Data

Purpose, role and importance in today's (digital) world

DATA powers nearly everything, from your favourite apps to business decisions.

Every click, purchase, or online search generates data. Data are the language used, to communicate and collaborate through an internal and/or external connected IT network. When properly governed and used wisely, data helps people, companies, and governments make better choices.

For enterprises, they are the most valuable non-valuated asset that needs to be managed effectively throughout its data lifecycle. Data are to be governed, controlled, and protected to ensure effective transactions, to derive meaningful insights, and make informed decisions


In Society

    • Public health: Data helps track disease outbreaks and improve healthcare services.
    • Transportation: Traffic apps use data to suggest faster routes.
    • Safety: Emergency services use data to respond quicker and more effectively.

In Companies

    • Cost savings: Data shows what’s working and what isn’t, helping reduce waste.
    • Better decisions: Companies analyse customer data to offer products you actually want.
    • Innovation: New services and products—like smart assistants or personalized shopping—are built on data.

What is Data Management?

Data Management is the development, execution, and supervision of plans, policies, programs, and practices that deliver, control, protect, and enhance the value of data and information assets throughout their lifecycles (reference: DAMA-DMBoK). It is a broad range of disciplines and activities that in one or the other way, serve the meaning and understanding of data, and their intended usage.

Why is (adequate) Data Governance important?

Data governance is the exercise of authority, control, and shared decision-making over the management of data assets (reference DAMA-DMBoK). It entails the defining and enforcing of data policies, procedures, roles and responsibilities, governance bodies, standards, rules, systems, and metrics to make sure the data are correct for an effective and efficient use by the organization to achieve its goals. It means managing data so it’s accurate, secure, and used responsibly. Key words: unambiguous (true or false) – unique, complete, correct, compliant and consistent.

Building Blocks of Data Governance

Data Architecture

In essence, provides the blueprint for how an organization manages its data assets. It encompasses how data are stored, accessed, and moved, ensuring scalability, integrity, and alignment with business strategy.

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Metadata Management

The practice of organizing and maintaining data about data; what it is, where it came from, how it’s structured, and how it should be used. Commonly maintained in a Data Catalog.

Master Data Management (MDM)

Relies on master data governance. It is the combination of people, technology and procedures, applied to maintain and deliver data that are: understood, trusted, controlled, accessible, and fit for purpose with a distinct role for the – Master Data Change Management to control and manage modifications to their core, business-critical master data. Assessing prior and tracking changes post with clear communication, including who-what-where-when, is vital for maintaining data integrity and facilitating audits.  

Data Security Management

The policies, procedures, and technologies used to protect digital information from unauthorized access, use, disclosure, disruption, modification, or destruction. It ensures the confidentiality, integrity, and availability of data throughout its lifecycle. 

Data Quality Management (DQM)

Is a comprehensive set of practices, processes, and technologies used to ensure that an organization’s data assets are trusted assets.

  • Good data = effective transactions; smart decisions
  • Bad data = wasted money; wrong conclusions

How are Data and Artificial Intelligence (AI) related?

AI is the ability of computer systems to perform tasks that typically require human intelligence, such as learning, problem-solving, and decision-making. AI systems use algorithms and computational models to analyse data, learn patterns, and make predictions or decisions. AI tools, like chatbots and self-driving cars, learn by analysing data.

       Key enhancements include: 
  • Automated Operations: AI excels in automating repetitive tasks like data entry, invoice processing, and customer queries, thereby freeing up valuable human resources for strategic tasks
  • Advanced Analytics: AI’s capability to sift through and make sense of vast datasets can unearth insights that predict customer behaviour, optimise inventory, and streamline supply chains
  • Personalised Experiences: By analysing user interaction patterns, AI can customise the user interface to individual preferences, significantly enhancing user satisfaction and productivity

To train AI, huge amounts of data are needed. AI requires clean, organised, and accessible data to function effectively. Ensuring the data meets these criteria is crucial for the success of AI integration. The AI models need to be trained with historical data and rigorously tested to ensure their accuracy and effectiveness before full deployment. If the data are flawed, AI will learn incorrectly. Poor-quality data = biased or dangerous AI. That’s why:

  • Clean, well-governed data is essential for safe, fair, and effective AI.
  • Ethical use of data matters – to avoid discrimination or misuse.