Data Engineers vs Data Scientists

People data, transactional data, market data, product data, and economic data all have a major impact on creating a competitive advantage and building an effective marketing strategy. Exploring big data sets within your industry sector can expose hidden market variations, allowing businesses to increase workflow quality and optimize operations.

Business data analytics involves analyzing extremely large data sets and implementing the IT architecture for computational algorithms that can reveal market trends, uncover consumer patterns, and increase business intelligence. This data may be structured or unstructured and will always require data experts to capture, store, query, and translate huge volumes of information.

When companies require professional data talent, knowing the difference between data engineers and data scientists will eliminate many potential difficulties and limitations down the road. The two professions do have some overlap; however, the duties of each are very distinct.

What is a Data Engineer

Data engineers work with raw, unformatted data that requires validation due to possible errors from humans, machines, and processes. The main objective of a data engineer is to translate volumes of data into a usable format which may involve constructing processing systems that can test, develop, and construct data architectures.

The information is usually a large-scale database pulled from three main sources:

  • Transactional Data – pulled from business websites which may include invoices, payment orders, storage records, and delivery receipts.
  • Social Media Data – gleaned from online social platforms where consumers tweet, like, upload pictures, post comments, and share reviews on the business they frequent.
  • Machine Data – which is data generated by industrial equipment and smart sensors which is captured, processed and defined as manufacturing or process information.

A data engineer can improve the quality and reliability of your data by employing a number of coded languages and software tools to acquire existing data from other IT systems for further processing by a Data Scientist.

What is a Data Scientist

After business data has been acquired, cleaned, and manipulated, it is now ready for a data scientist that can leverage that data and find hidden patterns. A data scientist will implement statistical analysis algorithms to gain either a predictive model of future market trends or a prescriptive model to determine the best course of action a business should take.

Expect a data scientist to also apply external research gleaned from existing industry information to get full insight into the captured data. A data scientist will always use controlled data or existing information to support or refute the results of the data they are analyzing. Expect a data scientist to perform the following:

  • Interpret complex digital information.
  • Present a clear story to stakeholders based on data.
  • Construct the data to assist in business decision-making

Impact of Data on Business Performance

Data offers a high value to the performance of a business and can help dictate the best ways to use financial resources, how to increase consumer engagement, and can facilitate managerial strategies from sales and marketing to plant floor automation.

Choose Sequent Solutions for Affordable IT Services

When you need to accelerate your data strategies, contact Sequent Solutions for affordable IT services and data experts that can supply the staff you need to capture, sort, analyze, and implement business data that can put you ahead of the competition.MARCH 4, 2019

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