In today’s business world, data is the key to answering many pressing questions, such as “How fast are we growing,” “What is our competition doing,” “What products or services should we develop next,” and “What’s keeping us from being more efficient?” But the path from accumulating data to realizing insightful answers isn’t straightforward. Data in its raw form doesn’t do anything. Rather, companies require human professionals to move data along from that raw state to one that is usable for gleaning valuable information.
Those professionals are called data engineers. The practice of data engineering involves collecting, processing, and transforming data so it can be used by others for analysis, leading to ideas that can help companies thrive and grow. For example, a company might learn through data analysis that its operations that use robotics are 34 percent more efficient than those that don’t. This fact can make the company more willing to invest in additional robotics, confident that it will be able to boost production and, therefore, revenue.
So, what is data engineering? In the sections below we explore more about data engineering, including how it differs from data science, what specifically data engineers actually do, what tools they use, and whether data engineering is something your company needs. But first, let’s take a look at why data is important for companies in the first place.
The Value of Data
As more data is produced, the possibility for greater levels of analysis exists, potentially leading to significant insights that can help companies meet and exceed their goals. In fact, many sources, including Gartner, put data front and center in terms of what makes companies successful in the current business climate. The following benefits show the value of data for companies of all sizes.
Stay Ahead of the Competition
The best way to beat your competition isn’t to copy what they’re doing. Even if you have similar target markets, there are differences, and the way to success is to find out what works best for yours. Data can help you do so in numerous ways. For example, you can learn which products or services they are most interested in now, what features they would like to see added or removed, what social media platforms they spend the most time on, what their biggest barriers to entry are, and what customer service problems you need to fix.
Use of data to achieve success is so powerful that companies that don’t do it on at least some level risk falling behind. A small camping supply retailer that doesn’t know a national camping supply chain is opening a location nearby can’t get out ahead of it with promotional activities and sales based on a “neighbor” theme, whereas the company with access to such information can.
Improve Customer Experience
When you take the time to improve the areas mentioned above, you are likely to enhance the customer experience (CX) and, therefore, increase loyalty and retention. That means your customers will shop with you more often, spend more when they do, and refer their friends, family members, and coworkers to you. All these factors add up to more revenue, greater exposure, and increased success.
A bank that gathers CX information might discover that customers don’t feel tellers and customer care representatives are friendly. The bank could institute rules asking these professionals to call customers by name and ask them how their day is going. While this one step may not entirely raise low scores, it may put the bank on the right track toward resolving the issue. And it wouldn’t have been tried without the insight that data provided.
Make Decisions Based on Facts
Businesses thrive when their leaders move forward based on what is truly happening in their company according to evidence rather than anecdotes, guesswork, or intuition. Data gives companies that evidence. For example, slow sales might appear to be due to the economy or other uncontrollable factors. But in looking at the data, company leaders might find that, in fact, the reduced volume started long before the economy started to tank and is, in reality, due to poor customer reviews based on inefficient service.
If the company had acted on the notion that the economy was to blame, it might lower prices, hold sales, and do other things to encourage buyers who don’t have as much cash to spend. These efforts might have drawn in more customers but wouldn’t have solved the real problem. What the company actually needs is a customer care overhaul, which is a much different approach.
Plan for the Future
No one knows exactly what the future holds, and that includes data engineers and data scientists. Yet, data can be useful for identifying trends and seeing probable patterns. Companies that make decisions about the future based on this kind of information are much more likely to be right about their assumptions and get the best return on investment (ROI).
For instance, a clothing retailer might consider installing new technology like virtual dressing rooms with mirrors that help shoppers see themselves in articles of clothing without actually trying them on. Data can be used to determine which competitors already have this technology and how customers feel about it. Of course, there are other factors leading to customer sentiment, but the company can get an understanding of how this technology is likely to impact future sales.
Other Benefits
Other benefits include improved operations such as greater efficiency from both humans and machines, better inventory and supply chain management, the potential for more targeted innovation, and more effective promotional campaigns.
Data Engineering vs. Data Science
So, what is the best way to define data engineering? A good place to start is to differentiate it from data science. At a high level, data must be gathered, moved, stored, and analyzed. In the past, companies would only hire data scientists, who were expected to perform all those functions. Now the field has expanded, and data engineers are responsible for gathering and managing data, while data scientists analyze it to answer tough business questions, provide valuable insights, and predict industry trends.
Data engineers and data scientists perform complementary functions, with data engineers providing quality data that enables data scientists to come up with the most accurate conclusions possible. At the same time, data scientists can evaluate the type and quality of the data and share that information with data engineers, to improve the process even more.
Both data engineers and data scientists commonly hold degrees in computer science, IT, mathematics, statistics, or economics. Before working as data engineers, these professionals may gain experience in software engineering, including knowledge of programming languages, or more business-focused work such as statistical analysis.
To get hired, they may be required to have data engineering certifications such as Google’s Professional Data Engineer or IBM Certified Data Engineer. They may also be required to have experience performing Extract, Transform, and Load (ETL) operations.
Data scientists are expected to understand a variety of techniques for extracting meaning from data and be familiar with a number of programming languages and tools. To be hired, they may be asked to analyze a data set and present their findings.
Both data engineers and data scientists must also have excellent communication skills to understand what company leaders need and to present their processes and findings. Additionally, according to CIO, “Aligning data strategies with business goals is important, especially when large and complex datasets and databases are involved.”
