Data governance in Ghana's digital economy
The fast-paced and ever-evolving technology landscape of the global marketplace, and the transition towards a digital economy, have raised concerns about the internal data processes.
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Moreover, the complexity of modern business environments, and the influx of data from multiple sources necessitate advanced technology systems and models that can provide actionable insights.
Despite heavy investment in internal data systems, these technologies create significant procedural gaps in the data value chain that need to be addressed.
As Ghana's digital economy continues to grow and evolve, it becomes critical to keep data secured and organised.
Data governance
This is where data governance comes in ― by establishing rules and guidelines for processing data, we can ensure that the technology we rely on remains reliable, safe and effective.
Importance, data governance
Data governance is a standardised internal process that ensures the security of data throughout its life cycle.
However, organisations may need to access data from third parties when adopting advanced technologies, such as, Artificial Intelligence (AI), Predictive Analytics, and Deep Learning models.
This can lead to data breaches in terms of privacy, security and integrity.
Therefore, it is essential to enforce policies to prevent the exploitation of third-party data.
To address these, organisations must enforce policies to ensure that data from third parties is exclusive and in a format that cannot be exploited.
A way organisations can help prevent the exploitation of customers’ financial data or information is to proactively predict fraudulent activities using real-time data feeds.
In that case, a Neural Network model or algorithmic patterns can help predict data outcomes under uncertain financial circumstances.
It is always better to stay alert and take the necessary steps to prevent any unwarranted mishaps.
Internal mechanisms
Fortunately, data governance adopts internal mechanisms that restrict the access and processing of data.
It also simplifies compliance processes for external data policies, such as, government agencies’ standards and best practices, as well as stakeholder agreements.
As the financial technology industry expands, companies need to follow proper procedures to monitor transactions and protect financial data.
These measures must comply with the global regulatory landscape, as well as local policies to prevent the misuse of financial data.
As tax administration departments increasingly use AI tools to respond to public enquiries caution should be exercised.
Additionally, the Revenue Authority's commitment to expand the tax net has led to the development of predictive or algorithmic models to detect tax evasion.
However, it is crucial to establish clear standards and policies to avoid data inaccuracies or misrepresenting facts in sensitive financial and legal situations.
In recent years, medical institutions have also been using information systems to manage their operations.
For example, laboratories often collect and store patients’ data over time.
Critical
Nonetheless, with the rise in cyber attacks, it has become critical for these institutions to invest in secure databases.
While developed economies have been successful in implementing such systems, emerging markets often face challenges due to lack of personnel with the expertise to manage the technology systems.
As a result, these entities must ensure that their professionals are equipped with the necessary skills and knowledge to effectively manage and protect internal data processes.
In essence, the tools and technologies used for collecting and processing data are only as unbiased as the accuracy and quality of the primary data sources.
Thus, as emerging economies continue to develop their data governance policies, they must also consider models that ensure the consistency or authenticity of primary data sources.
Moreover, complying with a regulatory regime that has several laws on data privacy and protection will require organisations to implement proper data governance initiatives to automate compliance with established regulations or laws.
Encryption and security features such as cryptographic keys can help protect data during transmission.
Most consequentially, boardroom discussions ought to prioritise data governance in its core strategic objectives as policies on the digital economy continue to evolve.
In conclusion, to ensure confidentiality, integrity and security, while minimising the risk of interception and unauthorised access, institutions must implement a robust data governance framework.
The writer is a Corporate Governance & Data Analyst.
E-mail: [email protected]
