The EZtek Software outlines six core capabilities that organisations must consider when establishing a robust Big Data practice. Each capability is essential for building a successful and sustainable Big Data organisation.
1. Big Data Strategy
Data has become one of the most valuable strategic assets for organisations today. The ability to analyse large data sets and identify patterns provides organisations with a significant competitive edge. For example:
- Netflix uses user behaviour data to determine which movies or series to produce.
- Alibaba leverages data to identify reliable suppliers, recommend them on their platform, and even make lending decisions.
These examples demonstrate that Big Data is not just about technology—it’s about Big Business.
To generate tangible returns from Big Data investments, organisations need a well-defined Big Data Strategy. This involves determining where to focus analytics efforts, understanding how to maximise return on investment (ROI), and setting clear objectives amidst the overwhelming amount of data available. A structured strategy serves as the foundation for effective Big Data initiatives and ensures organisations can achieve meaningful results without getting lost in the vastness of their datasets.
2. Big Data Architecture
Working with massive datasets requires specialised capabilities for storage and processing. For this reason, organisations need a robust Big Data Architecture that provides the IT infrastructure necessary to support advanced analytics.
This capability addresses key questions such as:
- How should organisations design their architecture to support Big Data initiatives?
- What are the storage and processing requirements to handle enormous datasets?
The Big Data Architecture component of the framework explores the technical capabilities needed to manage Big Data environments effectively. It also identifies the roles involved in maintaining and optimising these architectures and examines best practices for designing scalable, efficient systems.
In line with the framework’s vendor-independent approach, the architecture is aligned with established models such as the Big Data Reference Architecture from the National Institute of Standards and Technology (NIST). This ensures the framework is adaptable and applicable to a wide range of organisations and technologies.
3. Big Data Algorithms
A core capability in working with data is a strong understanding of statistics and algorithms. Big Data professionals must have a solid foundation in these areas to extract meaningful insights from large datasets. Algorithms, which are precise instructions for solving specific problems, play a critical role in this process. They are used to perform calculations, process data, and execute automated reasoning tasks.
By applying algorithms to vast amounts of data, organisations can uncover valuable insights and generate actionable knowledge.
The Big Data Algorithms element of the framework focuses on the technical skills required for anyone working in the field of Big Data. It provides a foundation in basic statistical operations and introduces different categories of algorithms, helping professionals build the expertise necessary to succeed in data-driven roles.
4. Big Data Processes
For Big Data to thrive within an organisation, skills and technology alone are not enough—processes are equally essential. Structured processes provide direction, create measurable steps, and enable effective management of Big Data initiatives on a daily basis.
Processes also help organisations embed Big Data practices into their operations. By standardising procedures and workflows, Big Data analysis becomes less reliant on individual expertise and more integrated into the organisation’s routine practices. This approach enhances the organisation’s ability to capture long-term value and ensures the continuity of Big Data efforts, regardless of team changes or personnel shifts.
Incorporating structured processes ensures that Big Data initiatives align with organisational goals and remain scalable, reliable, and effective over time.
5. Big Data Functions
Big Data Functions focus on the organisational aspects of managing Big Data initiatives within enterprises. This component of the framework addresses how organisations can structure themselves to establish Big Data roles and responsibilities effectively. Organisational culture, structure, and job roles play a significant role in determining the success of Big Data projects.
In this section, the framework provides guidance on best practices for setting up a Big Data Centre of Excellence (BDCoE). It also highlights critical success factors for initiating Big Data projects, ensuring that organisations have the right foundation to maximise their efforts. By addressing the non-technical aspects of Big Data, this element ensures that enterprises are equipped to foster collaboration and innovation across teams.
6. Artificial Intelligence
The final element of the Big Data Framework focuses on Artificial Intelligence (AI), one of the most transformative fields in the modern world. AI offers vast potential for organisations, but many are uncertain about where to begin their AI journey.
In this section, the framework explores the relationship between Big Data and AI, detailing how AI builds on the foundational capabilities developed through the other elements of the Big Data Framework. It takes a functional approach, demonstrating how AI can deliver tangible business benefits when properly integrated into enterprise systems.
The framework positions AI as the next logical step for organisations that have successfully implemented the other Big Data capabilities. Depicted as a continuous lifecycle, AI leverages organisational data to learn and improve over time, delivering sustained value and competitive advantage.
