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How banks and financial institutions can harness alternative data to make better decisions

The dependency by banking and financial institutions on traditional data sets has resulted in gaps of information in decision making processes.  

Quarterly earnings reports and industry whitepapers are often slow and infrequent and as these traditional sources of information are readily available, they tend to offer limited actionable intelligence or insight. No executive should go to their board and present a plan based on data from 6 months ago when they could be using data from yesterday. While traditional sources have served them well in the past, the rise of Big Data has brought about an exponential amount of new data that they could be overlooking - at their peril.

The value of traditional data is being eroded as financial institutions realise the potential of alternative data sources to gain a competitive advantage over their peers. Better-informed commercial decisions are the backbone of every successful business. 

Alternative data is non-personal data that helps businesses to make better informed decisions, from hard-to-access sources. The data is anonymised, processed and translated to create fast and reliable intelligence to generate predictive insights and inform business strategies.

Put simply, alternative data is data retrieved from non-traditional sources. It can come from anywhere, including satellites, point-of-sale transactions, and wearables. It informs companies of market trends and insights, and stress tests traditional sources like censuses and surveys to create a fuller, more accurate picture of what is going on.

Another benefit of alternative data over traditional data is its frequency. Instead of data being collected every 6 months or yearly, alternative data is accessible monthly, daily or even in real time. Companies can conduct more information-dense and precise analysis, resulting in not just richer insights into present performance but a forecast into the future.

In recent years there has been an increase in alternative data use by financial institutions. 78 per cent of hedge funds use or expect to use alternative data, while according to research by the Financial Times, investment groups have been doubling their spend on alternative data. Similarly, Deloitte says that spending on alternative data by trading and asset management firms may exceed $7bn by 2020. 

In the world of finance, there is significant value in non-personal, aggregated information that is used to identify business or investment opportunities. 

Alternative data is vital to produce reliable, up-to-date intelligence but to truly take full advantage of it, the right expertise is needed to separate the signal from the noise. 

The future challenge for banks and financial institutions will be not the ability to effectively analyse huge volumes of traditional customer data. Instead, it will be how they can harness the next wave of non-personal alternative data to stay better informed to serve the needs of its existing and new customers.

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Comments: (1)

Tayloe Draughon
Tayloe Draughon - CloudQuant - Chicago 28 August, 2019, 14:16Be the first to give this comment the thumbs up 0 likes

Alternative data is now the differentiator.

The problem is, which alternative data should I use? How can I be sure and how can I easily test it?

One of the success stories we recently saw of AltData use in trading was a result of Natural Language Processing (NLP) on StockTwits. The vendor producing the AltData signal showed a rapid uptick in positive sentiment from the StockTwits community in GOOGL 2 hours preceeding the earnings surprise.

There is a screenshot of this at https://info.cloudquant.com/2019/07/alt311303/?utm_source=finextra

BTW - Great post Hamza. But I slightly disagree with your definition of AltData. It is often non-personal data but it doesn't have to be. It could be, and sometimes is, very personal data but from a data source that your business does not typically utilize.