The Value of Data Engineering

Data Engineers Enable Better Data

Data is the fuel of most businesses. Better data means better decisions. Better decisions causes an organization to make more money. A good data engineer will help you to have a real data strategy, improve data quality, establish data standards, increase accessibility of data, increase data reliability, create better products and improve your customer's experience. So a good data engineer is a good investment.

Data Engineers Improve Data Quality

One of the biggest was a data engineer adds value to an organization is they improve the the quality of the data. They bring in standardization. For example, a field in an app was a text input (where the user can put whatever they want) and users have said the same thing seven different ways. This can be grasped by people, but impossible for computers. It will skew reports and lead to incorrect decisions. Data engineers will fix this and hopefully get the software engineers to turn the field into a dropdown list that has a standardized options.

Data Engineers Provide Access To Data

Organizations benefit most when data is shared. One of the biggest problems organizations face when it comes to data is called a data silo. This means the data is stuck in a single app, maintained by a single department, etc. and others who would get value from it, can't get to it. A good data engineer solves this problem. They will get the ability to get data into the hands of those that need it.

For example, the marketing team could really use the past week's sales data to build their next campaign but its stuck in some app they don't have in an improper format. The data engineer gets them this data.

Data Engineers Improve Products

• They move data using a Data Pipeline and a process called Extract Transform Load(ETL). It extracts data from a myriad of sources systems and files manipulates it so it is standardized and delivers it to a destination at the right time in the correct format.
• They clean data for data scientists and machine learning engineers so they can build high quality models. These can do cool stuff such as predictive analytics, detect churn, customer segmentation and personalized recommendations.
• The build data warehouses. These are specialized database designed for analytics which are source data for many dashboards built by a data analysts and used by executives.
• The sync up databases in both directions so similar data in separate databases (like customer service and sales) both get the most accurate data and they don't update an address in one of them and fail to do so in the other.