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In the world of finance, investment managers are always on the lookout for new sources of information which will provide a previously untapped source for creating Alpha. These very data sources are called Alternative Data or data fatigue. Data because, well, information. Alternative because they are above and beyond the typical company data sets. At the risk of overplaying the cliche, this is what triggers the ‘unfair advantage’ we all keep seeking.
The market for Alternative Data is still very raw and nascent. The company spending it is increasing multifold year on year. Some of the more popular sources of Alternative Data were credit card transactions, data scraped online, geolocation data from mobiles, etc. Intuitively, you can say that these sources would come under the radar for violating serious privacy issues. Ergo, the need for less intrusive alternate data points.
While the alternative data market is currently flourishing despite security concerns—and predicted to be worth a whopping $350 million in 2020, almost double from $183 million in 2016—we have witnessed companies suffer from alternative data fatigue in 2019. Alternative data fatigue you ask? Yes, it is just as self explanatory. The old adage, “Old wine in a new bottle” comes to mind. The same bunch of datasets are being repackaged and sold to the same groups of hedge fund managers, who lose valuable time preparing the data rather than analyzing it. By then, the market becomes saturated with new companies: all of which will be offering the same promise of competitive insights.
There are some basic hygiene order steps you can take to avoid alternative data fatigue. The first step is to ensure that you are securing tailored and business-specific information that will drive your business forward. That is the big differentiator if you want to go easy on the fatigue. Thanks to the repercussions of the ever growing increase of interest in alternative data, there are a multitude of research firms that offer the best alternative data insights and/or selling alternative datasets to investors, money managers, travel agents and anybody else looking to ride this pony. Imagine the same datasets, the same insights drawn from the same dataset, the same actionable points basis the insights drawn from the same data set by firm A and firm B. With just one small difference. They are both completely unaware that they’re using the same information against each other.
Alternative data feeds can be anything that is not considered ‘traditional’ by a particular company. The lines are truly getting blurred. What was then considered unconventional is one of the biggest marketing decision drivers today. Care has to be taken that what is considered ‘alternative’ for you, shouldn’t become the industry standard in the upcoming year. That will mean that everybody will have equal access to the data you spent a lot of energy collating.
It is task scraping and maintaining ‘mainstream’ data as is. To take out the time and energy to scrape and maintain and handle large amounts of alternative data is absolutely tangential to the main circle of concern. So what is the big culprit here? Yes, you guessed it. Lethargy.
Alternative datasets usually comprise information pulled from a plethora of sources and websites. The data scraping process can derail you from analysing the finds and drawing insights. Not to mention the increased scope for human error and duplicity. Scraping is the first big step. Then is standardising it. It has to be in a ‘standard’ format to make any sense. This is the only way the machine can learn and automate the process.
Now that we have the format in place, the next is drawing the true intent of the data collected. You know how American English and British English vastly differ? We aren’t the only ones who find that annoying. For example, there are two distinct ways in which a single date can be written. May 27, 2021 can be written as 5/27/2021 or 27/5/2021, and we don’t have to tell you the kind of repercussion this difference would make on the data collected. Hence, you have to scrape, standardise, draw insights and take strategic business decisions. With the least amount of human effort.
We never mention problems we do not have solutions to. There are a bunch of things you could begin with:
There are a bazillion other ways to absolutely avoid data fatigue by extracting custom data. It is beautiful. Sentiment analysis of your comment section across social media channels, credit card data to crack consumer spending behavior (however frowned upon). Using satellite and/or surveillance images to count cars in parking lots. You just have to tell your web scraper service provider to collect the exact nature of data you need. That is all. Kill the fatigue. You are meant for better things.
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