The following video highlights the relationship between data engineers and data scientists.
What Does a Data Engineer Do?
So, exactly what is a data engineer? It’s a professional who prepares data for others to find meaning within it. According to CIO, “Data engineers design, build, and optimize systems for data collection, storage, access, and analytics at scale. They create data pipelines used by data scientists, data-centric applications, and other data consumers.”
Dataquest identifies three main roles for data engineers: generalist, pipeline-centric, and database-centric. A smaller company might employ a data engineer generalist, who must provide end-to-end services that serve the entire organization. That may include some or all of the data analysis, which in larger firms is the responsibility of data scientists. In such smaller companies, the data analysis may take the place of more in-depth data engineering required by larger businesses.
Pipeline-specific data engineers are more commonly found in mid-sized companies with more complex data needs. They typically work within a team to convert raw data into useful formats for analysis, such as tools that enable company professionals to perform their own data analysis tasks.
A database-centric data engineer focuses on the development of analytics databases, including ETL tasks to move data into warehouses that can then be accessed for reporting, analytics, and data mining. These professionals typically work at larger companies with data that comes from a variety of sources.
Specific tasks include the following.
Designing
In designing data environments, data engineers must first gather data requirements from company leaders, such as how data will be used, which team members need access to it, and how long it should be stored.
Processing
Data in its raw form cannot be used for analysis, so it must be processed to serve this function. To do so, data engineers use tools that retrieve data from different sources, convert it into specified formats, assign metadata, and store the data in a storage system. The processing may include detecting and correcting errors, interpreting specific data that may have more than one meaning, and removing duplicated data.
Checking
Data engineers must perform data integrity checks to ensure data is usable. Several types of data integrity checks can be used, including aggregate, anomaly, category, null, and uniqueness. For example, aggregate checks ensure that data remains intact as it goes through the ETL process.
Storing
Data engineers use specialized storage technologies such as relational databases, NoSQL databases, Hadoop, Amazon S3, and Azure.
Managing
Data management includes maintaining metadata, which is information about the data. It may include the format, which technology the data is associated with, the size of data sets, where the data originated, and who owns the data.
Securing
As with all technology functions, data management requires attention to security and governance. Data engineers use such methods as lightweight directory access protocol (LDAP), encryption, and auditing access to the data.
What Tools Do Data Engineers Use?
Data engineers use a variety of tools in their work. Some of them are listed here.
- ETL Tools. ETL tools are used to access data from a variety of sources, transform it into an analysis-ready form, and move it to another location. Examples include Informatica and SAP Data Services.
- SQL. Data engineers use Structured Query Language (SQL) to query relational databases and to perform ETL tasks within them. SQL is supported by many applications.
- Python. Another programming language, Python is used for performing ETL tasks, sometimes replacing ETL tools for this purpose. It is known for its flexibility, ease of use, and extensive libraries for accessing databases and storage applications.
- Snowflake. Snowflake is a cloud-based provider of data storage and analytics services that enables users to easily move to a cloud-based data analysis system. It integrates with other tools, including ODBC, JDBC, JavaScript, Python, Spark, R, and Node.js.
- Microsoft Power BI. Microsoft Power BI helps companies view data by converting data sets into dashboards and reports that are friendly to nontechnical business users. This format enables more professionals outside of the data realm to benefit from access to valuable insights.
- Tableau. Tableau is another business-friendly data dashboard that helps users prepare and analyze data without the need for technical skills. Final products include charts, plots, and graphs that enable professionals to visualize data in useful ways.
- Spark and Hadoop. Spark and Hadoop are used in situations in which large data sets exist on a group of computers and the engineer wants to apply the power of those systems to a specific task. They are less commonly used than Python.
- HDFS and Amazon S3. HDFS and Amazon S3 are two alternatives used to store data. The benefit of these services is that they are expandable, so they can accommodate any amount of data. They can be integrated into a company’s data processing environments.
Is Data Engineering Right for Your Company?
If your business uses large-scale data sets, you should consider hiring at least one data professional. In smaller organizations, a data engineer can usually handle all the data functions, from collection to processing to analysis. Larger organizations should hire one person for each discrete function.
There are many factors that could go into the decision to hire one or more data engineers. But the bottom line is, if you rely on data to make smart decisions, keep customers happy, stay ahead of your competition, find the right areas in which to innovate, and ensure the highest possible ROI, you need a data engineer to provide the best quality data.
The phrase “garbage in, garbage out,” credited to an early IBM programmer and instructor, applies to data engineering as much as it does to software engineering. For applications, it means that computers only process what engineers give them to process. For data, it means that analysis is only as good as the quality of the data. Without quality data you end up with erroneous outcomes that might be worse than not having data insight at all.
For example, imagine that your data analysis reveals that the best place to open a new brick-and-mortar shop is actually the worst. If you move forward based on this analysis, you end up spending time, effort, and money on something that is not likely to produce a positive return. Good data engineers prevent this type of thing from happening by ensuring the data is of the best possible quality to begin with, providing the foundation for accurate analyses.
Data Engineering Enables Accurate Data Analysis
As we have seen, data engineering is just one part of the data analysis process. But it is a highly important one. With the right skills and tools, data engineers ensure that data from disparate sources is transformed into the right formats and the right databases to ensure data scientists can access and trust it to perform their analyses. Companies that rely on data to make important decisions must use data engineers to get the most informative and accurate results.
