven guidance and decision making to improve efficiency and quality, and to reduce costs.

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NLP: Geeking out with Words
posted by Mosaic Data Science

While experts may debate exactly what makes a human being human, there are a couple of unique traits that everybody agrees upon. One of those traits is Language: the capacity to communicate one’s thoughts, ideas, and feelings to others through a highly complex system of vocal, visual, and orthographic signals. No other species on earth can do that in the same way or with the same level of complexity.

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pics-blog-mngins-5How to Make the Most of Your Data-Science Dollar

posted by Mosaic Data Science

Data scientists are a scarce commodity, and are likely to remain so for years to come.[i] At the same time, data science can create a substantial competitive advantage for early adopters who make the best use of their scarce data-science resources.

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pics-blog-manag-4The Role of Industry Experience in Data Science
posted by Mosaic Data Science

Executives considering how to apply data science to their organizations often ask Mosaic about “relevant industry experience.”  Historically this has been a legitimate question to aim at a management consultant.  Each industry has had its own set of best practices.  A consultant’s responsibility has generally been to provide expertise about these practices and guide the customer in applying them profitably.  For example, two decades ago a fashion retailer might reasonably ask a business consultant about her or his expertise with the Quick Response method, then a best practice for fashion retail.[i]  Posing the same sort of question now to a data scientist assumes that industry experience continues to play the same role in data science that it has historically in management consulting.
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The Executive Role in a Data-Driven Organization
posted by Mosaic Data Science

Executives know that one must effect a variety of organizational changes in a timely fashion, to support a technology change.  Otherwise, the organization may resist or reject the change. These changes may involve the formal and informal reward systems, organization structure, resource allocations, and cultural norms.

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pics-blog-manag-2Sample Size Matters
posted by Mosaic Data Science

Given the current shortage of data scientists in the U.S. labor market, some argue that employers should simply train internal IT staff to program in a language such as Python or R having strong data-analysis capabilities, and then have these programmers do the company’s data science.  Or they may hire analysts with statistical training, but little or no background in optimization.  (We discuss this risk in our white paper “Standing up a Data Science Group.”)

This post illustrates an important risk in this homegrown approach to data science.  The programmers or statisticians may, in some sense, perform a correct statistical analysis.  They may nevertheless fail to arrive at a good solution to an important optimization problem.   And it is almost always the optimization problem that the business really cares about.  Treating an optimization problem as a purely statistical problem can cost a business millions in lost revenue or cost reductions, in the name of minimizing data science labor expense.

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pics-blog-manag-1Small Data, Big ROI

posted by Mosaic Data Science 

Welcome to Mosaic Data Science, and thanks for reading our blog!  We’ll frequently opine here about various technical and managerial data science topics, so visit often.

The phrase ‘big data’ has become enormously popular in the business press.  Like many business buzz phrases, it has lost much of its original meaning.  More often these days when a business writer says “big data” they mean data science, or data science applied to a large data set.  Some traditional-BI vendors try to capitalize on the buzz by identifying new features of their offerings as supporting “big data,” even though they work in the traditional relational-database paradigm, which big data by definition does not fit.

The phrase does have a clear (and useful) original definition.  Big data is data that is too big to be stored economically in a relational database.  Just what that means depends on whose budget we’re talking about, and what year.  Regardless, many new data-storage technologies have been invented out of the need to store data that’s too expensive to manage with a relational database.  There’s just too much of it.

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