Today, data is the asset that businesses are gathering critical insights on to improve business performance and protect the asset. But who will glean insights? Who will process all the collated raw data? Everything is done either by a data analyst or a data scientist. While data analysts and data scientists both work with data, the main difference lies in what they do with it.

Data analysts examine large data sets to identify trends, develop charts and create visual presentations to help businesses make more strategic decisions. Data scientists, on the other hand, design and construct new processes for data modeling and production using prototypes, algorithms, predictive models and custom analysis.

The emergence of e-commerce is a good example of digital flux across industries. E-commerce has been established for a few decades, but has just recently become mainstream for all enterprises, large and small, with the global epidemic forcing laggards into the new paradigm. Because of this transformation, huge volumes of useable data are generated by customers’ online travels and buying patterns. This data may now be processed and learned from, allowing for more precise company plans and decisions with more realistic outcome projections.

Experienced data scientists and data managers are tasked with developing a company’s best practices, from cleaning to processing and storing data. They work cross functionally with other teams throughout their organization, such as marketing, customer success and operations. They are highly sought after in today’s data and tech heavy economy, and their salaries and job growth clearly reflect that.

In this world of big data, basic data literacy—the ability to analyze, interpret, and even question data—is an increasingly valuable skill.

So, while data scientists are masters in predicting the future, basing their forecasts on patterns from the past detected in the data, data analysts extract the most relevant information from the same data sets. You might say that, if the former asks questions to try and map out what will happen in the next few years, the latter is responsible for answering questions that are already on the table.

Data analytics focuses more on viewing the historical data in context while data science focuses more on machine learning and predictive modeling. Data science is a multi-disciplinary blend that involves algorithm development, data inference and predictive modeling to solve analytically complex business problems.

At the end of the day, however, content needs to be findable, and that happens with a strong, standards-based taxonomy. Access Innovations is one of a very small number of companies able to help its clients generate ANSI/ISO/W3C-compliant taxonomies and associated rule bases for machine-assisted indexing.

Melody K. Smith

Data Harmony is an award-winning semantic suite that
leverages explainable AI.
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