The Future of AI in Data Platforms: Trends and Predictions

Data management and integrated platforms can be overwhelming, demanding substantial time and resources to gather, process, and analyse vast amounts of information. To tackle these challenges, about 64% of companies plan to invest in artificial intelligence (AI) to streamline their data platforms (warehousing processes and enhance the accuracy of their insights. AI's capabilities surpass traditional data analytics, identifying patterns and trends often missed by manual methods, leading to increased efficiency and improved accuracy. The current use of AI in data Lakehouses/warehousing is merely the beginning, with endless possibilities for new applications as long as computing power, improved quality, and engineering at scale can be provided .

AI and Data Platforms: Current Trends

AI is already reshaping data platform solutions and its processes, significantly improving speed and accuracy. Companies leveraging AI-powered trends have seen better decision-making and heightened efficiency.

1. AI-Assisted ETL Processes

A notable trend in data platforms is using AI to assist in the extract, transform, and load (ETL) process. ETL involves extracting data from various sources, transforming it into a suitable format, and loading it into a data warehouse for analysis. AI-powered ETL tools automate repetitive tasks, optimise performance, and reduce the potential for human error. By handling low-level tasks, AI allows data engineers to focus on more complex responsibilities such as designing data models, training machine learning algorithms, and creating data visualisations.

For instance, Coca-Cola employs AI-powered ETL tools to automate data integration tasks across its global supply chain, optimising procurement and sourcing processes. This automation has enabled the company to streamline its operations and improve efficiency significantly.

2. Smart Data Modelling

AI-powered tools for intelligent data modelling represent another emerging trend. Data modelling involves defining and structuring data for adequate storage and retrieval. AI can analyse data sources and automatically generate data models, considering the relationships between data points. This approach saves time and resources for data scientists, who would otherwise spend hours manually creating data models. Additionally, AI-powered data modelling enhances data accuracy and completeness.

Walmart exemplifies this trend by using AI-powered smart data modelling techniques for supply chain management and customer analytics. This optimisation allows Walmart to quickly and accurately identify trends in customer behaviour and forecast demand for specific products, ensuring a smooth customer shopping experience.

3. Automated Data Cleansing

Automated data cleansing through left-shift, or data preparation, involves using AI to detect and remove inaccuracies, inconsistencies, errors, and missing information from a data warehouse. This process ensures that the data is accurate and reliable. AI-powered data cleansing tools leverage advanced algorithms and robust computing power to efficiently process and clean massive amounts of data, handling diverse data types comprehensively.

For example, GE Healthcare uses AI-powered data cleansing tools to improve data quality in its electronic medical records. This reduces the risk of patient diagnosis and treatment errors, ultimately enhancing patient care.

4. Continuous Data Quality Monitoring

Continuous data quality monitoring is transforming the way businesses manage their data. Unlike traditional periodic checks, continuous monitoring involves real-time data quality assessment. AI technology ensures that data remains clean, accurate, and up-to-date by automatically detecting anomalies and errors as they occur, streamlining the data management process.

Airbnb has implemented AI-powered data quality monitoring tools to identify and correct real-time data quality issues. This results in more accurate search results and pricing algorithms, improving the overall user experience.

AI and Data Platforms: Future Predictions

As AI advances rapidly, its potential applications in data Platforms are expanding. Here are some predictions for the future of AI in this field:

1. Automated Schema Design

AI-powered schema design tools will analyse data sources and suggest the best schema design, resulting in more efficient and accurate data platforms. A schema defines the structure of a database, including how data is organised and how relationships between data entities are managed. This technology will benefit businesses dealing with large and complex data sets, such as financial institutions, healthcare organisations, and e-commerce companies.

For example, an e-commerce company could use an AI-powered schema design tool to optimise its data warehouse schema for different types of products, enabling easy addition of new product categories as the company expands.

2. AI-Driven Data Curation

Manual data curation has become increasingly time-consuming with the rise of big data. Data curation involves managing and maintaining data to ensure its quality and usability. AI-powered data curation tools automate data cleaning and organisation, allowing businesses to derive critical insights more efficiently. AI will classify data using machine learning algorithms based on criteria like keywords, metadata, or content type, ensuring consistency and saving time.

A healthcare organisation, for instance, could use an AI-powered data curation tool to analyse patient data and identify trends or correlations between symptoms and diagnoses, leading to improved patient care and outcomes.

3. Intelligent Data Discovery

Intelligent Data Discovery (IDD) will become a crucial trend in business intelligence as data Platforms grow more complex. IDD systems can automatically identify patterns, trends, and relationships in large datasets, providing real-time data analysis and instant insights that help businesses make informed decisions.

A transportation company, for example, could use an IDD system to analyse customer data and identify patterns in travel habits, leading to new service offerings or pricing models. By analysing customer feedback and sentiment, IDD systems can help businesses better understand their customers and improve their products or services.

AI as an enabler

AI will play an increasingly pivotal role in the future of data Platforms. By utilising machine learning models, natural language processing, and other advanced data science techniques, data Lakes, warehousing systems, Meshes, or Factories will become more intelligent and efficient at analysing complex data sets.

A successful AI-enabled and integrated data platform tool should possess advanced data mapping and transformation capabilities, automated code generation, support for multiple data formats, seamless integration with data lakes, and real-time learning capabilities. These intelligent and autonomous data Platform systems will identify patterns and trends that are not immediately apparent, providing insights and recommendations that help businesses stay ahead of the curve.

As we progress, we can expect to see more innovative solutions that push the boundaries of what is possible to leverage data as a competitive advantage. These advancements will help businesses of all sizes unlock the full potential of their data, leading to more informed decision-making and a more significant competitive advantage.