An organisation’s data is only valuable to the degree that it can be translated into meaningful insights that inspire decisions and action. This is why it’s so important businesses understand how to manage and interpret data to uncover its value.  

Despite many organisations perceiving data as a strategic asset and increasing their investment in data-driven projects, few manage data appropriately. Information silos can create insular management systems in companies that prevent access to collections of data in isolated systems. 

Organisations are also quick to recognise they have an abundance of data but do not know what to do with it or where to start in their quest to understand it. Unable to gain meaningful insights, distrust in data can begin to form and create an upending cycle of lost value and time.  

“When analysed appropriately, [data] allows us to make predictions about the future and can thus improve the quality of decision making and be the basis for improving efficiency and effectiveness of processes. However, while some businesses are reaping the benefits, others still don’t realise the potential.” – Professor Marta Indulska, UQ Business School  

The true value of data can be discovered by implementing effective management systems and a work culture that engages all employees.  

Why is effective data management important? 

A significant amount of value can come from improving data processes and analytics; however, a great number of risks can also occur, affecting customers and organisations. Without proper data management practices in place, businesses are exposed to risks in privacy, negligence, transparency, and accuracy, which can have devastating financial consequences. 

These risks can include: 

Unintended errors 

Data bias 

Insufficient disclosure 

Consumer privacy 

Lack of transparency 

Data breaches  

Incorrect or incomplete data 

Lack of autonomy 

Corrupted data 


System gaming 

Algorithm inaccuracies

It is important for businesses to understand that poor data management can cause internal consequences for employees, owners, board members and stakeholders, as well as external consequences for customers and clients.



How to manage data effectively  

“Ultimately, data becomes the fuel that helps power multiple use cases or opportunities that the business may want to go after as part of [a digital] transformation.” – Anil Chakravarthy, CEO Informatica  

For data to become the fuel in an organisation, data needs to be effectively managed through a continuous lifecycle with four stages: 

Creation   Data can be captured and integrated into an organisation’s system through observations and technology   Operation   Once data is identified, it can flow through to build or inform digital assets    Refinement    Data is dissected further to make more nuanced or high-level insights    Retainment   Safekeeping the access and storage of data.


The role of Corporate Digital Responsibility (CDR) 

For organisations to see outcomes and a return on their investment in data analytics technologies and capabilities, the organisation’s culture and management need to embrace corporate digital responsibility (CDR). CDR creates shared values and standards into a set of principles across departments regarding the creation of data and operation of digital technology.  

The policies and governance derived from CDR can initially require significant investments and run against operational goals. These factors can create tensions within an organisation and stop them from excelling. Issues that can arise include short-term sales, customer experience, cost reductions, managerial pushback, and poor practices.  

Organisations can create an internal culture that actively develops CDR in all departments by: 

  • Engaging each employee in issues of data transparency 

  • Implementing ethical KPIs that reward employees for doing the right thing and keep everyone in the organisation accountable 

  • Adopting a formalised digital governance structure across all roles and organisational units 

  • Promoting knowledge sharing and team integration for greater data proficiency  

  • Investing in senior technology and information positions to enhance data literacy in business policies. 


The role of technology 

Data consumption is the process that transforms raw data into information for business intelligence (BI) software, which can then create useful patterns and analyses. A process commonly identified as gold mining, the insights generated from data consumption can help organisations improve operations by informing decisions and actions in a variety of industries.  

As the capacity and variety of data increases, emerging technologies that easily expand as more data is gathered and processed become necessary. New decentralised approaches, such as data mesh, aim to make sure each domain is discoverable, secure, and governed by an open standard to easily distribute data across departments without duplicating data and obscuring data ownership. Large organisations that have more complex data models, data volumes and data domains can see positive changes in how they handle data with a decentralised data infrastructure. In particular, data management in healthcare services can benefit from a data mesh architecture that unifies large quantities of data to reduce variations in services. 

What is data mesh and how can it improve business performance? 

