Building an Organization Based on Data Engineering Solutions

Building an Organization Based on Data Engineering Solutions

Whether your organization is primarily comprised of data analysts, data engineers, or data scientists, you can create a data culture. However, the path and technologies to becoming a data-driven innovator differ, and success is determined by implementing the right technology in a way that complements a company’s culture. In this blog, we will explore why organizations need to be driven by data engineering solutions

Not all businesses are the same. Although all businesses have similar functions (sales, engineering, and marketing), not all functions have the same impact on overall business decisions. Some businesses are more focused on engineering, while others are more focused on sales and marketing. In practice, all businesses are a mash-up of all of these functions. Similarly, the data strategy may be more focused on data analysts, while others may be more focused on data engineering. Culture is a synthesis of several factors, including business requirements, organizational culture, and organizational skills. 

Traditionally, engineering-focused organizations primarily came from technology-driven digital backgrounds. To create repeatable data pipelines, they created their own frameworks or used programming frameworks. Some of this is due to the manner in which the data is received, the shape in which the data is received, and the speed with which the data arrives. If your data allows it, your organization can focus on data analysis rather than data engineering. You can focus on data analysis rather than data engineering if you can use an Extract-Load-Transform (ELT) approach rather than the traditional Extract-Transform-Load (ETL). Data that may be imported directly into the data warehouse, for example, enables data analysts to undertake data engineering work and apply transformations to the data. 

This does not happen very often, however. Sometimes your data is jumbled, inconsistent, voluminous, and encoded in old file formats or as part of legacy databases or systems, with limited possibility for data analysts to act on. 

Or perhaps you need to stream data and use complicated event processing to acquire competitive insights in near real-time. The value of data diminishes dramatically over time. In batch mode, most businesses can process data by the next day. However, not many people are likely to receive such information when the following second data is provided. 

In these scenarios, you must have the ability to unearth the insights concealed in that jumble of data, which may be chaotic or rapidly changing (or both!). Almost as essential, you must have the necessary tools and mechanisms in place to empower that talent. 

What are the appropriate solutions? The cloud provides the scale and flexibility necessary for data demands in such complicated settings. Gone are the days when data teams had to beg for the resources they needed to make a difference in the business. Because data processing systems are no longer rare, your data strategy should not artificially create scarcity. 


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