ShareAuthors: Aleksandr Andrejcuk & Marko Zagar
In today’s data-driven world, businesses generate and manage vast amounts of data daily, often measured in millions of terabytes. This explosion of decentralised data presents new challenges, particularly in effectively harnessing this data to extract valuable insights.
As AI evolves from a futuristic concept to a business necessity, the success of AI projects increasingly depends not only on the sophistication of algorithms but on the quality and accessibility of the underlying data. This is where data engineering emerges as a critical (albeit often unsung) hero.
The backbone of AI: Understanding data engineering
Imagine attempting to construct a building without a solid foundation—it would be unstable and prone to collapse. Similarly, AI models require a robust data infrastructure to function effectively. Data engineering is the process of designing, building, and maintaining the architecture that allows data to be collected, stored, processed, and transformed into a high-quality format suitable for AI models.
Data engineers act as the architects and builders of this data infrastructure. They create the pipelines and platforms that enable data to flow seamlessly from various sources to its destination, ensuring that data scientists and analysts have the resources they need to extract valuable insights. Without a strong data foundation, even the most advanced AI models will struggle to deliver accurate and actionable results.
From raw data to actionable insights
Data in its raw form is often messy, incomplete, and unstructured. The process of converting this chaotic information into structured, actionable insights involves several key steps:
- Data ingestion: Gathering data from multiple sources such as databases, APIs, IoT devices, and social media. This requires understanding the different formats and structures of data and devising ways to bring them together cohesively.
- Data cleaning: Removing errors, duplicates, and inconsistencies to ensure data quality. This meticulous process involves rectifying inaccuracies, filling in missing values, and ensuring uniformity across the dataset.
- Data integration: Combining data from various sources to provide a unified view. Effective data integration is crucial for comprehensive analysis and accurate AI models.
- Data storage: Utilising databases and data warehouses to store large volumes of data efficiently. Choosing the right storage solutions ensures scalability and security, accommodating the growing data needs of the organisation.
- Data transformation: Converting data into formats suitable for analysis and machine learning models, including normalising, aggregating, and encoding data to be effectively used by AI algorithms.
Ensuring Data Quality and Consistency
For AI models to generate reliable insights, the data they are trained on must be of high quality. Poor quality data leads to inaccurate models and flawed predictions. Data engineers play a crucial role in implementing data quality checks and validation processes, ensuring consistency and accuracy, and maintaining high data quality, which is the bedrock of any successful AI project.
Addressing the challenges of decentralised data
Traditional enterprise data management systems often struggle with the large amounts of decentralised data generated by modern businesses. For example, in industries like telecommunications or pharmacovigilance, data is often scattered across various systems, existing in both structured and unstructured forms. This creates significant challenges in data integration, quality control, and accessibility, which can hinder the extraction of valuable business insights.
Although tools using AI methodologies (such as the widely available pre-trained large-language models (LLMs)), by themselves can greatly improve the productivity of their users, there is a critical data engineering component to the implementation of those tools which significantly impacts the way the users’ interface and interact with the value provided by LLMs. If not considering this component when building a custom AI and LLM powered solution, a significant portion of value is left on the table which manifests as lacklustre reports, dashboards and/or business performance metrics while leaving the AI agents without a structured foundation for connecting the disparate data sources.
For example, at a leading US developer and operator of telecommunications infrastructure we, at Elixirr Digital, developed a comprehensive data strategy system that consolidated client’s data residing in multiple systems and developed reporting and dashboarding functionality on top of the enterprise data warehouse. This process freed the client’s staff from performing weekly repetitive tasks, saving numerous hours of manual sourcing, analysing and reporting on data.
Similarly, at a large telecommunications client, the sales department deals with various unstructured marketing, contract, but also structured, domain specific location data as a backbone for selling their services, reaching out to new clients and retaining their existing customer base. Luckily, that niche has decent support in terms of either data sources (B2B APIs such as ZoomInfo) or rich-featured CRM tools (Salesforce, for example). Still, the need remains for integrating those sources with proprietary enterprise data that exists only in the enterprise’s opaque storage systems.
Use cases
It is clear from the examples above that a modern enterprise deals with specific domain data of various origins brought together by analysts who often don’t have direct control over the data’s quality or freshness.
We can use these solutions to solve the mentioned problems:
- Unstructured data – much of the ingested data is comprised of unstructured documents which represent various internal memos, marketing information, contract information and corporate guideline documentation.
What’s more, the documents contain information embedded into the files as tables, graphs or images. These need to be engineered in order to support RAG. RAG, or retrieval-augmented generation, is a technique for enhancing accuracy and reliability of generative AI models with information retrieved from external sources.
