5 Steps to Creating Business Value using Data Engineering
In the contemporary business world, data represents an untapped source of value. In almost every industry, data is produced from a variety of internal and external sources. Data engineering is rapidly evolving from a business option to a necessity in your company. Business operations generate a growing amount of data, which may be utilized to open new revenue sources and reveal fresh information about your consumers and their behavior.
To implement the techniques and procedures that are part of a data engineering project and add value to your customers and your business, our data engineering team will need to understand how you view your data from a business viewpoint.
Data discovery and interpreting
Data discovery is the process of exploring your existing data to see what insights it contains. By creating a data catalog, data engineering allows you to gain a better understanding of your data. A data catalog is an organized inventory of your data that allows you to understand what data exists and how it is formatted within your company. A data catalog enables your team to quickly find a specific dataset or file and use the information to optimize operations, create new products or services. Data engineering also aids in data comprehension by generating data dictionaries that explain the structure of datasets. The entire process of aggregating data from disparate sources and updating it into a data warehouse employs an “ETL” framework, which stands for Extract-Transform-Load.
Data warehouse to aggregate data
As your company begins to investigate the data it has gathered, it will recognize the potential for developing new products and services. As this occurs, the data engineering team will need to be able to access and use the data in a timely and efficient manner. Data engineers can assist in the development of a data architecture that will allow your company to quickly access the data it requires. A data warehouse or a data lake enables easy and seamless access to data. A data warehouse is a centralized storage location for raw data. It is a system that gives you quick and easy access to your raw data.
This data warehouse could be built with a mix of technologies such as data virtualization, data integration, and data migration. By understanding your organization’s needs and goals, data engineers will help you decide which of these tools is best for your business. Data engineers can also assist you in determining which data warehouse software is best suited to your needs.
Data Virtualization for Live Access to Data
Data virtualization is a method of storing raw data while also providing a customized user interface for accessing and using that data. Data virtualization enables you to create a database that only stores the information required by your business. Depending on your organization’s needs, this database contains all your raw data as well as filtered data. Data engineering will assist you in determining how to construct a data warehouse that houses all your raw data, while remaining user-friendly enough for your employees to access the information quickly and easily using ad hoc query methods.
Data Platform to manage Data Life Cycle
A data platform is a centralized tool for managing the life cycle of your data. This data platform will help you automate common data tasks like data ingestion and cleansing, as well as store your data in a centralized location. Dashboards, reports, and data feeds to other applications are created, to improve the efficacy of the stored data. A data platform is also used to train AI systems, build machine learning models, and perform predictive analytics. By understanding your company’s needs and goals, data engineers will help you decide which tools would be best for this part of your data platform.
Standards and best Practices to manage data accuracy and consistency
Increasing data accuracy and consistency, reducing data latency, and improving data quality are all examples of organizational efficiencies. Data engineers will assist you in increasing the accuracy and consistency of your data by implementing standards and best practices. The amount of time it takes to transfer data from one location to another is referred to as data latency. Data latency is frequently caused by your data being stored in multiple systems. Data engineers will assist you in reducing data latency by implementing centralized data storage and data ingestion methods that keep all your data in one place. The accuracy and consistency of your data are referred to as data quality. Data engineers will assist you in improving data quality by developing data quality checks for new data and implementing quality control measures to ensure that your data is accurate and consistent.
Summary of the engagement
⦁ Discover and interpret
⦁ Build an ETL Framework – collect all data into a data warehouse of a data lake
⦁ Cleanse or scrub the data of inaccurate and unnecessary data; find relationship across the entire data set
⦁ Analyze how your business uses data
⦁ Build dashboards, reports and data pipelines to other applications and business entities
⦁ Further analyze data to find deeper insights in the stored data. Use artificial intelligence and machine learning technologies, predictive analytics.
Data engineering is a critical role in any company that relies on data to run its operations. Data engineers must understand how your business views data to create a centralized data architecture that allows your company to quickly access and use data most efficiently.
InnoBoon has a track record of implementing data engineering projects across multiple domains, and geographies. We use tools like Redshift, Snowflake, Airflow, Spark, Kafka, and others.
Let’s have a conversation on how best your data can be engineered to extract tangible value for your organization.