The Three Biggest Data Challenges For Australian Organizations

From government services and bank accounts to retail offers, Australians expect easy, digital access to everything they need. It isn’t surprising to note that Australia is a global leader in open data initiatives. The volume and types of data being gathered by Australian organizations are continually exploding. That said, not all the data collected is being put to use. Let’s take a look at the top three data challenges for Australian organizations and possible solutions.

Complexities of data management

Given the varied types of data being handled, sources and systems, managing data isn’t easy. Data is being stored on-premises and in the cloud simultaneously, there’s the impact of data from IoT devices and AI-based tools to consider and so on.

Though the data required may exist, in many cases, it may not be possible to find data that suits the user’s requirement. Or, the data may not be machine-readable. 93% of decision-makers who participated in a survey listed complexity in data management and storage as a factor impeding digital transformation.

Unfortunately, a siloed approach to data management is more common as compared to an enterprise-wide approach. As a result, many organizations use about 23 different data management tools. To work effectively, all data management tools must be set up to work together. This becomes harder as the number of data management tools increases. Hence, using multiple-point solutions for data integrations can cause several issues such as:

• Data becomes difficult to find
• Data is often misunderstood
• Data quality suffers leading to poor trust
• Systems are harder to scale
• Data and applications become more fragmented
• Data is harder to share

Simplifying data management

Investing in simplified data management systems gives businesses more control over their data and could significantly improve data quality. This begins with realizing that not all data is equally valuable. Businesses must focus on identifying and collecting only as much data as required.

While there may be many data sources, the format in which data is collected must be standardized. For example, any date captured by a database could be standardized to the DD/MM/YYYY format. This makes it easier to integrate data from all sources and maintain a central database in place of siloed data. Data centralization can simplify management, reduce costs and enhance accessibility.

Further, all data entering the system must be verified to meet data quality standards of accuracy, completeness, validity and so on. To build trust in the data, the data management system must also be designed to document data lineage. This includes establishing where the data was sourced from, when and how it was transformed or enriched and so on.

High cost of data management

Irrespective of the sector or business size, all organizations are striving to optimize costs. Hand-coded and limited point solutions can be expensive to maintain. In addition, they are slow to render. This increases exposure to operational risks and costs. Changes required to scale the system up can also put unnecessary pressure on data engineers. Overall, this limits innovation and makes the system expensive to maintain.

Cost optimization

Lowering the complexity of data management has a direct impact on the total cost of data ownership. It drives cost efficiency across all stages of data management. To begin with, replacing manual or code-based processes with Artificial Intelligence or Machine Learning technologies can automate repetitive tasks and make them more efficient. This allows human resources to focus on more value-driven tasks and activities. It also acts as a cost-effective way to scale data systems.

Similarly, businesses may choose to use low code/ no code data processing solutions to analyze data and derive insights while avoiding cost overruns. This enables seamless connectivity between data sources, tools and systems. In turn, it reduces the pressure on expensive technical resources and facilitates rapid scaling of infrastructure as required.

Data breaches

Unfortunately, data breaches are not uncommon in Australia. The Latitude data breach gave fraudsters unauthorized access to the personal details of over 14 million customers in 2023. Data breaches damage an organization’s reputation. It makes customers think twice before sharing their data. Some people may even intentionally submit fake email addresses and phone numbers in sign-up forms. This further lowers the value of data and insights derived from it.

While it may not be possible to guarantee 100% protection against data breaches, ignoring data privacy norms and compliance often increases the risk of such fraudulent activities.

Keeping data protected

Any organization that handles data must pay attention to data privacy and compliance with data regulations. Doing so would also reduce the firm’s exposure fraud and associated remediation costs.

Hence, it is important to regulate who can access data and how. Rather than completely restrict access to datasets, conditions must be set that allow data to be accessed when required while keeping it safe. For example, some sensitive data may be anonymized.

Further, to ensure compliance with data regulations, all existing data must be regularly validated. This helps weed out decayed data that may be used to gain unauthorized access and cements the subscriber’s assent to having their data stored with the organization.

Building trust in data

As the amount of data organizations deal with increases, so does the need to design data management systems that inspire trust in the data and allow traceable access. Along with standards and specifications for data quality control, organizations must define processes that measure data quality levels and identify opportunities for improvement. At the same time, there is a need to avoid cost overruns.

Data verification and regular validation will go a long way toward meeting quality dimensions such as accuracy, completeness, integrity, reasonability and uniqueness. This is an activity that can be easily automated at data collection points with the help of verification tools. Once verified, data can be brought together in a central database with secure, controlled access. This is key to empowering data users without compromising on security. Done right, it can help unlock the full potential of data, transform organizations and take Australia into a brighter future.