Data mesh is a decentralised architectural approach that unlocks value from data at scale. The new model was first explored by Zhamak Dehghani in 2019 and was designed to easily access, share, and manage high quantities of analytical data in complex environments. The structure provides a foundation for visualisations and reports to help businesses gain greater insights through machine learning analytics and data-intensive applications. 

Dehghani has described data mesh as the “essential ingredient for organisations to move from intuition and gut-driven decision making to taking action-based observations and data-driven predictions.” 

The paradigm aims to address the common failure modes of traditional centralised data platforms, such as data lakes and data warehouses, by shifting to an eco-system where data products can easily access and communicate with one another. This situates knowledge domains as the priority concern, while data lakes and data warehouses become additional nodes in the mesh to keep data products connected.  


  • Hyper-personalised customer experiences based on data insights 

  • Reductions to operational time and costs through data-driven optimisations  

  • Enhanced access to and understanding of business analytics, data mining and data infrastructure in all departments. 

Improving organisational capacity to self-serve data can help businesses improve system processes and see more return on their investment into training and technology. However, transitioning to a new model can come with challenges. These challenges include legacy culture resistance, migrating from legacy systems, and competing business priorities. 


Challenges to overcome: 
  • Implementing a product-centric operating model that discovers and analyses multiple domains 

  • Adopting a modern software fabric to enable data serving instead of data ingesting 

  • Increasing data literacy across the organisation to support multiple domain-centric teams in building and managing products. 

While these challenges are expected to decrease as technologies evolve, they present opportunities for businesses to transform their processes and systems. Data mesh creates a foundation for future insight architecture that allows organisations to embrace a new language and set of governing principles to derive value from data. 

Data ingestion is the process that moves data through centralised pipelines for extracting and loading. Data mesh streamlines this process to serve data in a unified eco-system of data products that enable proficient discovery and usability.


Organisational value of data analytics in practice: healthcare example 

Discovering and using quality data freely can improve processes, support, and development across a variety of industries, including healthcare. Health and medical organisations generate valuable insights from the digital collection and coding of patient data, while also holding important obligations to mitigate risks for patients, pharmaceuticals, governments, and the community.  

Operational practices can be improved by clinicians transitioning from using paper notes to digitally collecting and sharing codable data from their observations, creating a widespread positive impact on healthcare services. 

“On a wider scale, analysing data from medical and hospital records could lead to major advances in medical research and can assist in identifying risk factors and understanding best practice for treating specific conditions.” – Professor Marta Indulska, UQ Business School 

Managing data as an asset helps enhance how clinicians make diagnoses and provide patient treatments by reducing variations in practice. 

Dr. Mark Nelson is the Director of Physiotherapy at QEII Hospital (MSHHS), a UQ alumnus, and UQ Future of Health research hub partner. His research on the use of telehealth discovered that despite an uptake in usage during the pandemic, telehealth services declined when restrictions began easing in 2021.  

The challenges telehealth experienced before COVID-19 have persisted due to complicated systems and equipment, as well as a lack of training and clinician access to admin support.  

Nelson noted, “it only takes a few extra steps in a process for people to say it’s too hard and stop using it.” 

Learn more about how UQ partners with industry to prepare for the Future of Health  

Despite the growing need for the electronic health service, telehealth has been unable to refine data capture and streamline processes with effective data analytics and management. Further investment in data analytics could help collect and create relevant data patterns to optimise practices for telehealth patient visits by identifying missing values, redundant attributes, and inconsistent data. 

As technology and systems continue to expand, data analytics can simplify health services for clinicians in practices by documenting reoccurring patient issues. Staff in the health sector will also require training to understand how to collect, store and analyse data to improve access to comprehensive and quality data than can ultimately improve patient outcomes. 

Enhance your understanding of business analytics  

Advance your technical expertise in data analytics and develop business leadership your skills with a UQ Master of Business Analytics. The program helps students gain valuable insight from innovative technologies and practices to improve data management practices and, ultimately, business results.