This process enables searching through dozens of documents for specific, domain centric information in a way that is natural for the users, using simple and direct language commands. This agent can also return the actual document sources of the information it delivered so it can be verified or expanded upon as needed.
- Semi-structured and structured data – for this kind of source data, especially with data which is based on a number of slowly changing dimensions, such as lead prospecting data, we have to integrate all the disparate sources into one cohesive whole, what is commonly referred to as a data warehouse.
This kind of overarching architecture enables efficient analytics on the underlying data by splitting it into slowly changing dimensions used for lookup and (relatively) “fast” changing facts, which represent data points specific to the business domain and are described with references to those dimensions. This provides a way of integrating different spheres of business operations into a single data source on which critical business insights can be made and delivered to the stakeholders of the collected data.
Data sources
Some of that enterprise data is structured data stored in an internal database which the users need to operate mainly manually. Plus, they have a bunch of procedure documentation, contract templates and actual contract documents regarding their clients on the business side as well. It is expected, then, that much of the productive time is spent on just sifting through the data available or fighting custom database systems instead of chasing leads or performing domain specific analytics.
These extensive amounts of data originate from various sources. This is especially true for sales lead prospecting, a process integral to the functioning of business development departments across a wide range of industries. The general types of sources include:
- External data sources – different APIs with data exports conducted in certain intervals or unstructured file downloads that need additional, manual processing.
- Internal data stores – these could be managed databases, company data, contracts or templates.
A major issue with internally built custom data stores is the amount of overhead required for development and maintenance of such systems. The internal knowledge about the workings of these systems is not easily transferable and the transfer process is rather sluggish, making it hard to justify the initial and continuous costs of deployment of such systems.
Moreover, all these sources and systems collectively send data in all three data structure types:
- structured (relational database exports)
- semi-structured (XML, JSON)
- unstructured (PDF documents, files)
Both the listed external and internal sources are usually searched for useful data points “by hand” while “indexing” information on the files is located separate from the data itself.
Data quality
The users of data also combat various uncaught data quality issues which further complicate the process by which users extract value from their datasets:
- Missing metadata – incorrectly labelled fields, different labels across different datasets, incorrect or missing (catch-all) data types or undefined unique keys for records.
- General data quality – missing values, duplicate records, desynchronisation between different sources and the resulting staleness in various sub-components of the data model.
These are also the first problems to solve when engineering a robust data management system.
Agents and Warehouses: Building the Foundation for AI
To navigate the complexities of decentralised data, modern enterprises need to consolidate their data sources and create a cohesive data warehouse. This involves identifying all available data sources, determining the update cadence for each, and setting data quality expectations. For instance, at a leading US telecommunications company, a comprehensive data strategy was developed to consolidate data across multiple systems, leading to significant improvements in productivity and data accessibility.
The next step involves integrating AI methodologies, such as retrieval-augmented generation (RAG), which enhances the accuracy and reliability of AI models by leveraging information retrieved from external sources. By building a robust data warehouse, companies can facilitate seamless data navigation and deliver critical business insights through intuitive user interfaces.
Scalability, security, and compliance
AI projects often require processing vast amounts of data in real-time. Data engineers design scalable data pipelines using distributed computing frameworks and cloud-based solutions to handle large data volumes without compromising performance. Moreover, they implement robust security measures and ensure compliance with regulations like GDPR, CCPA, and HIPAA to protect sensitive data from unauthorised access.
The competitive edge: Unlocking the full potential of AI
Organisations that effectively leverage data engineering gain a competitive edge by transforming raw data into valuable insights. This not only enhances operational efficiency but also drives innovation and growth. For example, at a global investment management firm, a customised end-to-end solution automated up to 94.5% of the company’s processes related to ESG impact, demonstrating the profound impact of AI-powered automation.
The Future of AI and Data Engineering
As AI continues to evolve, the role of data engineering will become even more critical. Advancements in big data, cloud computing, and machine learning will require data engineers to continuously adapt and innovate. By investing in strong data engineering teams, organisations can navigate the complexities of AI development and unlock the full potential of their data, driving their digital transformation journey.
In conclusion…
Data engineering is the backbone of successful AI projects. By ensuring data quality, scalability, performance, security, and compliance, data engineers enable organisations to harness the power of AI, transforming decentralised data into actionable insights that fuel business success.
At Elixirr Digital, we have a strong foundation in conventional data engineering and data strategy development and with our commitment to innovation using the latest technologies and methodologies, we would be immensely interested in transforming the way your enterprise analyses data, all the while helping you grow and be more efficient, at scale.
Want to discuss the way your organisation analyses data? Contact us today to start the conversation.
