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The Importance of Data Quality and Specification in its Value-Add Potential


The year is 2022, and the use of data is becoming increasingly widespread. More and more firms are dedicating large sums of capital towards data analytics initiatives and transformations, in hopes of reaping some of the wide variety of benefits it could bring.

At the core of this movement is the idea that data can provide immense value to a firm through a plethora of facets, such as personalized customer experience and production optimization. This value can be considered the excess return from acquiring the data, once all implementation costs are factored in. Though, many use cases require a critical mass of relevant data to adequately make predictions and assist the firm in making decisions. Two such characteristics of this data are its quality and its specification.

Data quality has always been important and is of the utmost importance in modelling and extracting insights. Without “clean” data, a prospective user would have to first acquire and refine relevant data before they can focus on the true application of analytics. In many circumstances this equates to higher costs and longer lag times for implementation – both undesirable outcomes. This also encapsulates another important facet: specification. For analytics to be applicable in its industry, the input data must also align. For example, a model for manufacturers to streamline their equipment monitoring requires substantial amounts of data on equipment health over time and in different environments.

This boils down to one simple fact: The cleaner and more relevant the data is, the higher its value-add potential.

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