How we are helping build data-driven organisations

According to Gartner, “transforming into a (truly) digital business is the number one priority of most organisations. However, a digital business cannot exist without data and analytics. If an organisation struggles with digital transformation, perhaps they haven't given enough thought to data and the potential for valuable insights.”

Data is the lifeblood of successful enterprises. Growing organisations must tap into all available information and develop ways to use it as a valuable asset. By exploring, organising and analysing data from every transaction and touchpoint, organisations can gain vital insights about customers and potential market opportunities. Ideally, these insights should drive product roadmap priorities and map out where and how digital activities can maximise business impact. Driving a business strategy with data to continuously improve and shape the customer experience is one of the greatest challenges facing companies today.

As artificial intelligence becomes more ubiquitous, coupled with the exponential growth in connected devices and systems, the volume of data will continue to grow exponentially. Successful companies can leverage a wide variety of data streams to drive and optimise digital experiences. Data pipelines for AI and sensor-driven analytics must be designed with scalability, security and availability in mind to meet the needs of modern enterprises and the expectations of end users.

We conducted a Q&A with Chris Gojlo, our Data Architect, on the challenges of working with data and on building bulk data processing tools for one of our major strategic clients.

What are the main challenges large organisations face when dealing with data?

Inefficient data governance leads to the acceptance of low-quality data circulating in corporate systems, with incomplete or invalid data points. Poor quality datasets shared by downstream components force each consumer to create protection mechanisms that increase pipeline complexity and latency. A lack of consistent rules regarding data discovery, reuse policies or regulations governing associations across data contributes to increased time-to-insight.

As data streams flow through different channels using varied formats and semantics, it is essential that the data dictionary supports real-time profiling and auditing. This improves operational efficiency, ensures compliance and increases customer satisfaction by flagging data issues as soon as they occur.

This has contributed to the continuing trend of creating self-service data roadmaps to support discovery, quality scoring, lineage and governance. The self-service approach, combined with automation across governance processes, builds the capabilities required to democratise data and reduce time-to-insight — a significant bottleneck in modern architectures.

What is the role of data architecture and governance in building trust between data providers and consumers?

A successful enterprise data strategy requires a clear plan to unlock the value of data assets for business purposes. It demands a systematic governance approach covering ownership, integrity, compliance, access methods and dataset relationships.

With increasing regulatory demands, organisations must build data-driven solutions aligned with current regulatory frameworks and evaluate existing systems against new restrictions. This requires a coherent strategy defining how sensitive data is stored, processed, accessed and protected.

Trust between data services and recipients is built through high-quality data supported by metrics and metadata that reflect business semantics. Reliability must be operational and contextual — delivering relevant, accurate data to customers consistently.

How were these challenges addressed in bulk data processing tools?

Understanding the problem domain and business context is the starting point. By leveraging Domain-Driven Design (DDD), we refined conceptual models aligned to the target domain and structured the data architecture accordingly. This helped control architectural complexity and expose misalignments across services operating within the same domain.

Operating within a bounded context with strong information architecture requirements required addressing governance issues including ownership, quality, integrity, security and compliance. Two priorities for our client were auditing data changes across the pipeline (lineage) and assuring data quality.

The self-service capability allowed users to automatically check data quality across multiple sources against defined quality factors without manual intervention. Managing traceability and exposing audit events delivered compliance and security improvements while strengthening user trust. Knowing what happened to data at any stage of processing is fundamental to trust.

What is the impact of poor data quality, particularly in AI and data science?

Without high-quality data, organisations cannot respond effectively to market changes, assess competitive positioning or produce reliable analytics. This can result in flawed strategies.

There are two key aspects:

First, automation and AI can improve data quality and governance. Data quality spans consistency, integrity, accuracy and completeness. Traditional profiling combined with machine learning and semantic analysis can address complex challenges such as disambiguation and relationship extraction.

Second, access to verified, consistent and reliable data is critical throughout AI and data science pipelines. In machine learning, training datasets must be free from bias and inaccuracies. Models trained on biased or unrepresentative data produce unreliable outcomes and introduce ethical, legal and safety risks.

High data volume does not eliminate the need for quality checkpoints. While ML can tolerate some noise, this is only effective when foundational quality controls are in place.