Businesses nowadays dedicate most of their time to processing the data they receive. It is one story when you are a startup company with a few sources of data you can manage by yourself. Yet, it is a completely different scenario if your firm’s data is growing in a geometric progression every day. This is where artificial intelligence (AI) can be of use.
According to the research, 76% of data-driven companies were investing more in AI than in IT technologies in 2021. This statistic is not surprising, as machine learning (ML) with its proper application can optimize data pipelines, so your data flows smoothly from different sources for further analysis.
In this article, we will delve into the potential of AI technologies and how they can optimize your data pipelines.
What Challenges AI Can Overcome in Building Data Pipelines?
Implementing ML into data management processes is the decision that will save you both time and money and increase your performance. These are the challenges ML can help you overcome in data processing.
— Tailored results. A great benefit of AI-powered tools for data pipelines is that you can set the requirements and get specific outcomes. You can identify what data types you need to gather, in what format it should be, or the sources it should be taken from. Machines will provide you with tailored solutions, following your guidelines.
— Scalability and flexibility. For a growing business, thinking about the future is part of the strategy. As the company develops, its data also increases and new solutions are needed. Well-built ML algorithms are scalable and flexible, so you can handle more data without sacrificing the quality.
— Storage optimization. In data pipelines, how the data is stored is the main question. If it involves large amounts of data, compressing it and improving storage logical organization with an AI platform will be very useful as you will get access to any data you want in the best possible manner.
— Cleansing and validation. The goal of gathering data is to send it for further analysis. However, only high-quality data should be used if you expect good results. If set right, AI mechanisms can validate the quality of your data, so you will be sure you operate with error-free information.
Solutions AI Offers in Data Pipeline Optimization
Modern companies are already implementing AI technologies in their data pipelines. One of the examples is DataStax company which builds smarter data pipelines with the help of ML. The following are the solutions AI offers in optimizing data transport and storage:
Smart Integration
Artificial intelligence can analyze your data sources, determining only pertinent data. As data structures evolve, ML algorithms can adapt, paving the way for fresh data insights. AI-powered tools also tackle data integration, covering such processes as data mapping and transformation.Real-time Data Procurement
The integration of AI into your data pipelines boosts your analyses’ efficiency immediately. Unlike some traditional methods, AI can process the data streams in real time, facilitating intricate operations and instantaneously delivering results. That’s why by incorporating AI algorithms, you’ll experience an immediate uplift in processing capacity.
Implementation of Automated QA
Another solution that will significantly improve the quality of the data pipeline is the integration of an AI-based quality assurance process. As a result, it not only refines operational quality but also preempts significant issues during data operations. AI-based QA will also prevent you from making incompetent decisions based on incorrect data, positively affecting the business.
AI-Driven Data Transformation
Data transformation, a key process in the data pipeline flow, involves careful data inspection, cleansing, and enrichment. By implementing AI into the data procurement process, you will get a significant improvement in the outcome quality, filling data gaps, ensuring data relevance, and ultimately yielding accurate outcomes.
Final Thoughts
AI is opening the doors of optimization and robust quality for data pipelines. Leveraging ML mechanisms allows for seamless data collection from various sources, efficient compression, transformation, improvement, and storage.
AI-powered tools help improve the efficiency of data pipeline processes. ML models streamline the gathering of relevant data, facilitate real-time processing, cleaning, and normalization, fix errors, as well as automate and optimize the entirety of data pipeline operations.
