Unlock Alpha: Investment Decisions Powered by Alternative Data
British mathematician Clive Humby made a powerful statement when he said, “Data is the new oil”. This rings true especially when applied to the fascinating world of alternative data. When the immense potential of big data and data analytics first came to light, among the fields that could extract maximum benefit from exact and reliable data was finance and investment. Big data took the guesswork and bias out of investment decisions, turning them into a precise science for both portfolio managers and their clients. Before this, investors had to rely only on traditional data from company-originated sources such as periodic publications of company financials and market research. With rapid penetration of internet connectivity, there is a whole host of real-time consumer insights waiting to be gleaned from alternative data wells. Alternative data is big data drawn from non-traditional sources that offer deeper, more nuanced insights by scrutinizing a broad or narrow spectrum of individual and business actions, as the case may be. Deloitte estimates that by 2030, the global revenue generated by alternative data providers across all industries could grow by as much as 29x, at a compound annual growth rate of 53%, and possibly reach US$137 billion. Some popular alternative data sources include search trends, web scraping, crowdsourcing, credit cards and POS systems, social media sentiment analysis, satellite imagery, geolocation data, news feeds, and web traffic. For instance, 5.9 million Google searches happen per minute, with 66% of web traffic referrals coming in from the search giant and 63% of searches taking place via mobile phones in the U.S.Alternative data is helping asset managers assess risk better and detect frauds faster, forecast demand and revenue more efficiently, conduct frequent portfolio health checks, build better financial products and ultimately, enhance customer experience.
Benefits of big data and alternative data
The investment management sector is expected to be at the forefront of the growth in revenue for alternative data. This means that in order to be in top form for generating alpha, wealth management firms need to chart out a detailed roadmap to work with these data sources. Firstly, figuring out and zeroing in on the broad varieties of alternative data to be sourced, learning to decipher them right, layering them and using them in diverse permutations to enable the data ‘to speak’ for varied investment requirements.The easiest way to go about this? Put in place an intelligent AI/ML system to fully utilize the ‘information advantage’.By installing such a system with adequate room for growth, it is possible to easily process, manage and analyze large volumes of unstructured data and train it to recognize and spot particular trends while achieving consistent quality and results. Another benefit of leveraging AI is to discern minor, out-of-the-ordinary details that are easily missed with high visibility items. This could be a key decision-making ‘pivot-or-hold’ moment for an investor. It could also result in a windfall by providing a crucial insight in another area not on the investor’s radar. Traditional data sources and informal ways to glean information may not be reliable due to various reasons such as inherent bias or ‘information fixing’. The extent of making a decision could only range to whether or not such a deal should go through, in case of a merger or acquisition. However, alternative data such as historical trends, region-specific analysis, etc. sidesteps the risk of information-fixing, and the bigger decision on how the deal can be structured will not be a matter of forecast but of precision.It can also alert the investor by identifying high-potential risks, decoding employment and pay packet trends, gaining accurate market visibility and ascertaining revenue signals. Unleash superior credit risk assessment. Empower your investment decisions with Credit Pulse.
Challenges of alternative data
Many investment firms are still on the first rung of the ladder — combating challenges with linking data from varied sources and processing the unstructured data into a format that can be easily employed to provide insights. Challenges such as inadequate technical expertise to understand how to work with alternative data, what kind of systems and tools are needed to set up, run and gradually build an intelligent system, or the system having insufficient capability to analyse the information could hamper the success that the firm aims to achieve from their data efforts. Data privacy and the ethics associated with data use and data sharing is also gaining prominence. Cybersecurity can get fairly complicated with such large data volumes. In parallel, incomplete data sets, low quality of data and unverified/fake data offer incomplete analysis without an ‘ear to the ground’, which can defeat the purpose. There could be too many rapid changes in trends, not offering real insights. Without institutionalizing processes, it is impossible to draw the right insights and conclusions to help clients make faster and more efficient decisions. In the long run, this could affect the competitiveness and standing of the investment advisory, leading to business nosediving and profit margins taking a hit.
Conclusion
Research by Deloitte found that number of alternative data sets applicable to financial services increased by ~36% over the last two years and the number of alternative-data providers increased by ~29% over the same period. So, financial advisory firms that watch the booming alternative data game from the side lines will definitely be at a disadvantage. They will neither be able to refresh their capabilities and add on new dimensions to their offerings but their asset base will also stagnate and decline over time.The way to go about it is to get the basics right. First, defining the business requirement and building a use case in to zero in on the right sets of alternative data required is key. Then, having a direct view into how the raw data is processed helps ‘lighten the burden’ from the start so that the right information can be extracted; this quickens the cycle to take the right decisions within the required timeframe. This will also help bring on board the right people with the kind of skillsets required to decode the data and put in place the foundational system. Asset management firms and wealth management consultancies keen to reap the benefits of big data and analytics can’t afford to get the technology behind their systems wrong. Working with experts on the subject of data, analytics, AI and Gen AI, such as DPA ensures that all the right factors are considered in setting up such a system, that is trust-worthy, reliable and easily maintained in the future.In today’s data-driven investment landscape, asset management firms and wealth consultancies are under pressure to unlock the power of big data and analytics. But navigating this complex world can be risky. Building an analytics system that’s unreliable or insecure can lead to costly mistakes and missed opportunities. That’s where Decimal Point Analytics (DPA) steps in. We’re a team of experts in data, analytics, AI, and generative AI, specifically focused on empowering financial institutions. By partnering with DPA, you can ensure all the right factors are considered when setting up your system. We’ll help you build a secure, future-proof foundation that delivers trustworthy, reliable data and insights. Ultimately, this translates to superior investment decisions for you and your clients.
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