AT A recent conference at fintech hub Lattice80 in Singapore, one of the panels of industry practitioners discussed how Big Data can be transformed into meaningful data for financial companies.
To the practitioners, the potential of Big Data analytics to help companies is a given, but difficulties lie in the gateways to harnessing data that are relevant. This requires human intervention and therein lies the problem.
There is still a lot of resistance within companies as employees from top to bottom tend to rely on the status quo of legacy systems that already serve them well. Those running such legacy systems view these as justifications for their roles within companies. They are bogged down by traditional ideas about running their businesses, the mechanics of which are being superseded by new ideas and concepts driven by technology.
They are not willing to use the endless torrent of data available to them in more creative ways.
Disruption does not sit well even though the value-add of Big Data analytics to businesses is intuitive. People start to worry about their own roles within companies as decision-making becomes data-driven rather than people-driven. A fear of redundancy is present, and understandable.
As a consequence, the vast pools of data about customers, partners, suppliers and other stakeholders that companies churn out lie dormant. It is a waste because such databases can be analysed to help find new growth opportunities that can divert companies to more prosperous areas of activity, or shape businesses to be more operationally efficient.
Two things to consider about Big Data:
1. Under current difficult market conditions, when bottom lines are being pressured across many industries, Big Data analytics can give companies the edge.
2. Meanwhile, if companies don’t do anything about it, there is also a danger that Big Data analytics will lower barriers to entry.
We are already seeing this happen in China with the likes of Alibaba and Tencent, leveraging on their technology and going into new areas, including banking, asset management, shipping and media. We should not forget that Alibaba and Tencent started off as data-driven technology companies engaged in e-commerce.
One of the panellists at the conference described what is happening rather well when he said that “data is the new balance sheet.”
This is not just a catchy tagline. Basically it underpins how companies can potentially monetize the mountains of data they receive on a daily basis as a result of their business operations, creating a new balance sheet of business activity.
For instance, think about the masses of structured data that are generated at bank ATMs, or point-of-sale terminals, or by credit card usage. Or unstructured data from social media outlets like Facebook or Twitter or YouTube videos.
By distilling, harnessing, curating, shaping and analysing this data, companies can find ways to tailor their products and services to better serve all their stakeholders.
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Dealing With Privacy
One of the big challenges that Big Data analytics faces is how to treat data privacy considerations. It may be seen as a problem now, but we are already seeing data privacy being steadily diluted. In today’s tech-savvy world, almost everyone leaves an electronic footprint. Singapore is a good example of this.
Apart from the banking sector, the issue of privacy rarely comes up even though Singapore is arguably one of the most intrusive places in the non-Communist world. There is a hunger for information about people on the island. From in-vehicle units in cars to the upcoming satellite electronic road pricing (ERP) and cameras everywhere — from the streets to the shopping malls and lift lobbies of public housing flats — Singaporeans are always being watched.
If you book a golf game at the Marina Bay golf course, you have to put your full name, gender, identity card number and nationality because of its tiered system of pricing for locals, permanent residents and visitors. Singaporeans are happy to do so as their golf games are cheaper.
However, it is another example of how conveying private information in Singapore is institutionalised.
Still, data privacy doesn’t seem to be a big issue on the island, especially with Millennials. The broad view in the city state is that if you have nothing to hide, why care about privacy? Generations following the Millennials will likely continue to put less of a premium on privacy, until there is no concept of what it is. This is good for Big Data analytics because it means more information about individuals can be gathered, unlocked and analysed.
Quid Pro Quo
Having said that, at present, the provision of data cannot be just a one-way street. People are more willing to share data if they receive value from doing so.
This suggests that companies have to seriously view their customers and other stakeholders as partners, and not pay lip service to this concept. They have to be more transparent and accountable to their partners.
The more they trust their partners, the more open everyone will be about their data. If this is achievable, then Big Data-driven decisions will be more acceptable to companies because they are underpinned by the human touch. This would be an outcome that is favourable to all in the long run.
Thus It Was Unboxed by One-Five-Four Analytics presents alternative angles to current events. Reach us at firstname.lastname@example.org
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