Introduction

The data generated by connected devices and other new sources of data have transformed logistics and commerce. It has transformed maintenance of all kinds, in virtually all verticals. It is fueling an ongoing revolution in sales and marketing. It is one of several intersecting factors that have completely transformed data management. To understand the import and ramifications of this transformation, it is helpful to have a sense of what analytics are and how they work. After all, even if we treat data management as an end unto itself, the creation, preservation, and maintenance of data is always adjunct to other purposes. The analysis is just one of these purposes.

Analytics As a Site of a Rapid and Ongoing Transformation

Innovation in analytics is not just a function of fusing Lego-like blocks of data together to create larger ensembles of models. Recent analytic innovation is characterized by the intersection of three distinct trends: first, the capacity to cost-effectively collect, store, and process more and different types of data; second, the mainstream uptake of ML and especially of advanced ML techniques; and third, the application of this data (of different types and sizes) and of these advanced ML techniques to new problems that involve asking new kinds of questions.

Analytic practices are also changing. The BI practice area is now complemented by new practice areas such as data science and ML/artificial intelligence (AI) development. The people and machines who work with data no longer expect to use a single means of access an ODBC interface and a single common language (SQL) to access, manipulate, and query data. And analytics as such is no longer the remit of a single practice area or a single domain: the data warehouse and BI; data science and its products; ML engineering and its products, etc. Rather, almost all applications and services will incorporate analytic capabilities, with the result that the consumption of analytics will, in a sense, become commoditized.

Business Intelligent Provides Industry-specific Uses and Benefits.

Most BI work consists of combining customer, product, sales, and similar data into multidimensional views. The warehouse is still the killer app for asking questions of this kind. But access to data of diverse shapes and sizes permits businesses to ask new, different, more ambitious questions that involve discovering as-yet-unknown relationships between bits and pieces of data. Consider the twenty-first-century cargo ship.

Like other modes of commercial transport—railcars, tractor-trailers, and aircraft the cargo ship now bristles with sensors of different types: temperature sensors; sensors that record the frequency and impact of bumps or jostle; sensors that measure motion; sensors that detect chemicals and gases, such as those correlated with cargo spoilage. These sensors generate enormous volumes of data, a small subset of which gets transmitted back to the shipping company, sometimes in real-time.

This data is a potential treasure trove for business. Raw sensor data is of limited use in data warehouse-driven analytic development, where modelers and business analysts construct analytic views grounded in known relationships in available data. But the data generated by sensors lets an organization ask questions that have a definitive inductive quality: they're attempts to reason backward from effects to causes, attempts to discover unknown relationships that permit businesses to diagnose problems in the present, attempts to make predictions and to take action.