Adopting DataOps would mean that organizations will likely be successfully able to use their data to:
- Mitigate compliance, regulatory and security risks
- Find brand-new sources of customers while increasing sales from existing customers.
- Gain a 360-degree customer view to create personalized client experiences.
- Compress the product development cycle.
- Reduce operational expenses.
From a security stand point, company boards will now play a much bigger role in helping secure their data reputation. Specifically, boards will demand businesses have a clear data strategy and compliance program as well as the necessary controls in place around security, data privacy and data ethics. Thus having an end to end data management strategy and methodology will better prepare these companies for security demands.
As the region leads in global IoT spending, what are the challenges and opportunities posed by the proliferation of IoT devices?
Ong: The proliferation of IoT devices opens the doors to having a more complete view of the enterprise than ever before. The ability to measure, monitor and manage any asset from any location from anywhere at anytime cannot be discounted. And IoT devices are combined with edge computing the use cases can be even more interesting.
Some potential advantages to be reaped from the proliferation of IoT devices would be enhanced decision making through machine learning and AI as we would have more data sets for decisions to be made on. Tasks can be streamlined, and more staff could potentially work remotely leading to increased productivity. Further down the line, IoT would also likely create new consumer demands and potentially new revenue lines for organizations.
But the technology is not without its challenges. Two major challenges that we potentially see not just with proliferation of IoT devices but also as companies adopt 5G technologies will be that firstly the amount and variety of data transmitted could potentially be too much for current IT infrastructures to chew on. There may be a need to modernize IT infrastructure to handle the influx of data and convert it all into actionable business insights.
It is not just a scalability issue either, as IoT devices often have differing standards and the different systems connected through IoT continue to create interoperability challenges. Overall there is a considerable deal of complexity that comes with the IoT.
Secondly, securing these data both at the edge devices or during transmission will also be an area that organizations may need to revisit with regards to their security strategy.
What are some key principles and/or best practices for striking a balance between innovation and security?
Ong: Intelligent data governance should be the end goal that we are striving towards in terms of striking that balance between innovation and security. It is about protecting a company’s most sensitive and strategic data, but also leveraging it as a competitive advantage.
At its most foundational level, data governance means bringing data under control and keeping it protected. But it also means knowing where data originated, where it is currently located, who can access it, what it contains, and how long it should be retained. Intelligent data governance also implies that trivial data is distinguished from strategically important information that can be used to produce a competitive edge.
As organisations are usually operating under a high level of pressure and scrutiny, businesses often over-invest when first implementing a data governance strategy. They may create a new functional silo specifically dedicated to data security or build an entirely new IT infrastructure.
The truth is, intelligent data governance simply means practical planning. By using software to move data to a central hub — then managing access, protection, and retention for each data point — organizations are well on their way to achieving data governance success. Data analytics can help sift through the huge volume of information and separate the most important insights for creating business value.
Once data is centralized and thoughtfully managed, businesses can easily respond to regulatory inquiries, identify customer needs, anticipate emerging issues, and explore new business opportunities. They can protect their information assets while still allowing key stakeholders in the business to leverage data for improved decision making.
One example of a best practice is the use of automated solutions specifically for the task of data governance. After the IT team has defined rules for tagging files, establish security protocols and enabling access, automation can take over and ensure the rules are applied universally. The rule sets can even be customized as needed for employees across different geographies.
This ultimately means the employees who need quick access to data can still get it easily and quickly, whilst the information remains inaccessible to those who should not have access.
