Businesses worldwide are realizing the importance of the considerable volume of data they possess and the need to extract value from it and incorporate it into enhancing Customer Experience or simplifying operational processes for improved results. To do this, they are constantly looking to partner with experts who can guide them on what to do with that data. This is where data engineering services providers come into play.
Data engineering consulting is an inclusive term that encompasses multiple processes and business functions. Data engineering involves integrating several technology components into a seamless solution with a common goal: to extract valuable information from data to generate value.
For example, McDonald’s collaborated with data engineering firms to automate verbal order intake via machine learning and natural language processing (NLP). The business need was to shorten wait times, improve order accuracy and free up restaurant employees to focus on enhancing one-to-one service. Data engineering enabled McDonald’s to understand how orders are placed nature of questions or requests and use that insight to design the solution.
Challenges faced by the manufacturer
- Identifying the right business areas to apply Data Engineering. Companies need to validate the availability of relevant data for the project and quantify the impact to justify the effort to be undertaken.
- A need for end-to-end ML engineering expertise. In addition to technical skills, domain experience is vital to analyzing and interpreting the data, which is then used to model the ML solution.
- The complexity of NLP itself. In addition to the technical dimension, understanding the user behavior and application of Human Factors Design are critical requirements for a successful outcome.
How data engineering consulting helps
McDonald deployed and implemented business intelligence and analytics systems to overcome most challenges for decision support. In addition, they also used sophisticated NLP solutions and ML to listen to customers’ verbal orders and automatically put them through to the kitchen.
Data engineering: To turn data into valuable insights
Usually, data engineers are responsible for developing data pipelines to bring together information from several source systems. Data must be consolidated, cleansed, and structured for proper use in analytics applications. This data is then stored in Data Lakes or Data Warehouses, which makes data easily accessible for processing and further use.
The amount of data an engineer gets to work with varies with the company, particularly its size. The larger businesses have complex analytics architecture and may require more data engineers. Some businesses are more data-intensive, including retail, healthcare, and financial services.
In most cases, data engineers work in tandem with data scientists to enhance or create new data models, improve the AI algorithms, and create opportunities for customer innovation that are beneficial for the business.
What can a data engineer do for a business?
Typically, a data engineer’s day-to-day work revolves around managing Data pipelines, managing Data Lakes, and DataOps activities.
Managing data pipelines
ETL (Extract, Transform, Load) processes are a critical activity. It involves developing data extraction, transformation, and loading tasks, transferring them between several environments, and purging them so that they arrive in a regular and structured way in the hands of analysts and data scientists.
Extraction
For Mcdonald’s, this would involve extracting audio data of the order being placed, transaction record of the order from the POS, images from the store CCTV and more. Data pipelines from each source to the Data Lake need to be implemented. The source data will be tagged and annotated for easy reference.
Transformation
Next, data engineers coordinate the cleaning of the data, eliminate duplicates, fix errors, tag missing records, discard unusable material, and classify them with annotations & descriptions to aid in processing.
Load
The data is loaded into its destination: a database located on a company’s server or a data warehouse in the cloud. In addition to the correct export, one of the primary concerns in this final stage is security surveillance. The data engineer has to guarantee that the information is safe from unauthorized access within the organization and external cyberattacks.
Managing data lakes/DWH
Given the large amounts of data involved, Data Engineers need to design the Data Lake or DWH such that the desired information can be located and retrieved quickly, with minimum latency. In the case of Cloud-based storage, bandwidth requirements are associated costs for data retrieval that play a vital role in the design and operations of the data lake.
DataOps
With the constant ingress of data, Data Engineers need to ensure that the latest and correct version of data is available to the Analysts and Data Scientists for analysis and modeling. Updated data models are then correctly deployed into production for use in Customer Applications. DataOps is similar to DevOps in software development, the difference being that this involves the flow and use of data between Developers, Analysts, and Production.
Data Visualization & Analytics
The processed data is either collated and analyzed for Management decisions or used in real-time applications to enhance service delivery. Visualization tools such as Grafana, PowerBI, Tableau, and Google Charts pull relevant data from the DWH and provide various decision support options to suit every business.
What are data engineering services?
Data engineering services help businesses replace their costly, in-house data infrastructure and transform their information pipelines into robust systems with the aid of data engineers.
With the growing importance of data across all business verticals, data engineering services will become a helpful resource that enables businesses to extract valuable information.
The primary reason behind the growth of these services is that they ensure data availability in the right format at the right time.
How can data engineering consulting help businesses?
Almost all businesses face data-related roadblocks that require a certain degree of creativity and technical expertise. Data engineering consulting companies can quickly help businesses resolve such issues by comprehensively understanding data pipelines. They play a crucial role in advancing a company’s data science initiative.
For example, NextGen Healthcare was searching to provide health data to its Health Information Exchange customers in a modern platform that was easy to use, scale, and can create value-driven insights. With the help of a data engineering company, they built a new analytics solution for their existing platform, which enables their customers to use health data to its full potential for analytics and reporting.
Nowadays, many businesses are undergoing a digital transformation using design-led engineering and test-driven automation. This is why partnering with a reliable data engineering consulting service provider is necessary for businesses that want to compete in today’s competitive environment.
Data engineering consultants like Trigent can ensure that a business’s data analysis process is straightforward and effective. While every company has different data analysis requirements, many of them can benefit from collaborating with one on the team. Some of the most common ways that Trigent can help businesses include: creating and maintaining or improving infrastructure, solving complicated business problems, real-time interactive analytics, enhanced business intelligence through data models, streamlining data science processes, machine learning, data pipelines, and continued focus on cutting-edge practices in data science.
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