How Will Data Engineering Services Change In The Future?

 


Technological advancements have resulted in AI and Ml being integrated with data technologies. The field of data engineering is undergoing a complete transformation. New technologies like the Internet of Things or IoT, hybrid cloud, serverless computing etc., are impacting data engineering in a big way. Hence, the role of data engineers in the digital preparedness of a company has become even more crucial. To maximise the benefits of these upcoming changes, companies have to collaborate with data engineering consulting service providers.

Most companies will see the role and importance of data executives increasing in the next few years. Today data analytics play a key role in the decision-making process of a company. In the past, data analytics was considered important, but today it is an important business function. No one can deny that leveraging data science provides companies with immense power to take precise decisions that enhance company growth and profitability.

Future of data engineering

Such is the scope of data analytics that different people view different things in the future. Some of the emerging trends that are set to redefine this space are:

  • Data team specialisation: A data team consists of data engineers and data analysts. With an increase in the investment of data engineering services, the specialisation of the data teams is a foregone conclusion. Companies have realised and started to reap the results of investing in a good data team. Hence, there is a definite shift towards adding specialisation to the data team. The team in the future is likely to consist of separate front and backend data engineers, a visualisation lead etc., in addition to the data engineer and analyst.
  • Data as a product: The ability to measure, develop and manage data by adopting relevant practices will make data function a product in the future. On a broader spectrum, this would result in companies transitioning towards agile project management. This further means an innovative evolution towards those data tools that enable version control, monitoring and cross-organisation collaboration.
  • Technological shifts: The technological aspects of data engineering that are slated to witness a major shift in the future are:
    • An increase in the need for real-time data processing will culminate with database streaming becoming a reality. The traditional batch ETL functions will get replaced by streaming ETL. All ETL functions will take place in real-time thereby enabling companies to make changes in the data as it is being streamed.
    • Data sources and data storage warehouses will witness increased connectivity. The resultant environment created will see a reduction in the time taken for multiple source data retrieval. It will also allow users to analyse data belonging to different periods and make precise future predictions.
    • Self-service analytics to be made possible by using smart tools thereby further enabling companies to gain important insights into the working of their companies. Complementing the use of smart tools with the data analytical skills of data scientists and the right infrastructure would help in the identification of key data patterns and trends. The future would see companies taking more data-driven decisions and formulating data-driven strategies.
    • Automation of all data-related functions will make decision-making more data-centric, enhance digital transformation and allow the implementation of AI initiatives. Automation of data science would make data more agile, enable democratisation and operationalize it to shorten its implementation and remove all obstacles in the part of the same.
  • Data quality management: With a humongous increase in the collection of data, data management would continue to be a prominent part of the future of data science. Data engineering solutions dealing with the different aspects of data management would continue to play a critical role. Companies would see marked changes in data management, especially in areas related to:
    • Data cleaning and preparation
    • Good data harvesting
    • Effective data distribution
    • Distributed data management
    • Data contextualisation
    • Data security and accessibility etc

The future holds a lot of promise for data engineering. Previously, the involved companies, like Neuronimbus, were more focused on data collection and visualisation. However, with advances in technology, the shift has been towards finding better and more effective ways to manage, track and transform data. Consequently, to keep up with the changing times, companies too have to change their objectives and goals since data engineering is currently driven by accessibility, flexibility and efficiency.

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