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  • michaelthould

Big Data and Analytics Roles: Data Analyst, Data Scientist, Data Engineer and Data Architect

Updated: May 30, 2019

We may not know it, but we’re consuming huge amounts of data every day. Whether it’s through Siri, Google, Microsoft, Amazon or Oracle — all these applications are increasingly shaping how we interact with reality. The massive amounts of data analysis can be transformational and so they need to be interpreted by specialists acting within different roles.

Over the past few years, the responsibilities that come with these data-related roles have evolved to include designing and building systems that organize millions of data records, finding patterns in consumer and customer behaviour, and predicting future trends that improve outcomes. Below are the aforementioned roles and what they can do:

Data Architect

Data architects build complex computer database systems for companies, both public and private. Together with a team of software designers, design analysts, and other specialists, they determine the needs of the database and the data that is available, then they develop a comprehensive blueprint for creating, testing, and maintaining that database. The collection of these master blueprints, designed to align IT programs and information assets with business strategy, is called the Enterprise Data Architecture (EDA). This EDA is used to guide integration, quality enhancement, and successful data delivery to the business.

Data Engineer

Data management systems are built and optimized by the data engineer. The data engineer makes sure that any information is properly received, transformed, and stored since every business relies on its data to be accurate and accessible to key decision-making teams and individuals. Using different tools, the data engineer works on the “back-end” to constantly improve information pipelines so that any data needed is categorised, processed, stored and distributed efficiently. A good data engineer saves a lot of time and effort for the rest of the organization.

Data Analyst

Depending on the industry, data analysts can also be called business analysts, business intelligence analysts, operations analysts, and database analysts. However, no matter the job title, the data analyst’s specialty is still the same: generating data sets and interpreting these to answer questions, and communicating the results to their internal clients, so that they can make more informed business decisions. Some common tasks done by the data analyst include; data cleansing and data visualization.

The data analyst is very valuable because he or she can extract new data sets using the data systems built by the data engineer to identify key trends that provide customer or business insight, as well as running analyses on business issues or anomalies. The reports generated from these analyses need to provide a clear summary that allows strategic decision makers to better understand where the organization is and how the business can make strategic decisions that will provide better customer and as a result better business outcomes.

Data Scientist

Someone who is an expert in statistics and can build machine learning models to make predictions and answer key business questions is called a data scientist. Similar to the data analyst, the data scientist can also identify, clean, analyse, and visualize data. However, a data scientist has more insight and depth in terms of analytical and data handling skill because he or she is able to train and optimize machine learning models and extract knowledge and/or insights from data in various forms, structured or unstructured, including to data mining.

Enterprise data science takes advantage of recent advances in machine learning algorithms and cloud computing infrastructure to extract actionable information from digital assets and use it as a driver for operational efficiencies, knowledge discovery, and new opportunities. All of this can lead to substantial value creation.

The data science field is constantly evolving, but regardless of industry, the ability to systematically exploit digital data assets will prove to be the single most important differentiator between business success and failure over the next decade. The constant need to adapt to quickly advancing technologies can be addressed by such experts.

Contact us to find out how our Fusion Professionals can help identify and meet your Data Management needs from strategy and architecture to design, implementation, and management.

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