Artificial Intelligence (AI) and data engineering are closely interlinked. On one hand, making sense of unstructured data is the process known as data science or data engineering. On the other side of the same coin, AI-programmed computers have the ability to learn as they go, getting better at solving particular sorts of problems as they accumulate more data. So one cannot exist without the other.
Large amounts of data are required for the development of important machine learning algorithms. Machine learning requires data from a number of sources, in various forms, and from a variety of business processes in order to broaden and deepen the conclusions and findings made by the algorithm.
Therefore pairing data engineering efforts with artificial intelligence tools is the ultimate combination required to generate the best insights from the available data. In this article, we explore further how data engineering and AI actually go hand in hand.
A well-designed data pipeline connects several datasets to a business intelligence tool invisibly, enabling clients, internal teams, and other stakeholders to undertake sophisticated analysis and make the most of their data.
The intriguing difficulties that data engineers face include moving terabytes of data from their current location to a location where it can be studied, converting the data using a variety of libraries and services, and maintaining the pipeline's stability. But the process step involving data preparation has its own problems.
It can be a creative process, and it's unquestionably vital, but it can be difficult to save and automate the recurrent utilization of the reasoning every X hours. Currently, using machine learning and artificial intelligence will help to tackle this problem.
Business intelligence's next evolution, augmented analytics, incorporates AI components at each stage of the BI process. In today's sophisticated AI analytics systems, AI can help users in a wide variety of ways, but for the sake of this article, we'll keep our attention on data preparation.
There are three steps of the data preparation process; data cleansing and transformation, extracting and loading, and evaluating the prepared data where AI can be useful.
Although most businesses have sizable data holdings, unprocessed data isn't very useful. Even worse, non-normalized data analysis yields results that could be harmful and deceptive. In keeping with the oil analogy, you require a steady and dependable pipeline to transport your data from its storage location to the processing location where its true worth may be realized.
Data engineers have the capacity to process the data as it is being moved, bringing it closer to being in a useful state when it reaches the BI system. BI solutions are already utilizing AI in a number of ways to assist with the data purification process.
These are some of the ways that AI can help data engineers in this regard:
Customer Success Story: Daffodil helps reduce data redundancy by 30% for a maritime logistics provider.
There is a significant difference between the way data science and AI interact with data. Data science deals with pre-processing analysis, prediction, and visualization, whereas AI refers to the implementation of various predictive models that help in foreseeing data-based events. The following are some ways where AI can help fill the gaps presented by the data processing approach taken with data science:
Data engineers working with vast volumes of imperfect data would greatly benefit from an AI system that can be created to perform the task of outlier detection. As tables are constructed and fresh data is loaded, the AI will keep an eye on them and check the results.
The system could check for characteristics such as uniqueness, referential integrity (to values that are keys in other tables), skewed distribution, null values, and accepted values as it reads the data within a column.
A formula for catastrophe is to trust your facts without double-checking your work. In the aftermath, testing your AI-prepared data might be a lot easier if you have a few questions that you roughly know the answers to.
You can tell that the preparation process was successful if your responses fall within acceptable bounds. If there are significant differences, you might need to retrain the system or change how severe or lax the settings are.
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Routine tasks like removing redundant data, completing dataset gaps, and alerting human engineers to anomalies are all areas where AI analytics systems can really add value. By handling the labor-intensive tasks that humans don't really want to do anyway, these systems can support dedicated data engineers as they take on difficult problems that will eventually yield greater rewards for the company.
To enhance your data engineering and processing capabilities with AI, you can book a free consultation with us today.