The data industry has well and truly emerged over the last few years, with it having a distinct place within the wider digital economy. Data plays an increasingly important role in our lives, and as we continue to create data and realise it’s important, we also need to keep an eye on how it’s growing and where it’s going.
I spoke with leaders in the sector from, The Coca Cola company, Singlife with Aviva, Bank of Singapore and Qicstart to share their thoughts on the current and future data landscape, as well as workplace transformation.
How would you describe the current data landscape?
There are a few key trends in data now, namely cloud and hybrid datalake, ML & AI an automation. Mass adoption of the internet and the digitization of the economy since 2010 have seen a significant shift in our data practices and processes. While there are hundreds of tools and frameworks popping up in a rapidly evolving data landscape, architects and technology leaders find it extremely difficult to navigate the plethora of technologies that are all positioned as the "next silver bullet." Vibhanshu Singh - Director of Analytics & Insights, The Coca Cola Company
I believe, with recession upon us, we are looking at a focus on delivery and ROI of projects opposed to just a “growing as fast as we can” approach. Data is becoming an integral part of business processes. Instead of being available just in dashboards, it will start to get integrated with internal backend applications. The current trends I am seeing are with data applications such as chatGPT, and many more. Nikolay Novozhilov - Head of Data Science, Bank of Singapore
Currently there is an increase in the amount of data, online platforms that can analyse large datasets, and an increasing sophistication on the use of data within the enterprise. It has become a necessary function to compete within all industries. In terms of trends, there is some emphasis on AI and data governance along with issues of fairness and ethics within the data space. There is also a need to monitor model performance, with more and more data being stored and processed on the cloud. So, there will be an increase in automation of various data processes to improve the efficiency and effectiveness in providing relevant insights. Senior Data Scientist, Reinsurance Sector
I see companies heading into DDOM (data driven operating models) with hybrid cloud brands and a marketplace of tools for data talents to just buy, download and use. We have the leaders in data such as Google, Amazon, Netflix and Facebook, but most companies are only getting started with data. They are building a data team and doing data projects, however, eventually they get stuck, either because they don't know what they don't know or they cannot scale because they don't understand that building a data platform should be a hybrid cloud strategy (not one cloud brand but many) with many tools (data ingestion, data cleaning, data engineering, data modelling, data communication, data management, model management, reporting, data shift detection etc.) Most end up lost in the jungle. Andrew Liew – Data Science Partner, Qicstart
Data is a common thing when a business requires strategic and tactical initiatives. Every organisation will need someone who can turn data into actionable insights. Data-driven culture is inked in people’s mind when they are looking for answers. Alongside this, AI has grown faster than people initially thought. Current trends being ChatGPT, AI in arts and AI in transportation, with ChatGPT having tremendous focus. Ge Bin - Manager, Data Scientist, Singlife with Aviva
What does the data landscape look like in 3-5 years?
In the future, we will continue exploring innovative uses of data and settle on useful use cases which will become business-as-usual. Organisations may experiment on how they wish to structure their data teams, either centralised, decentralised or mixed/hybrid. There may be some new types of data emerging which may be useful for the enterprise, but I expect these to be few. Senior Data Scientist, Reinsurance Sector
I see a data evolution, that will move into a main highway with multiple branches of libraries for different use cases. Data evolution will mean that companies will eventually move into BYOS (bring your own stack) to foster tool diversity and hire data scientists with diverse skills and diverse preference for tools. A key consideration will look like this: A tool can cost around $2,000 per year but can save $50,000 in a salary bump and 60% chance of quitting. Andrew Liew – Data Science Partner, Qicstart
In the coming years data will be a big focus and it will continue to grow, with the adoption rate of monetising it. Vibhanshu Singh - Director of Analytics & Insights, The Coca Cola Company
How has the data sector been influenced by workplace transformation?
As a result of the increasing importance of digital technologies and knowledge-intensive work, more and more employees are now able to work independently of a specific location, and often also independently of fixed working hours. This provides companies with the potential to use their office space more efficiently, cut the time staff spend travelling to work and enhance employee satisfaction. To tap into this potential a modern workplace needs to offer flexibility over where – and when – employees work. Vibhanshu Singh - Director of Analytics & Insights, The Coca Cola Company
I feel that data teams were able to adapt quite well, and hybrid mode is here to stay. However, I’m cautious about security, remote access to data opens new kinds of threats in this regard. It will take a few big leaks around the world and the industry might get more conservative. Nikolay Novozhilov - Head of Data Science, Bank of Singapore
Hybrid and work from home are the dominate preference for current data scientists. Incoming data scientists requires a six month period, with three days on site, to familiarise themselves with cloud environment and tools (especially if the tools are not BYOS). Currently the statistics for companies that BYOS (bring your own stack) or allow tool diversity (same statistical analyses but company allow talents to buy any tools that they are expert at) are, 50% less attrition and 80% job satisfaction opposed to companies that follow a strict singular tool policy. Andrew Liew – Data Science Partner, Qicstart
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