By Dominika Oliver, Senior Global Manager, Software Engineering at Red Hat
During this decade, data has become an important tool in enterprise decision-making, especially as it sets the stage for realising AI-enabled applications and infrastructure. The push to become more data-centric is already happening in South Africa’s public and private sectors. The country’s government plans to implement a new digital identity system in a collaborative project between the South African Revenue Service, SA Reserve Bank, and the Department of Home Affairs. Such an undertaking requires collaborating agencies to share and exchange data while ensuring the appropriate infrastructure and data management controls are in place.
For these kinds of projects and others, local businesses need to get a firm grasp on their data, enhance their management capabilities, eliminate any blind spots they may have, and work to bring their workloads together via a common, scalable infrastructure. This isn’t just about breaking down data and analytics silos; it’s about breaking down barriers to innovation. In doing so, with the help of the right platforms and an eye towards hybrid cloud (an essential part of AI transformation), businesses become more agile, efficient and future-ready.
Have a full view of your data landscape
Often, organisations’ IT teams are forced to create data silos, complete with their own IT operations model, in the face of having to deal with too much data. Not only does this result in unnecessary duplications where the same data is stored in multiple locations, but it can also lead to increased expenditure thanks to different management and provisioning tools. More than that, the data in each silo may be incomplete, incompatible or inconsistent, which impacts collaboration between departments, issues with compliance, and any big data or AI initiative.
A centralised approach to data management just doesn’t work in the golden age of decentralised infrastructure and systems. Handling your organisation’s data from one location doesn’t work when there is so much of it, WAN bandwidths are too low, and a single point of failure represents too much of a security risk. Modern data management helps to break down data silos by strengthening system connectivity and optimising the flow of data, while application programming interfaces (APIs) and connectors enable real-time data access and sharing. This is the first step for organisations leveraging data to train and deploy AI models and applications, and no AI strategy is complete without it.
Maintain a focus on governance
As modern data management becomes critical for any business transformation, so does data governance as the mechanism to uphold data integrity and security. Governance is a subset of data management, but one influences the other, and both require team collaboration for effective implementation and oversight. At the end of the day, both serve to extract more value from an organisation’s data, ensure security and compliance, and uphold the quality of the data for more accurate analytics.
Data governance frameworks require extensive planning and the input of all relevant stakeholders, but they can be supported in several ways. You can classify data based on predefined categories, assess the state of their governance initiatives with the help of maturity models, and establish a central data catalogue to more easily locate data and enforce policies.
Governance becomes even more important when organisations set out to use their own data to train AI-based models. Widespread AI adoption requires businesses to embrace AI governance across the entire data lifecycle to give confidence to their customers and stakeholders and, importantly, regulators, especially when the data in question may be sensitive or proprietary.
Failing that, businesses risk data privacy incidents, regulatory violations, and the exposure of sensitive information or intellectual property, all resulting from inadequate oversight of the data used to train models. All successful and responsible AI integrations start with effective data governance, and that’s a lesson enterprises cannot afford to learn only after an incident has taken place.
Realise a resilient data ecosystem
For many businesses, the solution to breaking down data silos and consolidating storage and access has been to migrate their workloads onto a common infrastructure that leverages a shared storage solution. Software-defined storage platforms let businesses easily share large-scale data sets between clusters, increasing the agility of their data teams, cutting costs and reducing risks associated with data duplication.
A hybrid or multi-cloud approach to data storage unlocks further benefits, including increased flexibility and access, as well as the ability to comply with local and international regulations. It also empowers organisations to pursue their AI projects with confidence, accessing and leveraging data responsibly and consistently.
Additionally. embracing open source data storage platforms offers businesses a unique advantage: access to community-led innovation. Leveraging the collective community expertise can deliver enhanced features, increased stability, and cutting-edge solutions that outpace what any single vendor can achieve.
A resilient data ecosystem begins with communication and collaborative planning. Businesses across South Africa should always consult with their software and platform vendors on the best steps forward. With the right approach and expertise at hand, they can transform themselves into data-driven and innovative enterprises.
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