What is Data Engineering and How Does It Benefit Your Business

Everyone knows the power of data. So, it isn’t surprising to know that businesses collect data every time the customer interacts with the brand. About 328.77 million terabytes of data are created every day. That said, simply holding on to a lot of data does not make it useful.

Let’s say, your sales data shows you sold 1000 pieces of a red jacket in a month – unless you can compare this with sales for other jackets or sales in the rest of the year, it doesn’t help much. This is where data engineers come in. Data engineering helps uncover the potential behind datasets and add value to the business.

What Is Data Engineering?

Data engineering refers to creating and maintaining systems to collect data from various sources, validate it and prepare it for use by analysts and data scientists. Once they have validated the data, data engineers convert raw data to usable data by standardizing formats, optimizing data infrastructure and so on.

What Do Data Engineers Do?

Data engineers must cover multiple facets simultaneously while transforming data sets. Some of the key tasks performed by data engineers are:

• Acquisition: Data engineers must identify reliable sources to collect data
• Cleansing: All data entering the system must be verified against third-party databases to maintain a clean database.
• Conversion: Data engineers must create a standard data format and convert data from other formats to this format.
• Disambiguation: Data engineers prepare datasets so that they can be interpreted in different ways.
• Deduplication: Data engineers remove duplicate copies to ensure each record is unique.

Understanding The Importance Of Data Engineering

Data engineering is useful across industries ranging from retail and finance to healthcare and education. It helps streamline data so that data scientists and analysts can derive actionable insights from the data sets. Some of the benefits of data engineering include:

Improved Data Quality

Being faced with a lot of information can be overwhelming. Data engineers help sift through all the data to segregate the good and bad information and put it in a structured format. Data engineers are responsible for verifying data details and validating them to be current.

In standardizing formats, they make records easier to compare against each other and remove duplicates from the set. They also perform compliance checks. The result of these efforts is a clean, reliable dataset.

Handle Large Datasets

For businesses to stay ahead of the competition, they need to be able to process large amounts of data quickly. They cannot afford to compromise on the quality of data being worked with. Bad quality data can cost businesses as much as $15 million per year. It is only through the models built by data engineers that businesses can process all the data available to them without compromising on accuracy.

Further by streamlining data processes and removing duplicates, data engineering makes it easier for businesses to extract actionable insights and make critical real-time decisions. This can prove invaluable to businesses that must stay tuned to market conditions.

Keep Data Secure

Customers are happy to share their data in exchange for personalized services. That said, they expect the businesses collecting data to keep it secure. Hence, businesses must comply with data regulations.

Failure to do so can result in heavy fines and the loss of customer trust. Even without customer data being exposed to third parties, Amazon was fined $877 million in 2021 for GDPR breaches.

Data engineers create structures that ensure controlled access and put in security protocols such as specialized encryption techniques. This keeps data safe from data breaches and cyber-attacks. In addition, by deduplicating records, data engineering eliminates false positives and maintains system integrity.

Bridging Data Systems

With data being collected from different sources and by different departments, it is easy for databases to be siloed. Data engineering can help bridge these databases and create a centralized database. This helps data users access more comprehensive datasets and gives them a more well-rounded view of customer journeys.

When businesses have the ability to analyse integrated data, they can uncover new trends and opportunities that might otherwise be overlooked.

Applying Machine Learning Tools to Data Engineering

As with other fields of data processing, Machine Learning (ML) and Artificial Intelligence (AI) can be significant aids to data engineering.

It can automate steps for data preparation, quality monitoring, modeling and so on to optimize pipelines. Moreover, it is scalable and helps businesses process large datasets. For example, a data verification tool automatically compares incoming data against reliable third-party databases.

It flags invalid values and detects inconsistencies. With the right tool, you can also enrich records with missing information. This helps create consistently high-quality datasets and allows data scientists to focus on more complicated tasks such as creating data architectures.

Machine Learning can also help with data integration. It combines data from multiple sources and creates a centralized database. In doing so, these tools also identify data in different formats and standardize them to make records comparable.

Machine Learning algorithms can be very helpful in identifying connections between data characteristics such as product ID, colour, customer name, etc.

Data is not always structured in the required format. Unstructured data must first be transformed into structured data before it can be analyzed. AI-based algorithms help data engineers by identifying patterns and processing data to enhance the overall data quality.

Machine Learning models can also be used to create sophisticated predictive models to identify trends. In turn, this can be used to make reliable estimates about expected changes to market performance or customer behaviour.

In Conclusion

Businesses wanting to leverage the power of data must pay attention to data engineering to uncover its full potential. It’s all about transforming unstructured data, verifying it and creating systems to make it useful.

Machine Learning tools for data verification make it easier for data engineers to achieve this. It is easy to set up, lets you process large datasets and creates a unified database. Get started today.